EURUSD_LSTM.ipynb

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 "cells": [
  {
   "source": [
    "This document is to explore building an LSTM deep learning model on the obtained data set."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "import MetaTrader5 as mt5\n",
    "from datetime import datetime\n",
    "import pytz\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt    \n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "#build connection\n",
    "mt5.initialize(\n",
    "    path = \"C:\\\\Users\\\\silin\\\\AppData\\\\Roaming\\\\MetaTrader 5\\\\terminal64.exe\",\n",
    "    login = 35146240,\n",
    "    password = \"xt7lixru\"\n",
    "    )\n",
    "\n",
    "# query data\n",
    "timezone = pytz.timezone(\"Etc/UTC\")\n",
    "utc_from = datetime(2020, 9, 1, tzinfo=timezone)\n",
    "\n",
    "rates = mt5.copy_rates_from('EURUSD', mt5.TIMEFRAME_M10, utc_from, 80000)\n",
    "\n",
    "#off connection\n",
    "mt5.shutdown()\n",
    "\n",
    "# cleanup data\n",
    "eurusd = pd.DataFrame(rates)\n",
    "eurusd['time']=pd.to_datetime(eurusd['time'], unit='s')\n",
    "#rates_frame.to_csv(\"EURUSD_200923_190425.csv\")"
   ]
  },
  {
   "source": [
    "Below is the code to generate LSTM model. Keras and Tensorflow is used here.    \n",
    "- Data preprocessing:   \n",
    "    1. normalization (sklearn.preprocessing.MinMaxScaler)   \n",
    "    2. split train/test Data   \n",
    "    3. prepare target column [$Y_{t}=f(X_{t-1},...)$]   \n",
    "    4. reshape to numpy array: In keras sequential model, the input shape = (samples, step, feature)\n",
    "\n",
    "- Model setup:\n",
    "    1. in this model shown, 1st layer is an LSTM layer with 64 hidden units. input shape is (batch, step, feature) and output shape is (batch, 64)   \n",
    "    2. 2nd layer is dense layer, reduce shape to (batch,16)   \n",
    "    3. 3rd layer is a linear dense, output the result shape (batch, 1)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Bidirectional\n",
    "from keras.callbacks import EarlyStopping\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "68000 8000 4000\n(67994, 5, 4)\n(7994, 5, 4)\n(3994, 5, 4)\n"
    }
   ],
   "source": [
    "# fix random seed for reproducibility\n",
    "np.random.seed(7)\n",
    "\n",
    "dataset = eurusd.loc[:,['open','high','low','close']]\n",
    "dataset = dataset.values\n",
    "dataset = dataset.astype('float32')\n",
    "\n",
    "# normalize the dataset\n",
    "#scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "#dataset = scaler.fit_transform(dataset)\n",
    "\n",
    "# split into train and test sets\n",
    "train_size = int(len(dataset) * 0.85)\n",
    "val_size = int(len(dataset) * 0.10)\n",
    "test_size = len(dataset) - train_size - val_size\n",
    "train, val, test = dataset[0:train_size,:], dataset[train_size:(train_size+val_size),:], dataset[(train_size+val_size):len(dataset),:]\n",
    "print(len(train), len(val), len(test))\n",
    "\n",
    "\n",
    "# convert an array of values into a dataset matrix\n",
    "def create_dataset(dataset, look_back=1):\n",
    "\tdataX, dataY = [], []\n",
    "\tfor i in range(len(dataset)-look_back-1):\n",
    "\t\ta = dataset[i:(i+look_back), :]\n",
    "\t\tdataX.append(a)\n",
    "\t\tdataY.append(dataset[i + look_back, 3])\n",
    "\treturn np.array(dataX), np.array(dataY)\n",
    "\n",
    "# reshape into X=t and Y=t+1\n",
    "look_back = 5\n",
    "trainX, trainY = create_dataset(train, look_back)\n",
    "valX, valY = create_dataset(val, look_back)\n",
    "testX, testY = create_dataset(test, look_back)\n",
    "\n",
    "\n",
    "# reshape input to be [samples, time steps, features]\n",
    "#trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))\n",
    "#valX = np.reshape(valX, (valX.shape[0], 1, valX.shape[1]))\n",
    "#testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))\n",
    "\n",
    "print(trainX.shape)\n",
    "print(valX.shape)\n",
    "print(testX.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "\" # fix random seed for reproducibility\\nnp.random.seed(7)\\n\\ndataset = eurusd.loc[:,['close']].values\\ndataset = dataset.astype('float32')\\n\\n# normalize the dataset\\nscaler = MinMaxScaler(feature_range=(0, 1))\\ndataset = scaler.fit_transform(dataset)\\n\\n# split into train and test sets\\ntrain_size = int(len(dataset) * 0.8)\\ntest_size = len(dataset) - train_size\\ntrain, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]\\nprint(len(train), len(test))\\n\\n\\n# convert an array of values into a dataset matrix\\ndef create_dataset(dataset, look_back=1):\\n\\tdataX, dataY = [], []\\n\\tfor i in range(len(dataset)-look_back-1):\\n\\t\\ta = dataset[i:(i+look_back), 0]\\n\\t\\tdataX.append(a)\\n\\t\\tdataY.append(dataset[i + look_back, 0])\\n\\treturn np.array(dataX), np.array(dataY)\\n\\n# reshape into X=t and Y=t+1\\nlook_back = 1\\ntrainX, trainY = create_dataset(train, look_back)\\ntestX, testY = create_dataset(test, look_back)\\n\\n# reshape input to be [samples, time steps, features]\\ntrainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))\\ntestX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))\\n \""
     },
     "metadata": {},
     "execution_count": 152
    }
   ],
   "source": [
    "\"\"\" # fix random seed for reproducibility\n",
    "np.random.seed(7)\n",
    "\n",
    "dataset = eurusd.loc[:,['close']].values\n",
    "dataset = dataset.astype('float32')\n",
    "\n",
    "# normalize the dataset\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "dataset = scaler.fit_transform(dataset)\n",
    "\n",
    "# split into train and test sets\n",
    "train_size = int(len(dataset) * 0.8)\n",
    "test_size = len(dataset) - train_size\n",
    "train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]\n",
    "print(len(train), len(test))\n",
    "\n",
    "\n",
    "# convert an array of values into a dataset matrix\n",
    "def create_dataset(dataset, look_back=1):\n",
    "\tdataX, dataY = [], []\n",
    "\tfor i in range(len(dataset)-look_back-1):\n",
    "\t\ta = dataset[i:(i+look_back), 0]\n",
    "\t\tdataX.append(a)\n",
    "\t\tdataY.append(dataset[i + look_back, 0])\n",
    "\treturn np.array(dataX), np.array(dataY)\n",
    "\n",
    "# reshape into X=t and Y=t+1\n",
    "look_back = 1\n",
    "trainX, trainY = create_dataset(train, look_back)\n",
    "testX, testY = create_dataset(test, look_back)\n",
    "\n",
    "# reshape input to be [samples, time steps, features]\n",
    "trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))\n",
    "testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))\n",
    " \"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Model: \"sequential_8\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nlstm_8 (LSTM)                (None, 16)                1344      \n_________________________________________________________________\ndense_13 (Dense)             (None, 8)                 136       \n_________________________________________________________________\ndense_14 (Dense)             (None, 1)                 9         \n=================================================================\nTotal params: 1,489\nTrainable params: 1,489\nNon-trainable params: 0\n_________________________________________________________________\nTrain on 67994 samples, validate on 7994 samples\nEpoch 1/100\n - 5s - loss: 0.0080 - val_loss: 6.6166e-05\nEpoch 2/100\n - 5s - loss: 4.1349e-05 - val_loss: 6.7376e-06\nEpoch 3/100\n - 5s - loss: 2.5990e-06 - val_loss: 7.5227e-07\nEpoch 4/100\n - 5s - loss: 5.6749e-07 - val_loss: 1.3967e-06\nEpoch 5/100\n - 6s - loss: 1.0103e-06 - val_loss: 1.2328e-06\nEpoch 6/100\n - 5s - loss: 1.5241e-06 - val_loss: 3.9258e-06\nEpoch 7/100\n - 5s - loss: 1.8579e-06 - val_loss: 6.8561e-07\nEpoch 8/100\n - 5s - loss: 2.0962e-06 - val_loss: 7.9287e-07\nEpoch 9/100\n - 5s - loss: 2.0408e-06 - val_loss: 4.9038e-06\nEpoch 10/100\n - 5s - loss: 1.8108e-06 - val_loss: 4.5230e-06\nEpoch 11/100\n - 5s - loss: 1.9263e-06 - val_loss: 3.9201e-06\nEpoch 12/100\n - 5s - loss: 1.9013e-06 - val_loss: 5.2758e-07\nEpoch 13/100\n - 5s - loss: 1.8616e-06 - val_loss: 1.6650e-06\nEpoch 14/100\n - 5s - loss: 1.7355e-06 - val_loss: 1.1891e-06\nEpoch 15/100\n - 5s - loss: 1.8144e-06 - val_loss: 5.0907e-06\nEpoch 16/100\n - 5s - loss: 1.8559e-06 - val_loss: 4.9592e-07\nEpoch 17/100\n - 5s - loss: 1.7055e-06 - val_loss: 5.9970e-07\nEpoch 18/100\n - 5s - loss: 1.7187e-06 - val_loss: 3.1975e-06\nEpoch 19/100\n - 5s - loss: 1.7006e-06 - val_loss: 4.3333e-06\nEpoch 20/100\n - 5s - loss: 1.5653e-06 - val_loss: 4.7901e-07\nEpoch 21/100\n - 5s - loss: 1.6016e-06 - val_loss: 1.4999e-06\nEpoch 22/100\n - 5s - loss: 1.6580e-06 - val_loss: 6.4503e-07\nEpoch 23/100\n - 5s - loss: 1.4901e-06 - val_loss: 5.3932e-07\nEpoch 24/100\n - 5s - loss: 1.5616e-06 - val_loss: 2.8831e-06\nEpoch 25/100\n - 5s - loss: 1.5569e-06 - val_loss: 1.1587e-06\nEpoch 26/100\n - 5s - loss: 1.5507e-06 - val_loss: 8.8559e-07\nEpoch 27/100\n - 6s - loss: 1.4269e-06 - val_loss: 1.2575e-06\nEpoch 28/100\n - 5s - loss: 1.5112e-06 - val_loss: 4.1608e-06\nEpoch 29/100\n - 5s - loss: 1.3995e-06 - val_loss: 4.9873e-07\nEpoch 30/100\n - 5s - loss: 1.3343e-06 - val_loss: 6.7672e-07\nEpoch 31/100\n - 5s - loss: 1.4473e-06 - val_loss: 7.2561e-07\nEpoch 32/100\n - 5s - loss: 1.3459e-06 - val_loss: 4.4274e-07\nEpoch 33/100\n - 5s - loss: 