2021-05-18_model_single.ipynb

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 "nbformat": 4,
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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import seaborn as sn \n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "plt.style.use(\"dark_background\")\n",
    "import pickle\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import svm\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import precision_score, confusion_matrix, f1_score, accuracy_score, recall_score, classification_report, roc_auc_score\n",
    "from sklearn.preprocessing import label_binarize, normalize\n",
    "\n",
    "from utils import DataLoader, DataProcesser, label_plot, TimeKeeper\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_dic = {1: 'Still', 2: 'Walking', 3: 'Run', 4: 'Bike', 5: 'Car', 6: 'Bus', 7: 'Train', 8: 'Subway'}"
   ]
  },
  {
   "source": [
    "# Data"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## Train-Test"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/mid_res/data_ft_v1.csv\").fillna(0)\n",
    "col_drop_x = ['time', 'label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['speed_log'] = df.apply(lambda x: np.log(x['speed'] + 1), axis = 1) # 运行一遍\n",
    "# df['acc_wd_std'] = df[['label', 'acc']].rolling(10).std()['acc']\n",
    "# df['speed_wd_std'] = df[['label', 'speed']].rolling(10).std()['speed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_drop = ['latitude',\n",
    " 'longitude',\n",
    " 'altitude',\n",
    " 'time_dlt',\n",
    " 'valid_dlt',\n",
    " 'east',\n",
    " 'north',\n",
    " 'east_dlt',\n",
    " 'north_dlt',\n",
    " 'east_speed',\n",
    " 'north_speed',\n",
    " 'cells_ctype_mode',\n",
    " 'speed_dif',\n",
    " 'speed_dlt',\n",
    " 'speed' # 使用 speed_log\n",
    " ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "['time',\n",
       " 'label',\n",
       " 'accuracy',\n",
       " 'num_gps',\n",
       " 'num_wifi',\n",
       " 'num_cells',\n",
       " 'acc',\n",
       " 'wifi_rssi_mode',\n",
       " 'wifi_rssi_mean',\n",
       " 'wifi_rssi_min',\n",
       " 'wifi_rssi_max',\n",
       " 'wifi_rssi_std',\n",
       " 'wifi_freq_5ratio',\n",
       " 'cells_isRegistered_mean',\n",
       " 'cells_asuLevel_mean',\n",
       " 'cells_dbm_mean',\n",
       " 'cells_level_mean',\n",
       " 'cells_asuLevel_min',\n",
       " 'cells_dbm_min',\n",
       " 'cells_level_min',\n",
       " 'cells_asuLevel_max',\n",
       " 'cells_dbm_max',\n",
       " 'cells_level_max',\n",
       " 'cells_asuLevel_std',\n",
       " 'cells_dbm_std',\n",
       " 'gps_snr_mean',\n",
       " 'gps_snr_min',\n",
       " 'gps_snr_max',\n",
       " 'gps_snr_std',\n",
       " 'speed_log',\n",
       " 'acc_wd_std',\n",
       " 'speed_wd_std']"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "list(df.drop(col_drop, axis = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_ratio = 0.2\n",
    "X = df.drop(col_drop_x + col_drop, axis = 1).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sequence\n",
    "# len_train = int(X.shape[0] * train_ratio)\n",
    "# len_test = X.shape[0] - len_train\n",
    "# X_train, X_test, y_train, y_test = X.iloc[:len_train,:], X.iloc[-len_test:,:], df['label'][:len_train], df['label'][-len_test:]\n",
    "# random split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, df['label'], test_size = test_ratio)"
   ]
  },
  {
   "source": [
    "## Validate"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# val = DataLoader.SHLDataLoader('data/validate/')\n",
    "# val.load_all()\n",
    "# dp_val = DataProcesser.DataProcesser(val)\n",
    "# dp_val.process_pipe()\n",
    "\n",
    "# val['speed_log'] = val.apply(lambda x: np.log(x['speed'] + 1), axis = 1)\n",
    "# val['acc_wd_std'] = val[['label', 'acc']].rolling(10).std()['acc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save\n",
    "# file = open('data/mid_res/val_ft.pickle', 'wb')\n",
    "# pickle.dump(val, file)\n",
    "# file.close()\n",
    "# load\n",
    "with open('data/mid_res/val_ft.pickle', 'rb') as file:\n",
    "    val = pickle.load(file)\n",
    "val.df['speed_wd_std'] = val.df[['label', 'speed']].rolling(10).std()['speed']\n",
    "X_val = val.df.drop(col_drop_x + col_drop, axis = 1).fillna(0)\n",
    "y_val = val.df['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list(val.df)"
   ]
  },
  {
   "source": [
    "---\n",
    "# Model"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## Random Forest"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(n_estimators = 20, random_state = 0, max_depth = 8)\n",
    "rf.fit(X_train, y_train)\n",
    "pred_rf = rf.predict(X_test) # label predict\n",
    "pred_prob_rf = rf.predict_proba(X_test) # prob predict"
   ]
  },
  {
   "source": [
    "## LightGBM"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import xgboost as xgb # 太慢\n",
    "import lightgbm as lgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009645 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 4493\n",
      "[LightGBM] [Info] Number of data points in the train set: 784421, number of used features: 30\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Info] Start training from score -2.081408\n",
      "[LightGBM] [Info] Start training from score -2.076484\n",
      "[LightGBM] [Info] Start training from score -3.143184\n",
      "[LightGBM] [Info] Start training from score -2.123376\n",
      "[LightGBM] [Info] Start training from score -1.822800\n",
      "[LightGBM] [Info] Start training from score -1.934871\n",
      "[LightGBM] [Info] Start training from score -1.835721\n",
      "[LightGBM] [Info] Start training from score -2.107108\n",
      "[1]\tvalid_0's multi_logloss: 1.68542\n",
      "[2]\tvalid_0's multi_logloss: 1.4874\n",
      "[3]\tvalid_0's multi_logloss: 1.34429\n",
      "[4]\tvalid_0's multi_logloss: 1.23185\n",
      "[5]\tvalid_0's multi_logloss: 1.13885\n",
      "[6]\tvalid_0's multi_logloss: 1.06267\n",
      "[7]\tvalid_0's multi_logloss: 0.997234\n",
      "[8]\tvalid_0's multi_logloss: 0.941991\n",
      "[9]\tvalid_0's multi_logloss: 0.893352\n",
      "[10]\tvalid_0's multi_logloss: 0.851821\n",
      "[11]\tvalid_0's multi_logloss: 0.814644\n",
      "[12]\tvalid_0's multi_logloss: 0.779824\n",
      "[13]\tvalid_0's multi_logloss: 0.75091\n",
      "[14]\tvalid_0's multi_logloss: 0.726701\n",
      "[15]\tvalid_0's multi_logloss: 0.70505\n",
      "[16]\tvalid_0's multi_logloss: 0.684274\n",
      "[17]\tvalid_0's multi_logloss: 0.664881\n",
      "[18]\tvalid_0's multi_logloss: 0.646851\n",
      "[19]\tvalid_0's multi_logloss: 0.629887\n",
      "[20]\tvalid_0's multi_logloss: 0.613798\n",
      "[21]\tvalid_0's multi_logloss: 0.600653\n",
      "[22]\tvalid_0's multi_logloss: 0.587151\n",
      "[23]\tvalid_0's multi_logloss: 0.57594\n",
      "[24]\tvalid_0's multi_logloss: 0.562247\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[25]\tvalid_0's multi_logloss: 0.552304\n",
      "[26]\tvalid_0's multi_logloss: 0.542582\n",
      "[27]\tvalid_0's multi_logloss: 0.532014\n",
      "[28]\tvalid_0's multi_logloss: 0.523932\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[29]\tvalid_0's multi_logloss: 0.513791\n",
      "[30]\tvalid_0's multi_logloss: 0.506852\n",
      "[31]\tvalid_0's multi_logloss: 0.497313\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[32]\tvalid_0's multi_logloss: 0.489767\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[33]\tvalid_0's multi_logloss: 0.482801\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[34]\tvalid_0's multi_logloss: 0.473636\n",
      "[35]\tvalid_0's multi_logloss: 0.467426\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[36]\tvalid_0's multi_logloss: 0.461313\n",
      "[37]\tvalid_0's multi_logloss: 0.454632\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[38]\tvalid_0's multi_logloss: 0.447812\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[39]\tvalid_0's multi_logloss: 0.442543\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[40]\tvalid_0's multi_logloss: 0.436016\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[41]\tvalid_0's multi_logloss: 0.430792\n",
      "[42]\tvalid_0's multi_logloss: 0.422926\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[43]\tvalid_0's multi_logloss: 0.417683\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[44]\tvalid_0's multi_logloss: 0.411241\n",
      "[45]\tvalid_0's multi_logloss: 0.406862\n",
      "[46]\tvalid_0's multi_logloss: 0.39956\n",
      "[47]\tvalid_0's multi_logloss: 0.394554\n",
      "[48]\tvalid_0's multi_logloss: 0.389453\n",
      "[49]\tvalid_0's multi_logloss: 0.382882\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[50]\tvalid_0's multi_logloss: 0.379647\n",
      "[51]\tvalid_0's multi_logloss: 0.37485\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[52]\tvalid_0's multi_logloss: 0.369929\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[53]\tvalid_0's multi_logloss: 0.365179\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[54]\tvalid_0's multi_logloss: 0.361766\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[55]\tvalid_0's multi_logloss: 0.357221\n",
      "[56]\tvalid_0's multi_logloss: 0.353227\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[57]\tvalid_0's multi_logloss: 0.34852\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[58]\tvalid_0's multi_logloss: 0.344684\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[59]\tvalid_0's multi_logloss: 0.341266\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[60]\tvalid_0's multi_logloss: 0.337648\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[61]\tvalid_0's multi_logloss: 0.33431\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[62]\tvalid_0's multi_logloss: 0.330821\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[63]\tvalid_0's multi_logloss: 0.327281\n",
      "[64]\tvalid_0's multi_logloss: 0.323778\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[65]\tvalid_0's multi_logloss: 0.319712\n",
      "[66]\tvalid_0's multi_logloss: 0.315617\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[67]\tvalid_0's multi_logloss: 0.312953\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[68]\tvalid_0's multi_logloss: 0.310022\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[69]\tvalid_0's multi_logloss: 0.307192\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[70]\tvalid_0's multi_logloss: 0.305487\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[71]\tvalid_0's multi_logloss: 0.303144\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[72]\tvalid_0's multi_logloss: 0.300344\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[73]\tvalid_0's multi_logloss: 0.298022\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[74]\tvalid_0's multi_logloss: 0.295472\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[75]\tvalid_0's multi_logloss: 0.293319\n",
      "[76]\tvalid_0's multi_logloss: 0.290383\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[77]\tvalid_0's multi_logloss: 0.28756\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[78]\tvalid_0's multi_logloss: 0.284993\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[79]\tvalid_0's multi_logloss: 0.282716\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[80]\tvalid_0's multi_logloss: 0.280159\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[81]\tvalid_0's multi_logloss: 0.277856\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[82]\tvalid_0's multi_logloss: 0.275077\n",
      "[83]\tvalid_0's multi_logloss: 0.272591\n",
      "[84]\tvalid_0's multi_logloss: 0.269571\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[85]\tvalid_0's multi_logloss: 0.268174\n",
      "[86]\tvalid_0's multi_logloss: 0.266017\n",
      "[87]\tvalid_0's multi_logloss: 0.263973\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[88]\tvalid_0's multi_logloss: 0.261492\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[89]\tvalid_0's multi_logloss: 0.258831\n",
      "[90]\tvalid_0's multi_logloss: 0.256284\n",
      "[91]\tvalid_0's multi_logloss: 0.254366\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[92]\tvalid_0's multi_logloss: 0.25268\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[93]\tvalid_0's multi_logloss: 0.250494\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[94]\tvalid_0's multi_logloss: 0.248336\n",
      "[95]\tvalid_0's multi_logloss: 0.246213\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[96]\tvalid_0's multi_logloss: 0.244728\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[97]\tvalid_0's multi_logloss: 0.242944\n",
      "[98]\tvalid_0's multi_logloss: 0.241336\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[99]\tvalid_0's multi_logloss: 0.239536\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[100]\tvalid_0's multi_logloss: 0.238125\n"
     ]
    }
   ],
   "source": [
    "train_data = lgb.Dataset(X_train, label = y_train - 1)\n",
    "test_data = lgb.Dataset(X_test, label = y_test - 1)\n",
    "params={\n",
    "    'learning_rate':0.1,\n",
    "    'lambda_l1':0.1,\n",
    "    'lambda_l2':0.2,\n",
    "    'max_depth':6,\n",
    "    'objective':'multiclass',\n",
    "    'num_class':8,  \n",
    "}\n",
    "model_lgb = lgb.train(params, train_data, valid_sets = [test_data])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_prob_lgb = model_lgb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.9964864617017849"
      ]
     },
     "metadata": {},
     "execution_count": 160
    }
   ],
   "source": [
    "roc_auc_score(label_binarize(y_test - 1, classes= list(range(8))), pred_prob_lgb, average='micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[23169,   370,     6,   121,    49,   111,   143,   472],\n",
       "       [  266, 22552,   267,   392,    39,   105,   261,   541],\n",
       "       [    6,   393,  7584,   415,     5,    26,     1,     2],\n",
       "       [  155,   670,   282, 21822,   471,   122,    31,    12],\n",
       "       [   67,    87,    20,  1145, 30133,   305,   173,    24],\n",
       "       [  255,   117,    17,   401,  1853, 25515,   145,    36],\n",
       "       [  209,    35,     2,   120,  1341,   496, 28846,    97],\n",
       "       [  169,    62,     0,    20,   408,   206,   179, 22762]])"
      ]
     },
     "metadata": {},
     "execution_count": 139
    }
   ],
   "source": [
    "pred_lgb = pred_prob_lgb.argmax(axis = 1) + 1\n",
    "conf = confusion_matrix(y_test , pred_lgb)\n",
    "conf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "metadata": {},
     "execution_count": 140
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "sn.heatmap(conf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              precision    recall  f1-score   support\n\n       Still       0.95      0.95      0.95     24441\n     Walking       0.93      0.92      0.93     24423\n         Run       0.93      0.90      0.91      8432\n        Bike       0.89      0.93      0.91     23565\n         Car       0.88      0.94      0.91     31954\n         Bus       0.95      0.90      0.92     28339\n       Train       0.97      0.93      0.95     31146\n      Subway       0.95      0.96      0.95     23806\n\n    accuracy                           0.93    196106\n   macro avg       0.93      0.93      0.93    196106\nweighted avg       0.93      0.93      0.93    196106\n\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred_lgb, target_names = list(label_dic.values())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_prob_lgb_val = model_lgb.predict(X_val)\n",
    "pred_lgb_val = pred_prob_lgb_val.argmax(axis = 1) + 1\n",
    "conf = confusion_matrix(val.df['label'] , pred_lgb_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[14998,  1331,     1,   141,     4,   197,  3138, 10026],\n",
       "       [ 3905, 14952,   258,  1285,   125,  2018,  1357,  2249],\n",
       "       [    5,   854,   218,  1672,     0,     2,    15,     8],\n",
       "       [  397,  1297,  1100,  7757,    27,     5,    27,  1421],\n",
       "       [  366,   369,    15,  1360,  8440,  4244,  3376,  2303],\n",
       "       [ 1255,   371,     2,    94,   106,  4599,  1807,   944],\n",
       "       [ 1371,  1467,     0,   112,  2812,   482, 11111,  4453],\n",
       "       [   11,   129,     0,     7,    77,    44,  1084, 20357]])"
      ]
     },
     "metadata": {},
     "execution_count": 143
    }
   ],
   "source": [
    "conf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "metadata": {},
     "execution_count": 144
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
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0kZGRVFRUAFBSUsKSJUsASE9Pp7i4GIDt27ezYMGCQWNS0hYRSzHcvh8j9dRTT3l2VgeYNWsWVVVVHDx4kO9973sA2Gw2nE6n5xyn04nNZvN81tzcDPSt69TZ2cm0ad4339aLSBGxliEk4/5r/wO8+eabnmWlB7NmzRp6enrYvLlvv4DW1lbuuusuOjo6SExM5A9/+APx8fE3XcbaMPrG0719NhAlbRGxlKH0oPuv/T8UmZmZPPLII9cNZ3R3d3uGUqqqqmhsbCQuLg6n03ndEIrdbqelpW/s3ul0MnPmTM6ePUtISAhRUVE3DMd8nYZHRMRSxnp4ZNGiRbz44ossXryYq1evetqnT59OcHBfSp01axYOh4NPP/0Ul8tFV1cXKSkpQF/C37VrFwBlZWWsWLECgKVLl3LgwIFBn6+etohYitE78K5aQ7Vlyxbmz5/P9OnTaW5uZu3atbz00kvccsst7Nu3D+h7GZmbm8u8efP45S9/SU9PD729vfzsZz/jwoULAOTm5lJUVERERATl5eWecfDCwkI2bdpEfX09HR0dg1aOAAQBYzp96JZbZo7l7X1y+6QpZocAQI/Ra3YI/O/bHjA7BABWtqvkz5/4T8lf3Yjv0frQ930+91uH/zji54039bRFxFIM9+j1tP2RkraIWMpolPL5MyVtEbEUw1BPW0QkYEz4nvYDDzyAYRh8+OGHzJ49m9TUVE6dOnXdLCAREX/hHsXqEX/kNWn/27/9G2lpaYSGhrJv3z5SUlI4ePAgeXl5JCQk8Otf/3q84hQR8cmEfhG5dOlS5syZwy233ILL5cJut9PV1cVvfvMbjhw5MmDS7j819O23t1NYuGX0IxcRuYkJnbR7enpwu91cvXqVxsZGurq6APjiiy9wuwceOOo/NdQf6rRFZOIYZOmOgOc1aXd3dxMREcHVq1dJSkrytEdGRnpN2iIiZpnQPe158+bR3d0NXL/yVFhYmGe+vIiIP5nQJX9fJuyvO3/+POfPnx+TgERERqJ3IlePiIgEmgnd0xYRCTQTekxbRCTQTOjqERGRQKOetohIAOl1W3tDLiVtEbEUDY+IiAQQt8WrR6z9e4SITDiGEeTzMZjCwkLa2to4ceKEp+32229n7969fPLJJ+zdu5epU6d6PsvLy6O+vp5Tp06xcOFCT3tiYiI1NTXU19ezfv16T3t4eDilpaXU19dTUVFBTEzMoDEpaYuIpRiG78dgioqKSE1Nva4tLy+P/fv3ExcXx/79+8nLywNg9uzZZGRkEB8fT2pqKhs2bPDszl5QUEBOTg4OhwOHw+G5Z1ZWFhcuXMDhcJCfn8+6desGjWlCDI9c+OKS2SEA0OM2f2PfXwZXmx0CAJunzTc7BErD/mp2CABccF81OwTOXesyOwQAWkbhHqM5PHL48OEber/p6enMnz8fgOLiYs9y1enp6ZSWltLd3U1TUxMNDQ0kJyfT1NREZGQkFRUVAJSUlLBkyRL27NlDeno6L7/8MgDbt2/njTfeGDQm9bRFxFJ63cE+H8MxY8YMXC4XAC6XizvuuAMAm81Gc3Oz5zyn04nNZsNms+F0Om9o//o1vb29dHZ2Mm3aNK/PV9IWEUsxhnBkZ2dTWVnpObKzs4f93KCgG3v4hmEM2O7tGm8mxPCIiEwcQxke6b/2v6/a2tqIjo7G5XIRHR1Ne3s70NeDnjnzq/0D7HY7LS0tOJ1O7Hb7De39rzl79iwhISFERUXR0dHh9fnqaYuIpYxm9cjNlJWVeZamXrFiBbt27fK0Z2RkEB4eTmxsLA6Hg6NHj+Jyuejq6iIlJQWAzMzM66758l5Lly7lwIEDgz5fPW0RsZTR3J5ly5YtzJ8/n+nTp9Pc3MzatWt55ZVX2LZtG1lZWZw5c4bHHnsMgLq6OrZt20ZdXR09PT2sXLnSs1lMbm4uRUVFREREUF5e7tkYvbCwkE2bNlFfX09HRwcZGRmDxhRE39DOmNF2Y1/xh+qRGZOnmh0CAL+dNMfsEFQ90o+/VI/UtR8d8T3+OGOpz+d+v237iJ833tTTFhFL6bH4jEglbRGxFAMlbRGRgGH1LceVtEXEUqze0x5yyV9xcfFYxCEiMircQzgCkdee9pe1hF8KCgriBz/4gWdVq/T09DELTERkOHot3tP2mrTtdjt1dXW89dZbnumY999/P6+99tp4xSciMiQW323M+/DI/fffz0cffcQ///M/09nZyR//+EeuXr3KoUOHOHTo0IDX9Z/Pn5X1D6MetIjIQNwE+XwEIq89bcMw+N3vfsc777xDfn4+bW1thIYO/u6y/3x+Ta4RkfFk8d3GfKseOXv2LI8//jh///d/z1//6h+zyEREbiZQXzD6akglf7t372b37t1jFYuIyIi5b7LcqZWoTltELMX8FX7GlpK2iFiK1atHlLRFxFICtSrEV0raImIpqh4REQkgGh4REQkgKvkTEQkgvRbvaWtjXxGxlNFa5S8uLo7q6mrP0dnZyerVq1m7di1Op9PTnpaW5rkmLy+P+vp6Tp06xcKFCz3tiYmJ1NTUUF9fz/r160f08ylpi4iljFbS/uSTT0hISCAhIYGkpCSuXLnCzp07AcjPz/d89uUmvbNnzyYjI4P4+HhSU1PZsGEDwcF9KbagoICcnBwcDgcOh4PU1NRh/3xjPjwyddLksX7EoC5+cdnsEPxG++WLZocAwE8vHzQ7BH7yrQfMDgGAM1+cNzsELv6/S2aHMGrGYovIBQsW0NjYyJkzZwY8Jz09ndLSUrq7u2lqaqKhoYHk5GSampqIjIykoqICgJKSEpYsWcKePXuGFYt62iJiKWOxCUJGRgZbt271fL9q1SqOHz9OYWGhZ38Bm81Gc3Oz5xyn04nNZsNms+F0Om9oHy4lbRGxlN4hHP2Xka6srCQ7O/uG+4WFhbF48WLeeecdoG+o4+6772bOnDm0trZ69hcIusmaJ1/uQ3Cz9uFS9YiIWMpQ6rT7LyM9kLS0NKqqqmhvbwfw/PvL699//32grwc9c+ZXS1Hb7XZaWlpwOp3Y7fYb2odLPW0RsZTRHh5Zvnz5dUMj0dHRnq8fffRRamtrASgrKyMjI4Pw8HBiY2NxOBwcPXoUl8tFV1cXKSkpAGRmZt6wleNQqKctIpYympNrIiIi+PGPf8wzzzzjaXv11VeZM2cOhmHQ1NTk+ayuro5t27ZRV1dHT08PK1euxO3uiyY3N5eioiIiIiIoLy/3VJwMRxBjPFV/RtTfjeXtfeIv1SM9bvMXjbT4vIMh8ZfqkcrLn5kdgt9Uj1y41DDie/xm5hM+n/u/mjeP+HnjTT1tEbEUrT0iIhJAzP99dmwpaYuIpbgtvjirkraIWIpW+etn7ty5JCcnU1tby759+8YqJhGRYbN2P3uQOu0jR454vn766ad54403uO2221i7di0vvvjimAcnIjJUYzGN3Z94TdphYWGer3Nycvjxj3/ML3/5SxYuXMgTT/heViMiMl56ggyfj0DkNWkHBwczdepUvvGNbxAUFMTnn38OwJUrV+jp6Rnwuv7z+X+64rHRjVhExAtjCEcg8jqmHRUVxUcffURQUBCGYTBjxgza2tqYPHnyTRdB+VL/+fz+MLlGRCaOQB328JXXpD1r1qybtrvdbh599NExCUhEZCRU8ncTV69epampaZRDEREZOWunbNVpi4jFTOjhERGRQNNr8b62kraIWIp62iIiAcRQT1tEJHCopy0iEkCsXvKnPSJFxFJGc0bk6dOnqampobq6msrKSgBuv/129u7dyyeffMLevXuZOnWq5/y8vDzq6+s5deoUCxcu9LQnJiZSU1NDfX0969evH9HPp6QtIpbSg+Hz4Ysf/OAHJCQk8MADfdvT5eXlsX//fuLi4ti/fz95eXkAzJ49m4yMDOLj40lNTWXDhg0EB/el2IKCAnJycnA4HDgcDlJTU4f98ylpi4ilGEP4ZzjS09MpLi4GoLi4mCVLlnjaS0tL6e7upqmpiYaGBpKTk4mOjiYyMpKKigoASkpKPNcMx5iPaYcEmf//hem3RpodAgCuSxfMDsFvzJl+t9kh8P65Y2aHAEB1jPnr86y5MtPsEADYMQob+47mi0jDMNi7dy+GYfCf//mfbNy4kRkzZuByuQBwuVzccccdANhsNk9iBnA6ndhsNq5du4bT6byhfbj0IlJELGUoPejs7GxycnI837/55puexe6gb+OX1tZWvvnNb7Jv3z5OnTo14L1utoieYRgDtg+XkraIWMpQetr9VyS9mdbWVgDOnTvHzp07SU5Opq2tjejoaFwuF9HR0bS3twN9PeiZM7/6jcVut9PS0oLT6cRut9/QPlzmj12IiIyiXsPw+fDm1ltvZcqUKZ6vFy5cSG1tLWVlZaxYsQKAFStWsGvXLgDKysrIyMggPDyc2NhYHA4HR48exeVy0dXVRUpKCgCZmZmea4ZDPW0RsZTRqtOeMWMGO3fuBCA0NJQtW7bwwQcfUFlZybZt28jKyuLMmTM89ljfRi91dXVs27aNuro6enp6WLlyJW53X78/NzeXoqIiIiIiKC8vp7y8fNhxBTHGKxneOfXesby9T7xt2DCe/OFFpH/8SfjHi8i6i2fMDgHwlxeR4WaHAMCOM++N+B4Zd6X7fG7pmeH3eM2inraIWIqmsYuIBBCrT2NX0hYRS9EqfyIiAWSwqpBAp6QtIpZi9eERr3XaycnJ3HbbbQBMmjSJl19+mbKyMl555RUiI/1jariISH/uIRyByGvS/v3vf8+VK1cAWL9+PVFRUaxbt44rV67w9ttvj0uAIiJDMdYLRpnN6/BIcHAwvb29ANx///0kJSUB8Kc//Ynq6uqxj05EZIgm9PBIbW0tTz75JADHjx/3JG2Hw8G1a9cGvC47O5vKykoqKyt5YsVjoxetiMggDMPw+QhEXnvaTz/9NOvXr+df/uVf+Pzzz/nLX/5Cc3Mzzc3NPP300wNe138RFn+YESkiE0evxXvaXpP2X//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     },
     "metadata": {}
    }
   ],
   "source": [
    "sn.heatmap(conf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              precision    recall  f1-score   support\n\n       Still       0.67      0.50      0.58     29836\n     Walking       0.72      0.57      0.64     26149\n         Run       0.14      0.08      0.10      2774\n        Bike       0.62      0.64      0.63     12031\n         Car       0.73      0.41      0.53     20473\n         Bus       0.40      0.50      0.44      9178\n       Train       0.51      0.51      0.51     21808\n      Subway       0.49      0.94      0.64     21709\n\n    accuracy                           0.57    143958\n   macro avg       0.53      0.52      0.51    143958\nweighted avg       0.60      0.57      0.57    143958\n\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_val, pred_lgb_val, target_names = list(label_dic.values())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.8852316487979953"
      ]
     },
     "metadata": {},
     "execution_count": 163
    }
   ],
   "source": [
    "roc_auc_score(label_binarize(y_val - 1, classes = list(range(8))), pred_prob_lgb_val, average='micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fac7a65d310>]"
      ]
     },
     "metadata": {},
     "execution_count": 186
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1440x864 with 1 Axes>",
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545.152208 \nL 151.455757 206.284675 \nL 151.490994 460.435325 \nL 151.50509 460.435325 \nL 151.526232 206.284675 \nL 151.610803 545.152208 \nL 151.660136 460.435325 \nL 151.716516 545.152208 \nL 151.751754 545.152208 \nL 151.815182 460.435325 \nL 151.857467 545.152208 \nL 151.87861 545.152208 \nL 151.9068 460.435325 \nL 151.984324 545.152208 \nL 152.252131 545.152208 \nL 152.273273 206.284675 \nL 152.350797 629.869091 \nL 152.357844 629.869091 \nL 152.477653 629.869091 \nL 152.541081 36.850909 \nL 152.583366 629.869091 \nL 153.795546 629.869091 \nL 153.880117 121.567792 \nL 153.90126 629.869091 \nL 154.387541 629.869091 \nL 154.422779 545.152208 \nL 154.493255 629.869091 \nL 155.930957 629.869091 \nL 156.022575 121.567792 \nL 156.03667 629.869091 \nL 156.149431 545.152208 \nL 156.466571 545.152208 \nL 156.473619 629.869091 \nL 156.572284 545.152208 \nL 157.037423 545.152208 \nL 157.107899 629.869091 \nL 157.143137 545.152208 \nL 157.284088 545.152208 \nL 157.319326 206.284675 \nL 157.389801 545.152208 \nL 157.460277 629.869091 \nL 157.495515 545.152208 \nL 157.77037 545.152208 \nL 157.847893 121.567792 \nL 157.85494 629.869091 \nL 157.876083 629.869091 \nL 157.946558 629.869091 \nL 157.974749 545.152208 \nL 158.052272 629.869091 \nL 158.235508 629.869091 \nL 158.291889 545.152208 \nL 158.341222 629.869091 \nL 158.40465 629.869091 \nL 158.453983 545.152208 \nL 158.510363 629.869091 \nL 158.524458 629.869091 \nL 158.587886 545.152208 \nL 158.630172 629.869091 \nL 158.897979 629.869091 \nL 159.01074 36.850909 \nL 159.088263 36.850909 \nL 159.095311 121.567792 \nL 159.193976 36.850909 \nL 159.2715 36.850909 \nL 159.384261 629.869091 \nL 159.419498 629.869091 \nL 159.433593 36.850909 \nL 159.525212 629.869091 \nL 159.652068 629.869091 \nL 159.807114 36.850909 \nL 159.90578 36.850909 \nL 159.912827 291.001558 \nL 160.011493 36.850909 \nL 160.314538 36.850909 \nL 160.321586 375.718442 \nL 160.420252 36.850909 \nL 160.533013 36.850909 \nL 160.554155 375.718442 \nL 160.638726 36.850909 \nL 160.673964 36.850909 \nL 160.723297 545.152208 \nL 160.779677 36.850909 \nL 160.885391 36.850909 \nL 160.906533 206.284675 \nL 160.991104 36.850909 \nL 160.998152 36.850909 \nL 161.096817 206.284675 \nL 161.103865 36.850909 \nL 161.202531 36.850909 \nL 161.287101 545.152208 \nL 161.315292 206.284675 \nL 161.37872 375.718442 \nL 161.428053 36.850909 \nL 161.46329 36.850909 \nL 161.576051 206.284675 \nL 161.583099 206.284675 \nL 161.674717 36.850909 \nL 161.597194 375.718442 \nL 161.688812 206.284675 \nL 161.717003 206.284675 \nL 161.766335 545.152208 \nL 161.829764 36.850909 \nL 161.850906 36.850909 \nL 161.865001 545.152208 \nL 161.963667 206.284675 \nL 161.970715 206.284675 \nL 161.991857 545.152208 \nL 162.034143 36.850909 \nL 162.076428 36.850909 \nL 162.090523 36.850909 \nL 162.210332 375.718442 \nL 162.33014 36.850909 \nL 162.344235 36.850909 \nL 162.414711 206.284675 \nL 162.449949 36.850909 \nL 162.456996 36.850909 \nL 162.492234 206.284675 \nL 162.56271 36.850909 \nL 162.633185 36.850909 \nL 162.640233 206.284675 \nL 162.738899 36.850909 \nL 162.830517 36.850909 \nL 162.943278 206.284675 \nL 162.957373 206.284675 \nL 163.020801 375.718442 \nL 163.070134 36.850909 \nL 163.119467 36.850909 \nL 163.232228 206.284675 \nL 163.239275 206.284675 \nL 163.352036 36.850909 \nL 163.359084 36.850909 \nL 163.415464 375.718442 \nL 163.464797 36.850909 \nL 163.471845 36.850909 \nL 163.535273 629.869091 \nL 163.577558 36.850909 \nL 163.619844 36.850909 \nL 163.704414 375.718442 \nL 163.732604 206.284675 \nL 163.80308 36.850909 \nL 163.781937 629.869091 \nL 163.824223 206.284675 \nL 163.838318 545.152208 \nL 163.859461 36.850909 \nL 163.929936 36.850909 \nL 163.979269 36.850909 \nL 164.070887 375.718442 \nL 164.084982 36.850909 \nL 164.09203 36.850909 \nL 164.204791 206.284675 \nL 164.211838 206.284675 \nL 164.268219 36.850909 \nL 164.317552 206.284675 \nL 164.331647 206.284675 \nL 164.444408 36.850909 \nL 164.458503 36.850909 \nL 164.479646 375.718442 \nL 164.564216 36.850909 \nL 164.754501 36.850909 \nL 164.867261 206.284675 \nL 164.930689 206.284675 \nL 165.04345 36.850909 \nL 165.128021 36.850909 \nL 165.156211 206.284675 \nL 165.233735 36.850909 \nL 165.311258 36.850909 \nL 165.318305 206.284675 \nL 165.416971 36.850909 \nL 165.508589 36.850909 \nL 165.515637 206.284675 \nL 165.614303 36.850909 \nL 165.705921 36.850909 \nL 165.811634 206.284675 \nL 165.924395 36.850909 \nL 165.945538 36.850909 \nL 165.952586 375.718442 \nL 166.051251 36.850909 \nL 166.213345 36.850909 \nL 166.333154 545.152208 \nL 166.882863 545.152208 \nL 166.896958 36.850909 \nL 166.904006 629.869091 \nL 166.988577 629.869091 \nL 167.002672 629.869091 \nL 167.023815 545.152208 \nL 167.108385 629.869091 \nL 167.143623 629.869091 \nL 167.256384 545.152208 \nL 167.340955 545.152208 \nL 167.439621 36.850909 \nL 167.362097 629.869091 \nL 167.446668 375.718442 \nL 167.545334 629.869091 \nL 167.559429 545.152208 \nL 167.587619 545.152208 \nL 167.70038 375.718442 \nL 167.735618 375.718442 \nL 167.742666 545.152208 \nL 167.841331 375.718442 \nL 168.722276 375.718442 \nL 168.729324 460.435325 \nL 168.82799 375.718442 \nL 168.954846 375.718442 \nL 168.975988 460.435325 \nL 169.060559 375.718442 \nL 169.131035 375.718442 \nL 169.14513 36.850909 \nL 169.236748 460.435325 \nL 169.349509 375.718442 \nL 169.497508 375.718442 \nL 169.525698 460.435325 \nL 169.603221 375.718442 \nL 169.631411 375.718442 \nL 169.638459 545.152208 \nL 169.737125 375.718442 \nL 169.75122 375.718442 \nL 169.765315 460.435325 \nL 169.856933 375.718442 \nL 171.125494 375.718442 \nL 171.132541 460.435325 \nL 171.231207 375.718442 \nL 171.238255 375.718442 \nL 171.25235 460.435325 \nL 171.266445 36.850909 \nL 171.343968 375.718442 \nL 171.351016 375.718442 \nL 171.421491 460.435325 \nL 171.456729 375.718442 \nL 171.463777 375.718442 \nL 171.477872 460.435325 \nL 171.56949 375.718442 \nL 171.703394 375.718442 \nL 171.816155 460.435325 \nL 171.928916 375.718442 \nL 171.950058 375.718442 \nL 172.055772 460.435325 \nL 172.161485 375.718442 \nL 172.168533 460.435325 \nL 172.267198 375.718442 \nL 172.274246 375.718442 \nL 172.330626 460.435325 \nL 172.379959 375.718442 \nL 172.394054 375.718442 \nL 172.401102 460.435325 \nL 172.499768 375.718442 \nL 172.506815 375.718442 \nL 172.584339 629.869091 \nL 172.619576 545.152208 \nL 172.661862 545.152208 \nL 172.774623 375.718442 \nL 172.802813 375.718442 \nL 172.908526 460.435325 \nL 172.986049 375.718442 \nL 173.000145 545.152208 \nL 173.007192 460.435325 \nL 173.021287 375.718442 \nL 173.112906 545.152208 \nL 173.225666 375.718442 \nL 173.296142 460.435325 \nL 173.33138 375.718442 \nL 173.38776 375.718442 \nL 173.444141 460.435325 \nL 173.493474 375.718442 \nL 173.507569 375.718442 \nL 173.599187 460.435325 \nL 173.613282 375.718442 \nL 173.881089 375.718442 \nL 173.958613 545.152208 \nL 173.986803 375.718442 \nL 174.184134 375.718442 \nL 174.19823 36.850909 \nL 174.205277 545.152208 \nL 174.289848 375.718442 \nL 174.339181 375.718442 \nL 174.381466 545.152208 \nL 174.444894 375.718442 \nL 174.59994 375.718442 \nL 174.628131 545.152208 \nL 174.614036 36.850909 \nL 174.705654 460.435325 \nL 174.811367 375.718442 \nL 174.825462 545.152208 \nL 174.917081 375.718442 \nL 175.043937 375.718442 \nL 175.058032 36.850909 \nL 175.14965 375.718442 \nL 175.198983 375.718442 \nL 175.206031 460.435325 \nL 175.304696 375.718442 \nL 175.4386 375.718442 \nL 175.445648 460.435325 \nL 175.502028 36.850909 \nL 175.544313 375.718442 \nL 175.579551 375.718442 \nL 175.692312 460.435325 \nL 175.805073 375.718442 \nL 175.819168 375.718442 \nL 175.861454 545.152208 \nL 175.924882 375.718442 \nL 176.058785 375.718442 \nL 176.129261 460.435325 \nL 176.164499 375.718442 \nL 176.298402 375.718442 \nL 176.404116 460.435325 \nL 176.453448 545.152208 \nL 176.516877 375.718442 \nL 176.65078 375.718442 \nL 176.714208 545.152208 \nL 176.756494 375.718442 \nL 176.946778 375.718442 \nL 177.003158 545.152208 \nL 177.052491 375.718442 \nL 177.094776 375.718442 \nL 177.101824 545.152208 \nL 177.20049 375.718442 \nL 177.214585 375.718442 \nL 177.320298 460.435325 \nL 177.397821 375.718442 \nL 177.426012 460.435325 \nL 177.433059 460.435325 \nL 177.482392 375.718442 \nL 177.524677 545.152208 \nL 177.538773 460.435325 \nL 177.616296 460.435325 \nL 177.623343 36.850909 \nL 177.651534 545.152208 \nL 177.722009 375.718442 \nL 177.792485 375.718442 \nL 177.877055 460.435325 \nL 177.898198 375.718442 \nL 177.919341 375.718442 \nL 177.961626 545.152208 \nL 178.032102 460.435325 \nL 178.046197 545.152208 \nL 178.144863 375.718442 \nL 178.201243 375.718442 \nL 178.215338 36.850909 \nL 178.257624 545.152208 \nL 178.306957 375.718442 \nL 178.398575 375.718442 \nL 178.504288 460.435325 \nL 178.574764 545.152208 \nL 178.617049 375.718442 \nL 178.645239 375.718442 \nL 178.758 629.869091 \nL 178.786191 629.869091 \nL 178.913047 375.718442 \nL 178.920094 375.718442 \nL 179.032855 629.869091 \nL 179.053998 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189.4421 545.152208 \nL 189.766287 545.152208 \nL 189.850858 375.718442 \nL 189.857906 629.869091 \nL 189.872001 629.869091 \nL 190.090475 629.869091 \nL 190.160951 375.718442 \nL 190.196189 629.869091 \nL 190.210284 629.869091 \nL 190.33714 375.718442 \nL 190.344187 375.718442 \nL 190.372378 36.850909 \nL 190.456948 545.152208 \nL 190.471043 545.152208 \nL 190.499234 375.718442 \nL 190.576757 545.152208 \nL 190.590852 545.152208 \nL 190.597899 629.869091 \nL 190.696565 545.152208 \nL 190.703613 545.152208 \nL 190.724756 121.567792 \nL 190.816374 629.869091 \nL 191.063038 629.869091 \nL 191.175799 36.850909 \nL 195.44662 36.850909 \nL 195.559381 629.869091 \nL 196.151376 629.869091 \nL 196.158424 121.567792 \nL 196.257089 629.869091 \nL 196.334613 629.869091 \nL 196.34166 121.567792 \nL 196.440326 629.869091 \nL 196.856132 629.869091 \nL 196.968893 121.567792 \nL 197.081654 629.869091 \nL 197.356509 629.869091 \nL 197.46927 36.850909 \nL 198.047169 36.850909 \nL 198.096502 629.869091 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629.869091 \nL 200.429244 36.850909 \nL 200.443339 375.718442 \nL 200.450387 375.718442 \nL 200.563148 36.850909 \nL 200.584291 36.850909 \nL 200.626576 629.869091 \nL 200.690004 545.152208 \nL 200.802765 36.850909 \nL 200.859145 36.850909 \nL 200.971906 545.152208 \nL 201.07762 36.850909 \nL 201.084667 375.718442 \nL 201.091715 375.718442 \nL 201.16219 629.869091 \nL 201.155143 36.850909 \nL 201.197428 545.152208 \nL 201.204476 375.718442 \nL 201.211523 629.869091 \nL 201.303142 545.152208 \nL 201.317237 545.152208 \nL 201.380665 629.869091 \nL 201.42295 545.152208 \nL 201.45114 545.152208 \nL 201.500473 36.850909 \nL 201.563901 375.718442 \nL 201.570949 375.718442 \nL 201.620282 629.869091 \nL 201.68371 545.152208 \nL 201.796471 375.718442 \nL 201.916279 629.869091 \nL 201.923327 629.869091 \nL 202.036088 545.152208 \nL 202.043135 545.152208 \nL 202.155896 36.850909 \nL 202.254562 545.152208 \nL 202.26161 36.850909 \nL 202.282752 36.850909 \nL 202.381418 545.152208 \nL 202.395513 375.718442 \nL 202.416656 375.718442 \nL 202.480084 36.850909 \nL 202.515322 545.152208 \nL 202.564655 36.850909 \nL 202.536464 629.869091 \nL 202.621035 545.152208 \nL 202.649225 545.152208 \nL 202.719701 36.850909 \nL 202.754939 545.152208 \nL 202.846557 545.152208 \nL 202.853605 629.869091 \nL 202.938175 545.152208 \nL 202.95227 36.850909 \nL 203.050936 375.718442 \nL 203.170745 629.869091 \nL 203.177792 629.869091 \nL 203.290553 36.850909 \nL 203.325791 545.152208 \nL 203.403314 375.718442 \nL 203.438552 375.718442 \nL 203.4456 36.850909 \nL 203.544265 375.718442 \nL 203.579503 375.718442 \nL 203.586551 36.850909 \nL 203.678169 629.869091 \nL 203.685217 629.869091 \nL 203.783882 36.850909 \nL 203.797978 545.152208 \nL 203.833215 545.152208 \nL 203.840263 36.850909 \nL 203.945976 375.718442 \nL 203.960071 375.718442 \nL 203.995309 545.152208 \nL 204.016452 36.850909 \nL 204.05169 36.850909 \nL 204.07988 36.850909 \nL 204.129213 545.152208 \nL 204.192641 375.718442 \nL 204.220831 375.718442 \nL 204.291307 36.850909 \nL 204.326544 375.718442 \nL 204.34064 375.718442 \nL 204.439305 629.869091 \nL 204.446353 36.850909 \nL 204.545019 375.718442 \nL 204.636637 545.152208 \nL 204.700065 36.850909 \nL 204.74235 545.152208 \nL 204.749398 545.152208 \nL 204.798731 36.850909 \nL 204.862159 375.718442 \nL 204.869206 375.718442 \nL 204.890349 36.850909 \nL 204.981967 545.152208 \nL 204.989015 545.152208 \nL 205.094728 375.718442 \nL 205.158156 545.152208 \nL 205.200442 375.718442 \nL 205.221584 375.718442 \nL 205.228632 545.152208 \nL 205.32025 291.001558 \nL 205.327298 375.718442 \nL 205.341393 291.001558 \nL 205.433011 375.718442 \nL 205.440059 375.718442 \nL 205.447106 460.435325 \nL 205.545772 375.718442 \nL 205.686723 375.718442 \nL 205.750151 545.152208 \nL 205.792437 375.718442 \nL 206.50424 375.718442 \nL 206.560621 545.152208 \nL 206.567668 121.567792 \nL 206.609954 375.718442 \nL 206.638144 375.718442 \nL 206.652239 291.001558 \nL 206.743857 375.718442 \nL 206.757952 375.718442 \nL 206.765 291.001558 \nL 206.863666 375.718442 \nL 206.976427 545.152208 \nL 207.089188 375.718442 \nL 207.237186 375.718442 \nL 207.244234 460.435325 \nL 207.3429 375.718442 \nL 207.490898 375.718442 \nL 207.533184 545.152208 \nL 207.512041 36.850909 \nL 207.596612 375.718442 \nL 207.603659 291.001558 \nL 207.702325 375.718442 \nL 207.737563 375.718442 \nL 207.836229 545.152208 \nL 207.843276 375.718442 \nL 207.850324 375.718442 \nL 207.885562 545.152208 \nL 207.956037 375.718442 \nL 207.991275 375.718442 \nL 208.047656 545.152208 \nL 208.096989 375.718442 \nL 208.146321 375.718442 \nL 208.244987 460.435325 \nL 208.252035 375.718442 \nL 208.350701 375.718442 \nL 208.371843 460.435325 \nL 208.456414 375.718442 \nL 208.533937 375.718442 \nL 208.632603 545.152208 \nL 208.639651 375.718442 \nL 208.674888 206.284675 \nL 208.681936 545.152208 \nL 208.745364 375.718442 \nL 208.752412 375.718442 \nL 208.759459 291.001558 \nL 208.801744 545.152208 \nL 208.858125 375.718442 \nL 208.87222 375.718442 \nL 208.977933 545.152208 \nL 209.062504 375.718442 \nL 209.083647 545.152208 \nL 209.097742 545.152208 \nL 209.203455 375.718442 \nL 209.309169 545.152208 \nL 209.337359 36.850909 \nL 209.42193 375.718442 \nL 209.5065 375.718442 \nL 209.527643 545.152208 \nL 209.534691 291.001558 \nL 209.612214 375.718442 \nL 209.626309 375.718442 \nL 209.633356 36.850909 \nL 209.640404 545.152208 \nL 209.732022 375.718442 \nL 209.746117 375.718442 \nL 209.753165 460.435325 \nL 209.851831 375.718442 \nL 209.880021 375.718442 \nL 209.943449 545.152208 \nL 209.971639 291.001558 \nL 209.985734 375.718442 \nL 210.133733 375.718442 \nL 210.218304 545.152208 \nL 210.232399 291.001558 \nL 210.239446 291.001558 \nL 210.246494 36.850909 \nL 210.260589 375.718442 \nL 210.34516 291.001558 \nL 210.359255 291.001558 \nL 210.472016 375.718442 \nL 210.479063 375.718442 \nL 210.584777 460.435325 \nL 210.697538 375.718442 \nL 210.775061 375.718442 \nL 210.866679 545.152208 \nL 210.880774 375.718442 \nL 210.993535 375.718442 \nL 211.049916 545.152208 \nL 211.099249 375.718442 \nL 211.106296 375.718442 \nL 211.148582 545.152208 \nL 211.21201 375.718442 \nL 211.26839 375.718442 \nL 211.381151 545.152208 \nL 211.395246 545.152208 \nL 211.444579 629.869091 \nL 211.486864 545.152208 \nL 211.599625 375.718442 \nL 211.747624 375.718442 \nL 211.754672 36.850909 \nL 211.853337 375.718442 \nL 211.973146 375.718442 \nL 212.092954 629.869091 \nL 212.212763 629.869091 \nL 212.219811 375.718442 \nL 212.318476 629.869091 \nL 212.339619 629.869091 \nL 212.417142 375.718442 \nL 212.445332 629.869091 \nL 213.241707 629.869091 \nL 213.255802 545.152208 \nL 213.34742 629.869091 \nL 214.524362 629.869091 \nL 214.601885 375.718442 \nL 214.630076 629.869091 \nL 215.222071 629.869091 \nL 215.257308 545.152208 \nL 215.327784 629.869091 \nL 215.447593 629.869091 \nL 215.45464 36.850909 \nL 215.553306 460.435325 \nL 215.637877 629.869091 \nL 215.666067 545.152208 \nL 215.680162 545.152208 \nL 215.792923 375.718442 \nL 215.814066 375.718442 \nL 215.856351 545.152208 \nL 215.926827 460.435325 \nL 215.940922 460.435325 \nL 215.947969 545.152208 \nL 215.955017 375.718442 \nL 216.039588 375.718442 \nL 216.06073 375.718442 \nL 216.173491 629.869091 \nL 216.286252 375.718442 \nL 216.363775 375.718442 \nL 216.441298 545.152208 \nL 216.469489 375.718442 \nL 216.532917 375.718442 \nL 216.547012 460.435325 \nL 216.63863 375.718442 \nL 216.737296 375.718442 \nL 216.751391 545.152208 \nL 216.850057 460.435325 \nL 216.857104 545.152208 \nL 216.892342 375.718442 \nL 216.95577 460.435325 \nL 216.991008 460.435325 \nL 217.012151 291.001558 \nL 217.103769 375.718442 \nL 217.117864 375.718442 \nL 217.223577 545.152208 \nL 217.336338 375.718442 \nL 217.420909 460.435325 \nL 217.442052 375.718442 \nL 217.604146 375.718442 \nL 217.716907 545.152208 \nL 217.829668 375.718442 \nL 217.928333 545.152208 \nL 217.935381 375.718442 \nL 217.991761 375.718442 \nL 217.998809 36.850909 \nL 218.069285 545.152208 \nL 218.097475 375.718442 \nL 218.132713 375.718442 \nL 218.160903 545.152208 \nL 218.238426 375.718442 \nL 218.280711 375.718442 \nL 218.386425 545.152208 \nL 218.499186 375.718442 \nL 218.562614 375.718442 \nL 218.583756 545.152208 \nL 218.675375 460.435325 \nL 218.788136 375.718442 \nL 218.795183 545.152208 \nL 218.893849 375.718442 \nL 218.900897 375.718442 \nL 219.013657 545.152208 \nL 219.126418 375.718442 \nL 219.133466 375.718442 \nL 219.147561 36.850909 \nL 219.239179 375.718442 \nL 219.274417 375.718442 \nL 219.316702 545.152208 \nL 219.387178 460.435325 \nL 219.394226 460.435325 \nL 219.415368 545.152208 \nL 219.492891 375.718442 \nL 219.6127 629.869091 \nL 219.781841 629.869091 \nL 219.908697 375.718442 \nL 219.95803 375.718442 \nL 220.070791 545.152208 \nL 220.113077 545.152208 \nL 220.120124 36.850909 \nL 220.21879 545.152208 \nL 220.338599 545.152208 \nL 220.416122 375.718442 \nL 220.451359 460.435325 \nL 220.465455 460.435325 \nL 220.486597 545.152208 \nL 220.47955 375.718442 \nL 220.56412 375.718442 \nL 220.613453 375.718442 \nL 220.726214 545.152208 \nL 220.740309 545.152208 \nL 220.79669 291.001558 \nL 220.85307 375.718442 \nL 220.909451 375.718442 \nL 220.986974 460.435325 \nL 221.015164 375.718442 \nL 221.078592 375.718442 \nL 221.092687 545.152208 \nL 221.184306 375.718442 \nL 221.233639 375.718442 \nL 221.240686 545.152208 \nL 221.325257 291.001558 \nL 221.339352 375.718442 \nL 221.395732 375.718442 \nL 221.452113 545.152208 \nL 221.501446 375.718442 \nL 221.578969 375.718442 \nL 221.607159 460.435325 \nL 221.684682 375.718442 \nL 221.741063 375.718442 \nL 221.853824 460.435325 \nL 221.924299 460.435325 \nL 222.03706 375.718442 \nL 222.044108 375.718442 \nL 222.114583 545.152208 \nL 222.149821 375.718442 \nL 222.234392 375.718442 \nL 222.241439 121.567792 \nL 222.304868 545.152208 \nL 222.340105 375.718442 \nL 222.466961 375.718442 \nL 222.523342 545.152208 \nL 222.537437 291.001558 \nL 222.572675 375.718442 \nL 222.607913 375.718442 \nL 222.685436 460.435325 \nL 222.713626 375.718442 \nL 222.762959 375.718442 \nL 222.777054 460.435325 \nL 222.868672 375.718442 \nL 222.87572 375.718442 \nL 222.889815 545.152208 \nL 222.981433 375.718442 \nL 223.009623 375.718442 \nL 223.058956 545.152208 \nL 223.122384 460.435325 \nL 223.129432 460.435325 \nL 223.143527 545.152208 \nL 223.242193 375.718442 \nL 223.362001 545.152208 \nL 223.390192 545.152208 \nL 223.397239 375.718442 \nL 223.411334 629.869091 \nL 223.495905 629.869091 \nL 223.517048 629.869091 \nL 223.524095 36.850909 \nL 223.629809 375.718442 \nL 223.721427 375.718442 \nL 223.834188 629.869091 \nL 223.841235 629.869091 \nL 223.862378 375.718442 \nL 223.946949 629.869091 \nL 224.383897 629.869091 \nL 224.397993 545.152208 \nL 224.489611 629.869091 \nL 224.553039 629.869091 \nL 224.567134 545.152208 \nL 224.658752 629.869091 \nL 224.736275 629.869091 \nL 224.757418 545.152208 \nL 224.841989 629.869091 \nL 225.511507 629.869091 \nL 225.581982 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229.817565 545.152208 \nL 229.916231 375.718442 \nL 230.000802 375.718442 \nL 230.014897 545.152208 \nL 230.106515 375.718442 \nL 230.12061 375.718442 \nL 230.240419 545.152208 \nL 230.35318 375.718442 \nL 230.40956 375.718442 \nL 230.451846 460.435325 \nL 230.515274 375.718442 \nL 230.543464 375.718442 \nL 230.656225 460.435325 \nL 230.684415 460.435325 \nL 230.783081 375.718442 \nL 230.790129 460.435325 \nL 230.888794 460.435325 \nL 231.001555 375.718442 \nL 231.050888 545.152208 \nL 231.107269 375.718442 \nL 231.114316 375.718442 \nL 231.227077 545.152208 \nL 231.24822 375.718442 \nL 231.332791 545.152208 \nL 231.360981 545.152208 \nL 231.410314 375.718442 \nL 231.466694 545.152208 \nL 231.776787 545.152208 \nL 231.896595 375.718442 \nL 231.91069 375.718442 \nL 232.023451 460.435325 \nL 232.044594 545.152208 \nL 232.136212 375.718442 \nL 232.150307 375.718442 \nL 232.185545 545.152208 \nL 232.256021 375.718442 \nL 232.291259 375.718442 \nL 232.305354 545.152208 \nL 232.396972 375.718442 \nL 232.566113 375.718442 \nL 232.636589 545.152208 \nL 232.671827 375.718442 \nL 232.77754 375.718442 \nL 232.826873 545.152208 \nL 232.883254 375.718442 \nL 232.890301 375.718442 \nL 233.003062 460.435325 \nL 233.01011 460.435325 \nL 233.0383 545.152208 \nL 233.045347 375.718442 \nL 233.108775 375.718442 \nL 233.172204 375.718442 \nL 233.249727 545.152208 \nL 233.284964 460.435325 \nL 233.29906 460.435325 \nL 233.334297 545.152208 \nL 233.313155 375.718442 \nL 233.404773 460.435325 \nL 233.461153 460.435325 \nL 233.552772 375.718442 \nL 233.559819 545.152208 \nL 233.580962 545.152208 \nL 233.693723 375.718442 \nL 233.736008 375.718442 \nL 233.743056 460.435325 \nL 233.841722 375.718442 \nL 233.862864 375.718442 \nL 233.898102 460.435325 \nL 233.968578 375.718442 \nL 233.996768 375.718442 \nL 234.017911 545.152208 \nL 234.109529 460.435325 \nL 234.137719 460.435325 \nL 234.25048 375.718442 \nL 234.349146 375.718442 \nL 234.433717 545.152208 \nL 234.461907 460.435325 \nL 234.48305 460.435325 \nL 234.581715 375.718442 \nL 234.588763 545.152208 \nL 234.687429 375.718442 \nL 234.85657 375.718442 \nL 234.969331 460.435325 \nL 235.025712 460.435325 \nL 235.138472 375.718442 \nL 235.17371 375.718442 \nL 235.223043 545.152208 \nL 235.279424 375.718442 \nL 235.286471 375.718442 \nL 235.371042 545.152208 \nL 235.392185 375.718442 \nL 235.49085 375.718442 \nL 235.561326 545.152208 \nL 235.596564 375.718442 \nL 235.667039 375.718442 \nL 235.709325 460.435325 \nL 235.772753 375.718442 \nL 235.850276 375.718442 \nL 235.963037 460.435325 \nL 236.026465 36.850909 \nL 236.075798 375.718442 \nL 236.287225 375.718442 \nL 236.399986 460.435325 \nL 236.414081 460.435325 \nL 236.505699 375.718442 \nL 