4.7453e-06 - val_loss: 4.8709e-07\nEpoch 34/100\n - 5s - loss: 6.2753e-07 - val_loss: 2.0226e-06\nEpoch 35/100\n - 5s - loss: 1.3937e-06 - val_loss: 4.3207e-07\nEpoch 36/100\n - 5s - loss: 1.3884e-06 - val_loss: 1.1462e-06\nEpoch 37/100\n - 5s - loss: 1.2864e-06 - val_loss: 7.0279e-07\nEpoch 38/100\n - 5s - loss: 1.3050e-06 - val_loss: 4.5738e-07\nEpoch 39/100\n - 5s - loss: 1.2636e-06 - val_loss: 4.5628e-07\nEpoch 40/100\n - 5s - loss: 1.4074e-06 - val_loss: 5.5976e-07\nEpoch 41/100\n - 5s - loss: 1.2592e-06 - val_loss: 6.8916e-07\nEpoch 42/100\n - 5s - loss: 1.2769e-06 - val_loss: 1.5583e-06\nEpoch 43/100\n - 5s - loss: 1.2619e-06 - val_loss: 4.9522e-07\nEpoch 44/100\n - 5s - loss: 1.2491e-06 - val_loss: 8.4233e-07\nEpoch 45/100\n - 5s - loss: 1.2663e-06 - val_loss: 1.3512e-06\nEpoch 46/100\n - 5s - loss: 1.1352e-06 - val_loss: 1.6835e-06\nEpoch 47/100\n - 5s - loss: 1.2432e-06 - val_loss: 4.0982e-07\nEpoch 48/100\n - 5s - loss: 1.2217e-06 - val_loss: 4.5816e-07\nEpoch 49/100\n - 5s - loss: 1.1082e-06 - val_loss: 1.8231e-06\nEpoch 50/100\n - 5s - loss: 1.1601e-06 - val_loss: 1.7574e-06\nEpoch 51/100\n - 5s - loss: 1.1318e-06 - val_loss: 1.0185e-06\nEpoch 52/100\n - 5s - loss: 1.1672e-06 - val_loss: 1.2640e-06\nEpoch 53/100\n - 5s - loss: 1.1795e-06 - val_loss: 4.8213e-06\nEpoch 54/100\n - 5s - loss: 1.1199e-06 - val_loss: 8.8860e-07\nEpoch 55/100\n - 5s - loss: 1.1407e-06 - val_loss: 7.9895e-07\nEpoch 56/100\n - 5s - loss: 1.0835e-06 - val_loss: 8.1881e-07\nEpoch 57/100\n - 5s - loss: 1.1047e-06 - val_loss: 4.6792e-07\nEpoch 58/100\n - 5s - loss: 1.0621e-06 - val_loss: 1.3298e-06\nEpoch 59/100\n - 5s - loss: 1.0693e-06 - val_loss: 1.6815e-06\nEpoch 60/100\n - 5s - loss: 1.0832e-06 - val_loss: 1.2202e-06\nEpoch 61/100\n - 5s - loss: 1.0379e-06 - val_loss: 3.8264e-07\nEpoch 62/100\n - 5s - loss: 1.1349e-06 - val_loss: 5.6638e-07\nEpoch 63/100\n - 5s - loss: 1.0277e-06 - val_loss: 6.7067e-07\nEpoch 64/100\n - 5s - loss: 1.0970e-06 - val_loss: 3.7741e-07\nEpoch 65/100\n - 5s - loss: 9.8404e-07 - val_loss: 4.4019e-07\nEpoch 66/100\n - 5s - loss: 1.0426e-06 - val_loss: 8.0160e-07\nEpoch 67/100\n - 5s - loss: 9.6815e-07 - val_loss: 4.2215e-07\nEpoch 68/100\n - 5s - loss: 1.0231e-06 - val_loss: 3.7208e-06\nEpoch 69/100\n - 5s - loss: 9.7010e-07 - val_loss: 6.1438e-07\nEpoch 70/100\n - 5s - loss: 9.6048e-07 - val_loss: 2.4571e-06\nEpoch 71/100\n - 5s - loss: 1.0318e-06 - val_loss: 3.2824e-07\nEpoch 72/100\n - 5s - loss: 9.9657e-07 - val_loss: 4.1190e-07\nEpoch 73/100\n - 5s - loss: 9.9907e-07 - val_loss: 3.5809e-07\nEpoch 74/100\n - 5s - loss: 9.3241e-07 - val_loss: 2.3622e-06\nEpoch 75/100\n - 5s - loss: 1.0044e-06 - val_loss: 3.2048e-07\nEpoch 76/100\n - 5s - loss: 9.6367e-07 - val_loss: 1.1275e-06\nEpoch 77/100\n - 5s - loss: 9.0370e-07 - val_loss: 1.0320e-06\nEpoch 78/100\n - 5s - loss: 9.8714e-07 - val_loss: 6.2442e-07\nEpoch 79/100\n - 5s - loss: 9.8277e-07 - val_loss: 4.0107e-07\nEpoch 80/100\n - 5s - loss: 8.8500e-07 - val_loss: 4.8483e-07\nEpoch 81/100\n - 5s - loss: 9.1906e-07 - val_loss: 1.3258e-06\nEpoch 82/100\n - 5s - loss: 8.9820e-07 - val_loss: 1.1952e-06\nEpoch 83/100\n - 5s - loss: 9.0865e-07 - val_loss: 6.6796e-07\nEpoch 84/100\n - 5s - loss: 9.1479e-07 - val_loss: 3.8675e-07\nEpoch 85/100\n - 5s - loss: 9.1477e-07 - val_loss: 7.2216e-07\nEpoch 86/100\n - 5s - loss: 8.7929e-07 - val_loss: 3.8825e-06\nEpoch 87/100\n - 5s - loss: 8.6832e-07 - val_loss: 5.5942e-07\nEpoch 88/100\n - 5s - loss: 9.7034e-07 - val_loss: 5.8491e-07\nEpoch 89/100\n - 5s - loss: 8.8828e-07 - val_loss: 3.7477e-07\nEpoch 90/100\n - 5s - loss: 8.9516e-07 - val_loss: 3.4859e-07\nEpoch 91/100\n - 5s - loss: 8.6476e-07 - val_loss: 5.2803e-07\nEpoch 92/100\n - 5s - loss: 8.1874e-07 - val_loss: 3.3872e-06\nEpoch 93/100\n - 5s - loss: 8.4982e-07 - val_loss: 8.9394e-07\nEpoch 94/100\n - 5s - loss: 8.5759e-07 - val_loss: 2.0928e-06\nEpoch 95/100\n - 5s - loss: 8.0628e-07 - val_loss: 4.3934e-07\nEpoch 96/100\n - 5s - loss: 8.2828e-07 - val_loss: 1.4987e-06\nEpoch 97/100\n - 5s - loss: 8.5938e-07 - val_loss: 1.2109e-06\nEpoch 98/100\n - 5s - loss: 7.8604e-07 - val_loss: 9.5249e-07\nEpoch 99/100\n - 5s - loss: 8.3820e-07 - val_loss: 2.7954e-07\nEpoch 100/100\n - 5s - loss: 8.3998e-07 - val_loss: 2.9207e-06\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<keras.callbacks.callbacks.History at 0x186669dad48>"
     },
     "metadata": {},
     "execution_count": 153
    }
   ],
   "source": [
    "# create and fit the LSTM network\n",
    "model = Sequential()\n",
    "model.add(LSTM(16, input_shape=(look_back, 4)))\n",
    "model.add(Dense(8, activation=\"relu\"))\n",
    "model.add(Dense(1, activation=\"linear\"))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam', )\n",
    "model.summary()\n",
    "model.fit(trainX, trainY, epochs=100, batch_size=50, verbose=2, validation_data=(valX, valY), callbacks=[EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50)])"
   ]
  },
  {
   "source": [
    "After model training, below will be the model predictions. We will predict trainX and testX separately and compare with trainY and testY.   \n",
    "\n",
    "Since we use the MinMaxScaler, the output from the model will also be a scaled value between 0 and 1. We need the `scaler.inverse_transform()` to map back to the actual value.\n",
    "\n",
    "The common evaluation metric is RMSE/MAE. Since our data is very small close to 1, MAE will be better choice.  "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Train Score: 0.00 MAE\nTest Score: 0.00 MAE\n"
    }
   ],
   "source": [
    "# make predictions\n",
    "trainPredict = model.predict(trainX)\n",
    "valPredict = model.predict(valX)\n",
    "testPredict = model.predict(testX)\n",
    "\n",
    "# invert predictions\n",
    "#trainPredict = scaler.inverse_transform(trainPredict)\n",
    "#trainY = scaler.inverse_transform([trainY])\n",
    "#testPredict = scaler.inverse_transform(testPredict)\n",
    "#testY = scaler.inverse_transform([testY])\n",
    "\n",
    "# calculate root mean squared error\n",
    "trainScore = mean_absolute_error(trainY, trainPredict[:,0])\n",
    "print('Train Score: %.2f MAE' % (trainScore))\n",
    "testScore = mean_absolute_error(testY, testPredict[:,0])\n",
    "print('Test Score: %.2f MAE' % (testScore))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"LSTM_eurusd2.model\")"
   ]
  },
  {
   "source": [
    "Another view of the prediction result is plot the prediction vs actual. Below is the sample code for draw a line plot."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "(67994, 1) (68000, 4)\n(7994, 1) (8000, 4)\n(3994, 1) (4000, 4)\n"
    }
   ],
   "source": [
    "print(trainPredict.shape, train.shape)\n",
    "print(valPredict.shape, val.shape)\n",
    "print(testPredict.shape, test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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114.597471 122.967127 \r\nL 114.62791 120.766802 \r\nL 114.734446 118.123246 \r\nL 114.749666 118.65318 \r\nL 114.890446 117.293267 \r\nL 114.772495 118.824248 \r\nL 114.898056 117.362522 \r\nL 115.133959 111.928805 \r\nL 114.955129 118.015676 \r\nL 115.172007 113.625279 \r\nL 115.175812 114.032713 \r\nL 115.25191 110.654336 \r\nL 115.255715 110.798782 \r\nL 115.25952 110.768922 \r\nL 115.270934 111.412362 \r\nL 115.308983 112.233527 \r\nL 115.366056 111.36955 \r\nL 115.388886 112.127216 \r\nL 115.40791 110.537952 \r\nL 115.484008 112.282455 \r\nL 115.525861 113.129162 \r\nL 115.5563 109.316909 \r\nL 115.628593 104.543182 \r\nL 115.674251 107.614139 \r\nL 115.681861 106.943536 \r\nL 115.735129 109.705095 \r\nL 115.769373 109.071009 \r\nL 115.780788 110.683657 \r\nL 115.803617 108.439981 \r\nL 115.87591 109.230385 \r\nL 116.012886 105.857225 \r\nL 116.0243 106.487174 \r\nL 116.028105 106.877699 \r\nL 116.108008 104.126753 \r\nL 116.165081 102.795801 \r\nL 116.142251 105.630753 \r\nL 116.218349 103.892186 \r\nL 116.222154 104.000476 \r\nL 116.244983 101.764534 \r\nL 116.264008 98.982468 \r\nL 116.347715 102.349692 \r\nL 116.397178 108.462646 \r\nL 116.465666 106.651947 \r\nL 116.480886 106.52477 \r\nL 116.4923 107.262648 \r\nL 116.556983 106.770849 \r\nL 116.560788 106.930045 \r\nL 116.614056 104.842147 \r\nL 116.697764 92.38689 \r\nL 116.754837 95.935615 \r\nL 116.830934 101.894769 \r\nL 116.773861 95.234433 \r\nL 116.857569 96.289984 \r\nL 116.903227 91.989529 \r\nL 116.9603 96.89439 \r\nL 116.964105 96.87856 \r\nL 116.97552 97.004298 \r\nL 117.005959 95.517028 \r\nL 117.009764 95.1614 \r\nL 117.093471 96.77135 \r\nL 117.097276 96.688424 \r\nL 117.12391 97.481887 \r\nL 117.180983 94.33376 \r\nL 117.196203 93.482915 \r\nL 117.226642 100.089466 \r\nL 117.238056 100.087847 \r\nL 117.363617 105.306064 \r\nL 117.367422 104.992169 \r\nL 117.378837 102.440893 \r\nL 117.439715 109.29964 \r\nL 117.466349 108.97675 \r\nL 117.485374 110.224777 \r\nL 117.569081 107.711636 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103.586026 \r\nL 120.932593 105.137514 \r\nL 120.985862 105.42065 \r\nL 121.061959 102.941147 \r\nL 121.176106 106.003649 \r\nL 121.20274 104.089158 \r\nL 121.240789 101.966542 \r\nL 121.316886 102.509608 \r\nL 121.366349 111.327997 \r\nL 121.408203 102.478668 \r\nL 121.434837 103.728673 \r\nL 121.453862 102.999249 \r\nL 121.526154 104.572683 \r\nL 121.529959 104.568006 \r\nL 121.67835 100.021291 \r\nL 121.693569 100.590979 \r\nL 121.720203 100.489705 \r\nL 121.712593 101.097529 \r\nL 121.727813 101.032771 \r\nL 121.743032 101.89351 \r\nL 121.796301 97.52326 \r\nL 121.81152 97.99779 \r\nL 121.838154 98.55057 \r\nL 121.887618 96.367694 \r\nL 122.016984 92.103754 \r\nL 122.028398 92.373398 \r\nL 122.039813 93.117033 \r\nL 122.115911 92.161676 \r\nL 122.119715 92.192976 \r\nL 122.127325 91.725641 \r\nL 122.169179 96.767033 \r\nL 122.184398 96.537143 \r\nL 122.218642 98.725236 \r\nL 122.268106 96.413564 \r\nL 122.317569 97.56967 \r\nL 122.397472 94.784906 \r\nL 122.424106 97.75279 \r\nL 122.439325 99.405193 \r\nL 122.44313 99.625909 \r\nL 122.500203 97.314776 \r\nL 122.515423 97.526678 \r\nL 122.530642 96.170722 \r\nL 122.59152 98.256462 \r\nL 122.610545 97.926917 \r\nL 122.618154 98.112376 \r\nL 122.663813 96.638777 \r\nL 122.709472 94.615636 \r\nL 122.777959 95.778398 \r\nL 122.846447 93.272632 \r\nL 122.895911 95.003103 \r\nL 122.899715 94.91604 \r\nL 122.930155 96.646512 \r\nL 122.933959 96.979834 \r\nL 122.994837 93.715683 \r\nL 122.998642 93.810841 \r\nL 123.105179 82.377857 \r\nL 123.116594 82.46528 \r\nL 123.120398 83.152432 \r\nL 123.21552 81.06975 \r\nL 123.306837 78.836866 \r\nL 123.253569 81.951355 \r\nL 123.337276 80.412279 \r\nL 123.447618 85.533719 \r\nL 123.352496 80.067624 \r\nL 123.519911 83.665098 \r\nL 123.523716 83.101885 \r\nL 123.596008 86.86647 \r\nL 123.603618 86.438529 \r\nL 123.641667 89.760782 \r\nL 123.725374 89.232287 \r\nL 123.736789 89.289849 \r\nL 123.748203 88.915513 \r\nL 123.885179 84.558935 \r\nL 123.900398 85.072859 \r\nL 123.991716 86.117978 \r\nL 123.934642 84.774434 \r\nL 124.01074 85.232056 \r\nL 124.052594 83.910278 \r\nL 124.094447 90.170375 \r\nL 124.098252 90.169116 \r\nL 124.212399 93.677368 \r\nL 124.269472 95.147729 \r\nL 124.33035 94.421183 \r\nL 124.334155 94.553217 \r\nL 124.368399 92.694489 \r\nL 124.410252 93.379662 \r\nL 124.452106 92.211324 \r\nL 124.482545 94.489179 \r\nL 124.520594 93.110738 \r\nL 124.547228 95.549587 \r\nL 124.653764 94.423342 \r\nL 124.707033 92.350014 \r\nL 124.767911 93.976513 \r\nL 124.775521 94.188415 \r\nL 124.859228 93.248168 \r\nL 124.912496 90.957902 \r\nL 124.984789 92.274103 \r\nL 124.996203 91.858394 \r\nL 125.072301 97.468036 \r\nL 125.140789 101.154372 \r\nL 125.182643 98.410261 \r\nL 125.201667 96.691662 \r\nL 125.228301 99.984235 \r\nL 125.277764 106.214652 \r\nL 125.342447 102.332064 \r\nL 125.483228 104.256269 \r\nL 125.40713 102.094978 \r\nL 125.487033 104.252491 \r\nL 125.544106 102.258312 \r\nL 125.58596 104.466192 \r\nL 125.681082 106.75466 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130.935619 98.055533 \r\nL 130.947033 98.245849 \r\nL 131.042155 97.088663 \r\nL 130.977472 98.446598 \r\nL 131.04596 97.874211 \r\nL 131.144887 100.044855 \r\nL 131.160106 99.250134 \r\nL 131.171521 98.392453 \r\nL 131.220985 102.340698 \r\nL 131.259033 100.683079 \r\nL 131.319911 101.409086 \r\nL 131.323716 97.106292 \r\nL 131.445472 90.204553 \r\nL 131.453082 90.718658 \r\nL 131.50635 88.745344 \r\nL 131.612887 87.613882 \r\nL 131.593863 89.080646 \r\nL 131.616692 88.523369 \r\nL 131.681375 92.743957 \r\nL 131.628106 88.302113 \r\nL 131.738448 90.625838 \r\nL 131.746058 90.395228 \r\nL 131.803131 93.117213 \r\nL 131.83357 97.840573 \r\nL 131.924887 97.191556 \r\nL 131.99718 96.72566 \r\nL 131.955326 97.269445 \r\nL 132.016204 97.264049 \r\nL 132.122741 98.497684 \r\nL 132.065668 97.219258 \r\nL 132.13035 98.239913 \r\nL 132.297765 92.428622 \r\nL 132.335814 95.239829 \r\nL 132.339619 95.826786 \r\nL 132.358643 92.231651 \r\nL 132.44235 95.000045 \r\nL 132.495619 96.063692 \r\nL 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110.390628 \r\nL 134.32957 109.832812 \r\nL 134.333375 109.479882 \r\nL 134.398058 112.605704 \r\nL 134.405668 112.552998 \r\nL 134.447521 114.735695 \r\nL 134.512204 112.061559 \r\nL 134.516009 112.082245 \r\nL 134.519814 111.749102 \r\nL 134.573082 110.571231 \r\nL 134.63396 111.366132 \r\nL 134.782351 113.59362 \r\nL 134.816595 112.917441 \r\nL 134.900302 113.576171 \r\nL 134.828009 112.824081 \r\nL 134.907912 113.552606 \r\nL 134.934546 115.265809 \r\nL 134.980204 113.005223 \r\nL 135.010643 113.325055 \r\nL 135.033473 111.84534 \r\nL 135.090546 114.905504 \r\nL 135.094351 114.690904 \r\nL 135.200887 115.792685 \r\nL 135.128595 114.503466 \r\nL 135.212302 115.645002 \r\nL 135.318839 115.067039 \r\nL 135.231326 116.453035 \r\nL 135.334058 115.224076 \r\nL 135.512887 119.001613 \r\nL 135.425375 115.00318 \r\nL 135.524302 118.272728 \r\nL 135.535717 116.630399 \r\nL 135.6004 120.517844 \r\nL 135.611814 120.259532 \r\nL 135.623229 119.629223 \r\nL 135.634643 122.422082 \r\nL 135.638448 122.417585 \r\nL 135.699326 123.744939 \r\nL 135.653668 122.138227 \r\nL 135.775424 122.831855 \r\nL 135.89718 121.712445 \r\nL 135.908595 122.024002 \r\nL 135.939034 122.759542 \r\nL 135.988497 121.331633 \r\nL 135.999912 121.45935 \r\nL 136.121668 120.01741 \r\nL 136.026546 124.460333 \r\nL 136.125473 120.080189 \r\nL 136.171131 120.479889 \r\nL 136.186351 118.452611 \r\nL 136.270058 113.822071 \r\nL 136.315717 114.848841 \r\nL 136.319522 115.112549 \r\nL 136.35757 113.719537 \r\nL 136.410839 113.968855 \r\nL 136.452692 113.121967 \r\nL 136.517375 113.723495 \r\nL 136.696204 121.251585 \r\nL 136.700009 121.125128 \r\nL 136.711424 119.332777 \r\nL 136.776107 122.599806 \r\nL 136.802741 122.553576 \r\nL 136.886448 126.001208 \r\nL 136.939717 124.578336 \r\nL 137.095717 121.529684 \r\nL 137.114741 125.570929 \r\nL 137.217473 124.133126 \r\nL 137.369668 119.071047 \r\nL 137.426741 119.83375 \r\nL 137.434351 119.942759 \r\nL 137.468595 119.806228 \r\nL 137.594156 121.464567 \r\nL 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130.609982 \r\nL 154.286157 131.691797 \r\nL 154.35084 129.802309 \r\nL 154.385084 130.896535 \r\nL 154.598157 127.3318 \r\nL 154.628596 128.108354 \r\nL 154.776987 133.073116 \r\nL 154.799816 133.323513 \r\nL 154.853084 132.512602 \r\nL 154.872109 132.722704 \r\nL 154.879718 132.406471 \r\nL 154.902548 133.883128 \r\nL 154.96723 133.044695 \r\nL 155.050938 134.454075 \r\nL 155.073767 133.331068 \r\nL 155.077572 132.952415 \r\nL 155.127035 134.782541 \r\nL 155.180304 133.692812 \r\nL 155.30967 136.276287 \r\nL 155.237377 132.986592 \r\nL 155.328694 135.810931 \r\nL 155.340109 133.560058 \r\nL 155.45045 133.803979 \r\nL 155.454255 133.821608 \r\nL 155.488499 133.473715 \r\nL 155.507523 133.584702 \r\nL 155.640694 130.394663 \r\nL 155.655913 130.946183 \r\nL 155.663523 131.497344 \r\nL 155.712987 127.11684 \r\nL 155.720596 126.554167 \r\nL 155.789084 128.211786 \r\nL 155.838548 129.622246 \r\nL 155.888011 127.225669 \r\nL 155.891816 127.450703 \r\nL 155.975523 129.227944 \r\nL 155.907035 127.275317 \r\nL 156.021182 127.967506 \r\nL 156.055426 127.143643 \r\nL 156.116304 128.394727 \r\nL 156.131523 128.028666 \r\nL 156.180987 127.349788 \r\nL 156.211426 128.740462 \r\nL 156.23045 128.284819 \r\nL 156.234255 128.257836 \r\nL 156.24567 128.843894 \r\nL 156.356011 132.519977 \r\nL 156.38645 131.825809 \r\nL 156.504401 130.456183 \r\nL 156.515816 130.712875 \r\nL 156.538645 131.156286 \r\nL 156.595718 130.606025 \r\nL 156.70606 130.109189 \r\nL 156.614743 130.733382 \r\nL 156.709865 130.196792 \r\nL 156.744109 129.169482 \r\nL 156.824011 129.827852 \r\nL 156.983816 132.191871 \r\nL 156.881084 129.009206 \r\nL 157.00284 131.936437 \r\nL 157.006645 131.934638 \r\nL 157.109377 131.323217 \r\nL 157.078938 132.2086 \r\nL 157.124597 131.384737 \r\nL 157.16645 131.6873 \r\nL 157.208304 130.500614 \r\nL 157.253962 129.638256 \r\nL 157.31484 130.533172 \r\nL 157.32245 130.126638 \r\nL 157.459426 123.370244 \r\nL 157.607816 124.799771 \r\nL 157.687719 125.735881 \r\nL 157.718158 125.080748 \r\nL 157.79045 124.015303 \r\nL 157.828499 124.852657 \r\nL 157.832304 124.985051 \r\nL 157.885572 123.541492 \r\nL 157.995914 122.051883 \r\nL 158.003523 122.053682 \r\nL 158.170938 124.800131 \r\nL 158.174743 124.696699 \r\nL 158.231816 124.175938 \r\nL 158.262255 125.240484 \r\nL 158.269865 125.054126 \r\nL 158.296499 125.515705 \r\nL 158.361182 124.77189 \r\nL 158.372597 124.783942 \r\nL 158.501962 122.334119 \r\nL 158.520987 122.561851 \r\nL 158.543816 128.265931 \r\nL 158.631328 122.80847 \r\nL 158.680792 122.741014 \r\nL 158.661767 123.49814 \r\nL 158.703621 123.317538 \r\nL 158.711231 123.388232 \r\nL 158.764499 122.676796 \r\nL 158.775914 122.765118 \r\nL 158.863426 122.994829 \r\nL 158.901475 122.228169 \r\nL 158.958548 121.565301 \r\nL 158.977572 122.47119 \r\nL 159.008011 122.25623 \r\nL 159.186841 126.255922 \r\nL 159.034645 121.933341 \r\nL 159.213475 125.739838 \r\nL 159.262938 126.112016 \r\nL 159.540694 118.028627 \r\nL 159.632011 115.93911 \r\nL 159.552109 118.067662 \r\nL 159.666255 116.3836 \r\nL 159.799426 119.856955 \r\nL 159.83367 118.866701 \r\nL 159.951621 117.170407 \r\nL 159.985865 117.491318 \r\nL 160.020109 118.272009 \r\nL 160.050548 116.455014 \r\nL 160.069572 116.837444 \r\nL 160.080987 116.336651 \r\nL 160.15328 117.623352 \r\nL 160.176109 117.200627 \r\nL 160.179914 117.070572 \r\nL 160.236987 118.727651 \r\nL 160.248402 118.296652 \r\nL 160.305475 118.936135 \r\nL 160.263621 118.10148 \r\nL 160.366353 118.72945 \r\nL 160.434841 118.217144 \r\nL 160.381572 118.984884 \r\nL 160.45767 118.536256 \r\nL 160.472889 119.817021 \r\nL 160.545182 118.404762 \r\nL 160.564207 118.466462 \r\nL 160.625085 117.44167 \r\nL 160.644109 120.148725 \r\nL 160.647914 