236.519794 460.435325 \nL 236.540937 460.435325 \nL 236.653698 375.718442 \nL 236.843982 375.718442 \nL 236.900362 545.152208 \nL 236.956743 460.435325 \nL 237.069504 375.718442 \nL 237.104741 375.718442 \nL 237.217502 545.152208 \nL 237.22455 545.152208 \nL 237.344358 375.718442 \nL 237.428929 375.718442 \nL 237.450072 460.435325 \nL 237.534643 375.718442 \nL 237.612166 375.718442 \nL 237.619213 460.435325 \nL 237.717879 375.718442 \nL 237.781307 375.718442 \nL 237.894068 460.435325 \nL 237.985686 291.001558 \nL 238.006829 375.718442 \nL 238.11959 375.718442 \nL 238.14778 36.850909 \nL 238.232351 545.152208 \nL 238.309874 375.718442 \nL 238.338064 629.869091 \nL 238.359207 629.869091 \nL 238.479015 375.718442 \nL 238.584729 545.152208 \nL 238.683395 375.718442 \nL 238.690442 545.152208 \nL 238.704537 545.152208 \nL 238.824346 375.718442 \nL 238.887774 375.718442 \nL 239.000535 545.152208 \nL 239.099201 375.718442 \nL 239.106248 545.152208 \nL 239.120343 545.152208 \nL 239.233104 375.718442 \nL 239.254247 545.152208 \nL 239.345865 460.435325 \nL 239.430436 460.435325 \nL 239.500912 545.152208 \nL 239.444531 375.718442 \nL 239.536149 460.435325 \nL 239.606625 460.435325 \nL 239.719386 545.152208 \nL 239.768719 545.152208 \nL 239.88148 629.869091 \nL 239.994241 629.869091 \nL 240.121097 375.718442 \nL 240.142239 375.718442 \nL 240.255 460.435325 \nL 240.262048 460.435325 \nL 240.269095 36.850909 \nL 240.367761 375.718442 \nL 240.445284 375.718442 \nL 240.558045 460.435325 \nL 240.614426 460.435325 \nL 240.677854 375.718442 \nL 240.720139 460.435325 \nL 240.748329 460.435325 \nL 240.86109 375.718442 \nL 240.868138 375.718442 \nL 240.903376 460.435325 \nL 240.973851 375.718442 \nL 240.987946 375.718442 \nL 241.06547 545.152208 \nL 241.09366 375.718442 \nL 241.157088 375.718442 \nL 241.269849 460.435325 \nL 241.368515 460.435325 \nL 241.446038 375.718442 \nL 241.474228 460.435325 \nL 241.502418 460.435325 \nL 241.537656 375.718442 \nL 241.608132 460.435325 \nL 241.657465 460.435325 \nL 241.770226 375.718442 \nL 241.784321 375.718442 \nL 241.875939 460.435325 \nL 241.890034 375.718442 \nL 241.911177 375.718442 \nL 242.023938 460.435325 \nL 242.038033 460.435325 \nL 242.150794 375.718442 \nL 242.164889 375.718442 \nL 242.27765 629.869091 \nL 242.298792 629.869091 \nL 242.390411 36.850909 \nL 242.418601 375.718442 \nL 242.425649 375.718442 \nL 242.496124 545.152208 \nL 242.531362 375.718442 \nL 242.601838 375.718442 \nL 242.644123 545.152208 \nL 242.714598 460.435325 \nL 242.721646 460.435325 \nL 242.728694 545.152208 \nL 242.827359 460.435325 \nL 242.869645 375.718442 \nL 242.876692 545.152208 \nL 242.933073 460.435325 \nL 242.947168 460.435325 \nL 243.038786 545.152208 \nL 243.059929 375.718442 \nL 243.081072 375.718442 \nL 243.179737 629.869091 \nL 243.1445 36.850909 \nL 243.193832 545.152208 \nL 243.271356 375.718442 \nL 243.207928 629.869091 \nL 243.306593 460.435325 \nL 243.313641 460.435325 \nL 243.426402 545.152208 \nL 243.560306 545.152208 \nL 243.581448 460.435325 \nL 243.666019 545.152208 \nL 243.962016 545.152208 \nL 243.983159 36.850909 \nL 244.06773 460.435325 \nL 244.180491 545.152208 \nL 244.201633 545.152208 \nL 244.208681 460.435325 \nL 244.307347 545.152208 \nL 244.363727 545.152208 \nL 244.391917 375.718442 \nL 244.469441 545.152208 \nL 244.765438 545.152208 \nL 244.835914 375.718442 \nL 244.871152 545.152208 \nL 244.983912 545.152208 \nL 244.99096 375.718442 \nL 245.089626 545.152208 \nL 245.096673 629.869091 \nL 245.146006 375.718442 \nL 245.195339 545.152208 \nL 246.090379 545.152208 \nL 246.097427 460.435325 \nL 246.196093 545.152208 \nL 246.372282 545.152208 \nL 246.400472 460.435325 \nL 246.477995 545.152208 \nL 246.858563 545.152208 \nL 246.865611 375.718442 \nL 246.964277 545.152208 \nL 246.985419 545.152208 \nL 246.992467 375.718442 \nL 247.091133 545.152208 \nL 247.09818 545.152208 \nL 247.105228 375.718442 \nL 247.203894 545.152208 \nL 247.344845 545.152208 \nL 247.457606 629.869091 \nL 247.478748 629.869091 \nL 247.535129 375.718442 \nL 247.591509 545.152208 \nL 248.993974 545.152208 \nL 249.001021 206.284675 \nL 249.099687 545.152208 \nL 249.49435 545.152208 \nL 249.508445 375.718442 \nL 249.600064 545.152208 \nL 250.347105 545.152208 \nL 250.41758 206.284675 \nL 250.424628 629.869091 \nL 250.452818 545.152208 \nL 250.692435 545.152208 \nL 250.727673 121.567792 \nL 250.798149 545.152208 \nL 251.481762 545.152208 \nL 251.495857 375.718442 \nL 251.587475 545.152208 \nL 252.419087 545.152208 \nL 252.426135 629.869091 \nL 252.524801 545.152208 \nL 253.109748 545.152208 \nL 253.194319 629.869091 \nL 253.215461 545.152208 \nL 253.264794 545.152208 \nL 253.271842 629.869091 \nL 253.370508 545.152208 \nL 253.546697 545.152208 \nL 253.603077 375.718442 \nL 253.65241 545.152208 \nL 254.667259 545.152208 \nL 254.716591 206.284675 \nL 254.780019 629.869091 \nL 254.89278 629.869091 \nL 255.005541 545.152208 \nL 255.773725 545.152208 \nL 255.78782 375.718442 \nL 255.879439 545.152208 \nL 256.513719 545.152208 \nL 256.59829 36.850909 \nL 256.534862 629.869091 \nL 256.62648 121.567792 \nL 256.640575 121.567792 \nL 256.65467 36.850909 \nL 256.746288 121.567792 \nL 256.844954 545.152208 \nL 256.852002 121.567792 \nL 256.908382 121.567792 \nL 257.014096 291.001558 \nL 257.126857 121.567792 \nL 257.472187 121.567792 \nL 257.479235 291.001558 \nL 257.5779 121.567792 \nL 257.754089 121.567792 \nL 257.761137 629.869091 \nL 257.859803 121.567792 \nL 257.923231 121.567792 \nL 258.035992 629.869091 \nL 258.043039 629.869091 \nL 258.057134 36.850909 \nL 258.148753 629.869091 \nL 258.176943 629.869091 \nL 258.289704 121.567792 \nL 258.346084 121.567792 \nL 258.437703 36.850909 \nL 258.44475 629.869091 \nL 258.543416 121.567792 \nL 258.578654 121.567792 \nL 258.691415 36.850909 \nL 258.726653 36.850909 \nL 258.783033 545.152208 \nL 258.832366 36.850909 \nL 259.135411 36.850909 \nL 259.191791 545.152208 \nL 259.241124 121.567792 \nL 259.28341 36.850909 \nL 259.318648 291.001558 \nL 259.332743 291.001558 \nL 259.33979 291.001558 \nL 259.375028 121.567792 \nL 259.445504 291.001558 \nL 259.819024 291.001558 \nL 259.931785 36.850909 \nL 260.044546 121.567792 \nL 260.058641 121.567792 \nL 260.072736 375.718442 \nL 260.171402 291.001558 \nL 260.481495 291.001558 \nL 260.594256 121.567792 \nL 260.601303 121.567792 \nL 260.714064 291.001558 \nL 260.728159 291.001558 \nL 260.735207 545.152208 \nL 260.819778 36.850909 \nL 260.826825 121.567792 \nL 260.918443 36.850909 \nL 260.932539 121.567792 \nL 261.087585 121.567792 \nL 261.115775 36.850909 \nL 261.136918 629.869091 \nL 261.193298 121.567792 \nL 261.327202 121.567792 \nL 261.439963 291.001558 \nL 261.461105 291.001558 \nL 261.573866 121.567792 \nL 261.580914 121.567792 \nL 261.609104 291.001558 \nL 261.616152 36.850909 \nL 261.686627 36.850909 \nL 261.792341 36.850909 \nL 261.905102 121.567792 \nL 262.017863 36.850909 \nL 262.130624 545.152208 \nL 262.243385 36.850909 \nL 262.356145 121.567792 \nL 262.363193 121.567792 \nL 262.468906 36.850909 \nL 262.560525 121.567792 \nL 262.57462 36.850909 \nL 262.65919 36.850909 \nL 262.666238 545.152208 \nL 262.764904 36.850909 \nL 262.934045 36.850909 \nL 262.976331 121.567792 \nL 263.039759 36.850909 \nL 263.103187 36.850909 \nL 263.124329 545.152208 \nL 263.2089 36.850909 \nL 263.272328 36.850909 \nL 263.279376 121.567792 \nL 263.378042 36.850909 \nL 263.420327 36.850909 \nL 263.462612 545.152208 \nL 263.52604 121.567792 \nL 263.533088 121.567792 \nL 263.624706 36.850909 \nL 263.631754 545.152208 \nL 263.730419 36.850909 \nL 263.885466 36.850909 \nL 263.892513 545.152208 \nL 263.991179 36.850909 \nL 264.294224 36.850909 \nL 264.406985 629.869091 \nL 264.498603 629.869091 \nL 264.562031 121.567792 \nL 264.604317 629.869091 \nL 264.667745 629.869091 \nL 264.780506 121.567792 \nL 264.879172 121.567792 \nL 264.928504 36.850909 \nL 264.984885 121.567792 \nL 265.146979 121.567792 \nL 265.238597 36.850909 \nL 265.252692 121.567792 \nL 265.31612 121.567792 \nL 265.428881 629.869091 \nL 265.457071 629.869091 \nL 265.569832 36.850909 \nL 265.583927 36.850909 \nL 265.619165 629.869091 \nL 265.696688 121.567792 \nL 265.717831 121.567792 \nL 265.731926 629.869091 \nL 265.795354 36.850909 \nL 265.823544 36.850909 \nL 265.908115 36.850909 \nL 265.992686 121.567792 \nL 266.013829 36.850909 \nL 266.034971 36.850909 \nL 266.147732 121.567792 \nL 266.260493 36.850909 \nL 266.345064 36.850909 \nL 266.408492 121.567792 \nL 266.450777 36.850909 \nL 266.598776 36.850909 \nL 266.711537 121.567792 \nL 266.810203 36.850909 \nL 266.81725 121.567792 \nL 266.873631 121.567792 \nL 266.880678 36.850909 \nL 266.887726 545.152208 \nL 266.972297 121.567792 \nL 266.979344 545.152208 \nL 267.07801 206.284675 \nL 267.13439 206.284675 \nL 267.240104 121.567792 \nL 267.331722 291.001558 \nL 267.345817 121.567792 \nL 267.36696 121.567792 \nL 267.388103 206.284675 \nL 267.479721 36.850909 \nL 267.486768 36.850909 \nL 267.613624 291.001558 \nL 268.099906 291.001558 \nL 268.135144 121.567792 \nL 268.205619 291.001558 \nL 268.297238 291.001558 \nL 268.304285 121.567792 \nL 268.402951 291.001558 \nL 268.515712 291.001558 \nL 268.529807 36.850909 \nL 268.628473 121.567792 \nL 268.642568 121.567792 \nL 268.649616 291.001558 \nL 268.748281 121.567792 \nL 268.790567 121.567792 \nL 268.804662 36.850909 \nL 268.903328 291.001558 \nL 269.016089 291.001558 \nL 269.12885 121.567792 \nL 269.18523 121.567792 \nL 269.297991 291.001558 \nL 269.537608 291.001558 \nL 269.601036 545.152208 \nL 269.657417 36.850909 \nL 269.692654 36.850909 \nL 269.798368 629.869091 \nL 269.805415 121.567792 \nL 269.812463 121.567792 \nL 269.826558 629.869091 \nL 269.925224 545.152208 \nL 269.988652 545.152208 \nL 270.101413 36.850909 \nL 270.228269 291.001558 \nL 270.418553 291.001558 \nL 270.538361 36.850909 \nL 270.792074 36.850909 \nL 270.862549 545.152208 \nL 270.904835 121.567792 \nL 270.97531 121.567792 \nL 270.996453 629.869091 \nL 271.081024 121.567792 \nL 271.186737 121.567792 \nL 271.214927 291.001558 \nL 271.29245 121.567792 \nL 271.306545 121.567792 \nL 271.419306 291.001558 \nL 271.49683 291.001558 \nL 271.52502 121.567792 \nL 271.602543 291.001558 \nL 271.750542 291.001558 \nL 271.863303 121.567792 \nL 271.877398 121.567792 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121.567792 \nL 280.299231 121.567792 \nL 280.411992 545.152208 \nL 280.433134 545.152208 \nL 280.447229 36.850909 \nL 280.538848 545.152208 \nL 280.84894 545.152208 \nL 280.870083 36.850909 \nL 280.954654 545.152208 \nL 281.194271 545.152208 \nL 281.201318 36.850909 \nL 281.299984 545.152208 \nL 281.553696 545.152208 \nL 281.560744 36.850909 \nL 281.65941 545.152208 \nL 281.807408 545.152208 \nL 281.899027 121.567792 \nL 281.920169 206.284675 \nL 281.969502 206.284675 \nL 281.97655 375.718442 \nL 282.075216 206.284675 \nL 282.223214 206.284675 \nL 282.32188 629.869091 \nL 282.328928 206.284675 \nL 282.364166 206.284675 \nL 282.399403 629.869091 \nL 282.469879 206.284675 \nL 282.55445 206.284675 \nL 282.667211 545.152208 \nL 282.815209 545.152208 \nL 282.822257 629.869091 \nL 282.8434 206.284675 \nL 282.892732 206.284675 \nL 282.942065 206.284675 \nL 283.005493 629.869091 \nL 283.054826 545.152208 \nL 283.061874 545.152208 \nL 283.146445 36.850909 \nL 283.083017 629.869091 \nL 283.167587 545.152208 \nL 283.21692 545.152208 \nL 283.24511 121.567792 \nL 283.322634 545.152208 \nL 283.435394 545.152208 \nL 283.498823 36.850909 \nL 283.541108 545.152208 \nL 283.625679 545.152208 \nL 283.73844 36.850909 \nL 284.013294 36.850909 \nL 284.126055 121.567792 \nL 284.189483 121.567792 \nL 284.196531 36.850909 \nL 284.295197 121.567792 \nL 284.555956 121.567792 \nL 284.563004 36.850909 \nL 284.66167 121.567792 \nL 284.703955 121.567792 \nL 284.816716 36.850909 \nL 284.830811 36.850909 \nL 284.943572 121.567792 \nL 285.028143 121.567792 \nL 285.140904 36.850909 \nL 285.345283 36.850909 \nL 285.429854 121.567792 \nL 285.450996 36.850909 \nL 285.458044 36.850909 \nL 285.55671 545.152208 \nL 285.570805 121.567792 \nL 285.669471 121.567792 \nL 285.775184 36.850909 \nL 285.789279 121.567792 \nL 285.880897 36.850909 \nL 286.21918 36.850909 \nL 286.331941 121.567792 \nL 286.543368 121.567792 \nL 286.550416 36.850909 \nL 286.649081 121.567792 \nL 286.973269 121.567792 \nL 287.08603 36.850909 \nL 287.3186 36.850909 \nL 287.43136 121.567792 \nL 287.804881 121.567792 \nL 287.811929 36.850909 \nL 287.910594 121.567792 \nL 288.065641 121.567792 \nL 288.079736 36.850909 \nL 288.171354 121.567792 \nL 288.248877 121.567792 \nL 288.27002 36.850909 \nL 288.354591 121.567792 \nL 288.608303 121.567792 \nL 288.678778 36.850909 \nL 288.770397 629.869091 \nL 288.784492 121.567792 \nL 288.960681 121.567792 \nL 289.066394 36.850909 \nL 289.179155 121.567792 \nL 289.42582 121.567792 \nL 289.432867 36.850909 \nL 289.531533 121.567792 \nL 289.644294 121.567792 \nL 289.651342 206.284675 \nL 289.750007 121.567792 \nL 289.827531 121.567792 \nL 289.905054 36.850909 \nL 289.933244 121.567792 \nL 289.968482 121.567792 \nL 289.996672 36.850909 \nL 290.074195 121.567792 \nL 290.109433 121.567792 \nL 290.215146 36.850909 \nL 290.327907 121.567792 \nL 290.363145 121.567792 \nL 290.475906 36.850909 \nL 290.490001 36.850909 \nL 290.497049 291.001558 \nL 290.602762 121.567792 \nL 290.701428 36.850909 \nL 290.708475 121.567792 \nL 290.828284 121.567792 \nL 290.905807 36.850909 \nL 290.92695 121.567792 \nL 290.933997 206.284675 \nL 291.032663 121.567792 \nL 291.159519 121.567792 \nL 291.251137 36.850909 \nL 291.265233 121.567792 \nL 291.370946 121.567792 \nL 291.377994 291.001558 \nL 291.385041 36.850909 \nL 291.476659 36.850909 \nL 291.483707 36.850909 \nL 291.596468 121.567792 \nL 291.709229 36.850909 \nL 291.758562 36.850909 \nL 291.871323 121.567792 \nL 291.892465 121.567792 \nL 291.927703 545.152208 \nL 292.005226 36.850909 \nL 292.075702 36.850909 \nL 292.188463 121.567792 \nL 292.258938 121.567792 \nL 292.329414 36.850909 \nL 292.343509 121.567792 \nL 292.463318 545.152208 \nL 292.576079 121.567792 \nL 292.583126 121.567792 \nL 292.604269 629.869091 \nL 292.611316 36.850909 \nL 292.688839 36.850909 \nL 292.71703 36.850909 \nL 292.787505 291.001558 \nL 292.822743 36.850909 \nL 292.857981 36.850909 \nL 292.970742 121.567792 \nL 292.977789 121.567792 \nL 292.984837 545.152208 \nL 293.06236 36.850909 \nL 293.083503 121.567792 \nL 293.189216 121.567792 \nL 293.203311 545.152208 \nL 293.210359 36.850909 \nL 293.29493 36.850909 \nL 293.661403 36.850909 \nL 293.66845 291.001558 \nL 293.767116 36.850909 \nL 294.056066 36.850909 \nL 294.063114 291.001558 \nL 294.161779 36.850909 \nL 294.471872 36.850909 \nL 294.478919 291.001558 \nL 294.577585 36.850909 \nL 294.915868 36.850909 \nL 294.922916 121.567792 \nL 295.021582 36.850909 \nL 295.331674 36.850909 \nL 295.338722 375.718442 \nL 295.437388 36.850909 \nL 295.789765 36.850909 \nL 295.796813 291.001558 \nL 295.895479 36.850909 \nL 296.092811 36.850909 \nL 296.099858 375.718442 \nL 296.198524 36.850909 \nL 296.917375 36.850909 \nL 296.924422 291.001558 \nL 297.023088 36.850909 \nL 297.361371 36.850909 \nL 297.368419 291.001558 \nL 297.467085 36.850909 \nL 297.784225 36.850909 \nL 297.896986 545.152208 \nL 298.002699 36.850909 \nL 298.073175 545.152208 \nL 298.108412 36.850909 \nL 299.348783 36.850909 \nL 299.35583 291.001558 \nL 299.454496 36.850909 \nL 299.771636 36.850909 \nL 299.778684 291.001558 \nL 299.87735 36.850909 \nL 300.187442 36.850909 \nL 300.19449 291.001558 \nL 300.293156 36.850909 \nL 300.617343 36.850909 \nL 300.624391 291.001558 \nL 300.723057 36.850909 \nL 301.068387 36.850909 \nL 301.075435 291.001558 \nL 301.181148 121.567792 \nL 301.336194 121.567792 \nL 301.343242 36.850909 \nL 301.35029 545.152208 \nL 301.441908 121.567792 \nL 301.625144 121.567792 \nL 301.681525 545.152208 \nL 301.688572 36.850909 \nL 301.730858 121.567792 \nL 301.907047 121.567792 \nL 301.970475 36.850909 \nL 302.01276 545.152208 \nL 302.125521 36.850909 \nL 302.174854 36.850909 \nL 302.287615 545.152208 \nL 302.30171 545.152208 \nL 302.400376 36.850909 \nL 302.407423 545.152208 \nL 302.414471 545.152208 \nL 302.527232 36.850909 \nL 302.534279 36.850909 \nL 302.64704 545.152208 \nL 302.661136 545.152208 \nL 302.759801 36.850909 \nL 302.766849 545.152208 \nL 302.787992 545.152208 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291.001558 \nL 328.173299 36.850909 \nL 328.504534 36.850909 \nL 328.511582 291.001558 \nL 328.610248 36.850909 \nL 328.927388 36.850909 \nL 328.934436 291.001558 \nL 329.033101 36.850909 \nL 329.251576 36.850909 \nL 329.258623 291.001558 \nL 329.357289 36.850909 \nL 329.646239 36.850909 \nL 329.653287 121.567792 \nL 329.751952 36.850909 \nL 330.07614 36.850909 \nL 330.083188 121.567792 \nL 330.181854 36.850909 \nL 330.506041 36.850909 \nL 330.513089 206.284675 \nL 330.611755 36.850909 \nL 335.115145 36.850909 \nL 335.227906 121.567792 \nL 335.234953 121.567792 \nL 335.347714 545.152208 \nL 335.460475 121.567792 \nL 335.509808 121.567792 \nL 335.622569 206.284675 \nL 335.70714 206.284675 \nL 335.819901 121.567792 \nL 335.833996 545.152208 \nL 335.925614 121.567792 \nL 336.003137 121.567792 \nL 336.059518 291.001558 \nL 336.108851 121.567792 \nL 336.158184 121.567792 \nL 336.179326 545.152208 \nL 336.193421 36.850909 \nL 336.263897 36.850909 \nL 336.60218 36.850909 \nL 336.714941 545.152208 \nL 336.771321 121.567792 \nL 336.820654 545.152208 \nL 336.877035 545.152208 \nL 336.996843 206.284675 \nL 337.010938 206.284675 \nL 337.123699 545.152208 \nL 337.173032 545.152208 \nL 337.26465 629.869091 \nL 337.278745 545.152208 \nL 337.49722 545.152208 \nL 337.609981 36.850909 \nL 337.722742 545.152208 \nL 337.856645 545.152208 \nL 337.913026 36.850909 \nL 337.969406 121.567792 \nL 337.983501 121.567792 \nL 338.053977 629.869091 \nL 338.089215 121.567792 \nL 338.110357 121.567792 \nL 338.117405 36.850909 \nL 338.216071 121.567792 \nL 338.540259 121.567792 \nL 338.547306 36.850909 \nL 338.554354 545.152208 \nL 338.645972 121.567792 \nL 339.033588 121.567792 \nL 339.068825 545.152208 \nL 339.139301 206.284675 \nL 339.25911 206.284675 \nL 339.266157 36.850909 \nL 339.364823 121.567792 \nL 339.463489 206.284675 \nL 339.470536 121.567792 \nL 339.541012 121.567792 \nL 339.646725 206.284675 \nL 339.759486 121.567792 \nL 340.006151 121.567792 \nL 340.013198 291.001558 \nL 340.118912 206.284675 \nL 340.18234 121.567792 \nL 340.189387 545.152208 \nL 340.196435 545.152208 \nL 340.203482 545.152208 \nL 340.288053 121.567792 \nL 340.316243 206.284675 \nL 340.351481 206.284675 \nL 340.443099 36.850909 \nL 340.464242 121.567792 \nL 340.47129 121.567792 \nL 340.548813 206.284675 \nL 340.577003 121.567792 \nL 340.851858 121.567792 \nL 340.887096 545.152208 \nL 340.957571 121.567792 \nL 340.978714 121.567792 \nL 341.091475 629.869091 \nL 341.098522 629.869091 \nL 341.211283 545.152208 \nL 341.401567 545.152208 \nL 341.42271 629.869091 \nL 341.457948 206.284675 \nL 341.479091 206.284675 \nL 341.507281 206.284675 \nL 341.620042 629.869091 \nL 341.704613 375.718442 \nL 341.732803 545.152208 \nL 341.873754 545.152208 \nL 341.97242 375.718442 \nL 341.979467 545.152208 \nL 342.085181 545.152208 \nL 342.113371 375.718442 \nL 342.190894 545.152208 \nL 342.282512 545.152208 \nL 342.31775 629.869091 \nL 342.310703 36.850909 \nL 342.360036 545.152208 \nL 342.367083 36.850909 \nL 342.388226 629.869091 \nL 342.465749 545.152208 \nL 342.486892 545.152208 \nL 342.500987 375.718442 \nL 342.529177 629.869091 \nL 342.592605 629.869091 \nL 342.613748 629.869091 \nL 342.726509 545.152208 \nL 342.804032 545.152208 \nL 342.811079 375.718442 \nL 342.909745 545.152208 \nL 343.001363 545.152208 \nL 343.107077 629.869091 \nL 343.219838 121.567792 \nL 343.332599 629.869091 \nL 345.29182 629.869091 \nL 345.376391 121.567792 \nL 345.397533 629.869091 \nL 346.292573 629.869091 \nL 346.363049 545.152208 \nL 346.398287 629.869091 \nL 347.279232 629.869091 \nL 347.356755 545.152208 \nL 347.384945 629.869091 \nL 347.455421 629.869091 \nL 347.532944 545.152208 \nL 347.561134 629.869091 \nL 347.638657 629.869091 \nL 347.751418 545.152208 \nL 347.758466 629.869091 \nL 347.857132 545.152208 \nL 348.117891 545.152208 \nL 348.153129 629.869091 \nL 348.167224 206.284675 \nL 348.181319 206.284675 \nL 348.188367 36.850909 \nL 348.230652 629.869091 \nL 348.272938 545.152208 \nL 348.343413 545.152208 \nL 348.392746 206.284675 \nL 348.449126 545.152208 \nL 348.723981 545.152208 \nL 348.787409 629.869091 \nL 348.780362 206.284675 \nL 348.8156 545.152208 \nL 348.836742 206.284675 \nL 348.84379 629.869091 \nL 348.921313 545.152208 \nL 348.956551 545.152208 \nL 349.062264 629.869091 \nL 349.175025 545.152208 \nL 349.280738 545.152208 \nL 349.372357 629.869091 \nL 349.386452 545.152208 \nL 349.456927 545.152208 \nL 349.513308 629.869091 \nL 349.562641 545.152208 \nL 349.661307 545.152208 \nL 349.774068 629.869091 \nL 352.621281 629.869091 \nL 352.734042 121.567792 \nL 352.74109 121.567792 \nL 352.853851 629.869091 \nL 352.952517 629.869091 \nL 353.051183 121.567792 \nL 353.05823 629.869091 \nL 353.08642 629.869091 \nL 353.142801 121.567792 \nL 353.192134 629.869091 \nL 353.199181 629.869091 \nL 353.206229 121.567792 \nL 353.304895 629.869091 \nL 353.326037 629.869091 \nL 353.438798 121.567792 \nL 353.516321 36.850909 \nL 353.551559 629.869091 \nL 353.699558 629.869091 \nL 353.706606 36.850909 \nL 353.805271 629.869091 \nL 354.101269 629.869091 \nL 354.122412 36.850909 \nL 354.206982 629.869091 \nL 354.284505 629.869091 \nL 354.397266 36.850909 \nL 354.418409 36.850909 \nL 354.474789 545.152208 \nL 354.53117 121.567792 \nL 354.594598 121.567792 \nL 354.707359 36.850909 \nL 355.926587 36.850909 \nL 355.933634 121.567792 \nL 356.0323 36.850909 \nL 356.152109 36.850909 \nL 356.180299 121.567792 \nL 356.257822 36.850909 \nL 356.314202 36.850909 \nL 356.32125 121.567792 \nL 356.419916 36.850909 \nL 356.469249 36.850909 \nL 356.532677 121.567792 \nL 356.574962 36.850909 \nL 356.603152 36.850909 \nL 356.63839 629.869091 \nL 356.751151 545.152208 \nL 356.793436 545.152208 \nL 356.906197 36.850909 \nL 357.152862 36.850909 \nL 357.166957 545.152208 \nL 357.258575 36.850909 \nL 357.279718 36.850909 \nL 357.364289 629.869091 \nL 357.392479 545.152208 \nL 357.399526 545.152208 \nL 357.484097 206.284675 \nL 357.50524 545.152208 \nL 357.540478 545.152208 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360.528643 121.567792 \nL 360.641404 36.850909 \nL 360.662546 36.850909 \nL 360.697784 121.567792 \nL 360.76826 36.850909 \nL 385.223289 36.850909 \nL 385.244432 545.152208 \nL 385.329003 36.850909 \nL 385.343098 36.850909 \nL 385.350146 545.152208 \nL 385.448811 36.850909 \nL 386.153567 36.850909 \nL 386.266328 121.567792 \nL 386.590516 121.567792 \nL 386.597564 36.850909 \nL 386.696229 121.567792 \nL 387.506699 121.567792 \nL 387.513746 291.001558 \nL 387.612412 121.567792 \nL 387.725173 121.567792 \nL 387.732221 291.001558 \nL 387.830886 121.567792 \nL 388.155074 121.567792 \nL 388.162122 291.001558 \nL 388.260787 121.567792 \nL 388.584975 121.567792 \nL 388.683641 629.869091 \nL 388.690689 121.567792 \nL 390.325722 121.567792 \nL 390.33277 36.850909 \nL 390.431436 121.567792 \nL 392.996747 121.567792 \nL 393.003795 36.850909 \nL 393.10246 121.567792 \nL 393.9975 121.567792 \nL 394.004548 36.850909 \nL 394.103214 121.567792 \nL 394.180737 121.567792 \nL 394.194832 36.850909 \nL 394.28645 121.567792 \nL 394.399211 121.567792 \nL 394.406259 36.850909 \nL 394.504925 121.567792 \nL 394.695209 121.567792 \nL 394.702256 629.869091 \nL 394.800922 121.567792 \nL 394.829112 121.567792 \nL 394.83616 545.152208 \nL 394.934826 121.567792 \nL 395.230823 121.567792 \nL 395.237871 629.869091 \nL 395.336537 121.567792 \nL 396.013102 121.567792 \nL 396.02015 36.850909 \nL 396.118816 121.567792 \nL 396.252719 121.567792 \nL 396.259767 36.850909 \nL 396.358433 121.567792 \nL 396.675573 121.567792 \nL 396.68262 36.850909 \nL 396.781286 121.567792 \nL 397.683374 121.567792 \nL 397.796135 36.850909 \nL 397.908896 121.567792 \nL 397.979371 121.567792 \nL 398.056894 36.850909 \nL 398.085085 121.567792 \nL 398.366987 121.567792 \nL 398.44451 36.850909 \nL 398.4727 121.567792 \nL 398.585461 121.567792 \nL 398.698222 36.850909 \nL 398.810983 121.567792 \nL 398.853269 36.850909 \nL 398.916697 121.567792 \nL 399.099933 121.567792 \nL 399.184504 36.850909 \nL 399.205647 121.567792 \nL 399.212694 121.567792 \nL 399.276122 36.850909 \nL 399.318408 121.567792 \nL 399.402978 121.567792 \nL 399.417073 36.850909 \nL 399.508692 121.567792 \nL 399.543929 121.567792 \nL 399.60031 36.850909 \nL 399.649643 121.567792 \nL 399.684881 121.567792 \nL 399.797642 36.850909 \nL 399.804689 36.850909 \nL 399.91745 121.567792 \nL 400.079544 121.567792 \nL 400.15002 36.850909 \nL 400.185257 121.567792 \nL 400.298018 121.567792 \nL 400.340304 36.850909 \nL 400.403732 121.567792 \nL 400.509445 121.567792 \nL 400.565825 36.850909 \nL 400.615158 121.567792 \nL 400.727919 121.567792 \nL 400.770205 36.850909 \nL 400.833633 121.567792 \nL 400.939346 121.567792 \nL 400.995727 36.850909 \nL 401.04506 121.567792 \nL 401.136678 121.567792 \nL 401.178963 36.850909 \nL 401.242391 121.567792 \nL 401.326962 121.567792 \nL 401.355152 36.850909 \nL 401.432675 121.567792 \nL 401.538389 121.567792 \nL 401.594769 36.850909 \nL 401.644102 121.567792 \nL 401.658197 121.567792 \nL 401.672292 36.850909 \nL 401.763911 121.567792 \nL 401.989432 121.567792 \nL 402.003528 36.850909 \nL 402.095146 121.567792 \nL 402.186764 121.567792 \nL 402.200859 36.850909 \nL 402.292477 121.567792 \nL 402.370001 121.567792 \nL 402.377048 36.850909 \nL 402.475714 121.567792 \nL 402.532094 121.567792 \nL 402.54619 36.850909 \nL 402.637808 121.567792 \nL 402.651903 121.567792 \nL 402.665998 36.850909 \nL 402.757616 121.567792 \nL 402.933805 121.567792 \nL 403.046566 36.850909 \nL 403.053614 36.850909 \nL 403.166375 121.567792 \nL 403.335516 121.567792 \nL 403.349611 36.850909 \nL 403.44123 121.567792 \nL 403.737227 121.567792 \nL 403.849988 36.850909 \nL 403.871131 36.850909 \nL 403.983892 121.567792 \nL 404.153033 121.567792 \nL 404.167128 36.850909 \nL 404.258746 121.567792 \nL 404.491316 121.567792 \nL 404.498363 36.850909 \nL 404.597029 121.567792 \nL 404.730933 121.567792 \nL 404.73798 36.850909 \nL 404.836646 121.567792 \nL 404.935312 121.567792 \nL 405.033978 36.850909 \nL 405.041025 121.567792 \nL 405.090358 121.567792 \nL 405.181977 36.850909 \nL 405.196072 121.567792 \nL 405.54845 121.567792 \nL 405.661211 36.850909 \nL 405.668258 36.850909 \nL 405.781019 121.567792 \nL 405.809209 121.567792 \nL 405.823305 36.850909 \nL 405.914923 121.567792 \nL 405.978351 121.567792 \nL 406.020636 36.850909 \nL 406.084064 121.567792 \nL 406.098159 121.567792 \nL 406.112254 36.850909 \nL 406.203873 121.567792 \nL 406.295491 121.567792 \nL 406.309586 36.850909 \nL 406.401204 121.567792 \nL 406.408252 121.567792 \nL 406.485775 36.850909 \nL 406.513965 121.567792 \nL 406.612631 121.567792 \nL 406.619679 36.850909 \nL 406.718345 121.567792 \nL 406.78882 121.567792 \nL 406.901581 36.850909 \nL 406.929771 36.850909 \nL 407.042532 121.567792 \nL 407.04958 121.567792 \nL 407.07777 36.850909 \nL 407.155293 121.567792 \nL 407.401958 121.567792 \nL 407.507671 36.850909 \nL 407.620432 121.567792 \nL 407.803669 121.567792 \nL 407.909382 36.850909 \nL 408.022143 121.567792 \nL 408.085571 121.567792 \nL 408.198332 545.152208 \nL 408.226522 545.152208 \nL 408.240617 36.850909 \nL 408.332236 545.152208 \nL 408.635281 545.152208 \nL 408.670518 36.850909 \nL 408.740994 545.152208 \nL 408.783279 545.152208 \nL 408.797374 36.850909 \nL 408.888993 545.152208 \nL 409.044039 545.152208 \nL 409.1568 36.850909 \nL 409.41756 36.850909 \nL 409.530321 545.152208 \nL 409.671272 545.152208 \nL 409.678319 36.850909 \nL 409.776985 545.152208 \nL 410.065935 545.152208 \nL 410.08003 36.850909 \nL 410.171648 545.152208 \nL 410.474693 545.152208 \nL 410.495836 36.850909 \nL 410.580407 545.152208 \nL 410.848214 545.152208 \nL 410.855262 36.850909 \nL 410.953927 545.152208 \nL 410.996213 545.152208 \nL 411.010308 36.850909 \nL 411.101926 545.152208 \nL 411.404971 545.152208 \nL 411.412019 36.850909 \nL 411.510685 545.152208 \nL 411.799635 545.152208 \nL 411.81373 36.850909 \nL 411.905348 545.152208 \nL 411.954681 545.152208 \nL 412.025156 121.567792 \nL 412.060394 545.152208 \nL 412.095632 545.152208 \nL 412.194298 121.567792 \nL 412.201345 545.152208 \nL 412.215441 545.152208 \nL 412.278869 36.850909 \nL 412.328202 291.001558 \nL 412.398677 291.001558 \nL 412.469153 206.284675 \nL 412.511438 545.152208 \nL 412.518486 545.152208 \nL 412.631247 291.001558 \nL 412.659437 121.567792 \nL 412.744007 545.152208 \nL 412.79334 545.152208 \nL 412.849721 291.001558 \nL 412.899054 545.152208 \nL 412.906101 545.152208 \nL 412.913149 629.869091 \nL 413.011815 545.152208 \nL 413.089338 291.001558 \nL 413.117528 629.869091 \nL 413.230289 545.152208 \nL 413.441716 545.152208 \nL 413.448763 121.567792 \nL 413.547429 545.152208 \nL 413.723618 545.152208 \nL 413.794094 375.718442 \nL 413.829332 545.152208 \nL 413.857522 545.152208 \nL 413.878664 629.869091 \nL 413.963235 545.152208 \nL 414.068949 545.152208 \nL 414.075996 121.567792 \nL 414.083044 629.869091 \nL 414.174662 545.152208 \nL 414.18171 629.869091 \nL 414.280375 545.152208 \nL 414.322661 545.152208 \nL 414.329708 36.850909 \nL 414.357898 629.869091 \nL 414.428374 545.152208 \nL 414.660944 545.152208 \nL 414.682086 36.850909 \nL 414.766657 545.152208 \nL 414.971036 545.152208 \nL 415.041512 206.284675 \nL 415.07675 375.718442 \nL 415.18951 545.152208 \nL 415.668744 545.152208 \nL 415.675792 121.567792 \nL 415.774458 545.152208 \nL 415.908361 545.152208 \nL 415.922457 629.869091 \nL 415.957694 206.284675 \nL 416.014075 545.152208 \nL 416.190264 545.152208 \nL 416.211407 206.284675 \nL 416.295977 545.152208 \nL 416.408738 545.152208 \nL 416.521499 206.284675 \nL 416.535594 206.284675 \nL 416.627213 545.152208 \nL 416.641308 206.284675 \nL 416.676545 206.284675 \nL 416.775211 545.152208 \nL 416.782259 206.284675 \nL 416.789306 206.284675 \nL 416.89502 545.152208 \nL 417.000733 121.567792 \nL 416.993686 629.869091 \nL 417.007781 206.284675 \nL 417.106447 629.869091 \nL 417.113494 206.284675 \nL 417.127589 206.284675 \nL 417.24035 629.869091 \nL 417.310826 629.869091 \nL 417.317873 206.284675 \nL 417.416539 629.869091 \nL 417.613871 629.869091 \nL 417.691394 121.567792 \nL 417.719584 629.869091 \nL 417.733679 629.869091 \nL 417.768917 206.284675 \nL 417.839393 629.869091 \nL 418.121295 629.869091 \nL 418.234056 121.567792 \nL 418.375007 121.567792 \nL 418.382055 36.850909 \nL 418.480721 121.567792 \nL 418.790813 121.567792 \nL 418.797861 36.850909 \nL 418.804908 629.869091 \nL 418.896527 121.567792 \nL 419.023383 121.567792 \nL 419.065668 629.869091 \nL 419.129096 121.567792 \nL 419.164334 121.567792 \nL 419.255952 36.850909 \nL 419.277095 629.869091 \nL 419.66471 629.869091 \nL 419.671758 36.850909 \nL 419.770424 629.869091 \nL 419.791567 629.869091 \nL 419.812709 121.567792 \nL 419.89728 629.869091 \nL 419.904327 629.869091 \nL 420.010041 121.567792 \nL 420.031184 629.869091 \nL 420.101659 36.850909 \nL 420.115754 121.567792 \nL 420.362419 121.567792 \nL 420.411752 36.850909 \nL 420.468132 629.869091 \nL 420.580893 121.567792 \nL 420.623178 121.567792 \nL 420.630226 36.850909 \nL 420.637274 629.869091 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629.869091 \nL 423.428107 121.567792 \nL 423.463345 121.567792 \nL 423.53382 629.869091 \nL 423.569058 121.567792 \nL 423.618391 121.567792 \nL 423.710009 629.869091 \nL 423.688867 36.850909 \nL 423.724104 121.567792 \nL 423.745247 121.567792 \nL 423.79458 629.869091 \nL 423.850961 121.567792 \nL 423.907341 121.567792 \nL 424.020102 206.284675 \nL 424.104673 206.284675 \nL 424.196291 121.567792 \nL 424.210386 629.869091 \nL 424.294957 121.567792 \nL 424.323147 206.284675 \nL 424.435908 206.284675 \nL 424.541621 121.567792 \nL 424.569812 206.284675 \nL 424.647335 121.567792 \nL 424.654382 36.850909 \nL 424.753048 121.567792 \nL 424.795333 121.567792 \nL 424.858761 206.284675 \nL 424.901047 121.567792 \nL 424.936285 121.567792 \nL 425.049046 206.284675 \nL 425.098378 206.284675 \nL 425.126569 121.567792 \nL 425.204092 206.284675 \nL 425.394376 206.284675 \nL 425.52828 545.152208 \nL 425.535327 206.284675 \nL 425.633993 545.152208 \nL 425.803134 545.152208 \nL 425.908848 36.850909 \nL 425.915895 629.869091 \nL 426.014561 121.567792 \nL 426.324654 121.567792 \nL 426.331701 545.152208 \nL 426.430367 121.567792 \nL 426.754555 121.567792 \nL 426.761602 36.850909 \nL 426.76865 629.869091 \nL 426.860268 121.567792 \nL 427.332455 121.567792 \nL 427.339502 36.850909 \nL 427.438168 121.567792 \nL 427.776451 121.567792 \nL 427.783498 629.869091 \nL 427.882164 121.567792 \nL 428.178162 121.567792 \nL 428.185209 36.850909 \nL 428.192257 629.869091 \nL 428.283875 121.567792 \nL 428.593968 121.567792 \nL 428.601015 36.850909 \nL 428.699681 121.567792 \nL 428.882918 121.567792 \nL 428.889965 36.850909 \nL 428.988631 121.567792 \nL 429.115487 121.567792 \nL 429.122535 36.850909 \nL 429.129582 629.869091 \nL 429.2212 121.567792 \nL 429.439675 121.567792 \nL 429.552436 629.869091 \nL 429.665197 121.567792 \nL 429.679292 121.567792 \nL 429.77091 629.869091 \nL 429.785005 121.567792 \nL 429.947099 121.567792 \nL 429.954147 36.850909 \nL 429.961194 629.869091 \nL 430.052812 121.567792 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545.152208 \nL 432.963454 206.284675 \nL 433.055073 545.152208 \nL 433.076215 545.152208 \nL 433.188976 206.284675 \nL 433.308785 545.152208 \nL 433.315832 545.152208 \nL 433.329927 36.850909 \nL 433.428593 375.718442 \nL 433.541354 206.284675 \nL 433.58364 206.284675 \nL 433.590687 375.718442 \nL 433.689353 206.284675 \nL 433.759828 206.284675 \nL 433.766876 375.718442 \nL 433.865542 206.284675 \nL 433.964208 206.284675 \nL 434.006493 36.850909 \nL 434.069921 206.284675 \nL 434.429347 206.284675 \nL 434.436394 545.152208 \nL 434.53506 206.284675 \nL 434.816962 206.284675 \nL 434.82401 375.718442 \nL 434.922676 206.284675 \nL 435.176388 206.284675 \nL 435.232768 375.718442 \nL 435.282101 206.284675 \nL 435.676765 206.284675 \nL 435.683812 375.718442 \nL 435.782478 206.284675 \nL 436.106666 206.284675 \nL 436.113713 121.567792 \nL 436.212379 206.284675 \nL 436.480186 206.284675 \nL 436.550662 121.567792 \nL 436.5859 206.284675 \nL 436.769136 206.284675 \nL 436.776184 291.001558 \nL 436.87485 206.284675 \nL 437.206085 206.284675 \nL 437.241323 375.718442 \nL 437.311798 206.284675 \nL 437.396369 206.284675 \nL 437.417512 375.718442 \nL 437.502082 206.284675 \nL 437.50913 206.284675 \nL 437.621891 375.718442 \nL 437.734652 206.284675 \nL 438.023602 206.284675 \nL 438.030649 291.001558 \nL 438.129315 206.284675 \nL 438.488741 206.284675 \nL 438.495788 375.718442 \nL 438.594454 206.284675 \nL 438.66493 206.284675 \nL 438.671977 36.850909 \nL 438.770643 206.284675 \nL 439.151211 206.284675 \nL 439.158259 375.718442 \nL 439.256925 206.284675 \nL 439.341495 206.284675 \nL 439.348543 375.718442 \nL 439.447209 206.284675 \nL 439.750254 206.284675 \nL 439.757301 375.718442 \nL 439.855967 206.284675 \nL 440.16606 206.284675 \nL 440.236535 375.718442 \nL 440.271773 206.284675 \nL 440.342249 206.284675 \nL 440.447962 545.152208 \nL 440.370439 36.850909 \nL 440.45501 375.718442 \nL 440.504342 545.152208 \nL 440.567771 206.284675 \nL 440.588913 206.284675 \nL 440.708722 545.152208 \nL 440.743959 545.152208 \nL 440.814435 629.869091 \nL 440.849673 121.567792 \nL 440.920148 629.869091 \nL 440.962434 545.152208 \nL 440.969481 545.152208 \nL 441.004719 121.567792 \nL 441.032909 629.869091 \nL 441.075195 545.152208 \nL 441.314812 545.152208 \nL 441.321859 629.869091 \nL 441.420525 545.152208 \nL 441.815188 545.152208 \nL 441.822236 629.869091 \nL 441.920902 545.152208 \nL 442.216899 545.152208 \nL 442.223947 629.869091 \nL 442.315565 460.435325 \nL 442.357851 460.435325 \nL 442.470611 545.152208 \nL 442.576325 545.152208 \nL 442.597468 121.567792 \nL 442.682038 545.152208 \nL 443.055559 545.152208 \nL 443.16832 121.567792 \nL 443.281081 629.869091 \nL 443.365651 629.869091 \nL 443.492508 121.567792 \nL 443.499555 291.001558 \nL 443.51365 291.001558 \nL 443.626411 629.869091 \nL 443.654601 629.869091 \nL 443.689839 545.152208 \nL 443.760315 629.869091 \nL 443.77441 629.869091 \nL 443.901266 206.284675 \nL 443.922409 206.284675 \nL 444.014027 629.869091 \nL 444.021074 291.001558 \nL 444.133835 121.567792 \nL 444.204311 121.567792 \nL 444.317072 291.001558 \nL 444.458023 291.001558 \nL 444.465071 121.567792 \nL 444.563736 291.001558 \nL 444.634212 291.001558 \nL 444.648307 121.567792 \nL 444.739925 291.001558 \nL 444.810401 291.001558 \nL 444.831544 121.567792 \nL 444.916114 291.001558 \nL 444.979542 291.001558 \nL 444.98659 121.567792 \nL 445.092303 206.284675 \nL 445.183922 121.567792 \nL 445.190969 291.001558 \nL 445.198017 206.284675 \nL 445.261445 206.284675 \nL 445.324873 121.567792 \nL 445.33192 291.001558 \nL 445.360111 291.001558 \nL 445.367158 291.001558 \nL 445.402396 375.718442 \nL 445.465824 206.284675 \nL 445.472872 375.718442 \nL 445.550395 121.567792 \nL 445.571537 121.567792 \nL 445.627918 121.567792 \nL 445.705441 206.284675 \nL 445.733631 121.567792 \nL 445.797059 121.567792 \nL 445.860487 375.718442 \nL 445.90982 291.001558 \nL 446.029629 291.001558 \nL 446.121247 460.435325 \nL 446.128295 206.284675 \nL 446.135342 291.001558 \nL 446.17058 291.001558 \nL 446.184675 545.152208 \nL 446.290388 121.567792 \nL 446.318579 121.567792 \nL 446.325626 206.284675 \nL 446.424292 121.567792 \nL 446.544101 121.567792 \nL 446.558196 206.284675 \nL 446.649814 121.567792 \nL 446.656862 121.567792 \nL 446.762575 206.284675 \nL 446.875336 121.567792 \nL 446.889431 629.869091 \nL 446.981049 206.284675 \nL 447.079715 121.567792 \nL 447.136096 629.869091 \nL 447.185428 121.567792 \nL 447.234761 121.567792 \nL 447.340475 629.869091 \nL 447.347522 375.718442 \nL 447.35457 375.718442 \nL 447.460283 121.567792 \nL 447.467331 206.284675 \nL 447.678758 206.284675 \nL 447.791519 121.567792 \nL 447.798566 121.567792 \nL 447.904279 206.284675 \nL 447.925422 121.567792 \nL 448.009993 206.284675 \nL 448.031136 206.284675 \nL 448.052278 121.567792 \nL 448.059326 375.718442 \nL 448.122754 375.718442 \nL 448.193229 375.718442 \nL 448.30599 629.869091 \nL 448.320085 629.869091 \nL 448.446942 375.718442 \nL 448.531512 375.718442 \nL 448.644273 121.567792 \nL 448.982556 121.567792 \nL 448.989604 291.001558 \nL 449.088269 121.567792 \nL 449.137602 121.567792 \nL 449.222173 291.001558 \nL 449.243316 121.567792 \nL 449.645027 121.567792 \nL 449.652074 291.001558 \nL 449.75074 121.567792 \nL 449.941024 121.567792 \nL 449.983309 206.284675 \nL 450.046737 121.567792 \nL 450.060833 121.567792 \nL 450.06788 291.001558 \nL 450.173593 206.284675 \nL 450.286354 121.567792 \nL 450.476639 121.567792 \nL 450.483686 291.001558 \nL 450.582352 121.567792 \nL 450.681018 121.567792 \nL 450.709208 545.152208 \nL 450.786731 121.567792 \nL 450.90654 121.567792 \nL 450.913587 291.001558 \nL 451.012253 121.567792 \nL 451.392821 121.567792 \nL 451.399869 291.001558 \nL 451.498535 121.567792 \nL 451.822722 121.567792 \nL 451.82977 291.001558 \nL 451.928436 121.567792 \nL 452.238528 121.567792 \nL 452.245576 291.001558 \nL 452.344242 121.567792 \nL 452.640239 121.567792 \nL 452.647287 291.001558 \nL 452.753 206.284675 \nL 452.760048 206.284675 \nL 452.788238 375.718442 \nL 452.858713 121.567792 \nL 452.900999 375.718442 \nL 452.964427 121.567792 \nL 453.01376 121.567792 \nL 453.027855 375.718442 \nL 453.119473 121.567792 \nL 453.35909 121.567792 \nL 453.366138 291.001558 \nL 453.464804 121.567792 \nL 453.605755 121.567792 \nL 453.662135 545.152208 \nL 453.718516 375.718442 \nL 453.753753 375.718442 \nL 453.873562 121.567792 \nL 454.148417 121.567792 \nL 454.240035 291.001558 \nL 454.25413 121.567792 \nL 454.338701 121.567792 \nL 454.388034 206.284675 \nL 454.444414 121.567792 \nL 454.676984 121.567792 \nL 454.684031 291.001558 \nL 454.782697 121.567792 \nL 455.078695 121.567792 \nL 455.085742 291.001558 \nL 455.184408 121.567792 \nL 455.325359 121.567792 \nL 455.38174 375.718442 \nL 455.431072 121.567792 \nL 455.487453 121.567792 \nL 455.529738 291.001558 \nL 455.593166 121.567792 \nL 455.903259 121.567792 \nL 456.001925 291.001558 \nL 456.008972 121.567792 \nL 456.178114 121.567792 \nL 456.206304 291.001558 \nL 456.283827 121.567792 \nL 456.354303 121.567792 \nL 456.36135 291.001558 \nL 456.460016 121.567792 \nL 456.643253 121.567792 \nL 456.6503 545.152208 \nL 456.748966 121.567792 \nL 456.770109 121.567792 \nL 456.88287 291.001558 \nL 456.995631 121.567792 \nL 457.178867 121.567792 \nL 457.185915 291.001558 \nL 457.284581 121.567792 \nL 457.51715 121.567792 \nL 457.524198 36.850909 \nL 457.622863 121.567792 \nL 457.876575 121.567792 \nL 457.904766 291.001558 \nL 457.982289 121.567792 \nL 458.398095 121.567792 \nL 458.405142 291.001558 \nL 458.503808 121.567792 \nL 458.539046 121.567792 \nL 458.546094 291.001558 \nL 458.644759 121.567792 \nL 458.905519 121.567792 \nL 458.912567 291.001558 \nL 459.011232 121.567792 \nL 459.053518 121.567792 \nL 459.067613 291.001558 \nL 459.159231 121.567792 \nL 459.236754 121.567792 \nL 459.342468 291.001558 \nL 459.455229 121.567792 \nL 459.603227 121.567792 \nL 459.610275 36.850909 \nL 459.708941 121.567792 \nL 459.765321 121.567792 \nL 459.772369 291.001558 \nL 459.779416 36.850909 \nL 459.871035 36.850909 \nL 459.983796 121.567792 \nL 460.336174 121.567792 \nL 460.385506 291.001558 \nL 460.441887 121.567792 \nL 460.498267 121.567792 \nL 460.589886 291.001558 \nL 460.603981 121.567792 \nL 460.653314 121.567792 \nL 460.766075 291.001558 \nL 460.878836 121.567792 \nL 460.885883 121.567792 \nL 460.998644 291.001558 \nL 461.005692 291.001558 \nL 461.118453 121.567792 \nL 461.308737 121.567792 \nL 461.421498 291.001558 \nL 461.428545 291.001558 \nL 461.541306 121.567792 \nL 461.604734 121.567792 \nL 461.717495 291.001558 \nL 461.724543 291.001558 \nL 461.837304 121.567792 \nL 461.935969 121.567792 \nL 462.04873 291.001558 \nL 462.069873 291.001558 \nL 462.182634 121.567792 \nL 462.246062 121.567792 \nL 462.358823 291.001558 \nL 462.394061 291.001558 \nL 462.506822 121.567792 \nL 462.59844 121.567792 \nL 462.697106 291.001558 \nL 462.704153 121.567792 \nL 462.718249 121.567792 \nL 462.725296 291.001558 \nL 462.823962 121.567792 \nL 463.021294 121.567792 \nL 463.134055 291.001558 \nL 463.162245 291.001558 \nL 463.23272 121.567792 \nL 463.267958 291.001558 \nL 463.331386 291.001558 \nL 463.451195 121.567792 \nL 463.472337 121.567792 \nL 463.54986 545.152208 \nL 463.585098 375.718442 \nL 463.662621 121.567792 \nL 463.620336 545.152208 \nL 463.690812 545.152208 \nL 463.719002 545.152208 \nL 463.733097 121.567792 \nL 463.831763 629.869091 \nL 463.979762 629.869091 \nL 464.085475 206.284675 \nL 464.106618 375.718442 \nL 464.113665 375.718442 \nL 464.148903 206.284675 \nL 464.226426 291.001558 \nL 464.254616 291.001558 \nL 464.275759 375.718442 \nL 464.374425 121.567792 \nL 464.487186 545.152208 \nL 464.515376 545.152208 \nL 464.606994 121.567792 \nL 464.628137 206.284675 \nL 464.663375 206.284675 \nL 464.776136 121.567792 \nL 464.797278 121.567792 \nL 464.804326 291.001558 \nL 464.902992 121.567792 \nL 465.198989 121.567792 \nL 465.290608 291.001558 \nL 465.304703 121.567792 \nL 465.375178 121.567792 \nL 465.487939 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468.750959 291.001558 \nL 468.86372 121.567792 \nL 468.898958 121.567792 \nL 468.983529 291.001558 \nL 469.004671 121.567792 \nL 469.039909 121.567792 \nL 469.15267 291.001558 \nL 469.166765 291.001558 \nL 469.251336 121.567792 \nL 469.279526 375.718442 \nL 469.293621 375.718442 \nL 469.342954 121.567792 \nL 469.399334 375.718442 \nL 469.41343 375.718442 \nL 469.483905 121.567792 \nL 469.526191 291.001558 \nL 469.716475 291.001558 \nL 469.829236 121.567792 \nL 469.836283 121.567792 \nL 469.970187 545.152208 \nL 469.977234 375.718442 \nL 470.0759 545.152208 \nL 470.125233 545.152208 \nL 470.209804 121.567792 \nL 470.237994 206.284675 \nL 470.287327 206.284675 \nL 470.357803 545.152208 \nL 470.385993 121.567792 \nL 470.39304 121.567792 \nL 470.407135 121.567792 \nL 470.463516 545.152208 \nL 470.512849 206.284675 \nL 470.59742 206.284675 \nL 470.703133 545.152208 \nL 470.780656 206.284675 \nL 470.808846 545.152208 \nL 470.928655 545.152208 \nL 471.041416 121.567792 \nL 471.245795 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476.150896 121.567792 \nL 476.263657 545.152208 \nL 476.277752 545.152208 \nL 476.2848 36.850909 \nL 476.390513 121.567792 \nL 476.468036 291.001558 \nL 476.475084 36.850909 \nL 476.496226 36.850909 \nL 486.990042 36.850909 \nL 487.123945 545.152208 \nL 487.236706 121.567792 \nL 487.243754 121.567792 \nL 487.356515 291.001558 \nL 487.553847 291.001558 \nL 487.638417 121.567792 \nL 487.65956 291.001558 \nL 487.673655 291.001558 \nL 487.694798 545.152208 \nL 487.68775 121.567792 \nL 487.779368 291.001558 \nL 487.863939 291.001558 \nL 487.94851 121.567792 \nL 487.969653 291.001558 \nL 487.997843 291.001558 \nL 488.00489 121.567792 \nL 488.103556 291.001558 \nL 488.244507 291.001558 \nL 488.300888 545.152208 \nL 488.350221 291.001558 \nL 488.378411 291.001558 \nL 488.392506 375.718442 \nL 488.484124 291.001558 \nL 488.568695 291.001558 \nL 488.646218 545.152208 \nL 488.674408 291.001558 \nL 488.723741 291.001558 \nL 488.737836 375.718442 \nL 488.829455 291.001558 \nL 488.87174 291.001558 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291.001558 \nL 500.415642 291.001558 \nL 500.457927 206.284675 \nL 500.464975 545.152208 \nL 500.514308 291.001558 \nL 500.53545 629.869091 \nL 500.542498 206.284675 \nL 500.612973 206.284675 \nL 500.725734 36.850909 \nL 500.746877 36.850909 \nL 500.76802 375.718442 \nL 500.859638 291.001558 \nL 500.866686 291.001558 \nL 500.908971 375.718442 \nL 500.972399 291.001558 \nL 501.212016 291.001558 \nL 501.324777 206.284675 \nL 501.416395 