120.659052 \r\nL 160.701182 118.13242 \r\nL 160.750646 119.692362 \r\nL 160.765865 118.76057 \r\nL 160.815329 121.599479 \r\nL 160.849572 121.168659 \r\nL 160.857182 120.756368 \r\nL 160.906646 121.985867 \r\nL 160.948499 121.549471 \r\nL 160.952304 121.771446 \r\nL 161.020792 120.374837 \r\nL 161.146353 116.856332 \r\nL 161.169182 116.209654 \r\nL 161.24528 118.030966 \r\nL 161.302353 119.843644 \r\nL 161.370841 119.471466 \r\nL 161.378451 119.102887 \r\nL 161.397475 119.735534 \r\nL 161.481182 119.509242 \r\nL 161.484987 119.309752 \r\nL 161.572499 120.123181 \r\nL 161.580109 119.952113 \r\nL 161.587719 120.35577 \r\nL 161.660012 119.148217 \r\nL 161.667621 119.3281 \r\nL 161.675231 118.889366 \r\nL 161.70567 123.119307 \r\nL 161.823621 128.180127 \r\nL 161.842646 126.726675 \r\nL 161.911134 129.499387 \r\nL 161.933963 129.90772 \r\nL 161.991036 129.398832 \r\nL 162.021475 129.493271 \r\nL 162.02528 129.520973 \r\nL 162.044304 128.879151 \r\nL 162.124207 127.912282 \r\nL 162.162256 128.624797 \r\nL 162.17367 128.71186 \r\nL 162.185085 128.550865 \r\nL 162.276402 127.192571 \r\nL 162.310646 127.384326 \r\nL 162.32206 127.186095 \r\nL 162.360109 127.494774 \r\nL 162.409573 127.154076 \r\nL 162.481865 127.621411 \r\nL 162.546548 127.369755 \r\nL 162.576987 128.044316 \r\nL 162.679719 128.423508 \r\nL 162.63406 127.479484 \r\nL 162.691134 128.193438 \r\nL 162.858548 126.603815 \r\nL 162.713963 128.567414 \r\nL 162.869963 126.723617 \r\nL 162.881378 126.859428 \r\nL 162.908012 126.19692 \r\nL 162.938451 126.417996 \r\nL 163.033573 125.048549 \r\nL 163.060207 125.178784 \r\nL 163.075426 125.275381 \r\nL 163.193378 127.120978 \r\nL 163.155329 125.158817 \r\nL 163.235231 126.694836 \r\nL 163.25806 126.119751 \r\nL 163.334158 127.411848 \r\nL 163.341768 126.983727 \r\nL 163.391231 125.474332 \r\nL 163.497768 133.736343 \r\nL 163.501573 134.464509 \r\nL 163.551036 132.021522 \r\nL 163.596695 132.198346 \r\nL 163.646158 130.225213 \r\nL 163.726061 131.129484 \r\nL 163.878256 133.254618 \r\nL 163.927719 131.336888 \r\nL 163.980987 133.379096 \r\nL 163.992402 132.917697 \r\nL 163.996207 132.870388 \r\nL 164.011426 133.590099 \r\nL 164.148402 142.126971 \r\nL 164.152207 142.427735 \r\nL 164.235914 140.507128 \r\nL 164.239719 140.501731 \r\nL 164.296792 142.152155 \r\nL 164.346256 140.443629 \r\nL 164.350061 140.483743 \r\nL 164.551719 145.813128 \r\nL 164.418548 140.462697 \r\nL 164.555524 145.281035 \r\nL 164.597378 141.62366 \r\nL 164.681085 144.280347 \r\nL 164.68489 144.279987 \r\nL 164.696305 144.909757 \r\nL 164.783817 143.733144 \r\nL 164.806646 143.476991 \r\nL 164.829475 144.548552 \r\nL 164.837085 144.92001 \r\nL 164.875134 143.492101 \r\nL 164.932207 144.032649 \r\nL 164.985475 142.599703 \r\nL 165.046353 143.452887 \r\nL 165.092012 144.250846 \r\nL 165.076792 143.370321 \r\nL 165.099622 144.026892 \r\nL 165.149085 139.103143 \r\nL 165.213768 140.270942 \r\nL 165.29367 142.46587 \r\nL 165.362158 141.829266 \r\nL 165.46489 140.375814 \r\nL 165.396402 142.185074 \r\nL 165.495329 140.480145 \r\nL 165.563817 139.587208 \r\nL 165.601866 140.552638 \r\nL 165.651329 141.571314 \r\nL 165.700792 139.92107 \r\nL 165.811134 136.454191 \r\nL 165.849183 136.545032 \r\nL 165.860597 136.674008 \r\nL 165.92528 136.313343 \r\nL 165.986158 135.563772 \r\nL 165.9405 136.763769 \r\nL 166.035622 136.132381 \r\nL 166.062256 137.430234 \r\nL 166.111719 133.704324 \r\nL 166.225866 130.358506 \r\nL 166.233475 131.143514 \r\nL 166.271524 134.007067 \r\nL 166.313378 131.052494 \r\nL 166.343817 131.271591 \r\nL 166.347622 131.124267 \r\nL 166.427524 132.127473 \r\nL 166.431329 132.332179 \r\nL 166.492207 131.507417 \r\nL 166.526451 131.5639 \r\nL 166.693866 128.552664 \r\nL 166.606353 131.862146 \r\nL 166.70528 128.622818 \r\nL 166.762353 130.378293 \r\nL 166.792792 127.53129 \r\nL 166.796597 127.9148 \r\nL 166.8765 125.379533 \r\nL 166.880305 129.20294 \r\nL 166.899329 135.373636 \r\nL 166.994451 134.088734 \r\nL 166.998256 134.116436 \r\nL 167.02489 133.49584 \r\nL 167.047719 132.942161 \r\nL 167.070549 133.883847 \r\nL 167.139036 133.324052 \r\nL 167.165671 135.049847 \r\nL 167.203719 132.089338 \r\nL 167.253183 134.136942 \r\nL 167.283622 132.9141 \r\nL 167.333085 137.03845 \r\nL 167.367329 136.886089 \r\nL 167.473866 138.284497 \r\nL 167.481475 138.385591 \r\nL 167.530939 137.972221 \r\nL 167.561378 137.984813 \r\nL 167.572793 137.727221 \r\nL 167.6565 138.267768 \r\nL 167.782061 141.103799 \r\nL 167.789671 140.67208 \r\nL 167.793476 140.646357 \r\nL 167.801085 141.098762 \r\nL 167.8125 142.090635 \r\nL 167.869573 139.350482 \r\nL 167.979915 134.642053 \r\nL 167.991329 134.757897 \r\nL 168.025573 133.709181 \r\nL 168.036988 138.090944 \r\nL 168.090256 135.236565 \r\nL 168.113085 136.788413 \r\nL 168.192988 135.146804 \r\nL 168.200597 135.003437 \r\nL 168.227232 136.076977 \r\nL 168.276695 135.892417 \r\nL 168.303329 136.377921 \r\nL 168.28811 135.650655 \r\nL 168.318549 135.690589 \r\nL 168.360402 134.03225 \r\nL 168.402256 136.611229 \r\nL 168.42889 135.663786 \r\nL 168.4365 135.725126 \r\nL 168.466939 134.487534 \r\nL 168.493573 133.273146 \r\nL 168.57728 134.30837 \r\nL 168.596305 135.069634 \r\nL 168.657183 133.912808 \r\nL 168.683817 134.165184 \r\nL 168.794159 132.126393 \r\nL 168.809378 132.623589 \r\nL 168.816988 132.396217 \r\nL 168.836012 134.70699 \r\nL 168.885476 134.013003 \r\nL 169.018646 138.459523 \r\nL 169.071915 137.77489 \r\nL 169.148012 135.21426 \r\nL 169.186061 135.574745 \r\nL 169.193671 135.702461 \r\nL 169.220305 135.114605 \r\nL 169.345866 132.709753 \r\nL 169.357281 133.194897 \r\nL 169.429573 134.865827 \r\nL 169.383915 133.076534 \r\nL 169.471427 133.866039 \r\nL 169.585573 136.235993 \r\nL 169.555134 132.863912 \r\nL 169.616012 135.943144 \r\nL 169.730159 134.550133 \r\nL 169.745378 134.63144 \r\nL 169.806256 136.038122 \r\nL 169.870939 134.901084 \r\nL 169.905183 134.135143 \r\nL 169.931817 136.531541 \r\nL 169.935622 137.09943 \r\nL 169.973671 135.013331 \r\nL 170.026939 135.149682 \r\nL 170.072598 127.918398 \r\nL 170.175329 130.606565 \r\nL 170.182939 130.512666 \r\nL 170.205768 131.21223 \r\nL 170.220988 132.174422 \r\nL 170.297085 130.420566 \r\nL 170.30089 130.584799 \r\nL 170.335134 129.283707 \r\nL 170.430256 130.188697 \r\nL 170.445476 130.84383 \r\nL 170.506354 129.250069 \r\nL 170.62811 126.945952 \r\nL 170.548207 130.062599 \r\nL 170.631915 127.197248 \r\nL 170.730842 129.517375 \r\nL 170.757476 128.935274 \r\nL 170.765086 128.579107 \r\nL 170.860207 129.303314 \r\nL 170.883037 129.047341 \r\nL 170.94011 129.797812 \r\nL 170.943915 129.792595 \r\nL 170.959134 129.894229 \r\nL 170.978159 129.501905 \r\nL 171.065671 128.475494 \r\nL 171.016207 130.224134 \r\nL 171.09611 128.780755 \r\nL 171.107525 129.497768 \r\nL 171.153183 125.417668 \r\nL 171.210256 129.199162 \r\nL 171.233086 128.692793 \r\nL 171.297768 129.583392 \r\nL 171.309183 129.725499 \r\nL 171.324403 130.716473 \r\nL 171.396695 128.940851 \r\nL 171.404305 128.946787 \r\nL 171.503232 128.138934 \r\nL 171.438549 129.293241 \r\nL 171.526061 128.399404 \r\nL 171.628793 129.841344 \r\nL 171.590744 127.871628 \r\nL 171.647817 129.372929 \r\nL 171.655427 128.909551 \r\nL 171.7125 132.981915 \r\nL 171.731525 132.435252 \r\nL 171.803817 131.9323 \r\nL 171.857086 133.574989 \r\nL 171.948403 133.911729 \r\nL 171.902744 132.982095 \r\nL 171.963622 133.538832 \r\nL 172.070159 132.408629 \r\nL 172.089183 133.109812 \r\nL 172.134842 132.041489 \r\nL 172.100598 133.538113 \r\nL 172.157671 133.490624 \r\nL 172.210939 136.930161 \r\nL 172.260403 133.344379 \r\nL 172.268012 132.188273 \r\nL 172.366939 133.688675 \r\nL 172.378354 134.116256 \r\nL 172.458256 133.123303 \r\nL 172.469671 133.270447 \r\nL 172.587622 132.798255 \r\nL 172.591427 132.824158 \r\nL 172.690354 131.225901 \r\nL 172.6485 133.282499 \r\nL 172.709378 132.212737 \r\nL 172.838744 137.91268 \r\nL 172.861574 137.315289 \r\nL 172.926256 138.763705 \r\nL 173.013769 138.181065 \r\nL 173.1165 137.388502 \r\nL 173.044208 138.312559 \r\nL 173.158354 137.848462 \r\nL 173.253476 139.831848 \r\nL 173.192598 137.058597 \r\nL 173.283915 139.041803 \r\nL 173.375232 140.296665 \r\nL 173.325769 137.837669 \r\nL 173.398061 139.317384 \r\nL 173.409476 139.091991 \r\nL 173.458939 139.929704 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121.469243 \r\nL 180.429477 125.653495 \r\nL 180.456111 120.121023 \r\nL 180.524599 128.884008 \r\nL 180.532208 128.431783 \r\nL 180.631135 130.960214 \r\nL 180.657769 130.255434 \r\nL 180.718647 129.495789 \r\nL 180.756696 130.382251 \r\nL 180.771916 130.103972 \r\nL 180.901282 128.150087 \r\nL 180.867038 130.346814 \r\nL 180.905086 128.306045 \r\nL 180.965964 129.612713 \r\nL 180.981184 124.895829 \r\nL 181.015428 126.099244 \r\nL 180.996403 120.96863 \r\nL 181.038257 123.746558 \r\nL 181.080111 119.613394 \r\nL 181.152404 122.646756 \r\nL 181.163818 123.480332 \r\nL 181.213282 122.373874 \r\nL 181.26655 122.906866 \r\nL 181.403525 124.806967 \r\nL 181.452989 124.317146 \r\nL 181.479623 125.341218 \r\nL 181.574745 114.771851 \r\nL 181.597574 112.670102 \r\nL 181.612794 114.800992 \r\nL 181.685086 113.76091 \r\nL 181.780208 116.500884 \r\nL 181.806843 115.898277 \r\nL 181.810647 115.736922 \r\nL 181.88294 117.291828 \r\nL 181.981867 119.997623 \r\nL 182.03133 118.191421 \r\nL 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108.902819 \r\nL 189.679136 105.6804 \r\nL 189.800892 106.342368 \r\nL 189.857965 109.953873 \r\nL 189.812307 105.94123 \r\nL 189.956892 109.657067 \r\nL 189.994941 108.798307 \r\nL 190.044404 110.898797 \r\nL 190.18138 115.818768 \r\nL 190.265087 114.056458 \r\nL 190.299331 111.213052 \r\nL 190.364014 116.35302 \r\nL 190.443917 121.494427 \r\nL 190.539038 121.307709 \r\nL 190.561868 121.069904 \r\nL 190.626551 122.438092 \r\nL 190.637965 121.868043 \r\nL 190.645575 121.854732 \r\nL 190.64938 121.985147 \r\nL 190.660795 122.243818 \r\nL 190.683624 120.721471 \r\nL 190.695038 120.821846 \r\nL 190.786356 117.970345 \r\nL 190.843429 119.275754 \r\nL 190.892892 120.352891 \r\nL 190.934746 118.779638 \r\nL 190.949965 117.946241 \r\nL 191.026063 121.281086 \r\nL 191.029868 121.299254 \r\nL 191.056502 120.913586 \r\nL 191.144014 120.114367 \r\nL 191.174453 120.478809 \r\nL 191.182063 120.329507 \r\nL 191.239136 121.129445 \r\nL 191.246746 120.816989 \r\nL 191.284795 122.934928 \r\nL 191.338063 120.739999 \r\nL 191.349478 120.787668 \r\nL 191.440795 119.219271 \r\nL 191.410356 121.487592 \r\nL 191.452209 121.185568 \r\nL 191.478843 122.921616 \r\nL 191.573965 122.356245 \r\nL 191.58538 122.588114 \r\nL 191.631039 121.27515 \r\nL 191.703331 120.183442 \r\nL 191.722356 121.537239 \r\nL 191.74138 121.297635 \r\nL 191.828892 121.897724 \r\nL 191.775624 121.289001 \r\nL 191.844112 121.372107 \r\nL 191.924014 120.898476 \r\nL 191.863136 121.900422 \r\nL 191.950648 121.177294 \r\nL 191.969673 122.689028 \r\nL 192.0686 121.711905 \r\nL 192.182746 121.137 \r\nL 192.205575 121.30627 \r\nL 192.327331 122.30264 \r\nL 192.338746 122.239861 \r\nL 192.342551 122.200467 \r\nL 192.36538 123.057068 \r\nL 192.567039 132.293325 \r\nL 192.635526 131.189025 \r\nL 192.734453 129.885595 \r\nL 192.768697 129.932004 \r\nL 192.776307 130.646858 \r\nL 192.871429 129.648869 \r\nL 192.879039 129.880738 \r\nL 192.955136 129.245752 \r\nL 192.913283 130.946723 \r\nL 192.985575 129.818139 \r\nL 193.122551 132.705616 \r\nL 193.133966 132.099951 \r\nL 193.1606 131.258639 \r\nL 193.217673 132.712451 \r\nL 193.248112 131.900641 \r\nL 193.312795 131.51803 \r\nL 193.339429 132.39244 \r\nL 193.350844 132.173882 \r\nL 193.396502 132.385424 \r\nL 193.404112 132.006412 \r\nL 193.423136 131.558684 \r\nL 193.487819 133.112151 \r\nL 193.514453 134.572978 \r\nL 193.598161 133.463102 \r\nL 193.704697 132.387223 \r\nL 193.640014 133.564376 \r\nL 193.719917 132.577 \r\nL 193.723722 132.571783 \r\nL 193.727527 132.715869 \r\nL 193.731331 132.641577 \r\nL 193.860697 133.601431 \r\nL 193.868307 133.668708 \r\nL 193.91777 132.861214 \r\nL 193.921575 132.857976 \r\nL 193.93299 132.956192 \r\nL 193.997673 133.090205 \r\nL 193.990063 132.705975 \r\nL 194.001478 132.98983 \r\nL 194.069966 130.155059 \r\nL 194.115624 131.816815 \r\nL 194.41621 124.565924 \r\nL 194.134649 131.934099 \r\nL 194.420014 124.729797 \r\nL 194.423819 125.028402 \r\nL 194.488502 122.520478 \r\nL 194.503722 122.697842 \r\nL 194.515136 122.530371 \r\nL 194.545575 123.818332 \r\nL 194.602649 122.982956 \r\nL 194.7206 125.974765 \r\nL 194.682551 122.028679 \r\nL 194.732014 125.850286 \r\nL 194.754844 125.12356 \r\nL 194.796697 126.525386 \r\nL 194.849966 125.344636 \r\nL 194.956502 126.434725 \r\nL 194.964112 125.89004 \r\nL 195.082063 123.096462 \r\nL 195.097283 123.581066 \r\nL 195.253283 127.681492 \r\nL 195.104893 123.396686 \r\nL 195.291332 126.409722 \r\nL 195.390258 123.266631 \r\nL 195.420697 123.592399 \r\nL 195.432112 124.022318 \r\nL 195.458746 123.124884 \r\nL 195.50821 123.334807 \r\nL 195.512015 123.341103 \r\nL 195.515819 123.109054 \r\nL 195.553868 124.145898 \r\nL 195.622356 123.283181 \r\nL 195.736502 121.8112 \r\nL 195.702258 123.393628 \r\nL 195.759332 122.737416 \r\nL 195.900112 125.685874 \r\nL 195.999039 125.045671 \r\nL 196.014258 124.77117 \r\nL 196.075137 125.542867 \r\nL 196.097966 125.24606 \r\nL 196.467039 133.519585 \r\nL 196.1246 124.750304 \r\nL 196.535527 132.535087 \r\nL 196.623039 132.118119 \r\nL 196.604015 132.947558 \r\nL 196.645868 132.415645 \r\nL 196.7486 133.61852 \r\nL 196.706746 131.766628 \r\nL 196.760015 132.758501 \r\nL 196.847527 131.148551 \r\nL 196.771429 133.0366 \r\nL 196.91982 131.283283 \r\nL 196.923624 131.567858 \r\nL 196.957868 129.557848 \r\nL 197.014941 130.123939 \r\nL 197.033966 130.875669 \r\nL 197.094844 129.876241 \r\nL 197.121478 129.95449 \r\nL 197.281283 127.762259 \r\nL 197.148112 130.164953 \r\nL 197.353576 128.672106 \r\nL 197.395429 132.137906 \r\nL 197.482942 131.202516 \r\nL 197.642746 122.249754 \r\nL 197.646551 122.8851 \r\nL 197.661771 126.605794 \r\nL 197.756893 124.079161 \r\nL 197.775917 124.607297 \r\nL 197.924307 128.556262 \r\nL 198.034649 131.204135 \r\nL 197.939527 128.460744 \r\nL 198.057478 130.426862 \r\nL 198.072698 129.990107 \r\nL 198.103137 132.326783 \r\nL 198.1526 130.974784 \r\nL 198.274356 132.448563 \r\nL 198.278161 132.194569 \r\nL 198.396112 131.070302 \r\nL 198.415137 131.158265 \r\nL 198.506454 132.475546 \r\nL 198.47982 130.735361 \r\nL 198.529283 131.567498 \r\nL 198.533088 131.568757 \r\nL 198.552112 130.749751 \r\nL 198.605381 132.087179 \r\nL 198.643429 131.386716 \r\nL 198.79182 133.339882 \r\nL 198.807039 133.945367 \r\nL 198.92499 135.687891 \r\nL 198.928795 135.564671 \r\nL 198.936405 135.853922 \r\nL 198.94021 135.837193 \r\nL 199.122844 141.419853 \r\nL 198.959234 135.212101 \r\nL 199.141868 141.175032 \r\nL 199.187527 140.904669 \r\nL 199.240795 141.888447 \r\nL 199.297869 141.271809 \r\nL 199.370161 142.482959 \r\nL 199.488112 141.404742 \r\nL 199.453869 142.827794 \r\nL 199.495722 141.866501 \r\nL 199.507137 143.622516 \r\nL 199.57943 141.548109 \r\nL 199.613673 142.899927 \r\nL 199.651722 141.399706 \r\nL 199.697381 142.951554 \r\nL 199.72782 142.700258 \r\nL 199.73543 142.642156 \r\nL 199.739234 142.889134 \r\nL 199.781088 144.112157 \r\nL 199.838161 142.80441 \r\nL 199.849576 142.839667 \r\nL 199.853381 142.838408 \r\nL 199.857186 142.904964 \r\nL 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111.444021 \r\nL 109.601666 115.307902 \r\nL 109.685373 111.789216 \r\nL 109.692983 111.468126 \r\nL 109.734836 110.056406 \r\nL 109.791909 112.603725 \r\nL 109.803324 112.921578 \r\nL 109.837568 111.094869 \r\nL 109.848983 111.209095 \r\nL 109.887031 111.21683 \r\nL 109.963129 108.893645 \r\nL 110.141958 102.268745 \r\nL 110.195227 104.348369 \r\nL 110.286544 114.547537 \r\nL 110.336007 113.016736 \r\nL 110.434934 112.404055 \r\nL 110.362641 113.125745 \r\nL 110.442544 112.661288 \r\nL 110.575714 116.185909 \r\nL 110.579519 115.823086 \r\nL 110.724105 110.737622 \r\nL 110.72791 110.871275 \r\nL 110.743129 110.442435 \r\nL 110.765958 112.576743 \r\nL 110.777373 115.094381 \r\nL 110.830641 111.196323 \r\nL 110.872495 112.189096 \r\nL 110.902934 112.919059 \r\nL 111.009471 111.562384 \r\nL 111.165471 114.948316 \r\nL 111.332885 103.469102 \r\nL 111.17308 115.08071 \r\nL 111.447032 107.902311 \r\nL 111.450836 108.214048 \r\nL 111.473666 106.89155 \r\nL 111.557373 107.9194 \r\nL 111.610641 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124.520594 93.110738 \r\nL 124.547228 95.549587 \r\nL 124.653764 94.423342 \r\nL 124.707033 92.350014 \r\nL 124.767911 93.976513 \r\nL 124.775521 94.188415 \r\nL 124.859228 93.248168 \r\nL 124.912496 90.957902 \r\nL 124.984789 92.274103 \r\nL 124.996203 91.858394 \r\nL 125.072301 97.468036 \r\nL 125.140789 101.154372 \r\nL 125.182643 98.410261 \r\nL 125.201667 96.691662 \r\nL 125.228301 99.984235 \r\nL 125.277764 106.214652 \r\nL 125.342447 102.332064 \r\nL 125.483228 104.256269 \r\nL 125.40713 102.094978 \r\nL 125.487033 104.252491 \r\nL 125.544106 102.258312 \r\nL 125.58596 104.466192 \r\nL 125.681082 106.75466 \r\nL 125.616399 101.101127 \r\nL 125.711521 105.583803 \r\nL 125.837082 102.925677 \r\nL 125.859911 103.474679 \r\nL 125.905569 105.395466 \r\nL 125.977862 104.48508 \r\nL 125.989277 104.233244 \r\nL 126.034935 105.746597 \r\nL 126.130057 107.447388 \r\nL 126.118643 105.626615 \r\nL 126.149082 106.116975 \r\nL 126.248008 103.584407 \r\nL 126.282252 105.142011 \r\nL 126.316496 107.537329 \r\nL 126.404008 106.144677 \r\nL 126.407813 105.147048 \r\nL 126.51435 105.567794 \r\nL 126.540984 104.619632 \r\nL 126.628496 105.3129 \r\nL 126.636106 105.629673 \r\nL 126.723618 104.471948 \r\nL 126.727423 104.581497 \r\nL 126.750252 103.578471 \r\nL 126.773082 102.853004 \r\nL 126.83396 105.085888 \r\nL 126.879618 110.378037 \r\nL 126.970935 109.40667 \r\nL 127.008984 109.916638 \r\nL 127.039423 109.043667 \r\nL 127.050838 109.219052 \r\nL 127.115521 107.268225 \r\nL 127.168789 107.70552 \r\nL 127.275326 108.583347 \r\nL 127.233472 106.455874 \r\nL 127.28674 108.052693 \r\nL 127.298155 107.450446 \r\nL 127.355228 109.844325 \r\nL 127.359033 109.794137 \r\nL 127.381862 110.180705 \r\nL 127.438935 108.733909 \r\nL 127.45796 108.817914 \r\nL 127.560692 109.235602 \r\nL 127.530252 108.754236 \r\nL 127.572106 108.976031 \r\nL 127.640594 108.436923 \r\nL 127.682448 108.926383 \r\nL 127.773765 111.601599 \r\nL 127.804204 110.09706 \r\nL 127.808009 110.095261 \r\nL 127.922155 112.624052 \r\nL 127.830838 109.638899 \r\nL 127.937374 111.633078 \r\nL 128.013472 108.519489 \r\nL 128.062935 110.003521 \r\nL 128.078155 110.655775 \r\nL 128.146643 109.611556 \r\nL 128.154253 109.627926 \r\nL 128.177082 109.747548 \r\nL 128.306448 108.543593 \r\nL 128.382545 111.01662 \r\nL 128.428204 109.501109 \r\nL 128.54996 105.154963 \r\nL 128.473862 