206.284675 \nL 501.423443 545.152208 \nL 501.529156 375.718442 \nL 501.557346 375.718442 \nL 501.627822 291.001558 \nL 501.66306 375.718442 \nL 501.677155 375.718442 \nL 501.775821 291.001558 \nL 501.782868 375.718442 \nL 501.789916 375.718442 \nL 501.846296 121.567792 \nL 501.839249 545.152208 \nL 501.881534 375.718442 \nL 501.909724 545.152208 \nL 501.923819 291.001558 \nL 501.987247 375.718442 \nL 501.994295 375.718442 \nL 502.03658 545.152208 \nL 502.092961 36.850909 \nL 502.107056 545.152208 \nL 502.205722 291.001558 \nL 502.212769 291.001558 \nL 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505.468742 629.869091 \nL 505.581503 121.567792 \nL 505.58855 121.567792 \nL 505.701311 629.869091 \nL 505.715406 629.869091 \nL 505.828167 545.152208 \nL 505.8775 545.152208 \nL 505.947976 121.567792 \nL 505.955023 629.869091 \nL 505.983213 629.869091 \nL 506.088927 629.869091 \nL 506.159402 545.152208 \nL 506.19464 629.869091 \nL 506.22283 629.869091 \nL 506.272163 121.567792 \nL 506.328544 629.869091 \nL 506.370829 629.869091 \nL 506.377877 36.850909 \nL 506.476543 629.869091 \nL 506.596351 629.869091 \nL 506.687969 121.567792 \nL 506.702064 545.152208 \nL 506.74435 545.152208 \nL 506.751397 121.567792 \nL 506.779588 629.869091 \nL 506.850063 545.152208 \nL 506.962824 545.152208 \nL 507.075585 629.869091 \nL 507.294059 629.869091 \nL 507.308154 121.567792 \nL 507.399773 629.869091 \nL 507.413868 629.869091 \nL 507.427963 545.152208 \nL 507.519581 629.869091 \nL 507.66758 629.869091 \nL 507.766246 121.567792 \nL 507.773293 629.869091 \nL 507.829674 629.869091 \nL 507.942435 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515.539703 36.850909 \nL 515.638369 545.152208 \nL 515.842748 545.152208 \nL 515.906176 36.850909 \nL 515.948462 545.152208 \nL 516.06827 545.152208 \nL 516.075318 629.869091 \nL 516.082365 36.850909 \nL 516.131698 36.850909 \nL 517.872445 36.850909 \nL 517.893588 629.869091 \nL 517.985206 545.152208 \nL 518.295299 545.152208 \nL 518.393965 36.850909 \nL 518.351679 629.869091 \nL 518.401012 629.869091 \nL 518.429203 36.850909 \nL 518.541964 206.284675 \nL 518.577201 206.284675 \nL 518.647677 629.869091 \nL 518.682915 545.152208 \nL 518.689962 206.284675 \nL 518.788628 545.152208 \nL 518.809771 545.152208 \nL 518.816818 629.869091 \nL 518.915484 545.152208 \nL 519.204434 545.152208 \nL 519.211482 36.850909 \nL 519.310147 545.152208 \nL 519.33129 545.152208 \nL 519.338338 36.850909 \nL 519.345385 629.869091 \nL 519.437004 629.869091 \nL 519.803477 629.869091 \nL 519.810524 36.850909 \nL 519.90919 629.869091 \nL 520.057189 629.869091 \nL 520.064236 36.850909 \nL 520.162902 629.869091 \nL 520.451852 629.869091 \nL 520.4589 36.850909 \nL 520.557565 629.869091 \nL 520.613946 545.152208 \nL 520.663279 629.869091 \nL 520.670326 629.869091 \nL 520.783087 545.152208 \nL 520.924039 545.152208 \nL 521.043847 206.284675 \nL 521.06499 206.284675 \nL 521.086132 545.152208 \nL 521.170703 206.284675 \nL 521.360987 206.284675 \nL 521.368035 291.001558 \nL 521.466701 206.284675 \nL 521.656985 206.284675 \nL 521.664032 121.567792 \nL 521.762698 206.284675 \nL 521.769746 206.284675 \nL 521.776793 291.001558 \nL 521.875459 206.284675 \nL 521.903649 206.284675 \nL 521.910697 545.152208 \nL 521.938887 121.567792 \nL 522.009363 206.284675 \nL 522.199647 206.284675 \nL 522.206694 291.001558 \nL 522.30536 206.284675 \nL 522.643643 206.284675 \nL 522.65069 291.001558 \nL 522.749356 206.284675 \nL 522.756404 206.284675 \nL 522.805737 121.567792 \nL 522.862117 206.284675 \nL 523.101734 206.284675 \nL 523.108782 291.001558 \nL 523.151067 121.567792 \nL 523.207448 206.284675 \nL 523.531635 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545.152208 \nL 541.213961 545.152208 \nL 541.326722 375.718442 \nL 541.439483 545.152208 \nL 541.488816 545.152208 \nL 541.601577 375.718442 \nL 541.629767 375.718442 \nL 541.672052 545.152208 \nL 541.73548 375.718442 \nL 541.763671 375.718442 \nL 541.876431 545.152208 \nL 543.053374 545.152208 \nL 543.060421 375.718442 \nL 543.159087 545.152208 \nL 543.870891 545.152208 \nL 543.877938 629.869091 \nL 543.884986 375.718442 \nL 543.976604 545.152208 \nL 545.992206 545.152208 \nL 546.062682 36.850909 \nL 546.006301 629.869091 \nL 546.097919 629.869091 \nL 546.753342 629.869091 \nL 546.76039 545.152208 \nL 546.859056 629.869091 \nL 547.267814 629.869091 \nL 547.296004 545.152208 \nL 547.373528 629.869091 \nL 547.584954 629.869091 \nL 547.599049 121.567792 \nL 547.690668 629.869091 \nL 548.740754 629.869091 \nL 548.818277 36.850909 \nL 548.846467 629.869091 \nL 548.874658 629.869091 \nL 549.001514 375.718442 \nL 549.114275 375.718442 \nL 549.227036 291.001558 \nL 549.410272 291.001558 \nL 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545.152208 \nL 562.765396 121.567792 \nL 562.857015 291.001558 \nL 564.351097 291.001558 \nL 564.358145 36.850909 \nL 564.456811 291.001558 \nL 564.879664 291.001558 \nL 564.964235 121.567792 \nL 564.985378 291.001558 \nL 565.401184 291.001558 \nL 565.506897 375.718442 \nL 565.619658 291.001558 \nL 565.626705 291.001558 \nL 565.725371 375.718442 \nL 565.732419 291.001558 \nL 565.802894 291.001558 \nL 565.880418 375.718442 \nL 565.908608 291.001558 \nL 565.922703 291.001558 \nL 566.035464 375.718442 \nL 566.091844 375.718442 \nL 566.204605 291.001558 \nL 566.394889 291.001558 \nL 566.465365 375.718442 \nL 566.47946 36.850909 \nL 566.493555 291.001558 \nL 566.542888 36.850909 \nL 566.528793 375.718442 \nL 566.606316 206.284675 \nL 566.655649 375.718442 \nL 566.719077 291.001558 \nL 566.747267 291.001558 \nL 566.789553 375.718442 \nL 566.852981 291.001558 \nL 566.965742 36.850909 \nL 567.008027 36.850909 \nL 567.141931 375.718442 \nL 567.381548 375.718442 \nL 567.473166 206.284675 \nL 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629.869091 \nL 584.584639 121.567792 \nL 584.605782 629.869091 \nL 586.508623 629.869091 \nL 586.593194 121.567792 \nL 586.614336 629.869091 \nL 587.784231 629.869091 \nL 587.791279 36.850909 \nL 587.889944 629.869091 \nL 588.432606 629.869091 \nL 588.517177 121.567792 \nL 588.53832 629.869091 \nL 589.905546 629.869091 \nL 589.912594 36.850909 \nL 590.01126 629.869091 \nL 590.173353 629.869091 \nL 590.187449 545.152208 \nL 590.279067 629.869091 \nL 590.518684 629.869091 \nL 590.596207 121.567792 \nL 590.624397 629.869091 \nL 591.012013 629.869091 \nL 591.019061 545.152208 \nL 591.117726 629.869091 \nL 592.351049 629.869091 \nL 592.43562 121.567792 \nL 592.456763 629.869091 \nL 593.626657 629.869091 \nL 593.6478 121.567792 \nL 593.732371 629.869091 \nL 594.141129 629.869091 \nL 594.2257 121.567792 \nL 594.246843 629.869091 \nL 594.401889 629.869091 \nL 594.48646 121.567792 \nL 594.507602 629.869091 \nL 595.895971 629.869091 \nL 596.008732 121.567792 \nL 596.100351 121.567792 \nL 596.213112 629.869091 \nL 596.530252 629.869091 \nL 596.537299 545.152208 \nL 596.635965 629.869091 \nL 596.924915 629.869091 \nL 596.931963 36.850909 \nL 597.030628 629.869091 \nL 597.319578 629.869091 \nL 597.326626 121.567792 \nL 597.425292 629.869091 \nL 597.728337 629.869091 \nL 597.735384 36.850909 \nL 597.83405 629.869091 \nL 598.137095 629.869091 \nL 598.249856 545.152208 \nL 598.270999 545.152208 \nL 598.327379 375.718442 \nL 598.376712 545.152208 \nL 598.559949 545.152208 \nL 598.574044 375.718442 \nL 598.658615 629.869091 \nL 598.665662 545.152208 \nL 598.707947 629.869091 \nL 598.743185 545.152208 \nL 598.806613 36.850909 \nL 598.785471 629.869091 \nL 598.848899 545.152208 \nL 598.870041 545.152208 \nL 598.877089 206.284675 \nL 598.975755 629.869091 \nL 599.067373 206.284675 \nL 599.081468 629.869091 \nL 599.581845 629.869091 \nL 599.610035 375.718442 \nL 599.687558 629.869091 \nL 599.955365 629.869091 \nL 600.075174 36.850909 \nL 600.194982 629.869091 \nL 600.427552 629.869091 \nL 600.441647 206.284675 \nL 600.533265 629.869091 \nL 600.906786 629.869091 \nL 600.913833 36.850909 \nL 601.012499 629.869091 \nL 601.090022 629.869091 \nL 601.202783 36.850909 \nL 601.216878 36.850909 \nL 601.329639 629.869091 \nL 601.505828 629.869091 \nL 601.512876 36.850909 \nL 601.611542 629.869091 \nL 601.67497 629.869091 \nL 601.682017 36.850909 \nL 601.780683 629.869091 \nL 601.844111 629.869091 \nL 601.949825 206.284675 \nL 602.062586 629.869091 \nL 602.154204 629.869091 \nL 602.189442 206.284675 \nL 602.259917 629.869091 \nL 602.379726 629.869091 \nL 602.471344 36.850909 \nL 602.485439 629.869091 \nL 602.591152 629.869091 \nL 602.647533 36.850909 \nL 602.696866 629.869091 \nL 602.739151 629.869091 \nL 602.788484 36.850909 \nL 602.844865 629.869091 \nL 603.197243 629.869091 \nL 603.310004 36.850909 \nL 603.366384 36.850909 \nL 603.479145 629.869091 \nL 603.591906 36.850909 \nL 603.873808 36.850909 \nL 603.880856 121.567792 \nL 603.979522 36.850909 \nL 604.606754 36.850909 \nL 604.64904 629.869091 \nL 604.712468 36.850909 \nL 604.73361 36.850909 \nL 604.846371 629.869091 \nL 604.853419 629.869091 \nL 604.96618 36.850909 \nL 605.170559 36.850909 \nL 605.177607 121.567792 \nL 605.276272 36.850909 \nL 610.491466 36.850909 \nL 610.512609 121.567792 \nL 610.59718 36.850909 \nL 610.632417 36.850909 \nL 610.745178 121.567792 \nL 610.886129 121.567792 \nL 610.893177 36.850909 \nL 610.991843 121.567792 \nL 611.196222 121.567792 \nL 611.20327 36.850909 \nL 611.301935 121.567792 \nL 611.661361 121.567792 \nL 611.668409 36.850909 \nL 611.767074 121.567792 \nL 611.915073 121.567792 \nL 612.013739 36.850909 \nL 612.020786 121.567792 \nL 612.1265 121.567792 \nL 612.140595 36.850909 \nL 612.232213 121.567792 \nL 612.570496 121.567792 \nL 612.577544 36.850909 \nL 612.676209 121.567792 \nL 612.78897 121.567792 \nL 612.859446 36.850909 \nL 612.894684 121.567792 \nL 613.007445 121.567792 \nL 613.02154 36.850909 \nL 613.113158 121.567792 \nL 613.162491 121.567792 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616.580557 121.567792 \nL 616.693318 36.850909 \nL 616.714461 36.850909 \nL 616.827222 121.567792 \nL 617.010458 121.567792 \nL 617.031601 36.850909 \nL 617.116172 121.567792 \nL 617.355789 121.567792 \nL 617.362836 36.850909 \nL 617.461502 121.567792 \nL 617.517883 121.567792 \nL 617.581311 36.850909 \nL 617.623596 121.567792 \nL 617.651786 121.567792 \nL 617.708167 36.850909 \nL 617.7575 121.567792 \nL 617.961879 121.567792 \nL 618.07464 36.850909 \nL 618.081687 36.850909 \nL 618.194448 121.567792 \nL 618.405875 121.567792 \nL 618.412923 36.850909 \nL 618.511588 121.567792 \nL 618.807586 121.567792 \nL 618.821681 36.850909 \nL 618.913299 121.567792 \nL 619.237487 121.567792 \nL 619.251582 36.850909 \nL 619.3432 121.567792 \nL 619.477104 121.567792 \nL 619.484151 36.850909 \nL 619.582817 121.567792 \nL 619.589865 121.567792 \nL 619.702626 36.850909 \nL 619.815387 121.567792 \nL 619.850625 36.850909 \nL 619.9211 121.567792 \nL 620.019766 121.567792 \nL 620.033861 36.850909 \nL 620.125479 121.567792 \nL 620.146622 121.567792 \nL 620.160717 36.850909 \nL 620.252335 121.567792 \nL 620.273478 121.567792 \nL 620.386239 36.850909 \nL 620.393287 36.850909 \nL 620.491952 121.567792 \nL 620.499 36.850909 \nL 620.506048 36.850909 \nL 620.618808 121.567792 \nL 620.675189 121.567792 \nL 620.780902 36.850909 \nL 620.893663 121.567792 \nL 620.907758 121.567792 \nL 620.964139 36.850909 \nL 621.013472 121.567792 \nL 621.0769 121.567792 \nL 621.189661 36.850909 \nL 621.210803 36.850909 \nL 621.323564 121.567792 \nL 621.379945 121.567792 \nL 621.492706 36.850909 \nL 621.626609 36.850909 \nL 621.73937 121.567792 \nL 621.753465 121.567792 \nL 621.838036 36.850909 \nL 621.859179 121.567792 \nL 621.922607 121.567792 \nL 622.02832 36.850909 \nL 622.141081 121.567792 \nL 622.267937 121.567792 \nL 622.380698 36.850909 \nL 622.627363 36.850909 \nL 622.740124 121.567792 \nL 622.796504 121.567792 \nL 622.86698 36.850909 \nL 622.902218 121.567792 \nL 622.92336 121.567792 \nL 623.036121 36.850909 \nL 623.071359 36.850909 \nL 623.18412 121.567792 \nL 623.254596 121.567792 \nL 623.353261 36.850909 \nL 623.360309 121.567792 \nL 623.437832 121.567792 \nL 623.44488 36.850909 \nL 623.543545 121.567792 \nL 623.628116 121.567792 \nL 623.726782 36.850909 \nL 623.73383 121.567792 \nL 623.797258 121.567792 \nL 623.902971 36.850909 \nL 624.015732 121.567792 \nL 624.043922 121.567792 \nL 624.100303 36.850909 \nL 624.149636 121.567792 \nL 624.156683 121.567792 \nL 624.269444 36.850909 \nL 624.382205 121.567792 \nL 624.600679 121.567792 \nL 624.71344 36.850909 \nL 624.755726 36.850909 \nL 624.868487 121.567792 \nL 624.94601 121.567792 \nL 624.960105 36.850909 \nL 625.051723 121.567792 \nL 625.185627 121.567792 \nL 625.206769 36.850909 \nL 625.29134 121.567792 \nL 625.60848 121.567792 \nL 625.622575 36.850909 \nL 625.714194 121.567792 \nL 625.834002 121.567792 \nL 625.84105 36.850909 \nL 625.939716 121.567792 \nL 626.038381 121.567792 \nL 626.080667 36.850909 \nL 626.144095 121.567792 \nL 626.151142 121.567792 \nL 626.256856 36.850909 \nL 626.369617 121.567792 \nL 626.552853 121.567792 \nL 626.616281 36.850909 \nL 626.658567 121.567792 \nL 626.898184 121.567792 \nL 626.975707 36.850909 \nL 627.003897 121.567792 \nL 627.299894 121.567792 \nL 627.391513 36.850909 \nL 627.405608 121.567792 \nL 627.433798 121.567792 \nL 627.461988 36.850909 \nL 627.539511 121.567792 \nL 627.546559 121.567792 \nL 627.553607 36.850909 \nL 627.652272 121.567792 \nL 627.786176 121.567792 \nL 627.793224 36.850909 \nL 627.891889 121.567792 \nL 628.018745 121.567792 \nL 628.025793 36.850909 \nL 628.124459 121.567792 \nL 628.434551 121.567792 \nL 628.441599 36.850909 \nL 628.540265 121.567792 \nL 628.596645 121.567792 \nL 628.624836 36.850909 \nL 628.702359 121.567792 \nL 628.857405 121.567792 \nL 628.8715 36.850909 \nL 628.963118 121.567792 \nL 629.252068 121.567792 \nL 629.273211 36.850909 \nL 629.357782 121.567792 \nL 629.653779 121.567792 \nL 629.667874 36.850909 \nL 629.759493 121.567792 \nL 630.05549 121.567792 \nL 630.069585 36.850909 \nL 630.161203 121.567792 \nL 630.302155 121.567792 \nL 630.309202 36.850909 \nL 630.407868 121.567792 \nL 630.717961 121.567792 \nL 630.725008 36.850909 \nL 630.823674 121.567792 \nL 631.105576 121.567792 \nL 631.126719 36.850909 \nL 631.21129 121.567792 \nL 631.521382 121.567792 \nL 631.52843 36.850909 \nL 631.627096 121.567792 \nL 631.732809 121.567792 \nL 631.84557 36.850909 \nL 635.460968 36.850909 \nL 635.468015 121.567792 \nL 635.566681 36.850909 \nL 636.010677 36.850909 \nL 636.017725 121.567792 \nL 636.116391 36.850909 \nL 638.413895 36.850909 \nL 638.420943 121.567792 \nL 638.519608 36.850909 \nL 647.871719 36.850909 \nL 647.906957 545.152208 \nL 647.977433 36.850909 \nL 648.01267 36.850909 \nL 648.033813 545.152208 \nL 648.118384 36.850909 \nL 676.407286 36.850909 \nL 676.421381 545.152208 \nL 676.512999 36.850909 \nL 676.527094 36.850909 \nL 676.541189 545.152208 \nL 676.632808 36.850909 \nL 677.5208 36.850909 \nL 677.527848 545.152208 \nL 677.626513 36.850909 \nL 680.086112 36.850909 \nL 680.100207 545.152208 \nL 680.191825 36.850909 \nL 709.953667 36.850909 \nL 710.066428 545.152208 \nL 710.115761 545.152208 \nL 710.228521 36.850909 \nL 710.341282 545.152208 \nL 710.383568 545.152208 \nL 710.461091 36.850909 \nL 710.489281 545.152208 \nL 710.531567 545.152208 \nL 710.644327 36.850909 \nL 710.792326 36.850909 \nL 710.89804 545.152208 \nL 710.95442 36.850909 \nL 711.003753 545.152208 \nL 711.024896 545.152208 \nL 711.137657 36.850909 \nL 711.567558 36.850909 \nL 711.652128 545.152208 \nL 711.673271 36.850909 \nL 711.729652 36.850909 \nL 711.743747 545.152208 \nL 711.835365 36.850909 \nL 711.842412 36.850909 \nL 711.955173 545.152208 \nL 712.08203 545.152208 \nL 712.180695 36.850909 \nL 712.187743 545.152208 \nL 712.215933 545.152208 \nL 712.230028 36.850909 \nL 712.321647 545.152208 \nL 712.45555 545.152208 \nL 712.547168 36.850909 \nL 712.561264 545.152208 \nL 712.730405 545.152208 \nL 712.807928 36.850909 \nL 712.836118 545.152208 \nL 713.06164 545.152208 \nL 713.096878 36.850909 \nL 713.167354 545.152208 \nL 713.266019 545.152208 \nL 713.37878 36.850909 \nL 713.470399 36.850909 \nL 713.58316 545.152208 \nL 713.625445 545.152208 \nL 713.63954 36.850909 \nL 713.731158 545.152208 \nL 713.752301 545.152208 \nL 713.801634 36.850909 \nL 713.858014 545.152208 \nL 713.886205 545.152208 \nL 713.9003 36.850909 \nL 713.991918 545.152208 \nL 714.027156 545.152208 \nL 714.139917 36.850909 \nL 722.512417 36.850909 \nL 722.582892 629.869091 \nL 722.625178 545.152208 \nL 722.794319 545.152208 \nL 722.87889 629.869091 \nL 722.885937 36.850909 \nL 722.984603 629.869091 \nL 723.245363 629.869091 \nL 723.252411 36.850909 \nL 723.351076 629.869091 \nL 723.358124 629.869091 \nL 723.470885 545.152208 \nL 723.499075 629.869091 \nL 723.534313 545.152208 \nL 723.54136 36.850909 \nL 723.548408 629.869091 \nL 723.640026 545.152208 \nL 723.731645 545.152208 \nL 723.844406 629.869091 \nL 723.964214 629.869091 \nL 723.971262 36.850909 \nL 724.069927 629.869091 \nL 724.076975 629.869091 \nL 724.189736 545.152208 \nL 724.246116 545.152208 \nL 724.358877 629.869091 \nL 724.401163 629.869091 \nL 724.40821 36.850909 \nL 724.506876 629.869091 \nL 724.605542 629.869091 \nL 724.718303 545.152208 \nL 724.732398 629.869091 \nL 724.816969 545.152208 \nL 724.824016 36.850909 \nL 724.838111 629.869091 \nL 724.922682 629.869091 \nL 725.232775 629.869091 \nL 725.282108 36.850909 \nL 725.338488 629.869091 \nL 725.704961 629.869091 \nL 725.754294 36.850909 \nL 725.810674 629.869091 \nL 725.82477 629.869091 \nL 725.895245 545.152208 \nL 725.930483 629.869091 \nL 725.937531 629.869091 \nL 726.050291 545.152208 \nL 726.148957 545.152208 \nL 726.212385 36.850909 \nL 726.261718 629.869091 \nL 726.423812 629.869091 \nL 726.536573 545.152208 \nL 726.550668 545.152208 \nL 726.635239 629.869091 \nL 726.642286 36.850909 \nL 726.740952 545.152208 \nL 726.924189 545.152208 \nL 727.03695 629.869091 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545.152208 \nL 729.440167 629.869091 \nL 729.503595 545.152208 \nL 729.602261 545.152208 \nL 729.609309 36.850909 \nL 729.637499 629.869091 \nL 729.707975 545.152208 \nL 729.771403 545.152208 \nL 729.834831 36.850909 \nL 729.884164 629.869091 \nL 730.060353 629.869091 \nL 730.173114 545.152208 \nL 730.229494 545.152208 \nL 730.236542 36.850909 \nL 730.243589 629.869091 \nL 730.335207 545.152208 \nL 730.405683 545.152208 \nL 730.518444 629.869091 \nL 730.532539 629.869091 \nL 730.652348 36.850909 \nL 730.765108 545.152208 \nL 730.821489 545.152208 \nL 730.93425 629.869091 \nL 731.047011 36.850909 \nL 731.054058 629.869091 \nL 731.159772 545.152208 \nL 731.307771 545.152208 \nL 731.420531 629.869091 \nL 731.476912 629.869091 \nL 731.48396 36.850909 \nL 731.582625 629.869091 \nL 731.695386 545.152208 \nL 731.723577 545.152208 \nL 731.730624 36.850909 \nL 731.82929 545.152208 \nL 731.871575 545.152208 \nL 731.88567 629.869091 \nL 731.977289 545.152208 \nL 731.984336 545.152208 \nL 732.054812 629.869091 \nL 732.061859 36.850909 \nL 732.160525 545.152208 \nL 732.273286 629.869091 \nL 732.287381 629.869091 \nL 732.386047 545.152208 \nL 732.393095 629.869091 \nL 732.400142 629.869091 \nL 732.46357 36.850909 \nL 732.512903 545.152208 \nL 732.548141 545.152208 \nL 732.660902 36.850909 \nL 733.464324 36.850909 \nL 733.471371 121.567792 \nL 733.570037 36.850909 \nL 733.682798 36.850909 \nL 733.795559 545.152208 \nL 733.823749 545.152208 \nL 733.93651 36.850909 \nL 733.985843 36.850909 \nL 734.028128 545.152208 \nL 734.091556 36.850909 \nL 734.147937 36.850909 \nL 734.162032 545.152208 \nL 734.25365 36.850909 \nL 734.324126 36.850909 \nL 734.331173 545.152208 \nL 734.429839 121.567792 \nL 734.5426 36.850909 \nL 734.598981 36.850909 \nL 734.641266 121.567792 \nL 734.704694 36.850909 \nL 735.078215 36.850909 \nL 735.190976 121.567792 \nL 735.205071 121.567792 \nL 735.317832 36.850909 \nL 735.642019 36.850909 \nL 735.75478 121.567792 \nL 735.895731 121.567792 \nL 735.902779 36.850909 \nL 736.001445 121.567792 \nL 736.297442 121.567792 \nL 736.30449 36.850909 \nL 736.403156 121.567792 \nL 736.59344 121.567792 \nL 736.600487 629.869091 \nL 736.706201 545.152208 \nL 736.734391 545.152208 \nL 736.847152 629.869091 \nL 736.924675 629.869091 \nL 737.030388 121.567792 \nL 737.037436 629.869091 \nL 737.122007 36.850909 \nL 737.136102 121.567792 \nL 737.54486 121.567792 \nL 737.551908 36.850909 \nL 737.650574 121.567792 \nL 737.883143 121.567792 \nL 737.890191 36.850909 \nL 737.988856 121.567792 \nL 738.09457 121.567792 \nL 738.101617 36.850909 \nL 738.200283 121.567792 \nL 738.510376 121.567792 \nL 738.517423 36.850909 \nL 738.616089 121.567792 \nL 738.912087 121.567792 \nL 738.919134 545.152208 \nL 739.0178 121.567792 \nL 739.313798 121.567792 \nL 739.320845 36.850909 \nL 739.419511 121.567792 \nL 739.482939 121.567792 \nL 739.489987 629.869091 \nL 739.5957 545.152208 \nL 739.997411 545.152208 \nL 740.018553 629.869091 \nL 740.011506 36.850909 \nL 740.103124 545.152208 \nL 740.251123 545.152208 \nL 740.363884 629.869091 \nL 740.370931 629.869091 \nL 740.385027 545.152208 \nL 740.476645 629.869091 \nL 740.575311 629.869091 \nL 740.688072 121.567792 \nL 740.772642 629.869091 \nL 740.793785 121.567792 \nL 740.800833 36.850909 \nL 740.821975 629.869091 \nL 740.899498 121.567792 \nL 741.188448 121.567792 \nL 741.195496 36.850909 \nL 741.294162 121.567792 \nL 741.604254 121.567792 \nL 741.611302 36.850909 \nL 741.709968 121.567792 \nL 742.02006 121.567792 \nL 742.027108 36.850909 \nL 742.125774 121.567792 \nL 742.196249 121.567792 \nL 742.203297 36.850909 \nL 742.301963 121.567792 \nL 742.612055 121.567792 \nL 742.619103 36.850909 \nL 742.717769 121.567792 \nL 742.774149 121.567792 \nL 742.795292 36.850909 \nL 742.879862 121.567792 \nL 742.950338 121.567792 \nL 742.978528 36.850909 \nL 743.056051 121.567792 \nL 743.542333 121.567792 \nL 743.549381 36.850909 \nL 743.648046 121.567792 \nL 744.014519 121.567792 \nL 744.021567 36.850909 \nL 744.120233 121.567792 \nL 744.430325 121.567792 \nL 744.437373 36.850909 \nL 744.536039 121.567792 \nL 744.902512 121.567792 \nL 744.909559 36.850909 \nL 745.008225 121.567792 \nL 745.346508 121.567792 \nL 745.353556 36.850909 \nL 745.452221 121.567792 \nL 745.790504 121.567792 \nL 745.797552 36.850909 \nL 745.896218 121.567792 \nL 746.248596 121.567792 \nL 746.255643 36.850909 \nL 746.354309 121.567792 \nL 746.72783 121.567792 \nL 746.734877 36.850909 \nL 746.833543 121.567792 \nL 747.192969 121.567792 \nL 747.200016 36.850909 \nL 747.298682 121.567792 \nL 747.67925 121.567792 \nL 747.686298 36.850909 \nL 747.784964 121.567792 \nL 748.123246 121.567792 \nL 748.130294 36.850909 \nL 748.22896 121.567792 \nL 748.524957 121.567792 \nL 748.532005 36.850909 \nL 748.630671 121.567792 \nL 748.919621 121.567792 \nL 748.926668 36.850909 \nL 749.025334 121.567792 \nL 749.342474 121.567792 \nL 749.349522 36.850909 \nL 749.448187 121.567792 \nL 749.772375 121.567792 \nL 749.779423 36.850909 \nL 749.878089 121.567792 \nL 750.174086 121.567792 \nL 750.181134 36.850909 \nL 750.279799 121.567792 \nL 750.59694 121.567792 \nL 750.603987 