110.726469 \r\nL 128.568984 105.447272 \r\nL 128.671716 107.01531 \r\nL 128.694545 106.291462 \r\nL 128.793472 105.668888 \r\nL 128.70596 106.469366 \r\nL 128.808692 106.163925 \r\nL 129.01035 114.986631 \r\nL 128.839131 106.114277 \r\nL 129.021765 111.024355 \r\nL 129.036984 108.08885 \r\nL 129.105472 118.992798 \r\nL 129.135911 119.852818 \r\nL 129.185375 117.330503 \r\nL 129.212009 118.721176 \r\nL 129.219618 118.779458 \r\nL 129.234838 118.259777 \r\nL 129.32996 115.548585 \r\nL 129.360399 115.80132 \r\nL 129.725667 101.744027 \r\nL 129.432692 116.100465 \r\nL 129.763716 102.762163 \r\nL 129.767521 102.776734 \r\nL 129.775131 102.370199 \r\nL 129.778936 102.46104 \r\nL 129.893082 100.557881 \r\nL 129.915911 101.311409 \r\nL 129.984399 104.700939 \r\nL 130.033862 102.24518 \r\nL 130.174643 98.020815 \r\nL 130.068106 102.571668 \r\nL 130.178448 98.248907 \r\nL 130.292594 100.078853 \r\nL 130.304009 99.978299 \r\nL 130.448594 99.151558 \r\nL 130.376302 100.60573 \r\nL 130.452399 99.203724 \r\nL 130.471423 99.526793 \r\nL 130.528497 98.010382 \r\nL 130.539911 97.295888 \r\nL 130.623619 99.451422 \r\nL 130.699716 101.523851 \r\nL 130.745375 100.622639 \r\nL 130.825277 98.830647 \r\nL 130.867131 98.906378 \r\nL 130.920399 99.304278 \r\nL 130.935619 98.055533 \r\nL 130.947033 98.245849 \r\nL 131.042155 97.088663 \r\nL 130.977472 98.446598 \r\nL 131.04596 97.874211 \r\nL 131.144887 100.044855 \r\nL 131.160106 99.250134 \r\nL 131.171521 98.392453 \r\nL 131.220985 102.340698 \r\nL 131.259033 100.683079 \r\nL 131.319911 101.409086 \r\nL 131.323716 97.106292 \r\nL 131.445472 90.204553 \r\nL 131.453082 90.718658 \r\nL 131.50635 88.745344 \r\nL 131.612887 87.613882 \r\nL 131.593863 89.080646 \r\nL 131.616692 88.523369 \r\nL 131.681375 92.743957 \r\nL 131.628106 88.302113 \r\nL 131.738448 90.625838 \r\nL 131.746058 90.395228 \r\nL 131.803131 93.117213 \r\nL 131.83357 97.840573 \r\nL 131.924887 97.191556 \r\nL 131.99718 96.72566 \r\nL 131.955326 97.269445 \r\nL 132.016204 97.264049 \r\nL 132.122741 98.497684 \r\nL 132.065668 97.219258 \r\nL 132.13035 98.239913 \r\nL 132.297765 92.428622 \r\nL 132.335814 95.239829 \r\nL 132.339619 95.826786 \r\nL 132.358643 92.231651 \r\nL 132.44235 95.000045 \r\nL 132.495619 96.063692 \r\nL 132.453765 94.999326 \r\nL 132.571716 95.404961 \r\nL 132.586936 95.289117 \r\nL 132.59835 95.892443 \r\nL 132.602155 95.875894 \r\nL 132.704887 97.907669 \r\nL 132.727716 97.024445 \r\nL 132.803814 96.310671 \r\nL 132.765765 97.606186 \r\nL 132.830448 97.264229 \r\nL 132.898936 99.814785 \r\nL 132.841863 96.789878 \r\nL 133.009277 99.006932 \r\nL 133.134838 98.186667 \r\nL 133.146253 98.247108 \r\nL 133.283229 102.036696 \r\nL 133.363131 100.515968 \r\nL 133.40118 99.460417 \r\nL 133.446838 102.896716 \r\nL 133.469668 103.471441 \r\nL 133.503912 101.86329 \r\nL 133.54957 102.544685 \r\nL 133.55718 102.339259 \r\nL 133.583814 103.516951 \r\nL 133.644692 102.96705 \r\nL 133.659912 103.512634 \r\nL 133.793082 106.349924 \r\nL 133.800692 106.224905 \r\nL 133.87679 104.650572 \r\nL 133.899619 106.313767 \r\nL 133.941473 105.517786 \r\nL 134.070838 109.561009 \r\nL 134.101277 109.012727 \r\nL 134.105082 108.921886 \r\nL 134.127912 109.739993 \r\nL 134.196399 109.328241 \r\nL 134.283912 110.390628 \r\nL 134.32957 109.832812 \r\nL 134.333375 109.479882 \r\nL 134.398058 112.605704 \r\nL 134.405668 112.552998 \r\nL 134.447521 114.735695 \r\nL 134.512204 112.061559 \r\nL 134.516009 112.082245 \r\nL 134.519814 111.749102 \r\nL 134.573082 110.571231 \r\nL 134.63396 111.366132 \r\nL 134.782351 113.59362 \r\nL 134.816595 112.917441 \r\nL 134.900302 113.576171 \r\nL 134.828009 112.824081 \r\nL 134.907912 113.552606 \r\nL 134.934546 115.265809 \r\nL 134.980204 113.005223 \r\nL 135.010643 113.325055 \r\nL 135.033473 111.84534 \r\nL 135.090546 114.905504 \r\nL 135.094351 114.690904 \r\nL 135.200887 115.792685 \r\nL 135.128595 114.503466 \r\nL 135.212302 115.645002 \r\nL 135.318839 115.067039 \r\nL 135.231326 116.453035 \r\nL 135.334058 115.224076 \r\nL 135.512887 119.001613 \r\nL 135.425375 115.00318 \r\nL 135.524302 118.272728 \r\nL 135.535717 116.630399 \r\nL 135.6004 120.517844 \r\nL 135.611814 120.259532 \r\nL 135.623229 119.629223 \r\nL 135.634643 122.422082 \r\nL 135.638448 122.417585 \r\nL 135.699326 123.744939 \r\nL 135.653668 122.138227 \r\nL 135.775424 122.831855 \r\nL 135.89718 121.712445 \r\nL 135.908595 122.024002 \r\nL 135.939034 122.759542 \r\nL 135.988497 121.331633 \r\nL 135.999912 121.45935 \r\nL 136.121668 120.01741 \r\nL 136.026546 124.460333 \r\nL 136.125473 120.080189 \r\nL 136.171131 120.479889 \r\nL 136.186351 118.452611 \r\nL 136.270058 113.822071 \r\nL 136.315717 114.848841 \r\nL 136.319522 115.112549 \r\nL 136.35757 113.719537 \r\nL 136.410839 113.968855 \r\nL 136.452692 113.121967 \r\nL 136.517375 113.723495 \r\nL 136.696204 121.251585 \r\nL 136.700009 121.125128 \r\nL 136.711424 119.332777 \r\nL 136.776107 122.599806 \r\nL 136.802741 122.553576 \r\nL 136.886448 126.001208 \r\nL 136.939717 124.578336 \r\nL 137.095717 121.529684 \r\nL 137.114741 125.570929 \r\nL 137.217473 124.133126 \r\nL 137.369668 119.071047 \r\nL 137.426741 119.83375 \r\nL 137.434351 119.942759 \r\nL 137.468595 119.806228 \r\nL 137.594156 121.464567 \r\nL 137.609375 121.243671 \r\nL 137.624595 120.880667 \r\nL 137.636009 122.098473 \r\nL 137.696888 121.681865 \r\nL 137.772985 127.130691 \r\nL 137.909961 119.256327 \r\nL 137.913766 119.498988 \r\nL 137.993668 120.654015 \r\nL 137.9404 119.232942 \r\nL 138.020302 119.237439 \r\nL 138.07357 116.070244 \r\nL 138.153473 117.813667 \r\nL 138.199131 119.138144 \r\nL 138.23718 116.898065 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139.80479 114.582615 \r\nL 139.8124 114.430254 \r\nL 139.816205 114.612115 \r\nL 139.846644 110.836378 \r\nL 139.850449 110.973628 \r\nL 139.858058 110.875952 \r\nL 139.873278 114.043686 \r\nL 139.880888 114.98843 \r\nL 139.95318 111.039825 \r\nL 139.96079 111.344366 \r\nL 139.972205 109.921494 \r\nL 140.055912 114.885537 \r\nL 140.204302 113.076277 \r\nL 140.230937 113.403484 \r\nL 140.268985 113.260477 \r\nL 140.242351 113.785554 \r\nL 140.299424 113.60873 \r\nL 140.329863 113.993859 \r\nL 140.371717 112.098794 \r\nL 140.386937 112.334801 \r\nL 140.394546 111.848578 \r\nL 140.44401 113.573293 \r\nL 140.478254 113.524725 \r\nL 140.501083 115.493541 \r\nL 140.561961 111.830949 \r\nL 140.580985 112.995869 \r\nL 140.630449 112.174885 \r\nL 140.676107 114.134347 \r\nL 140.687522 114.369094 \r\nL 140.73318 113.624919 \r\nL 140.790254 111.969279 \r\nL 140.851132 112.914203 \r\nL 140.873961 112.867253 \r\nL 140.877766 113.052712 \r\nL 140.881571 113.098942 \r\nL 140.9044 112.139988 \r\nL 141.018546 109.439049 \r\nL 140.953863 112.16661 \r\nL 141.04518 110.392427 \r\nL 141.056595 110.923981 \r\nL 141.0604 110.877931 \r\nL 141.147912 113.022132 \r\nL 141.083229 110.428943 \r\nL 141.174546 111.524249 \r\nL 141.212595 109.28363 \r\nL 141.330546 110.183943 \r\nL 141.341961 110.582383 \r\nL 141.368595 109.40631 \r\nL 141.444693 110.567453 \r\nL 141.459912 111.2922 \r\nL 141.478937 111.228522 \r\nL 141.482741 111.714745 \r\nL 141.532205 109.102309 \r\nL 141.574059 109.66732 \r\nL 141.707229 107.478148 \r\nL 141.646351 111.490071 \r\nL 141.711034 107.481745 \r\nL 141.718644 107.912385 \r\nL 141.749083 104.707774 \r\nL 141.802351 105.591898 \r\nL 141.84801 105.032643 \r\nL 141.878449 106.15655 \r\nL 141.886059 105.964075 \r\nL 141.9964 107.822623 \r\nL 142.015424 107.714514 \r\nL 142.106742 104.156794 \r\nL 142.137181 105.996094 \r\nL 142.247522 109.101949 \r\nL 142.205668 105.811894 \r\nL 142.274156 107.953758 \r\nL 142.277961 107.953758 \r\nL 142.293181 108.734448 \r\nL 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147.091132 122.298503 \r\nL 147.125376 122.558253 \r\nL 147.228108 118.985963 \r\nL 147.243327 119.907502 \r\nL 147.258547 121.193483 \r\nL 147.361279 120.725429 \r\nL 147.505864 121.646428 \r\nL 147.513474 121.33721 \r\nL 147.600986 118.992978 \r\nL 147.63523 119.362278 \r\nL 147.779815 114.028936 \r\nL 147.79884 114.422519 \r\nL 147.912986 115.31078 \r\nL 147.852108 113.668271 \r\nL 147.916791 115.216341 \r\nL 147.924401 114.678492 \r\nL 148.015718 116.580032 \r\nL 148.061376 114.489255 \r\nL 148.099425 116.914614 \r\nL 148.179327 119.387821 \r\nL 148.221181 118.733588 \r\nL 148.270645 119.265681 \r\nL 148.232596 118.278664 \r\nL 148.339132 118.880372 \r\nL 148.491327 115.789807 \r\nL 148.358157 119.003771 \r\nL 148.506547 116.130325 \r\nL 148.540791 116.683465 \r\nL 148.597864 115.998111 \r\nL 148.613084 115.198173 \r\nL 148.700596 116.648567 \r\nL 148.71962 118.431025 \r\nL 148.753864 113.280264 \r\nL 148.776693 113.728172 \r\nL 148.780498 113.622401 \r\nL 148.799523 116.054595 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144.92001 \r\nL 164.875134 143.492101 \r\nL 164.932207 144.032649 \r\nL 164.985475 142.599703 \r\nL 165.046353 143.452887 \r\nL 165.092012 144.250846 \r\nL 165.076792 143.370321 \r\nL 165.099622 144.026892 \r\nL 165.149085 139.103143 \r\nL 165.213768 140.270942 \r\nL 165.29367 142.46587 \r\nL 165.362158 141.829266 \r\nL 165.46489 140.375814 \r\nL 165.396402 142.185074 \r\nL 165.495329 140.480145 \r\nL 165.563817 139.587208 \r\nL 165.601866 140.552638 \r\nL 165.651329 141.571314 \r\nL 165.700792 139.92107 \r\nL 165.811134 136.454191 \r\nL 165.849183 136.545032 \r\nL 165.860597 136.674008 \r\nL 165.92528 136.313343 \r\nL 165.986158 135.563772 \r\nL 165.9405 136.763769 \r\nL 166.035622 136.132381 \r\nL 166.062256 137.430234 \r\nL 166.111719 133.704324 \r\nL 166.225866 130.358506 \r\nL 166.233475 131.143514 \r\nL 166.271524 134.007067 \r\nL 166.313378 131.052494 \r\nL 166.343817 131.271591 \r\nL 166.347622 131.124267 \r\nL 166.427524 132.127473 \r\nL 166.431329 132.332179 \r\nL 166.492207 131.507417 \r\nL 166.526451 131.5639 \r\nL 166.693866 128.552664 \r\nL 166.606353 131.862146 \r\nL 166.70528 128.622818 \r\nL 166.762353 130.378293 \r\nL 166.792792 127.53129 \r\nL 166.796597 127.9148 \r\nL 166.8765 125.379533 \r\nL 166.880305 129.20294 \r\nL 166.899329 135.373636 \r\nL 166.994451 134.088734 \r\nL 166.998256 134.116436 \r\nL 167.02489 133.49584 \r\nL 167.047719 132.942161 \r\nL 167.070549 133.883847 \r\nL 167.139036 133.324052 \r\nL 167.165671 135.049847 \r\nL 167.203719 132.089338 \r\nL 167.253183 134.136942 \r\nL 167.283622 132.9141 \r\nL 167.333085 137.03845 \r\nL 167.367329 136.886089 \r\nL 167.473866 138.284497 \r\nL 167.481475 138.385591 \r\nL 167.530939 137.972221 \r\nL 167.561378 137.984813 \r\nL 167.572793 137.727221 \r\nL 167.6565 138.267768 \r\nL 167.782061 141.103799 \r\nL 167.789671 140.67208 \r\nL 167.793476 140.646357 \r\nL 167.801085 141.098762 \r\nL 167.8125 142.090635 \r\nL 167.869573 139.350482 \r\nL 167.979915 134.642053 \r\nL 167.991329 134.757897 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134.116256 \r\nL 172.458256 133.123303 \r\nL 172.469671 133.270447 \r\nL 172.587622 132.798255 \r\nL 172.591427 132.824158 \r\nL 172.690354 131.225901 \r\nL 172.6485 133.282499 \r\nL 172.709378 132.212737 \r\nL 172.838744 137.91268 \r\nL 172.861574 137.315289 \r\nL 172.926256 138.763705 \r\nL 173.013769 138.181065 \r\nL 173.1165 137.388502 \r\nL 173.044208 138.312559 \r\nL 173.158354 137.848462 \r\nL 173.253476 139.831848 \r\nL 173.192598 137.058597 \r\nL 173.283915 139.041803 \r\nL 173.375232 140.296665 \r\nL 173.325769 137.837669 \r\nL 173.398061 139.317384 \r\nL 173.409476 139.091991 \r\nL 173.458939 139.929704 \r\nL 173.496988 140.708596 \r\nL 173.542647 139.432509 \r\nL 173.565476 139.732913 \r\nL 173.706257 141.430286 \r\nL 173.611135 139.32296 \r\nL 173.74811 140.669382 \r\nL 173.831817 140.987055 \r\nL 173.869866 139.56814 \r\nL 173.881281 139.911177 \r\nL 173.923135 139.055295 \r\nL 173.934549 138.324611 \r\nL 174.029671 139.192185 \r\nL 174.0525 139.4147 \r\nL 174.117183 138.621957 \r\nL 174.273183 141.751376 \r\nL 174.326452 142.546098 \r\nL 174.307427 141.002885 \r\nL 174.391135 142.280052 \r\nL 174.474842 138.148506 \r\nL 174.398744 142.299839 \r\nL 174.604208 139.943915 \r\nL 174.611818 140.045369 \r\nL 174.661281 139.459491 \r\nL 174.703135 139.764752 \r\nL 174.722159 139.463089 \r\nL 174.775427 140.265905 \r\nL 174.85533 141.491626 \r\nL 174.874354 140.07451 \r\nL 174.878159 140.178302 \r\nL 174.961866 137.972401 \r\nL 174.999915 139.484855 \r\nL 175.026549 141.188164 \r\nL 175.136891 141.095704 \r\nL 175.140696 141.095164 \r\nL 175.144501 141.188703 \r\nL 175.197769 140.762381 \r\nL 175.262452 141.621861 \r\nL 175.296696 141.479394 \r\nL 175.353769 142.070129 \r\nL 175.368988 141.647044 \r\nL 175.448891 144.282326 \r\nL 175.498354 143.57053 \r\nL 175.502159 143.566033 \r\nL 175.505964 143.614062 \r\nL 175.650549 136.741644 \r\nL 175.532598 147.070148 \r\nL 175.665769 137.139004 \r\nL 175.677184 137.305036 \r\nL 175.719037 136.672209 \r\nL 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177.309476 139.788856 \r\nL 177.320891 139.49187 \r\nL 177.431232 138.594795 \r\nL 177.34372 140.046448 \r\nL 177.446452 138.902574 \r\nL 177.632891 141.499181 \r\nL 177.648111 140.790983 \r\nL 177.724208 142.35884 \r\nL 177.766062 144.779522 \r\nL 177.739428 142.008609 \r\nL 177.864989 144.562583 \r\nL 177.998159 142.938782 \r\nL 177.891623 144.576254 \r\nL 178.013379 143.464759 \r\nL 178.13133 144.939437 \r\nL 178.074257 143.23325 \r\nL 178.150354 144.108739 \r\nL 178.188403 143.155181 \r\nL 178.169379 144.162884 \r\nL 178.256891 144.161804 \r\nL 178.275915 146.113891 \r\nL 178.359623 143.473573 \r\nL 178.454745 144.846438 \r\nL 178.492794 144.30607 \r\nL 178.553672 143.608126 \r\nL 178.508013 144.573376 \r\nL 178.59172 144.212531 \r\nL 178.618355 145.089999 \r\nL 178.679233 143.497678 \r\nL 178.683037 143.614242 \r\nL 178.804794 140.560913 \r\nL 178.808598 140.720289 \r\nL 178.812403 140.823542 \r\nL 178.839037 138.766943 \r\nL 178.842842 138.23521 \r\nL 178.854257 142.319626 \r\nL 178.949379 138.662971 \r\nL 179.132013 135.908067 \r\nL 178.998842 139.740648 \r\nL 179.135818 136.187605 \r\nL 179.21572 139.44528 \r\nL 179.284208 138.208587 \r\nL 179.497281 125.466956 \r\nL 179.626647 126.620724 \r\nL 179.657086 127.180159 \r\nL 179.710355 125.989875 \r\nL 179.729379 126.600037 \r\nL 179.775038 123.572252 \r\nL 179.820696 127.471389 \r\nL 179.832111 126.972215 \r\nL 179.904403 129.276872 \r\nL 179.870159 125.181303 \r\nL 179.972891 128.749636 \r\nL 180.022355 124.929287 \r\nL 180.106062 126.204835 \r\nL 180.300111 123.701408 \r\nL 180.125086 126.379321 \r\nL 180.307721 124.371471 \r\nL 180.368599 121.469243 \r\nL 180.429477 125.653495 \r\nL 180.456111 120.121023 \r\nL 180.524599 128.884008 \r\nL 180.532208 128.431783 \r\nL 180.631135 130.960214 \r\nL 180.657769 130.255434 \r\nL 180.718647 129.495789 \r\nL 180.756696 130.382251 \r\nL 180.771916 130.103972 \r\nL 180.901282 128.150087 \r\nL 180.867038 130.346814 \r\nL 180.905086 128.306045 \r\nL 180.965964 129.612713 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135.212101 \r\nL 199.141868 141.175032 \r\nL 199.187527 140.904669 \r\nL 199.240795 141.888447 \r\nL 199.297869 141.271809 \r\nL 199.370161 142.482959 \r\nL 199.488112 141.404742 \r\nL 199.453869 142.827794 \r\nL 199.495722 141.866501 \r\nL 199.507137 143.622516 \r\nL 199.57943 141.548109 \r\nL 199.613673 142.899927 \r\nL 199.651722 141.399706 \r\nL 199.697381 142.951554 \r\nL 199.72782 142.700258 \r\nL 199.73543 142.642156 \r\nL 199.739234 142.889134 \r\nL 199.781088 144.112157 \r\nL 199.838161 142.80441 \r\nL 199.849576 142.839667 \r\nL 199.853381 142.838408 \r\nL 199.857186 142.904964 \r\nL 199.872405 142.863951 \r\nL 199.997966 144.013221 \r\nL 200.0246 143.765703 \r\nL 200.04743 144.467245 \r\nL 200.100698 145.085502 \r\nL 200.138747 142.817541 \r\nL 200.161576 147.625805 \r\nL 200.176795 140.679276 \r\nL 200.1806 141.077536 \r\nL 200.199625 137.377169 \r\nL 200.271917 144.282866 \r\nL 200.275722 143.828662 \r\nL 200.302356 141.974431 \r\nL 200.405088 142.262783 \r\nL 200.50782 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286.007242 144.432348 \r\nL 286.106169 142.854057 \r\nL 286.227925 145.123997 \r\nL 286.23934 143.940189 \r\nL 286.243144 143.946845 \r\nL 286.364901 134.965662 \r\nL 286.391535 137.497331 \r\nL 286.635047 127.702538 \r\nL 286.714949 128.943909 \r\nL 286.733974 130.451686 \r\nL 286.779632 125.057184 \r\nL 286.928023 112.210502 \r\nL 286.939437 117.256031 \r\nL 287.01934 119.559969 \r\nL 286.973681 115.721092 \r\nL 287.042169 116.713505 \r\nL 287.182949 98.659399 \r\nL 287.084023 120.178945 \r\nL 287.236218 103.01238 \r\nL 287.266657 105.222239 \r\nL 287.297096 99.018445 \r\nL 287.304705 95.043217 \r\nL 287.346559 107.507289 \r\nL 287.403632 100.674625 \r\nL 287.441681 93.841601 \r\nL 287.415047 102.218198 \r\nL 287.532998 94.76278 \r\nL 287.540608 93.962122 \r\nL 287.552023 98.025852 \r\nL 287.605291 96.83359 \r\nL 287.814559 112.045909 \r\nL 287.647145 96.429753 \r\nL 287.852608 111.015181 \r\nL 287.875437 106.047901 \r\nL 287.924901 112.830197 \r\nL 287.974364 107.132413 \r\nL 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289.526754 133.501057 \r\nL 289.800706 148.786588 \r\nL 289.842559 136.545392 \r\nL 289.911047 150.050804 \r\nL 290.059437 130.435316 \r\nL 290.082267 135.276679 \r\nL 290.192608 147.502226 \r\nL 290.204023 144.876478 \r\nL 290.314364 137.09979 \r\nL 290.344803 137.926351 \r\nL 290.363828 137.724882 \r\nL 290.375242 138.58724 \r\nL 290.390462 140.134951 \r\nL 290.45895 136.518229 \r\nL 290.474169 137.32932 \r\nL 290.500803 136.664114 \r\nL 290.538852 138.711899 \r\nL 290.546462 138.608466 \r\nL 290.740511 169.305087 \r\nL 290.801389 167.860989 \r\nL 290.92695 161.425326 \r\nL 290.930755 161.794625 \r\nL 290.968803 165.269599 \r\nL 291.018267 161.637228 \r\nL 291.07534 158.265147 \r\nL 291.105779 163.241781 \r\nL 291.117194 162.908459 \r\nL 291.181877 162.616689 \r\nL 291.242755 173.138207 \r\nL 291.334072 192.263515 \r\nL 291.39495 181.82924 \r\nL 291.482462 169.80732 \r\nL 291.516706 178.344192 \r\nL 291.569974 181.5837 \r\nL 291.52812 177.706868 \r\nL 291.627047 179.194138 \r\nL 291.646072 174.603892 \r\nL 291.676511 187.895064 \r\nL 291.68412 184.753053 \r\nL 291.69173 183.619972 \r\nL 291.737389 193.9733 \r\nL 291.881974 213.729456 \r\nL 291.798267 193.927969 \r\nL 291.889584 211.105327 \r\nL 291.965681 210.404864 \r\nL 291.916218 214.066556 \r\nL 291.977096 212.953442 \r\nL 292.045584 216.382006 \r\nL 292.076023 210.692856 \r\nL 292.193974 198.95659 \r\nL 292.197779 199.849887 \r\nL 292.300511 208.111899 \r\nL 292.220608 193.092954 \r\nL 292.32334 205.690498 \r\nL 292.33095 203.042445 \r\nL 292.414657 210.591042 \r\nL 292.48695 218.002929 \r\nL 292.540218 215.610669 \r\nL 292.677194 204.69143 \r\nL 292.680999 204.742876 \r\nL 292.711438 200.875938 \r\nL 292.749486 209.873131 \r\nL 292.802755 213.623325 \r\nL 292.768511 208.957168 \r\nL 292.836999 