36.850909 \nL 750.702653 121.567792 \nL 750.794271 121.567792 \nL 750.801319 36.850909 \nL 750.899985 121.567792 \nL 751.097316 121.567792 \nL 751.104364 36.850909 \nL 751.20303 121.567792 \nL 751.513122 121.567792 \nL 751.52017 36.850909 \nL 751.618836 121.567792 \nL 751.957118 121.567792 \nL 751.964166 545.152208 \nL 752.062832 121.567792 \nL 752.619589 121.567792 \nL 752.626637 545.152208 \nL 752.725302 121.567792 \nL 753.0213 121.567792 \nL 753.028347 36.850909 \nL 753.127013 121.567792 \nL 753.437106 121.567792 \nL 753.444153 36.850909 \nL 753.542819 121.567792 \nL 753.620342 121.567792 \nL 753.733103 629.869091 \nL 753.740151 629.869091 \nL 753.852912 121.567792 \nL 753.859959 121.567792 \nL 753.951578 629.869091 \nL 753.965673 121.567792 \nL 754.212337 121.567792 \nL 754.240528 629.869091 \nL 754.318051 121.567792 \nL 754.52243 121.567792 \nL 754.529478 36.850909 \nL 754.628143 121.567792 \nL 754.888903 121.567792 \nL 754.895951 629.869091 \nL 754.994616 121.567792 \nL 755.198996 121.567792 \nL 755.213091 629.869091 \nL 755.220138 36.850909 \nL 755.304709 121.567792 \nL 755.628897 121.567792 \nL 755.635944 36.850909 \nL 755.73461 121.567792 \nL 756.030608 121.567792 \nL 756.143369 36.850909 \nL 756.495746 36.850909 \nL 756.608507 121.567792 \nL 756.636698 121.567792 \nL 756.643745 36.850909 \nL 756.742411 121.567792 \nL 757.038409 121.567792 \nL 757.045456 36.850909 \nL 757.144122 121.567792 \nL 757.440119 121.567792 \nL 757.447167 36.850909 \nL 757.545833 121.567792 \nL 757.84183 121.567792 \nL 757.848878 36.850909 \nL 757.947544 121.567792 \nL 758.271731 121.567792 \nL 758.278779 36.850909 \nL 758.377445 121.567792 \nL 758.469063 121.567792 \nL 758.476111 36.850909 \nL 758.574776 121.567792 \nL 758.863726 121.567792 \nL 758.870774 36.850909 \nL 758.96944 121.567792 \nL 759.265437 121.567792 \nL 759.272485 36.850909 \nL 759.371151 121.567792 \nL 759.667148 121.567792 \nL 759.674196 36.850909 \nL 759.772861 121.567792 \nL 759.822194 121.567792 \nL 759.829242 36.850909 \nL 759.850385 629.869091 \nL 759.927908 121.567792 \nL 759.977241 121.567792 \nL 760.090002 629.869091 \nL 760.097049 629.869091 \nL 760.20981 121.567792 \nL 760.252095 121.567792 \nL 760.273238 629.869091 \nL 760.357809 121.567792 \nL 760.456475 121.567792 \nL 760.52695 629.869091 \nL 760.562188 121.567792 \nL 760.625616 121.567792 \nL 760.738377 629.869091 \nL 760.75952 629.869091 \nL 760.886376 121.567792 \nL 760.999137 629.869091 \nL 761.027327 629.869091 \nL 761.083707 121.567792 \nL 761.13304 629.869091 \nL 761.140088 629.869091 \nL 761.252849 36.850909 \nL 761.513609 36.850909 \nL 761.520656 545.152208 \nL 761.619322 36.850909 \nL 761.795511 36.850909 \nL 761.802558 121.567792 \nL 761.901224 36.850909 \nL 761.929414 36.850909 \nL 761.936462 206.284675 \nL 762.035128 36.850909 \nL 762.387506 36.850909 \nL 762.457981 629.869091 \nL 762.500267 545.152208 \nL 762.613028 121.567792 \nL 762.725789 629.869091 \nL 762.775122 629.869091 \nL 762.83855 545.152208 \nL 762.880835 629.869091 \nL 762.944263 629.869091 \nL 763.000643 206.284675 \nL 763.049976 629.869091 \nL 763.057024 629.869091 \nL 763.15569 206.284675 \nL 763.162737 629.869091 \nL 763.169785 629.869091 \nL 763.261403 206.284675 \nL 763.275498 629.869091 \nL 763.303688 629.869091 \nL 763.388259 121.567792 \nL 763.409402 629.869091 \nL 763.479877 629.869091 \nL 763.571496 206.284675 \nL 763.585591 629.869091 \nL 763.684257 629.869091 \nL 763.797018 206.284675 \nL 763.81816 206.284675 \nL 763.930921 629.869091 \nL 763.966159 629.869091 \nL 764.064825 206.284675 \nL 764.071872 629.869091 \nL 764.085968 629.869091 \nL 764.198728 121.567792 \nL 764.241014 629.869091 \nL 764.304442 121.567792 \nL 764.311489 121.567792 \nL 764.318537 629.869091 \nL 764.417203 121.567792 \nL 764.515869 121.567792 \nL 764.62863 629.869091 \nL 764.65682 629.869091 \nL 764.762533 121.567792 \nL 764.875294 629.869091 \nL 765.016245 206.284675 \nL 765.107864 206.284675 \nL 765.185387 121.567792 \nL 765.255862 629.869091 \nL 765.2911 121.567792 \nL 765.340433 121.567792 \nL 765.453194 629.869091 \nL 765.594145 629.869091 \nL 765.706906 206.284675 \nL 765.728049 206.284675 \nL 765.784429 121.567792 \nL 765.742144 629.869091 \nL 765.798524 629.869091 \nL 765.890143 629.869091 \nL 765.918333 206.284675 \nL 765.995856 629.869091 \nL 766.073379 629.869091 \nL 766.18614 121.567792 \nL 766.263663 629.869091 \nL 766.291854 206.284675 \nL 766.298901 121.567792 \nL 766.312996 629.869091 \nL 766.362329 629.869091 \nL 766.651279 629.869091 \nL 766.672422 121.567792 \nL 766.756992 629.869091 \nL 766.827468 629.869091 \nL 766.834516 121.567792 \nL 766.940229 206.284675 \nL 766.947277 206.284675 \nL 767.017752 629.869091 \nL 766.975467 121.567792 \nL 767.05299 206.284675 \nL 767.060037 206.284675 \nL 767.172798 629.869091 \nL 767.229179 629.869091 \nL 767.236226 36.850909 \nL 767.334892 629.869091 \nL 767.39832 629.869091 \nL 767.482891 121.567792 \nL 767.504034 629.869091 \nL 767.792984 629.869091 \nL 767.800031 121.567792 \nL 767.898697 629.869091 \nL 767.97622 629.869091 \nL 768.032601 121.567792 \nL 768.081934 629.869091 \nL 768.166504 629.869091 \nL 768.20879 121.567792 \nL 768.279265 206.284675 \nL 768.286313 206.284675 \nL 768.370883 629.869091 \nL 768.349741 121.567792 \nL 768.392026 121.567792 \nL 768.406121 121.567792 \nL 768.49774 629.869091 \nL 768.511835 121.567792 \nL 768.540025 121.567792 \nL 768.652786 629.869091 \nL 768.723261 629.869091 \nL 768.850117 206.284675 \nL 768.913545 629.869091 \nL 768.892403 121.567792 \nL 768.955831 121.567792 \nL 769.040402 629.869091 \nL 769.068592 206.284675 \nL 769.075639 206.284675 \nL 769.089734 629.869091 \nL 769.181353 206.284675 \nL 769.202495 206.284675 \nL 769.308209 629.869091 \nL 769.406875 121.567792 \nL 769.413922 545.152208 \nL 769.435065 545.152208 \nL 769.498493 629.869091 \nL 769.526683 545.152208 \nL 769.540778 629.869091 \nL 769.646492 121.567792 \nL 769.688777 121.567792 \nL 769.815633 629.869091 \nL 769.829728 545.152208 \nL 769.836776 545.152208 \nL 769.850871 629.869091 \nL 769.949537 206.284675 \nL 769.956584 206.284675 \nL 770.041155 629.869091 \nL 770.020012 121.567792 \nL 770.062298 206.284675 \nL 770.069345 206.284675 \nL 770.08344 629.869091 \nL 770.175059 206.284675 \nL 770.182106 206.284675 \nL 770.231439 629.869091 \nL 770.294867 545.152208 \nL 770.31601 545.152208 \nL 770.323057 629.869091 \nL 770.3442 206.284675 \nL 770.393533 206.284675 \nL 770.407628 206.284675 \nL 770.520389 629.869091 \nL 770.626102 206.284675 \nL 770.738863 629.869091 \nL 770.89391 629.869091 \nL 770.929147 121.567792 \nL 770.999623 629.869091 \nL 771.21105 629.869091 \nL 771.323811 206.284675 \nL 771.330858 206.284675 \nL 771.443619 629.869091 \nL 771.457714 629.869091 \nL 771.549333 121.567792 \nL 771.570475 206.284675 \nL 771.58457 206.284675 \nL 771.662094 121.567792 \nL 771.697331 629.869091 \nL 771.704379 629.869091 \nL 771.78895 121.567792 \nL 771.810092 206.284675 \nL 771.87352 629.869091 \nL 771.922853 545.152208 \nL 771.929901 545.152208 \nL 771.943996 629.869091 \nL 772.042662 121.567792 \nL 772.155423 629.869091 \nL 772.16247 545.152208 \nL 772.261136 629.869091 \nL 772.303421 629.869091 \nL 772.402087 206.284675 \nL 772.409135 629.869091 \nL 772.42323 629.869091 \nL 772.444373 121.567792 \nL 772.528943 629.869091 \nL 772.535991 629.869091 \nL 772.543038 291.001558 \nL 772.641704 629.869091 \nL 772.747418 629.869091 \nL 772.803798 121.567792 \nL 772.853131 629.869091 \nL 772.860179 629.869091 \nL 772.944749 291.001558 \nL 772.972939 375.718442 \nL 773.036368 121.567792 \nL 773.050463 629.869091 \nL 773.078653 375.718442 \nL 773.113891 375.718442 \nL 773.135033 291.001558 \nL 773.156176 629.869091 \nL 773.198461 629.869091 \nL 773.304175 629.869091 \nL 773.416936 121.567792 \nL 773.529697 629.869091 \nL 773.543792 629.869091 \nL 773.6636 121.567792 \nL 773.741123 629.869091 \nL 773.776361 291.001558 \nL 773.797504 291.001558 \nL 773.818647 629.869091 \nL 773.853884 121.567792 \nL 773.903217 121.567792 \nL 773.92436 121.567792 \nL 774.051216 629.869091 \nL 774.100549 629.869091 \nL 774.192167 291.001558 \nL 774.21331 375.718442 \nL 774.2415 375.718442 \nL 774.326071 629.869091 \nL 774.354261 291.001558 \nL 774.382451 291.001558 \nL 774.445879 629.869091 \nL 774.488165 121.567792 \nL 774.607973 375.718442 \nL 774.615021 121.567792 \nL 774.713687 375.718442 \nL 775.178825 375.718442 \nL 775.185873 121.567792 \nL 775.284539 375.718442 \nL 775.700345 375.718442 \nL 775.71444 121.567792 \nL 775.806058 375.718442 \nL 776.066818 375.718442 \nL 776.073865 121.567792 \nL 776.172531 375.718442 \nL 776.313482 375.718442 \nL 776.362815 460.435325 \nL 776.419196 375.718442 \nL 776.433291 375.718442 \nL 776.475576 460.435325 \nL 776.539004 375.718442 \nL 776.553099 375.718442 \nL 776.658813 460.435325 \nL 776.750431 375.718442 \nL 776.764526 460.435325 \nL 776.771574 460.435325 \nL 776.863192 375.718442 \nL 776.877287 460.435325 \nL 776.968905 460.435325 \nL 777.081666 375.718442 \nL 777.152142 460.435325 \nL 777.18738 375.718442 \nL 777.328331 375.718442 \nL 777.441092 460.435325 \nL 777.462235 460.435325 \nL 777.574996 375.718442 \nL 777.82166 375.718442 \nL 777.828708 36.850909 \nL 777.927373 375.718442 \nL 778.11061 375.718442 \nL 778.124705 121.567792 \nL 778.152895 545.152208 \nL 778.216323 375.718442 \nL 778.244514 375.718442 \nL 778.251561 629.869091 \nL 778.350227 375.718442 \nL 778.434798 375.718442 \nL 778.547559 460.435325 \nL 778.554606 460.435325 \nL 778.66032 375.718442 \nL 778.773081 460.435325 \nL 778.780128 460.435325 \nL 778.892889 375.718442 \nL 779.047935 375.718442 \nL 779.146601 460.435325 \nL 779.153649 375.718442 \nL 779.273457 375.718442 \nL 779.329838 460.435325 \nL 779.379171 375.718442 \nL 779.414408 375.718442 \nL 779.442599 629.869091 \nL 779.520122 375.718442 \nL 779.569455 375.718442 \nL 779.576502 460.435325 \nL 779.675168 375.718442 \nL 779.731549 375.718442 \nL 779.84431 460.435325 \nL 779.851357 460.435325 \nL 779.95707 375.718442 \nL 780.069831 460.435325 \nL 780.182592 375.718442 \nL 780.21783 375.718442 \nL 780.330591 460.435325 \nL 780.358781 121.567792 \nL 780.386972 545.152208 \nL 780.436304 460.435325 \nL 780.457447 460.435325 \nL 780.527923 375.718442 \nL 780.563161 460.435325 \nL 780.619541 460.435325 \nL 780.732302 375.718442 \nL 780.73935 375.718442 \nL 780.838015 460.435325 \nL 780.845063 375.718442 \nL 780.866206 375.718442 \nL 780.978967 460.435325 \nL 780.986014 460.435325 \nL 781.08468 375.718442 \nL 781.091727 460.435325 \nL 781.105823 460.435325 \nL 781.14106 375.718442 \nL 781.148108 545.152208 \nL 781.204488 545.152208 \nL 781.246774 545.152208 \nL 781.359535 375.718442 \nL 781.387725 375.718442 \nL 781.40182 545.152208 \nL 781.493438 375.718442 \nL 781.697818 375.718442 \nL 781.704865 545.152208 \nL 781.803531 375.718442 \nL 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545.152208 \nL 789.372609 375.718442 \nL 789.48537 545.152208 \nL 789.80251 545.152208 \nL 789.858891 629.869091 \nL 789.823653 375.718442 \nL 789.880034 545.152208 \nL 789.887081 206.284675 \nL 789.908224 629.869091 \nL 789.985747 629.869091 \nL 789.999842 629.869091 \nL 790.112603 375.718442 \nL 790.274697 375.718442 \nL 790.359268 121.567792 \nL 790.387458 629.869091 \nL 790.401553 121.567792 \nL 790.493171 629.869091 \nL 790.500219 629.869091 \nL 790.61298 206.284675 \nL 790.676408 629.869091 \nL 790.725741 375.718442 \nL 790.732788 375.718442 \nL 790.746883 206.284675 \nL 790.845549 629.869091 \nL 790.852597 629.869091 \nL 790.908977 121.567792 \nL 790.95831 545.152208 \nL 790.972405 545.152208 \nL 790.979453 121.567792 \nL 791.028786 629.869091 \nL 791.078119 629.869091 \nL 791.085166 629.869091 \nL 791.155642 545.152208 \nL 791.19088 629.869091 \nL 791.204975 629.869091 \nL 791.212022 375.718442 \nL 791.317736 545.152208 \nL 791.479829 545.152208 \nL 791.486877 206.284675 \nL 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795.553319 206.284675 \nL 795.651984 545.152208 \nL 795.729508 545.152208 \nL 795.842269 206.284675 \nL 795.940934 206.284675 \nL 796.0396 545.152208 \nL 796.046648 206.284675 \nL 796.060743 206.284675 \nL 796.110076 629.869091 \nL 796.166456 206.284675 \nL 796.187599 206.284675 \nL 796.286265 545.152208 \nL 796.293312 206.284675 \nL 796.307407 206.284675 \nL 796.370835 629.869091 \nL 796.413121 206.284675 \nL 796.490644 206.284675 \nL 796.497692 629.869091 \nL 796.603405 545.152208 \nL 796.610452 629.869091 \nL 796.64569 206.284675 \nL 796.680928 206.284675 \nL 796.702071 206.284675 \nL 796.779594 629.869091 \nL 796.814832 545.152208 \nL 796.983973 545.152208 \nL 797.005116 206.284675 \nL 797.061496 629.869091 \nL 797.160162 206.284675 \nL 797.16721 545.152208 \nL 797.181305 545.152208 \nL 797.279971 629.869091 \nL 797.287018 545.152208 \nL 797.301113 545.152208 \nL 797.343399 629.869091 \nL 797.378636 206.284675 \nL 797.385684 206.284675 \nL 797.463207 629.869091 \nL 797.491397 545.152208 \nL 797.505492 629.869091 \nL 797.597111 206.284675 \nL 797.709872 629.869091 \nL 797.780347 629.869091 \nL 797.879013 206.284675 \nL 797.886061 629.869091 \nL 798.012917 206.284675 \nL 798.125678 629.869091 \nL 798.196153 629.869091 \nL 798.308914 206.284675 \nL 798.485103 206.284675 \nL 798.590817 629.869091 \nL 798.682435 206.284675 \nL 798.69653 629.869091 \nL 798.703577 629.869091 \nL 798.802243 206.284675 \nL 798.809291 629.869091 \nL 798.816338 629.869091 \nL 798.872719 206.284675 \nL 798.922052 629.869091 \nL 798.936147 629.869091 \nL 798.950242 206.284675 \nL 799.04186 629.869091 \nL 799.182811 629.869091 \nL 799.189859 36.850909 \nL 799.295572 206.284675 \nL 799.408333 629.869091 \nL 799.415381 629.869091 \nL 799.464714 206.284675 \nL 799.521094 629.869091 \nL 799.528142 629.869091 \nL 799.577475 206.284675 \nL 799.633855 629.869091 \nL 800.599371 629.869091 \nL 800.606418 206.284675 \nL 800.705084 629.869091 \nL 800.909463 629.869091 \nL 800.916511 121.567792 \nL 801.015177 629.869091 \nL 801.297079 629.869091 \nL 801.395745 206.284675 \nL 801.40984 291.001558 \nL 801.416888 291.001558 \nL 801.536696 629.869091 \nL 801.649457 375.718442 \nL 801.705838 629.869091 \nL 801.762218 545.152208 \nL 801.832694 545.152208 \nL 801.860884 291.001558 \nL 801.924312 629.869091 \nL 802.030025 291.001558 \nL 802.128691 629.869091 \nL 802.114596 121.567792 \nL 802.135739 121.567792 \nL 802.368308 121.567792 \nL 802.481069 629.869091 \nL 802.650211 629.869091 \nL 802.770019 375.718442 \nL 802.875732 545.152208 \nL 802.932113 36.850909 \nL 802.981446 545.152208 \nL 803.002588 545.152208 \nL 803.037826 375.718442 \nL 803.108302 545.152208 \nL 803.312681 545.152208 \nL 803.404299 121.567792 \nL 803.418394 545.152208 \nL 803.932866 545.152208 \nL 804.031532 375.718442 \nL 804.03858 545.152208 \nL 804.09496 545.152208 \nL 804.179531 375.718442 \nL 804.130198 629.869091 \nL 804.200674 629.869091 \nL 804.214769 629.869091 \nL 804.320482 375.718442 \nL 804.32753 545.152208 \nL 804.348672 545.152208 \nL 804.461433 375.718442 \nL 804.468481 375.718442 \nL 804.553051 629.869091 \nL 804.574194 545.152208 \nL 804.61648 629.869091 \nL 804.686955 375.718442 \nL 804.72924 375.718442 \nL 804.736288 629.869091 \nL 804.834954 375.718442 \nL 804.975905 375.718442 \nL 805.081618 629.869091 \nL 805.088666 545.152208 \nL 805.123904 545.152208 \nL 805.194379 375.718442 \nL 805.215522 629.869091 \nL 805.229617 545.152208 \nL 805.236665 545.152208 \nL 805.300093 375.718442 \nL 805.349426 629.869091 \nL 805.363521 629.869091 \nL 805.511519 121.567792 \nL 805.581995 629.869091 \nL 805.62428 545.152208 \nL 805.638376 629.869091 \nL 805.729994 545.152208 \nL 805.737041 545.152208 \nL 805.849802 629.869091 \nL 806.181038 629.869091 \nL 806.293799 545.152208 \nL 806.321989 545.152208 \nL 806.329036 629.869091 \nL 806.427702 545.152208 \nL 806.49113 545.152208 \nL 806.498178 291.001558 \nL 806.540463 629.869091 \nL 806.596844 545.152208 \nL 806.794175 545.152208 \nL 806.836461 291.001558 \nL 806.906936 629.869091 \nL 806.970364 629.869091 \nL 807.06903 121.567792 \nL 807.083125 545.152208 \nL 807.118363 545.152208 \nL 807.231124 121.567792 \nL 807.266362 121.567792 \nL 807.273409 36.850909 \nL 807.372075 121.567792 \nL 807.456646 121.567792 \nL 807.491884 545.152208 \nL 807.498931 36.850909 \nL 807.562359 36.850909 \nL 807.67512 121.567792 \nL 807.872452 121.567792 \nL 807.879499 36.850909 \nL 807.978165 121.567792 \nL 808.288258 121.567792 \nL 808.295305 36.850909 \nL 808.393971 121.567792 \nL 808.718159 121.567792 \nL 808.725206 36.850909 \nL 808.823872 121.567792 \nL 809.14806 121.567792 \nL 809.260821 36.850909 \nL 831.101206 36.850909 \nL 831.108254 121.567792 \nL 831.20692 36.850909 \nL 848.473439 36.850909 \nL 848.61439 545.152208 \nL 848.713056 36.850909 \nL 848.727151 206.284675 \nL 848.790579 206.284675 \nL 848.797627 36.850909 \nL 848.811722 545.152208 \nL 848.896293 206.284675 \nL 848.910388 206.284675 \nL 849.030196 629.869091 \nL 849.051339 629.869091 \nL 849.1641 206.284675 \nL 849.178195 206.284675 \nL 849.19229 629.869091 \nL 849.283909 206.284675 \nL 849.410765 206.284675 \nL 849.530573 545.152208 \nL 849.537621 545.152208 \nL 849.608096 629.869091 \nL 849.650382 206.284675 \nL 849.784285 206.284675 \nL 849.833618 629.869091 \nL 849.889999 206.284675 \nL 849.925236 206.284675 \nL 849.946379 545.152208 \nL 850.037997 36.850909 \nL 850.052093 36.850909 \nL 850.122568 629.869091 \nL 850.164853 375.718442 \nL 850.185996 375.718442 \nL 850.284662 206.284675 \nL 850.37628 629.869091 \nL 850.390375 206.284675 \nL 850.418566 206.284675 \nL 850.517231 629.869091 \nL 850.524279 206.284675 \nL 850.728658 206.284675 \nL 850.820276 629.869091 \nL 850.834372 206.284675 \nL 850.869609 206.284675 \nL 850.940085 545.152208 \nL 850.975323 206.284675 \nL 851.045798 206.284675 \nL 851.158559 629.869091 \nL 851.193797 629.869091 \nL 851.306558 206.284675 \nL 851.405224 206.284675 \nL 851.517985 629.869091 \nL 851.658936 206.284675 \nL 851.722364 206.284675 \nL 851.750554 629.869091 \nL 851.828077 206.284675 \nL 851.891505 206.284675 \nL 851.961981 36.850909 \nL 851.912648 545.152208 \nL 851.997219 206.284675 \nL 852.032457 206.284675 \nL 852.095885 36.850909 \nL 852.053599 545.152208 \nL 852.13817 206.284675 \nL 852.173408 206.284675 \nL 852.250931 36.850909 \nL 852.293216 545.152208 \nL 852.307311 545.152208 \nL 852.314359 121.567792 \nL 852.420072 206.284675 \nL 852.42712 206.284675 \nL 852.441215 629.869091 \nL 852.532833 545.152208 \nL 852.617404 206.284675 \nL 852.561024 629.869091 \nL 852.638547 629.869091 \nL 852.645594 629.869091 \nL 852.786545 206.284675 \nL 853.026162 206.284675 \nL 853.075495 36.850909 \nL 853.131876 545.152208 \nL 853.145971 36.850909 \nL 853.244637 206.284675 \nL 853.272827 206.284675 \nL 853.329207 375.718442 \nL 853.37854 206.284675 \nL 853.491301 206.284675 \nL 853.597015 629.869091 \nL 853.709776 206.284675 \nL 853.815489 206.284675 \nL 853.822537 629.869091 \nL 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629.869091 \nL 856.888225 629.869091 \nL 857.043271 629.869091 \nL 857.085556 206.284675 \nL 857.148984 629.869091 \nL 857.374506 629.869091 \nL 857.45203 206.284675 \nL 857.48022 629.869091 \nL 857.487267 629.869091 \nL 857.578886 206.284675 \nL 857.592981 545.152208 \nL 857.642314 545.152208 \nL 857.656409 629.869091 \nL 857.755075 36.850909 \nL 857.867835 206.284675 \nL 858.128595 206.284675 \nL 858.170881 291.001558 \nL 858.241356 36.850909 \nL 858.276594 36.850909 \nL 858.389355 206.284675 \nL 858.607829 206.284675 \nL 858.614877 36.850909 \nL 858.713543 206.284675 \nL 858.833351 206.284675 \nL 858.917922 629.869091 \nL 858.896779 36.850909 \nL 858.946112 545.152208 \nL 858.967255 545.152208 \nL 859.044778 629.869091 \nL 859.051825 36.850909 \nL 859.150491 629.869091 \nL 859.178681 629.869091 \nL 859.284395 36.850909 \nL 859.291442 545.152208 \nL 859.368966 629.869091 \nL 859.460584 36.850909 \nL 859.474679 629.869091 \nL 859.552202 629.869091 \nL 859.566297 36.850909 \nL 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864.295209 629.869091 \nL 864.316352 545.152208 \nL 864.400923 629.869091 \nL 864.492541 629.869091 \nL 864.534826 545.152208 \nL 864.598254 629.869091 \nL 864.619397 629.869091 \nL 864.64054 36.850909 \nL 864.732158 545.152208 \nL 864.802634 545.152208 \nL 864.915395 629.869091 \nL 865.035203 629.869091 \nL 865.042251 36.850909 \nL 865.140916 629.869091 \nL 865.317105 629.869091 \nL 865.429866 36.850909 \nL 865.542627 545.152208 \nL 865.577865 545.152208 \nL 865.59196 121.567792 \nL 865.690626 629.869091 \nL 865.747006 629.869091 \nL 865.82453 545.152208 \nL 865.85272 629.869091 \nL 865.930243 629.869091 \nL 866.021861 545.152208 \nL 866.035956 629.869091 \nL 866.191003 629.869091 \nL 866.339001 121.567792 \nL 866.45881 121.567792 \nL 866.465858 36.850909 \nL 866.564523 121.567792 \nL 866.733665 121.567792 \nL 866.740712 629.869091 \nL 866.839378 121.567792 \nL 866.888711 121.567792 \nL 867.001472 629.869091 \nL 867.318612 629.869091 \nL 867.32566 36.850909 \nL 867.431373 206.284675 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545.152208 \nL 870.67325 206.284675 \nL 870.870582 206.284675 \nL 870.877629 291.001558 \nL 870.976295 206.284675 \nL 871.321626 206.284675 \nL 871.328673 121.567792 \nL 871.427339 206.284675 \nL 871.526005 206.284675 \nL 871.554195 629.869091 \nL 871.631718 206.284675 \nL 871.765622 206.284675 \nL 871.772669 121.567792 \nL 871.871335 206.284675 \nL 872.37876 206.284675 \nL 872.43514 545.152208 \nL 872.484473 206.284675 \nL 872.597234 206.284675 \nL 872.604281 291.001558 \nL 872.702947 206.284675 \nL 872.766375 206.284675 \nL 872.787518 121.567792 \nL 872.872089 206.284675 \nL 873.048278 206.284675 \nL 873.055325 375.718442 \nL 873.153991 206.284675 \nL 873.280847 206.284675 \nL 873.30199 121.567792 \nL 873.38656 206.284675 \nL 873.485226 206.284675 \nL 873.492274 375.718442 \nL 873.59094 206.284675 \nL 873.915127 206.284675 \nL 873.922175 121.567792 \nL 874.020841 206.284675 \nL 874.345028 206.284675 \nL 874.352076 121.567792 \nL 874.450742 206.284675 \nL 874.549408 206.284675 \nL 874.556455 36.850909 \nL 874.655121 206.284675 \nL 874.951119 206.284675 \nL 874.972261 121.567792 \nL 875.056832 206.284675 \nL 875.134355 206.284675 \nL 875.162545 121.567792 \nL 875.240068 206.284675 \nL 875.40921 206.284675 \nL 875.416257 291.001558 \nL 875.514923 206.284675 \nL 875.973015 206.284675 \nL 876.0153 629.869091 \nL 876.085776 121.567792 \nL 876.198537 206.284675 \nL 876.254917 206.284675 \nL 876.261965 291.001558 \nL 876.30425 121.567792 \nL 876.36063 206.284675 \nL 876.381773 121.567792 \nL 876.466344 206.284675 \nL 876.600247 206.284675 \nL 876.670723 121.567792 \nL 876.705961 206.284675 \nL 876.713008 206.284675 \nL 876.741199 121.567792 \nL 876.797579 291.001558 \nL 876.818722 206.284675 \nL 876.88215 206.284675 \nL 876.952625 121.567792 \nL 876.987863 629.869091 \nL 877.100624 121.567792 \nL 877.107672 121.567792 \nL 877.121767 629.869091 \nL 877.213385 206.284675 \nL 877.220433 206.284675 \nL 877.333194 121.567792 \nL 877.396622 121.567792 \nL 877.509382 206.284675 \nL 877.572811 206.284675 \nL 877.579858 121.567792 \nL 877.657381 291.001558 \nL 877.678524 206.284675 \nL 877.847665 206.284675 \nL 877.918141 121.567792 \nL 877.925188 629.869091 \nL 878.023854 121.567792 \nL 878.171853 121.567792 \nL 878.284614 206.284675 \nL 878.390327 206.284675 \nL 878.510136 36.850909 \nL 878.566516 629.869091 \nL 878.622897 121.567792 \nL 878.651087 121.567792 \nL 878.763848 206.284675 \nL 878.784991 206.284675 \nL 878.792038 36.850909 \nL 878.890704 206.284675 \nL 878.918894 206.284675 \nL 878.925942 629.869091 \nL 879.024608 206.284675 \nL 879.031655 206.284675 \nL 879.073941 629.869091 \nL 879.123274 121.567792 \nL 879.137369 206.284675 \nL 879.30651 206.284675 \nL 879.313558 629.869091 \nL 879.320605 121.567792 \nL 879.412223 206.284675 \nL 879.546127 206.284675 \nL 879.553175 291.001558 \nL 879.65184 206.284675 \nL 879.961933 206.284675 \nL 880.067646 121.567792 \nL 880.180407 206.284675 \nL 880.293168 206.284675 \nL 880.391834 121.567792 \nL 880.398882 291.001558 \nL 880.504595 206.284675 \nL 880.723069 206.284675 \nL 880.828783 36.850909 \nL 880.83583 121.567792 \nL 881.265731 121.567792 \nL 881.272779 291.001558 \nL 881.371445 121.567792 \nL 881.632205 121.567792 \nL 881.73087 291.001558 \nL 881.744965 206.284675 \nL 881.759061 206.284675 \nL 881.871822 121.567792 \nL 881.921154 206.284675 \nL 881.977535 121.567792 \nL 882.146676 121.567792 \nL 882.224199 206.284675 \nL 882.25239 121.567792 \nL 882.513149 121.567792 \nL 882.62591 206.284675 \nL 882.780957 206.284675 \nL 882.809147 121.567792 \nL 882.88667 206.284675 \nL 883.006479 206.284675 \nL 883.013526 291.001558 \nL 883.112192 206.284675 \nL 883.17562 206.284675 \nL 883.182668 291.001558 \nL 883.281333 206.284675 \nL 883.302476 206.284675 \nL 883.379999 121.567792 \nL 883.408189 206.284675 \nL 883.556188 206.284675 \nL 883.563236 121.567792 \nL 883.570283 291.001558 \nL 883.654854 291.001558 \nL 883.78171 291.001558 \nL 883.873328 121.567792 \nL 883.887423 291.001558 \nL 884.000184 291.001558 \nL 884.028375 121.567792 \nL 884.112945 206.284675 \nL 884.218659 206.284675 \nL 884.225706 121.567792 \nL 884.324372 206.284675 \nL 884.380753 206.284675 \nL 884.493513 121.567792 \nL 884.67675 121.567792 \nL 884.789511 206.284675 \nL 884.909319 206.284675 \nL 884.986843 291.001558 \nL 885.02208 121.567792 \nL 885.036176 121.567792 \nL 885.064366 206.284675 \nL 885.141889 121.567792 \nL 885.353316 121.567792 \nL 885.416744 291.001558 \nL 885.466077 206.284675 \nL 885.585885 206.284675 \nL 885.691599 36.850909 \nL 885.804359 206.284675 \nL 885.825502 206.284675 \nL 885.83255 291.001558 \nL 885.931216 206.284675 \nL 886.713495 206.284675 \nL 886.720542 291.001558 \nL 886.819208 206.284675 \nL 887.157491 206.284675 \nL 887.270252 629.869091 \nL 887.383013 206.284675 \nL 887.594439 206.284675 \nL 887.601487 375.718442 \nL 887.700153 206.284675 \nL 888.461289 206.284675 \nL 888.57405 291.001558 \nL 888.637478 291.001558 \nL 888.665668 121.567792 \nL 888.750239 206.284675 \nL 888.919381 206.284675 \nL 888.926428 375.718442 \nL 889.025094 206.284675 \nL 889.067379 206.284675 \nL 889.116712 121.567792 \nL 889.173093 206.284675 \nL 889.236521 206.284675 \nL 889.335187 121.567792 \nL 889.342234 291.001558 \nL 889.4409 121.567792 \nL 889.504328 121.567792 \nL 889.511376 291.001558 \nL 889.610041 121.567792 \nL 889.941277 121.567792 \nL 889.948324 291.001558 \nL 890.04699 121.567792 \nL 890.357083 121.567792 \nL 890.36413 291.001558 \nL 890.462796 121.567792 \nL 890.801079 121.567792 \nL 890.808126 291.001558 \nL 890.906792 121.567792 \nL 891.23098 121.567792 \nL 891.238027 291.001558 \nL 891.336693 121.567792 \nL 891.646786 121.567792 \nL 891.653833 206.284675 \nL 891.752499 121.567792 \nL 892.062592 121.567792 \nL 892.069639 375.718442 \nL 892.168305 121.567792 \nL 892.330399 121.567792 \nL 892.351542 206.284675 \nL 892.436113 121.567792 \nL 892.44316 121.567792 \nL 892.527731 545.152208 \nL 892.548873 121.567792 \nL 892.584111 121.567792 \nL 892.626397 375.718442 \nL 892.689825 121.567792 \nL 892.70392 121.567792 \nL 892.816681 629.869091 \nL 893.31001 629.869091 \nL 893.401628 121.567792 \nL 893.415723 629.869091 \nL 893.479151 629.869091 \nL 893.549627 121.567792 \nL 893.584865 629.869091 \nL 893.697626 629.869091 \nL 893.718768 121.567792 \nL 893.803339 629.869091 \nL 893.993623 629.869091 \nL 894.050004 121.567792 \nL 894.099336 629.869091 \nL 894.310763 629.869091 \nL 894.423524 121.567792 \nL 894.430572 121.567792 \nL 894.543333 629.869091 \nL 894.86752 629.869091 \nL 894.980281 121.567792 \nL 895.085995 121.567792 \nL 895.198756 629.869091 \nL 896.580077 629.869091 \nL 896.594172 36.850909 \nL 896.685791 629.869091 \nL 898.757773 629.869091 \nL 898.778916 36.850909 \nL 898.863486 629.869091 \nL 898.870534 629.869091 \nL 898.891677 545.152208 \nL 898.976247 629.869091 \nL 899.659861 629.869091 \nL 899.688051 545.152208 \nL 899.765574 629.869091 \nL 900.568996 629.869091 \nL 900.597186 545.152208 \nL 900.674709 629.869091 \nL 900.836803 629.869091 \nL 900.850898 36.850909 \nL 900.942516 629.869091 \nL 902.929928 629.869091 \nL 902.993356 36.850909 \nL 903.042689 375.718442 \nL 903.148402 629.869091 \nL 903.15545 545.152208 \nL 903.197735 545.152208 \nL 903.247068 629.869091 \nL 903.275258 460.435325 \nL 903.303449 545.152208 \nL 903.76154 545.152208 \nL 903.853158 629.869091 \nL 903.867253 545.152208 \nL 903.937729 545.152208 \nL 904.008204 375.718442 \nL 903.972967 629.869091 \nL 904.043442 545.152208 \nL 904.07868 545.152208 \nL 904.085728 629.869091 \nL 904.099823 460.435325 \nL 904.184393 545.152208 \nL 904.325345 545.152208 \nL 904.438106 460.435325 \nL 904.494486 375.718442 \nL 904.550866 545.152208 \nL 904.614295 545.152208 \nL 904.670675 375.718442 \nL 904.720008 545.152208 \nL 904.896197 545.152208 \nL 904.938482 629.869091 \nL 904.959625 375.718442 \nL 904.966672 36.850909 \nL 904.97372 545.152208 \nL 905.065338 545.152208 \nL 905.206289 545.152208 \nL 905.213337 629.869091 \nL 905.312003 545.152208 \nL 905.347241 545.152208 \nL 905.460002 460.435325 \nL 905.572763 545.152208 \nL 905.600953 545.152208 \nL 905.615048 36.850909 \nL 905.636191 629.869091 \nL 905.706666 545.152208 \nL 905.81238 545.152208 \nL 905.819427 629.869091 \nL 905.911045 545.152208 \nL 905.918093 375.718442 \nL 906.016759 545.152208 \nL 906.12952 545.152208 \nL 906.235233 460.435325 \nL 906.270471 375.718442 \nL 906.347994 545.152208 \nL 906.355042 545.152208 \nL 906.369137 629.869091 \nL 906.460755 545.152208 \nL 906.47485 545.152208 \nL 906.481898 460.435325 \nL 906.580563 545.152208 \nL 906.799038 545.152208 \nL 906.813133 36.850909 \nL 906.911799 629.869091 \nL 907.02456 545.152208 \nL 907.095035 545.152208 \nL 907.102083 629.869091 \nL 907.200749 545.152208 \nL 907.235986 545.152208 \nL 907.243034 629.869091 \nL 907.250082 375.718442 \nL 907.3417 545.152208 \nL 907.489699 545.152208 \nL 907.496746 375.718442 \nL 907.503794 629.869091 \nL 907.595412 545.152208 \nL 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36.850909 \nL 957.23137 629.869091 \nL 957.25956 121.567792 \nL 957.266608 121.567792 \nL 957.315941 36.850909 \nL 957.287751 545.152208 \nL 957.337084 545.152208 \nL 957.379369 545.152208 \nL 957.49213 36.850909 \nL 957.724699 36.850909 \nL 957.823365 629.869091 \nL 957.872698 545.152208 \nL 957.879746 545.152208 \nL 957.957269 375.718442 \nL 957.964316 629.869091 \nL 957.985459 629.869091 \nL 958.041839 629.869091 \nL 958.062982 375.718442 \nL 958.147553 629.869091 \nL 958.175743 629.869091 \nL 958.281456 545.152208 \nL 958.38717 629.869091 \nL 958.478788 375.718442 \nL 958.499931 545.152208 \nL 958.584502 545.152208 \nL 958.591549 629.869091 \nL 958.654977 375.718442 \nL 958.683167 545.152208 \nL 958.781833 375.718442 \nL 958.788881 545.152208 \nL 958.838214 545.152208 \nL 958.845261 375.718442 \nL 958.866404 629.869091 \nL 958.943927 629.869091 \nL 959.056688 545.152208 \nL 959.148306 545.152208 \nL 959.204687 629.869091 \nL 959.25402 545.152208 \nL 959.3104 545.152208 \nL 959.331543 375.718442 \nL 959.373828 629.869091 \nL 959.416113 545.152208 \nL 959.430209 545.152208 \nL 959.479542 121.567792 \nL 959.535922 545.152208 \nL 959.669826 545.152208 \nL 959.705063 375.718442 \nL 959.775539 545.152208 \nL 959.986966 545.152208 \nL 959.994013 629.869091 \nL 960.029251 460.435325 \nL 960.092679 545.152208 \nL 960.388677 545.152208 \nL 960.4662 629.869091 \nL 960.49439 545.152208 \nL 960.529628 545.152208 \nL 960.536675 629.869091 \nL 960.635341 545.152208 \nL 960.656484 545.152208 \nL 960.762197 375.718442 \nL 960.83972 629.869091 \nL 960.874958 545.152208 \nL 961.107528 545.152208 \nL 961.192098 375.718442 \nL 961.213241 545.152208 \nL 961.347145 545.152208 \nL 961.354192 629.869091 \nL 961.368287 375.718442 \nL 961.452858 545.152208 \nL 961.741808 545.152208 \nL 961.791141 629.869091 \nL 961.798188 36.850909 \nL 961.805236 36.850909 \nL 961.96733 36.850909 \nL 962.094186 629.869091 \nL 962.101233 545.152208 \nL 962.115329 121.567792 \nL 962.129424 629.869091 \nL 962.206947 545.152208 \nL 962.298565 545.152208 \nL 962.397231 629.869091 \nL 962.376088 375.718442 \nL 962.404279 545.152208 \nL 962.418374 545.152208 \nL 962.488849 629.869091 \nL 962.502944 36.850909 \nL 962.594563 629.869091 \nL 962.6298 629.869091 \nL 962.700276 545.152208 \nL 962.735514 629.869091 \nL 962.820084 629.869091 \nL 962.925798 121.567792 \nL 963.038559 629.869091 \nL 963.954741 629.869091 \nL 963.975884 545.152208 \nL 964.060455 629.869091 \nL 964.476261 629.869091 \nL 964.483308 36.850909 \nL 964.581974 629.869091 \nL 965.033018 629.869091 \nL 965.047113 545.152208 \nL 965.138731 629.869091 \nL 965.406539 629.869091 \nL 965.420634 545.152208 \nL 965.512252 629.869091 \nL 966.548243 629.869091 \nL 966.562338 121.567792 \nL 966.653957 629.869091 \nL 967.006335 629.869091 \nL 967.02043 545.152208 \nL 967.112048 629.869091 \nL 967.436236 629.869091 \nL 967.457378 545.152208 \nL 967.541949 629.869091 \nL 968.415846 629.869091 \nL 968.486322 121.567792 \nL 968.52156 629.869091 \nL 969.945167 629.869091 \nL 969.980404 375.718442 \nL 970.057928 545.152208 \nL 970.072023 545.152208 \nL 970.184784 629.869091 \nL 970.375068 629.869091 \nL 970.480781 36.850909 \nL 970.501924 545.152208 \nL 970.586495 121.567792 \nL 970.62878 121.567792 \nL 970.720398 545.152208 \nL 970.664018 36.850909 \nL 970.734493 121.567792 \nL 970.741541 121.567792 \nL 970.748588 206.284675 \nL 970.755636 36.850909 \nL 970.840207 36.850909 \nL 970.875444 36.850909 \nL 970.882492 121.567792 \nL 970.981158 36.850909 \nL 971.009348 36.850909 \nL 971.122109 121.567792 \nL 971.199632 121.567792 \nL 971.298298 36.850909 \nL 971.305346 121.567792 \nL 971.312393 121.567792 \nL 971.425154 36.850909 \nL 971.432202 36.850909 \nL 971.544963 121.567792 \nL 971.622486 121.567792 \nL 971.735247 291.001558 \nL 971.826865 291.001558 \nL 971.939626 121.567792 \nL 971.974864 121.567792 \nL 972.087625 291.001558 \nL 972.151053 291.001558 \nL 972.263814 121.567792 \nL 972.651429 121.567792 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36.850909 \nL 979.02947 36.850909 \nL 979.135184 545.152208 \nL 979.247945 36.850909 \nL 979.332515 545.152208 \nL 979.353658 36.850909 \nL 979.395943 36.850909 \nL 979.410038 545.152208 \nL 979.501657 121.567792 \nL 979.508704 36.850909 \nL 979.60737 121.567792 \nL 979.670798 121.567792 \nL 979.684893 36.850909 \nL 979.783559 545.152208 \nL 979.89632 36.850909 \nL 979.92451 545.152208 \nL 980.002033 121.567792 \nL 980.037271 121.567792 \nL 980.150032 36.850909 \nL 980.290983 36.850909 \nL 980.403744 545.152208 \nL 980.424887 545.152208 \nL 980.537648 36.850909 \nL 980.572886 545.152208 \nL 980.643361 36.850909 \nL 980.756122 121.567792 \nL 980.81955 121.567792 \nL 980.861836 36.850909 \nL 980.868883 545.152208 \nL 980.890026 545.152208 \nL 980.897073 545.152208 \nL 981.009834 36.850909 \nL 981.023929 36.850909 \nL 981.059167 121.567792 \nL 981.129643 36.850909 \nL 981.13669 36.850909 \nL 981.263546 291.001558 \nL 981.305832 291.001558 \nL 981.418593 36.850909 \nL 981.510211 36.850909 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629.869091 \nL 994.083056 36.850909 \nL 994.174675 629.869091 \nL 994.336768 629.869091 \nL 994.449529 375.718442 \nL 994.56229 545.152208 \nL 994.576385 545.152208 \nL 994.689146 629.869091 \nL 994.759622 629.869091 \nL 994.773717 545.152208 \nL 994.865335 629.869091 \nL 994.964001 629.869091 \nL 995.062667 545.152208 \nL 995.069715 629.869091 \nL 995.090857 629.869091 \nL 995.203618 545.152208 \nL 995.231808 545.152208 \nL 995.330474 629.869091 \nL 995.337522 545.152208 \nL 995.407997 375.718442 \nL 995.450283 460.435325 \nL 995.499616 629.869091 \nL 995.563044 545.152208 \nL 995.887231 545.152208 \nL 995.894279 629.869091 \nL 995.97885 545.152208 \nL 996.070468 375.718442 \nL 996.077516 629.869091 \nL 996.084563 629.869091 \nL 996.112753 629.869091 \nL 996.126848 545.152208 \nL 996.218467 629.869091 \nL 996.232562 629.869091 \nL 996.359418 375.718442 \nL 996.436941 375.718442 \nL 996.528559 629.869091 \nL 996.549702 545.152208 \nL 996.634273 545.152208 \nL 996.747034 375.718442 \nL 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     },
     "metadata": {}
    }
   ],
   "source": [
    "plt.figure(figsize = [20, 12])\n",
    "plt.plot(pred_lgb_val, alpha = 0.4)\n",
    "plt.plot(y_val)"
   ]
  },
  {
   "source": [
    "## Post-Process: Mode"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Start selecting top2\n",
      "Time used for select top2 in groups: 40.96\n",
      "Time used for get hit columns: 3.44\n",
      "Top 1 precision: 0.5726114561191459\n",
      "Top 2 precesion: 0.7288167382153128\n"
     ]
    }
   ],
   "source": [
    "pred_df_lgb = eval_df_top2(y_val, pred_prob_lgb_val, pred_lgb_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df_lgb = pd.DataFrame(pred_prob_lgb_val, columns = list(label_dic.values()))\n",
    "pred_df_lgb['pred'] = pred_lgb_val\n",
    "pred_df_lgb['label'] = y_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{1: 'Still',\n",
       " 2: 'Walking',\n",
       " 3: 'Run',\n",
       " 4: 'Bike',\n",
       " 5: 'Car',\n",
       " 6: 'Bus',\n",
       " 7: 'Train',\n",
       " 8: 'Subway'}"
      ]
     },
     "metadata": {},
     "execution_count": 448
    }
   ],
   "source": [
    "label_dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_lgb_val_roll = pd.Series(pred_lgb_val).rolling(180, center = True).agg(lambda x: Counter(x).most_common()[0][0]).fillna(method = 'bfill').fillna(method = 'ffill')\n",
    "pred_df_lgb['pred_roll'] = pred_lgb_val_roll.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fac50128be0>]"
      ]
     },
     "metadata": {},
     "execution_count": 505
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1440x864 with 1 Axes>",
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     },
     "metadata": {}
    }
   ],
   "source": [
    "plt.figure(figsize = [20, 12])\n",
    "plt.plot(pred_lgb_val_roll, alpha = 0.4)\n",
    "plt.plot(y_val, alpha = 0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Still       0.69      0.54      0.61     29836\n",
      "     Walking       0.82      0.64      0.72     26149\n",
      "         Run       0.00      0.00      0.00      2774\n",
      "        Bike       0.75      0.79      0.77     12031\n",
      "         Car       0.74      0.46      0.56     20473\n",
      "         Bus       0.42      0.54      0.48      9178\n",
      "       Train       0.53      0.54      0.54     21808\n",
      "      Subway       0.50      0.93      0.65     21709\n",
      "\n",
      "    accuracy                           0.62    143958\n",
      "   macro avg       0.56      0.56      0.54    143958\n",
      "weighted avg       0.64      0.62      0.61    143958\n",
      "\n",
      "[[16167   615     0    10     0    90  2823 10131]\n",
      " [ 3727 16642     0   561     0  1837   987  2395]\n",
      " [    0   783     0  1991     0     0     0     0]\n",
      " [  196   767   247  9534     0     0     0  1287]\n",
      " [  424   201     0   554  9355  4675  3480  1784]\n",
      " [ 1384   145     0     0     0  5001  1660   988]\n",
      " [ 1522  1096     0     0  3322   167 11772  3929]\n",
      " [    0    68     0     0     0     0  1415 20226]]\n"
     ]
    },
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sNgMAampqkJ+fDwDIy8tDdXU1AKC+vh6rVq3yGhOTNhGpiuj2vZlMJlgsFqmZTKZJ3evFF19EU1OT9P2SJUvQ0tKCEydO4IknngAA6HQ6CIIgHSMIAnQ6nfRZd3c3gJF1nQYHBzF//nyP9+SDSCJSl0kMj4xeRnqytm7dCqfTiT17RvYL6O3txeLFizEwMID09HR89NFHSE1NHXcZa1EcGU/39NlEmLSJSFXEIMweKSgowLPPPnvPcMbw8LA0lNLS0oLOzk4kJydDEIR7hlD0ej16ekbG7gVBQEJCAq5cuYLw8HDExcWNGY75Ng6PEJGqTGZ4ZCpWr16NLVu2YM2aNbh165bUHx8fj7C/TDZYsmQJDAYDvvrqKzgcDgwNDSErKwvASMJvaGgAADQ2NqKwsBAAsG7dOhw/ftzr/VlpE5GqiC7/zVjbu3cvVq5cifj4eHR3d2Pbtm144403cN999+Ho0aMARh5GlpSUYMWKFfjFL34Bp9MJl8uFn/70p7h27RoAoKSkBFVVVYiOjkZTU5M0Dl5ZWYndu3fDbrdjYGDA68wRANAACOjrQ5FRukBe3ifRkffJHQIA4LZzWO4Q8Gnc43KHAAB4+hrXYw8loTPl78K0r9H75FM+H/vgqd9O+37BxkqbiFRFdKv73RAmbSJSlWA8iJQTkzYRqYoostImIlKMGV9pL1u2DKIo4osvvkBKSgpycnLQ3t5+z1tAREShwu3H2SOhyGPS/pd/+Rfk5uYiIiICR48eRVZWFk6cOIHS0lKkpaXhrbfeClacREQ+mdEPItetW4elS5fivvvug8PhgF6vx9DQEH71q1+hubl5wqRtMplQXFwMAKj8cD8qK/f4P3IionHM6KTtdDrhdrtx69YtdHZ2YmhoCABw+/ZtuN0TDxyNfp8/FOZpE9HM4WXpDsXzmLSHh4cRHR2NW7duISMjQ+qPjY31mLSJiOQyoyvtFStWYHh45C2+0StPRUZGSu/LExGFkhk95e9uwv62r7/+Gl9//XVAAiIimg7XTJ49QkSkNDO60iYiUpoZPaZNRKQ0M3r2CBGR0rDSJiJSEJdb3RtyMWkTkapweISISEHcKp89ou7fI4hoxhFFjc/Nm8rKSvT19eH8+fNS37x583DkyBFcvHgRR44cwdy5c6XPSktLYbfb0d7ejuzsbKk/PT0dra2tsNvtKCsrk/qjoqJQW1sLu90Os9mMxMRErzExaRORqoii782bqqoq5OTk3NNXWlqKY8eOITk5GceOHUNpaSkAICUlBUajEampqcjJycH7778v7c5eXl6O4uJiGAwGGAwG6ZpFRUW4du0aDAYDduzYge3bt3uNKeDDI+Fh4YG+hVe37vyP3CEAANwhMNj27NAXcocAAPh03hNyh4DKWfJvtAwAfa4bcoeAS7evyh2C3/hzeOTUqVNjqt+8vDysXLkSAFBdXS0tV52Xl4fa2loMDw+jq6sLHR0dyMzMRFdXF2JjY2E2mwEANTU1yM/Px+HDh5GXl4c333wTAFBfX4+dO3d6jYmVNhGpissd5nMzmUywWCxSM5lMXq+/cOFCOBwOAIDD4cCCBQsAADqdDt3d3dJxgiBAp9NBp9NBEIQx/d8+x+VyYXBwEPPnz/d4fz6IJCJVmczvs6OXkZ4ujWZshS+K4oT9ns7xhJU2EamKW9T43Kair68PWq0WAKDVatHf3w9gpIJOSEiQjtPr9ejp6YEgCNDr9WP6v31OeHg44uLiMDAw4PH+TNpEpCr+nD0ynsbGRmlp6sLCQjQ0NEj9RqMRUVFRSEpKgsFgwOnTp+FwODA0NISsrCwAQEFBwT3n3L3WunXrcPz4ca/35/AIEamKP7dn2bt3L1auXIn4+Hh0d3dj27ZtePvtt1FXV4eioiJcvnwZzz33HADAZrOhrq4ONpsNTqcTmzZtkjaLKSkpQVVVFaKjo9HU1CRtjF5ZWYndu3fDbrdjYGAARqPRa0waTG4IaNJmzVocyMv7xOV2yR0CgNCYPTIrIkruEAAADfdnyh0CZ4+MEiqzRy59fXba1/jtwnU+H/tUX/207xdsrLSJSFWcKn8jkkmbiFRFBJM2EZFiqH3LcSZtIlIVtVfak57yV11dHYg4iIj8wj2JpkQeK+27cwnv0mg0+P73vy+tapWXlxewwIiIpsKl8krbY9LW6/Ww2WzYtWuX9DrmY489hnfffTdY8RERTYrKdxvzPDzy2GOP4cyZM/inf/onDA4O4re//S1u3bqFkydP4uTJkxOeN3oRlhdf3OD3oImIJuKGxuemRB4rbVEU8etf/xr79+/Hjh070NfXh4gI788uRy/CEgov1xDRzCH/K2yB5dPskStXruBHP/oR/v7v/x7ffPNNoGMiIpoypT5g9NWkpvwdOnQIhw4dClQsRETT5h5nuVM14TxtIlKV0FhpKHCYtIlIVdQ+e4RJm4hURamzQnzFpE1EqsLZI0RECsLhESIiBeGUPyIiBXGpvNLmxr5EpCr+WuUvOTkZVqtVaoODg3j11Vexbds2CIIg9efm5krnlJaWwm63o729HdnZ2VJ/eno6WltbYbfbUVZWNq2fj0mbiFTFX0n74sWLSEtLQ1paGjIyMnDz5k0cPHgQALBjxw7ps7ub9KakpMBoNCI1NRU5OTl4//33ERY2kmLLy8tRXFwMg8EAg8GAnJycKf98AR8e+ZtZMYG+hVf9N/4sdwgh47YzNDazXX3tv+UOAf9n0eNyhwAA+OpWn9whYOjOLblD8JtAbBG5atUqdHZ24vLlyxMek5eXh9raWgwPD6OrqwsdHR3IzMxEV1cXYmNjYTabAQA1NTXIz8/H4cOHpxQLK20iUpXJVNqjVyS1WCwwmUzjXtNoNGLfvn3S95s3b8a5c+dQWVkp7S+g0+nQ3d0tHSMIAnQ6HXQ6HQRBGNM/VUzaRKQqrkm0iooKLFu2TGp3VycdLTIyEmvWrMH+/fsBjAx1PPzww1i6dCl6e3ul/QU046x5cncfgvH6p4pJm4hUxa3xvfkiNzcXLS0t6O/vBwD09/fD7XZDFEVUVFQgMzMTwEgFnZCQIJ2n1+vR09MDQRCg1+vH9E8VkzYRqYq/94jcsGHDPUMjWq1W+nrt2rVoa2sDADQ2NsJoNCIqKgpJSUkwGAw4ffo0HA4HhoaGkJWVBQAoKCgYs5XjZHCeNhGpij9fromOjsYPf/hDvPTSS1LfO++8g6VLl0IURXR1dUmf2Ww21NXVwWazwel0YtOmTXC7R6IpKSlBVVUVoqOj0dTUJM04mQoNAvyq/qK5jwby8j4Jldkjal8TQWlCZfbIiaEOuUMImdkjXw/Zp32NXyU87/Ox/7d7z7TvF2ystIlIVbj2CBGRgnATBCIiBXGrfCCSSZuIVIWr/BERKYi66+xJJu3ly5cjMzMTbW1tOHr0aKBiIiKaMrVX2h5frmlubpa+3rhxI3bu3In7778f27Ztw5YtWwIeHBHRZDk1os9NiTwm7cjISOnr4uJi/PCHP8QvfvELZGdn4/nnJ54LOXoRlucLn/NftEREXoiTaErkcXgkLCwMc+fORVhYGDQaDf70pz8BAG7evAmn0znheRUVFdLCK6Hwcg0RzRxqHx7xmLTj4uJw5swZaDQaiKKIhQsXoq+vD3PmzBl35SoiIrnN6Cl/S5YsGbff7XZj7dq1AQmIiGg61J2ypzjl79atW+jq6vJzKERE0zejh0eIiJTGpfJam0mbiFSFlTYRkYKIrLSJiJSDlTYRkYKofcof94gkIlXx5xuRly5dQmtrK6xWKywWCwBg3rx5OHLkCC5evIgjR45g7ty50vGlpaWw2+1ob29Hdna21J+eno7W1lbY7XaUlZVN6+dj0iYiVXFC9Ln54vvf/z7S0tKwbNkyACOJ+dixY0hOTsaxY8dQWloKAEhJSYHRaERqaipycnLw/vvvIyxsJMWWl5ejuLgYBoMBBoMBOTk5U/75mLSJSFXESfw3FXl5eaiurgYAVFdXIz8/X+qvra3F8PAwurq60NHRgczMTGi1WsTGxsJsNgMAampqpHOmIuBj2uEa+f9dmBsdI3cIAIBrt67LHULIeGJBitwhYE9vs/eDguCrpclyh4Di3llyhwAAaPLDxr7+fBApiiKOHDkCURTxn//5n6ioqMDChQvhcDgAAA6HAwsWLAAA6HQ6KTEDgCAI0Ol0uHPnDgRBGNM/VXwQSUSqMpkK2mQyobi4WPr+gw8+kBa7A0b2EOjt7cUDDzyAo0ePor29fcJrjbcekyiKE/ZPFZM2EanKZCrt0SuSjqe3txcAcPXqVRw8eBCZmZno6+uDVquFw+GAVqtFf38/gJEKOiEhQTpXr9ejp6cHgiBAr9eP6Z8q+ccuiIj8yCWKPjdPZs+ejZiYGOnr7OxstLW1obGxEYWFhQCAwsJCNDQ0AAAaGxthNBoRFRWFpKQkGAwGnD59Gg6HA0NDQ8jKygIAFBQUSOdMBSttIlIVf83TXrhwIQ4ePAgAiIiIwN69e/Hpp5/CYrGgrq4ORUVFuHz5Mp57bmSjF5vNhrq6OthsNjidTmzatAlu90jdX1JSgqqqKkRHR6OpqQlNTU1TjkuDAK9kmDDvfwXy8j655RqWOwQAfBA5Wig8iPz86sTjk8HEB5F/1dQ99WR2l3Fxns/H1l6eesUrF1baRKQqfI2diEhB1P4aO5M2EakKV/kjIlIQb7NClI5Jm4hUhcMjREQKovYHkR5frsnMzMT9998PAJg1axbefPNNNDY24u2330ZsbGxQAiQimoxALxglN49J+8MPP8TNmzcBAGVlZYiLi8P27dtx8+ZN/OY3vwlKgEREk+GG6HNTIo/DI2FhYXC5XACAxx57DBkZGQCA3/3ud7BarROeN3oRlvo9/w97q+v9FS8RkUfTWYxJCTxW2m1tbXjhhRcAAOfOnZOStsFgwJ07dyY8r6KiAsuWLcOyZcuYsIkoqFwQfW5K5DFpb9y4EU899RQ6Ojrw6KOP4ve//z06OztRUVGBjRs3BitGIiKfzejhkW+++QY/+clPEBMTg4ceeggREREQBEFaipCIKNSofXjEpyl/169fR2tra6BjISKaNqVW0L7iPG0iUhWlTuXzFZM2EakKX2MnIlIQDo8QESkIkzYRkYKoffYIN/YlIlXx1zxtvV6P48ePw2azoa2tDa+88goAYNu2bRAEAVarFVarFbm5udI5paWlsNvtaG9vR3Z2ttSfnp6O1tZW2O12lJWVTevnY6VNRKrir9kjTqcTr7/+OqxWK2JiYnDmzBkcPXoUALBjxw68++679xyfkpICo9GI1NRULFq0CJ999hmSk5PhdrtRXl6O4uJimM1mHDp0CDk5OTh8+PCU4mKlTUSq4hLdPjdPHA6HtMbS9evXceHCBeh0ugmPz8vLQ21tLYaHh9HV1YWOjg5kZmZCq9UiNjYWZrMZAFBTU4P8/Pwp/3wBr7R7rg8E+hakQP/df0HuEEJG1sU+uUNAl/1juUMAAEQ98PC0rxGIMe3ExESkpaWhubkZy5cvx+bNm1FQUIAvvvgCr7/+Ov785z9Dp9NJiRkABEGATqfDnTt3IAjCmP6pYqVNRKoymTFtk8kEi8UiNZPJNOZ6c+bMwYEDB/Daa69haGgI5eXlePjhh7F06VL09vZKwyQajWbMuaIoTtg/VRzTJiJVmcyYdkVFBSoqKib8PCIiAgcOHMCePXtw8OBBALhn7aWKigp88sknAEYq6ISEBOkzvV6Pnp4eCIIAvV4/pn+qWGkTkaq4RdHn5k1lZSUuXLiAHTt2SH1arVb6eu3atWhrawMANDY2wmg0IioqCklJSTAYDDh9+jQcDgeGhoaQlZUFACgoKEBDQ8OUfz5W2kSkKv6aPbJ8+XIUFBSgtbVVeiC5detWbNiwAUuXLoUoiujq6sJLL70EALDZbKirq4PNZoPT6cSmTZvgdo887CwpKUFVVRWio6PR1NSEpqamKcelAQL7+lB45KJAXp5I8RbOmSt3CKp6EPnIA4/5fOyXV7+Y9v2CjZU2EamKL8MeSsakTUSqwqVZiYgUhJU2EZGCsNImIlIQl+iSO4SAYtImIlWZ0Uuzvvzyy/e8yUNEFOr8tTRrqPKYtH/5y1+iubkZJ0+eRElJCeLj44MVFxHRlIii6HNTIo9J+6uvvoJer8cvf/lLZGRkwGazoampCQUFBYiJiZnwvNGLsGwset7vQRMRTcSfr7GHIo9vRJ45cwYZGRnS9xEREcjNzcWGDRvw9NNPY8GCBV5vwDciiTzjG5F/5Y83IhfG/Z3Px/YNtk/7fsHm8UHkt5cUdDqd+Pjjj/Hxxx9j1qxZAQ2MiGgqvG1uoHQek/b69esn/Oz27dt+D4aIaLqUOlbtK49J2263BysOIiK/UOpYta84T5uIVGVGV9pEREqj1PnXvmLSJiJVYaVNRKQgM3r2CBGR0qj9QSQ39iUiVfHna+yrV69Ge3s77HY7tmzZEoTovWPSJiJVESfxnydhYWH4j//4D+Tm5uLRRx/Fhg0bkJKSEqSfwkNccgdARORP/qq0MzMz0dHRgUuXLuHOnTuora1FXl5ekH6KiTFpE5Gq+GvBKJ1Oh+7ubul7QRCg0+kCHb5PxFBvJpOJMYRQHKEQQ6jEEQoxhEocoRDDVGK2WCxSG/0zrFu3TqyoqJC+//GPfyy+9957sseMEAjAa7NYLIwhhOIIhRhCJY5QiCFU4giFGPzZHn/8cfHw4cPS96WlpWJpaanscXF4hIhoHBaLBQaDAUlJSYiMjITRaERjY6PcYXGeNhHReFwuFzZv3oxPP/0U4eHh+PDDD2Gz2eQOC0AI/BrirYXCWFkoxBAqcYRCDKESRyjEECpxhEIMM6F53LmGiIhCC8e0iYgUJKSTdii8QlpZWYm+vj6cP39elvsDgF6vx/Hjx2Gz2dDW1oZXXnlFljjuu+8+NDc34+zZs2hra8Obb74pSxzAyNtqLS0t+Phj+fY2vHTpElpbW2G1WmGxWGSJIS4uDvv378eFCxdgs9nw+OOPBz2G5ORkWK1WqQ0ODuLVV18NehwziexjNOO1sLAwsaOjQ1yyZIkYGRkpnj17VkxJSQl6HE8++aSYlpYmnj9/XrY/C61WK6alpYkAxJiYGPHLL7+U5c8CgDhnzhwRgBgRESGazWYxKytLljh+9rOfiXv27BE//vhj2f6/XLp0SZw/f75s9wcgVlVViUVFRSIAMTIyUoyLi5M1nrCwMLG3t1dcvHixrHGouYVspR0qr5CeOnUKAwMDQb/vaA6HA1arFQBw/fp1XLhwQbY3s27cuAEAiIyMRGRkpCxrF+t0OjzzzDPYtWtX0O8dSu6//36sWLEClZWVAIA7d+5gcHBQ1phWrVqFzs5OXL58WdY41Cxkk3Yov0Iqp8TERKSlpaG5uVmW+4eFhcFqtaK/vx9Hjx7F6dOngx7Dr3/9a/z85z+H2y3vusmiKOLIkSP44osvYDKZgn7/hx56CFevXsVvfvMbtLS0oKKiArNnzw56HKMZjUbs27dP1hjULmSTtkajGdOn9h0pvJkzZw4OHDiA1157DUNDQ7LE4Ha7kZaWBr1ej8zMTKSmpgb1/s888wz6+/vR0tIS1PuOZ/ny5cjIyEBubi42bdqEJ598Mqj3j4iIQHp6OsrLy5Geno4bN26gtLQ0qDGMFhkZiTVr1mD//v2yxTAThGzSFgQBCQkJ0vd6vR49PT0yRiSviIgIHDhwAHv27MHBgwflDgeDg4M4ceIEcnJygnrf5cuXY82aNbh06RJqa2vxgx/8ALt37w5qDHf19vYCAK5evYqDBw8iMzMzqPcXBAGCIEi/7dTX1yM9PT2oMYyWm5uLlpYW9Pf3yxbDTCH7wPp4LTw8XOzs7BSTkpKkB5GPPvqoLLEkJibK+iASgFhdXS3u2LFD1hji4+OlB12zZs0ST548KT7zzDOyxfPUU0/J9iBy9uzZYkxMjPT17373O3H16tVBj+PkyZNicnKyCEDctm2b+M4778j2/2Pfvn3iCy+8INv9Z1CTPYAJW25urvjll1+KHR0d4tatW2WJYe/evWJPT484PDwsdnd3iy+++GLQY1i+fLkoiqJ47tw50Wq1ilarVczNzQ16HN/5znfElpYW8dy5c+L58+fFf/7nf5b174ecSXvJkiXi2bNnxbNnz4ptbW2y/f383ve+J1osFvHcuXPiwYMHxblz58oSR3R0tPinP/1JjI2NlfXvxExofCOSiEhBQnZMm4iIxmLSJiJSECZtIiIFYdImIlIQJm0iIgVh0iYiUhAmbSIiBWHSJiJSkP8PpPFRvUZq1+wAAAAASUVORK5CYII=\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "print(classification_report(y_val, pred_lgb_val_roll, target_names = list(label_dic.values())))\n",
    "conf = confusion_matrix(y_val, pred_lgb_val_roll)\n",
    "print(conf)\n",
    "sn.heatmap(conf)"
   ]
  },
  {
   "source": [
    "## Post-Process: HMM"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hmmlearn import hmm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [],
   "source": [
    "states = list(label_dic.values())\n",
    "n_states = len(states)\n",
    "observations = pred_lgb_val_roll - 1\n",
    "# start_probability = np.zeros(8, dtype = int)\n",
    "# start_probability.put(int(pred_lgb_val_roll[0]) - 1, np.ones(8))\n",
    "# start_probability = start_probability.tolist()\n",
    "start_probability = pred_prob_lgb_val[:10,:].mean(axis = 0).tolist()\n",
    "conf = confusion_matrix(y_val, pred_lgb_val_roll)\n",
    "emission_probability = normalize(conf, axis = 1, norm = 'l1')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [],
   "source": [
    "transition_probability = np.array([\n",
    "  [0.80, 0.06, 0.04, 0.02, 0.02, 0.02, 0.02, 0.02],\n",
    "  [0.04, 0.80, 0.06, 0.02, 0.02, 0.02, 0.02, 0.02],\n",
    "  [0.01, 0.24, 0.70, 0.01, 0.01, 0.01, 0.01, 0.01],\n",
    "  [0.01, 0.08, 0.06, 0.80, 0.02, 0.01, 0.01, 0.01],\n",
    "  [0.02, 0.02, 0.02, 0.01, 0.90, 0.01, 0.01, 0.01],\n",
    "  [0.02, 0.02, 0.02, 0.01, 0.01, 0.90, 0.01, 0.01],\n",
    "  [0.02, 0.02, 0.02, 0.01, 0.01, 0.01, 0.90, 0.01],\n",
    "  [0.02, 0.02, 0.02, 0.01, 0.01, 0.01, 0.01, 0.90]\n",
    "])\n",
    "\n",
    "label_x = df['label']\n",
    "label_y = df['label'].shift(1).fillna(method = 'bfill').astype(int)\n",
    "t = np.zeros([8, 8])\n",
    "for i in range(len(label_x)):\n",
    "    t[label_x[i]-1, label_y[i]-1] += 1\n",
    "transition_probability = normalize(t, axis = 1, norm = 'l1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = hmm.MultinomialHMM(n_components = n_states, n_iter = 20, tol = 0.001)\n",
    "model.startprob_ = start_probability\n",
    "model.transmat_ = transition_probability\n",
    "model.emissionprob_ = emission_probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [],
   "source": [
    "seen = np.array(observations, dtype = int).reshape(1, -1).T\n",
    "logprob, pred_hmm = model.decode(seen, algorithm=\"viterbi\")\n",
    "pred_hmm = pred_hmm + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              precision    recall  f1-score   support\n\n           1       0.69      0.54      0.60     29836\n           2       0.78      0.63      0.70     26149\n           3       0.22      0.02      0.04      2774\n           4       0.73      0.78      0.75     12031\n           5       0.73      0.44      0.55     20473\n           6       0.41      0.54      0.47      9178\n           7       0.53      0.53      0.53     21808\n           8       0.50      0.93      0.65     21709\n\n    accuracy                           0.61    143958\n   macro avg       0.57      0.55      0.54    143958\nweighted avg       0.64      0.61      0.60    143958\n\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_val, pred_hmm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.plot(pred_hmm)\n",
    "# plt.plot(y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Help on package seqlearn:\n\nNAME\n    seqlearn\n\nPACKAGE CONTENTS\n    _decode (package)\n    _utils (package)\n    _version\n    base\n    datasets\n    evaluation\n    hmm\n    perceptron\n\nVERSION\n    0.2\n\nFILE\n    /opt/anaconda3/lib/python3.8/site-packages/seqlearn/__init__.py\n\n\n"
     ]
    }
   ],
   "source": [
    "help(seqlearn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "AttributeError",
     "evalue": "module 'seqlearn' has no attribute 'hmm'",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-513-321503a6f981>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mseqlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhmm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMultinomialHMM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdecode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'viterbi'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: module 'seqlearn' has no attribute 'hmm'"
     ]
    }
   ],
   "source": [
    "seqlearn.hmm.MultinomialHMM(decode = 'viterbi')"
   ]
  },
  {
   "source": [
    "## Post-Process: Rule"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_group_conscount(sr):\n",
    "    groups = groupby(sr.values.tolist())\n",
    "    grouped_elements = [list(group) for key, group in groups]\n",
    "    dur_df = pd.DataFrame({\"label\": [t[0] for t in grouped_elements],\n",
    "                        \"duration\": [len(t) for t in grouped_elements]})\n",
    "    return dur_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {},
   "outputs": [],
   "source": [
    "dur_df = get_group_conscount(df['label'])\n",
    "dur_df = get_group_conscount(pred_df_lgb['pred_roll'])\n",
    "dur_df = get_group_conscount(y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='label', ylabel='duration'>"
      ]
     },
     "metadata": {},
     "execution_count": 510
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "plt.figure(figsize = [12, 6])\n",
    "sn.boxplot(x = dur_df['label'], y = dur_df['duration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   count         mean          std     min      25%     50%      75%      max\n",
       "0   31.0   962.451613  1153.768025   106.0   330.00   630.0  1057.50   5834.0\n",
       "1   41.0   637.780488   585.951556    30.0   210.00   450.0   854.00   2610.0\n",
       "2    4.0   693.500000   777.128689   285.0   307.50   315.0   701.00   1859.0\n",
       "3    6.0  2005.166667   752.652753  1140.0  1395.00  1995.5  2482.75   3060.0\n",
       "4    6.0  3412.166667  2046.457859  1484.0  2002.50  2729.5  4469.75   6720.0\n",
       "5    6.0  1529.666667  1709.543994   241.0   944.75  1004.0  1031.00   4964.0\n",
       "6    9.0  2423.111111  1297.927429   434.0  1425.00  2970.0  3420.00   3720.0\n",
       "7    6.0  3618.166667  3774.312198  1350.0  1527.25  2004.0  3413.75  11099.0"
      ],
      "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>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>31.0</td>\n      <td>962.451613</td>\n      <td>1153.768025</td>\n      <td>106.0</td>\n      <td>330.00</td>\n      <td>630.0</td>\n      <td>1057.50</td>\n      <td>5834.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>41.0</td>\n      <td>637.780488</td>\n      <td>585.951556</td>\n      <td>30.0</td>\n      <td>210.00</td>\n      <td>450.0</td>\n      <td>854.00</td>\n      <td>2610.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.0</td>\n      <td>693.500000</td>\n      <td>777.128689</td>\n      <td>285.0</td>\n      <td>307.50</td>\n      <td>315.0</td>\n      <td>701.00</td>\n      <td>1859.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>6.0</td>\n      <td>2005.166667</td>\n      <td>752.652753</td>\n      <td>1140.0</td>\n      <td>1395.00</td>\n      <td>1995.5</td>\n      <td>2482.75</td>\n      <td>3060.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6.0</td>\n      <td>3412.166667</td>\n      <td>2046.457859</td>\n      <td>1484.0</td>\n      <td>2002.50</td>\n      <td>2729.5</td>\n      <td>4469.75</td>\n      <td>6720.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6.0</td>\n      <td>1529.666667</td>\n      <td>1709.543994</td>\n      <td>241.0</td>\n      <td>944.75</td>\n      <td>1004.0</td>\n      <td>1031.00</td>\n      <td>4964.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>9.0</td>\n      <td>2423.111111</td>\n      <td>1297.927429</td>\n      <td>434.0</td>\n      <td>1425.00</td>\n      <td>2970.0</td>\n      <td>3420.00</td>\n      <td>3720.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>6.0</td>\n      <td>3618.166667</td>\n      <td>3774.312198</td>\n      <td>1350.0</td>\n      <td>1527.25</td>\n      <td>2004.0</td>\n      <td>3413.75</td>\n      <td>11099.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 511
    }
   ],
   "source": [
    "dur_df.groupby(['label'], as_index = False).describe()['duration']"
   ]
  },
  {
   "source": [
    "## Transition Matrix"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_x = df['label']\n",
    "label_y = df['label'].shift(1).fillna(method = 'bfill').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[9.98724459e-01, 4.90592881e-04, 6.54123842e-05, 2.28943345e-04,\n",
       "        6.54123842e-05, 1.39001316e-04, 1.39001316e-04, 1.47177864e-04],\n",
       "       [1.87348289e-04, 9.98688562e-01, 8.96013554e-05, 1.87348289e-04,\n",
       "        8.96013554e-05, 3.42114266e-04, 1.95493866e-04, 2.19930600e-04],\n",
       "       [9.46163308e-05, 2.83848992e-04, 9.99148453e-01, 1.18270413e-04,\n",
       "        1.65578579e-04, 1.65578579e-04, 0.00000000e+00, 2.36540827e-05],\n",
       "       [1.78871068e-04, 1.78871068e-04, 5.11060194e-05, 9.99429316e-01,\n",
       "        6.81413592e-05, 1.70353398e-05, 7.66590292e-05, 0.00000000e+00],\n",
       "       [5.04111661e-05, 9.45209364e-05, 3.78083746e-05, 3.78083746e-05,\n",
       "        9.99779451e-01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [3.17700133e-04, 1.05900044e-04, 2.11800089e-05, 1.41200059e-05,\n",
       "        0.00000000e+00, 9.99519920e-01, 2.11800089e-05, 0.00000000e+00],\n",
       "       [2.04786894e-04, 1.02393447e-04, 1.27991809e-05, 2.55983617e-05,\n",
       "        0.00000000e+00, 0.00000000e+00, 9.99648023e-01, 6.39959043e-06],\n",
       "       [1.92980543e-04, 1.84590084e-04, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 1.67809167e-05, 9.99605648e-01]])"
      ]
     },
     "metadata": {},
     "execution_count": 438
    }
   ],
   "source": [
    "t = np.zeros([8, 8])\n",
    "for i in range(len(label_x)):\n",
    "    t[label_x[i]-1, label_y[i]-1] += 1\n",
    "normalize(t, axis = 1, norm = 'l1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          0         1        2         3         4         5         6  \\\n",
       "0  122145.0      60.0      8.0      28.0       8.0      17.0      17.0   \n",
       "1      23.0  122605.0     11.0      23.0      11.0      42.0      24.0   \n",
       "2       4.0      12.0  42240.0       5.0       7.0       7.0       0.0   \n",
       "3      21.0      21.0      6.0  117336.0       8.0       2.0       9.0   \n",
       "4       8.0      15.0      6.0       6.0  158660.0       0.0       0.0   \n",
       "5      45.0      15.0      3.0       2.0       0.0  141575.0       3.0   \n",
       "6      32.0      16.0      2.0       4.0       0.0       0.0  156205.0   \n",
       "7      23.0      22.0      0.0       0.0       0.0       0.0       2.0   \n",
       "\n",
       "          7  \n",
       "0      18.0  \n",
       "1      27.0  \n",
       "2       1.0  \n",
       "3       0.0  \n",
       "4       0.0  \n",
       "5       0.0  \n",
       "6       1.0  \n",
       "7  119136.0  "
      ],
      "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>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>122145.0</td>\n      <td>60.0</td>\n      <td>8.0</td>\n      <td>28.0</td>\n      <td>8.0</td>\n      <td>17.0</td>\n      <td>17.0</td>\n      <td>18.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>23.0</td>\n      <td>122605.0</td>\n      <td>11.0</td>\n      <td>23.0</td>\n      <td>11.0</td>\n      <td>42.0</td>\n      <td>24.0</td>\n      <td>27.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.0</td>\n      <td>12.0</td>\n      <td>42240.0</td>\n      <td>5.0</td>\n      <td>7.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>21.0</td>\n      <td>21.0</td>\n      <td>6.0</td>\n      <td>117336.0</td>\n      <td>8.0</td>\n      <td>2.0</td>\n      <td>9.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>8.0</td>\n      <td>15.0</td>\n      <td>6.0</td>\n      <td>6.0</td>\n      <td>158660.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>45.0</td>\n      <td>15.0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>141575.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>32.0</td>\n      <td>16.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>156205.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>23.0</td>\n      <td>22.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>119136.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 447
    }
   ],
   "source": [
    "pd.DataFrame(t)"
   ]
  },
  {
   "source": [
    "---\n",
    "# Evaluation"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## Test DataSet-Top1"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "macro score: 0.7644688014505321\nmicro score: 0.7364690524512254\nweighted score: 0.744723592560603\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[20643,   844,     0,   183,   459,   571,   936,   805],\n",
       "       [ 1081, 19404,   202,  1041,   227,  1051,   511,   906],\n",
       "       [   22,  2282,  3824,  1811,     5,   472,     9,     7],\n",
       "       [  212,  1017,   108, 18686,  1212,  2277,    29,    24],\n",
       "       [  405,   142,     9,  2253, 22681,  3669,  2473,   322],\n",
       "       [  892,   186,     8,  1953,  2620, 20054,  2552,    74],\n",
       "       [ 1724,    41,     1,   411,  3868,  3844, 20020,  1237],\n",
       "       [  524,    68,     8,   234,  1222,  1401,  1235, 19114]])"
      ]
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "source": [
    "pred = pred_rf\n",
    "print(\"macro score: {}\\nmicro score: {}\\nweighted score: {}\".format(precision_score(y_test, pred, average = \"macro\"),\n",
    "                                                                    precision_score(y_test, pred, average = \"micro\"),\n",
    "                                                                    precision_score(y_test, pred, average = \"weighted\")))\n",
    "conf = confusion_matrix(y_test, pred)\n",
    "conf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "metadata": {},
     "execution_count": 22
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "sn.heatmap(conf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              precision    recall  f1-score   support\n\n       Still       0.81      0.84      0.83     24441\n     Walking       0.81      0.79      0.80     24423\n         Run       0.92      0.45      0.61      8432\n        Bike       0.70      0.79      0.75     23565\n         Car       0.70      0.71      0.71     31954\n         Bus       0.60      0.71      0.65     28339\n       Train       0.72      0.64      0.68     31146\n      Subway       0.85      0.80      0.83     23806\n\n    accuracy                           0.74    196106\n   macro avg       0.76      0.72      0.73    196106\nweighted avg       0.74      0.74      0.74    196106\n\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred, target_names = list(label_dic.values())))"
   ]
  },
  {
   "source": [
    "## Test DataSet-Top2"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Time used for select top2 in groups: 58.44\n",
      "Time used for get hit columns: 4.95\n"
     ]
    }
   ],
   "source": [
    "pred_df = eval_df_top2(y_test, pred_prob_rf, pred_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def top(x, n):\n",
    "    return x.sort_values().to_list()[-n]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_df_top2(y_test, pred_prob, pred):\n",
    "    df1 = pd.DataFrame(pred_prob, columns = list(label_dic.values())) # prob\n",
    "    df2 = pd.DataFrame({\"label\": y_test}) # label\n",
    "    y_test_compare = pd.concat([df1.reset_index(drop = True), df2.reset_index(drop = True)], axis = 1, ignore_index = False)\n",
    "    \n",
    "    pred_df = pd.concat([df1.reset_index(drop = True), df2.reset_index(drop = True)], axis = 1, ignore_index = False)\n",
    "    pred_df['pred_top1'] = pred\n",
    "    timer = TimeKeeper.TimeKeeper()\n",
    "    print(\"Start selecting top2\")\n",
    "    pred_df['pred_top2'] = df1.apply(lambda x: np.where(x == top(x, n = 2))[0][0] + 1, axis = 1)\n",
    "    print(\"Time used for select top2 in groups: {}\".format(timer.get_update_time()))\n",
    "    pred_df['hit_top1'] = pred_df.apply(lambda x: int(x['pred_top1'] == x['label']), axis = 1)\n",
    "    pred_df['hit_top2'] = pred_df.apply(lambda x: int((x['pred_top1'] == x['label']) | (x['pred_top2'] == x['label'])), axis = 1)\n",
    "    print(\"Time used for get hit columns: {}\".format(timer.get_update_time()))\n",
    "\n",
    "    print(\"Top 1 precision: {}\\nTop 2 precesion: {}\".format(Counter(pred_df.hit_top1)[1]/np.sum(list(Counter(pred_df.hit_top1).values())),\n",
    "                                                Counter(pred_df.hit_top2)[1]/np.sum(list(Counter(pred_df.hit_top2).values()))))\n",
    "\n",
    "    return pred_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pred_df.to_csv('data/mid_res/pred_df.csv', header = True, index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Top 1 precision: 0.7364690524512254\nTop 2 precesion: 0.8892894659010943\n"
     ]
    }
   ],
   "source": [
    "print(\"Top 1 precision: {}\\nTop 2 precesion: {}\".format(Counter(pred_df.hit_top1)[1]/np.sum(list(Counter(pred_df.hit_top1).values())),\n",
    "                                                Counter(pred_df.hit_top2)[1]/np.sum(list(Counter(pred_df.hit_top2).values()))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.955293260987095"
      ]
     },
     "metadata": {},
     "execution_count": 84
    }
   ],
   "source": [
    "roc_auc_score(y_test, pred_prob_rf, multi_class = 'ovo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# random forest\n",
    "ft_df = pd.DataFrame({\"Feature\": list(X), \"Importance\": rf.feature_importances_})\n",
    "ft_df.sort_values(ascending = False, by = \"Importance\", inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "# feature importance\n",
    "plt.figure(figsize = [12, 8])\n",
    "plt.plot(ft_df['Importance'].reset_index(drop = True))\n",
    "plt.scatter(range(ft_df.shape[0]), ft_df['Importance'].reset_index(drop = True))\n",
    "plt.xticks(range(ft_df.shape[0]), ft_df['Feature'].reset_index(drop = True), rotation = 90);"
   ]
  },
  {
   "source": [
    "## Validation DataSet"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # load\n",
    "# with open('data/mid_res/val_ft.pickle', 'rb') as file:\n",
    "#     val = pickle.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "pred_rf_val = rf.predict(X_val)\n",
    "pred_prob_rf_val = rf.predict_proba(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              precision    recall  f1-score   support\n\n       Still       0.66      0.53      0.59     29836\n     Walking       0.77      0.46      0.57     26149\n         Run       0.62      0.05      0.09      2774\n        Bike       0.62      0.72      0.67     12031\n         Car       0.53      0.44      0.48     20473\n         Bus       0.27      0.55      0.36      9178\n       Train       0.39      0.55      0.45     21808\n      Subway       0.62      0.67      0.65     21709\n\n    accuracy                           0.54    143958\n   macro avg       0.56      0.50      0.48    143958\nweighted avg       0.59      0.54      0.54    143958\n\n"
     ]
    }
   ],
   "source": [
    "pred = pred_rf_val\n",
    "print(classification_report(val.df['label'], pred, target_names = list(label_dic.values())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[15962,  1066,     0,   249,   435,  1384,  7365,  3375],\n",
       "       [ 2705, 11900,    61,  1191,   953,  6287,  1704,  1348],\n",
       "       [   14,   256,   140,   789,   361,  1178,    27,     9],\n",
       "       [  306,  1421,    25,  8688,     2,   132,   130,  1327],\n",
       "       [  444,   272,     0,  2478,  9030,  3367,  3790,  1092],\n",
       "       [ 1779,    88,     0,   368,    73,  5052,  1089,   729],\n",
       "       [ 2668,    19,     0,   286,  4919,  1019, 12017,   880],\n",
       "       [  312,   340,     0,    22,  1177,   313,  4956, 14589]])"
      ]
     },
     "metadata": {},
     "execution_count": 36
    }
   ],
   "source": [
    "conf = confusion_matrix(val.df['label'], pred)\n",
    "conf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "metadata": {},
     "execution_count": 37
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "sn.heatmap(conf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df_val = eval_df_top2(val.df['label'], pred_prob_rf_val, pred_rf_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df_val.to_csv('data/mid_res/pred_v1_val.csv', header = True, index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Top 1 precision: 0.5375039942205365\nTop 2 precesion: 0.7501354561747177\n"
     ]
    }
   ],
   "source": [
    "print(\"Top 1 precision: {}\\nTop 2 precesion: {}\".format(Counter(pred_df_val.hit_top1)[1]/np.sum(list(Counter(pred_df_val.hit_top1).values())),\n",
    "                                                Counter(pred_df_val.hit_top2)[1]/np.sum(list(Counter(pred_df_val.hit_top2).values()))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}