212.75665 \r\nL 292.901682 193.545179 \r\nL 292.95495 202.123784 \r\nL 292.970169 199.166693 \r\nL 293.027243 205.544074 \r\nL 293.038657 205.362392 \r\nL 293.046267 206.019684 \r\nL 293.10334 200.79625 \r\nL 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193.182535 \r\nL 298.384511 196.531232 \r\nL 298.475828 194.840874 \r\nL 298.567145 196.567928 \r\nL 298.58617 194.978124 \r\nL 298.917194 176.879947 \r\nL 298.616609 195.564902 \r\nL 298.981877 179.739902 \r\nL 299.092219 180.906082 \r\nL 299.027536 178.604483 \r\nL 299.107438 180.481019 \r\nL 299.130267 179.515409 \r\nL 299.149292 181.143887 \r\nL 299.229194 185.335693 \r\nL 299.267243 183.549817 \r\nL 299.274853 182.391013 \r\nL 299.286267 188.173162 \r\nL 299.36617 184.516867 \r\nL 299.449877 181.793623 \r\nL 299.408024 184.78777 \r\nL 299.480316 183.77611 \r\nL 299.491731 183.344391 \r\nL 299.605877 186.538029 \r\nL 299.609682 186.71953 \r\nL 299.689585 185.352782 \r\nL 299.708609 183.635442 \r\nL 299.750463 185.896027 \r\nL 299.811341 184.823207 \r\nL 299.815146 184.738662 \r\nL 299.82656 186.635345 \r\nL 299.830365 187.340126 \r\nL 299.910268 182.911054 \r\nL 300.035829 173.93149 \r\nL 299.955926 186.334402 \r\nL 300.058658 175.110981 \r\nL 300.089097 172.667454 \r\nL 300.123341 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104.348369 \r\nL 110.286544 114.547537 \r\nL 110.336007 113.016736 \r\nL 110.434934 112.404055 \r\nL 110.362641 113.125745 \r\nL 110.442544 112.661288 \r\nL 110.575714 116.185909 \r\nL 110.579519 115.823086 \r\nL 110.724105 110.737622 \r\nL 110.72791 110.871275 \r\nL 110.743129 110.442435 \r\nL 110.765958 112.576743 \r\nL 110.777373 115.094381 \r\nL 110.830641 111.196323 \r\nL 110.872495 112.189096 \r\nL 110.902934 112.919059 \r\nL 111.009471 111.562384 \r\nL 111.165471 114.948316 \r\nL 111.332885 103.469102 \r\nL 111.17308 115.08071 \r\nL 111.447032 107.902311 \r\nL 111.450836 108.214048 \r\nL 111.473666 106.89155 \r\nL 111.557373 107.9194 \r\nL 111.610641 107.184219 \r\nL 111.652495 108.04226 \r\nL 111.694349 109.247294 \r\nL 111.728593 107.347193 \r\nL 111.766641 108.604573 \r\nL 111.770446 108.596838 \r\nL 111.774251 109.158072 \r\nL 111.838934 105.436479 \r\nL 111.861763 106.242174 \r\nL 111.873178 106.685405 \r\nL 111.983519 101.165525 \r\nL 112.044397 105.244185 \r\nL 112.101471 102.825662 \r\nL 112.128105 98.278587 \r\nL 112.230837 99.036793 \r\nL 112.322154 103.201257 \r\nL 112.253666 98.862666 \r\nL 112.367812 100.933656 \r\nL 112.375422 100.435201 \r\nL 112.413471 104.018104 \r\nL 112.4363 103.076418 \r\nL 112.542837 111.162685 \r\nL 112.558056 110.993955 \r\nL 112.59991 111.273852 \r\nL 112.645568 110.474814 \r\nL 112.892885 104.781526 \r\nL 112.91191 105.383774 \r\nL 113.022251 117.432676 \r\nL 113.056495 116.052256 \r\nL 113.071715 116.599459 \r\nL 113.086934 115.438137 \r\nL 113.163032 115.858163 \r\nL 113.227715 114.180757 \r\nL 113.311422 114.631363 \r\nL 113.360885 116.034268 \r\nL 113.414154 113.955544 \r\nL 113.456007 114.618231 \r\nL 113.459812 112.909526 \r\nL 113.5283 108.222682 \r\nL 113.577763 109.539244 \r\nL 113.585373 107.374176 \r\nL 113.608202 109.714629 \r\nL 113.69191 108.332051 \r\nL 113.703324 107.926955 \r\nL 113.85552 109.302158 \r\nL 113.912593 106.072544 \r\nL 113.9963 107.068555 \r\nL 114.076203 113.548829 \r\nL 114.15991 109.63692 \r\nL 114.175129 109.228226 \r\nL 114.236007 110.224417 \r\nL 114.239812 110.52662 \r\nL 114.270251 109.737294 \r\nL 114.342544 110.187361 \r\nL 114.380593 109.944519 \r\nL 114.414837 110.89358 \r\nL 114.4643 114.893452 \r\nL 114.586056 122.996627 \r\nL 114.589861 122.703419 \r\nL 114.597471 122.967127 \r\nL 114.62791 120.766802 \r\nL 114.734446 118.123246 \r\nL 114.749666 118.65318 \r\nL 114.890446 117.293267 \r\nL 114.772495 118.824248 \r\nL 114.898056 117.362522 \r\nL 115.133959 111.928805 \r\nL 114.955129 118.015676 \r\nL 115.172007 113.625279 \r\nL 115.175812 114.032713 \r\nL 115.25191 110.654336 \r\nL 115.255715 110.798782 \r\nL 115.25952 110.768922 \r\nL 115.270934 111.412362 \r\nL 115.308983 112.233527 \r\nL 115.366056 111.36955 \r\nL 115.388886 112.127216 \r\nL 115.40791 110.537952 \r\nL 115.484008 112.282455 \r\nL 115.525861 113.129162 \r\nL 115.5563 109.316909 \r\nL 115.628593 104.543182 \r\nL 115.674251 107.614139 \r\nL 115.681861 106.943536 \r\nL 115.735129 109.705095 \r\nL 115.769373 109.071009 \r\nL 115.780788 110.683657 \r\nL 115.803617 108.439981 \r\nL 115.87591 109.230385 \r\nL 116.012886 105.857225 \r\nL 116.0243 106.487174 \r\nL 116.028105 106.877699 \r\nL 116.108008 104.126753 \r\nL 116.165081 102.795801 \r\nL 116.142251 105.630753 \r\nL 116.218349 103.892186 \r\nL 116.222154 104.000476 \r\nL 116.244983 101.764534 \r\nL 116.264008 98.982468 \r\nL 116.347715 102.349692 \r\nL 116.397178 108.462646 \r\nL 116.465666 106.651947 \r\nL 116.480886 106.52477 \r\nL 116.4923 107.262648 \r\nL 116.556983 106.770849 \r\nL 116.560788 106.930045 \r\nL 116.614056 104.842147 \r\nL 116.697764 92.38689 \r\nL 116.754837 95.935615 \r\nL 116.830934 101.894769 \r\nL 116.773861 95.234433 \r\nL 116.857569 96.289984 \r\nL 116.903227 91.989529 \r\nL 116.9603 96.89439 \r\nL 116.964105 96.87856 \r\nL 116.97552 97.004298 \r\nL 117.005959 95.517028 \r\nL 117.009764 95.1614 \r\nL 117.093471 96.77135 \r\nL 117.097276 96.688424 \r\nL 117.12391 97.481887 \r\nL 117.180983 94.33376 \r\nL 117.196203 93.482915 \r\nL 117.226642 100.089466 \r\nL 117.238056 100.087847 \r\nL 117.363617 105.306064 \r\nL 117.367422 104.992169 \r\nL 117.378837 102.440893 \r\nL 117.439715 109.29964 \r\nL 117.466349 108.97675 \r\nL 117.485374 110.224777 \r\nL 117.569081 107.711636 \r\nL 117.683227 105.447632 \r\nL 117.702252 106.269696 \r\nL 117.7403 106.863309 \r\nL 117.759325 104.547499 \r\nL 117.831617 104.96033 \r\nL 117.884886 101.12595 \r\nL 117.892496 99.628247 \r\nL 117.915325 102.820985 \r\nL 117.976203 101.304034 \r\nL 118.124593 106.338231 \r\nL 118.280593 102.596132 \r\nL 118.181666 107.295747 \r\nL 118.292008 104.042388 \r\nL 118.360496 111.865846 \r\nL 118.425179 110.660452 \r\nL 118.482252 108.476137 \r\nL 118.550739 106.009765 \r\nL 118.607813 106.882916 \r\nL 118.623032 107.341617 \r\nL 118.649666 105.11377 \r\nL 118.653471 105.143091 \r\nL 118.710544 103.449675 \r\nL 118.737179 105.539372 \r\nL 118.748593 107.556936 \r\nL 118.798057 100.312881 \r\nL 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122.039813 93.117033 \r\nL 122.115911 92.161676 \r\nL 122.119715 92.192976 \r\nL 122.127325 91.725641 \r\nL 122.169179 96.767033 \r\nL 122.184398 96.537143 \r\nL 122.218642 98.725236 \r\nL 122.268106 96.413564 \r\nL 122.317569 97.56967 \r\nL 122.397472 94.784906 \r\nL 122.424106 97.75279 \r\nL 122.439325 99.405193 \r\nL 122.44313 99.625909 \r\nL 122.500203 97.314776 \r\nL 122.515423 97.526678 \r\nL 122.530642 96.170722 \r\nL 122.59152 98.256462 \r\nL 122.610545 97.926917 \r\nL 122.618154 98.112376 \r\nL 122.663813 96.638777 \r\nL 122.709472 94.615636 \r\nL 122.777959 95.778398 \r\nL 122.846447 93.272632 \r\nL 122.895911 95.003103 \r\nL 122.899715 94.91604 \r\nL 122.930155 96.646512 \r\nL 122.933959 96.979834 \r\nL 122.994837 93.715683 \r\nL 122.998642 93.810841 \r\nL 123.105179 82.377857 \r\nL 123.116594 82.46528 \r\nL 123.120398 83.152432 \r\nL 123.21552 81.06975 \r\nL 123.306837 78.836866 \r\nL 123.253569 81.951355 \r\nL 123.337276 80.412279 \r\nL 123.447618 85.533719 \r\nL 123.352496 80.067624 \r\nL 123.519911 83.665098 \r\nL 123.523716 83.101885 \r\nL 123.596008 86.86647 \r\nL 123.603618 86.438529 \r\nL 123.641667 89.760782 \r\nL 123.725374 89.232287 \r\nL 123.736789 89.289849 \r\nL 123.748203 88.915513 \r\nL 123.885179 84.558935 \r\nL 123.900398 85.072859 \r\nL 123.991716 86.117978 \r\nL 123.934642 84.774434 \r\nL 124.01074 85.232056 \r\nL 124.052594 83.910278 \r\nL 124.094447 90.170375 \r\nL 124.098252 90.169116 \r\nL 124.212399 93.677368 \r\nL 124.269472 95.147729 \r\nL 124.33035 94.421183 \r\nL 124.334155 94.553217 \r\nL 124.368399 92.694489 \r\nL 124.410252 93.379662 \r\nL 124.452106 92.211324 \r\nL 124.482545 94.489179 \r\nL 124.520594 93.110738 \r\nL 124.547228 95.549587 \r\nL 124.653764 94.423342 \r\nL 124.707033 92.350014 \r\nL 124.767911 93.976513 \r\nL 124.775521 94.188415 \r\nL 124.859228 93.248168 \r\nL 124.912496 90.957902 \r\nL 124.984789 92.274103 \r\nL 124.996203 91.858394 \r\nL 125.072301 97.468036 \r\nL 125.140789 101.154372 \r\nL 125.182643 98.410261 \r\nL 125.201667 96.691662 \r\nL 125.228301 99.984235 \r\nL 125.277764 106.214652 \r\nL 125.342447 102.332064 \r\nL 125.483228 104.256269 \r\nL 125.40713 102.094978 \r\nL 125.487033 104.252491 \r\nL 125.544106 102.258312 \r\nL 125.58596 104.466192 \r\nL 125.681082 106.75466 \r\nL 125.616399 101.101127 \r\nL 125.711521 105.583803 \r\nL 125.837082 102.925677 \r\nL 125.859911 103.474679 \r\nL 125.905569 105.395466 \r\nL 125.977862 104.48508 \r\nL 125.989277 104.233244 \r\nL 126.034935 105.746597 \r\nL 126.130057 107.447388 \r\nL 126.118643 105.626615 \r\nL 126.149082 106.116975 \r\nL 126.248008 103.584407 \r\nL 126.282252 105.142011 \r\nL 126.316496 107.537329 \r\nL 126.404008 106.144677 \r\nL 126.407813 105.147048 \r\nL 126.51435 105.567794 \r\nL 126.540984 104.619632 \r\nL 126.628496 105.3129 \r\nL 126.636106 105.629673 \r\nL 126.723618 104.471948 \r\nL 126.727423 104.581497 \r\nL 126.750252 103.578471 \r\nL 126.773082 102.853004 \r\nL 126.83396 105.085888 \r\nL 126.879618 110.378037 \r\nL 126.970935 109.40667 \r\nL 127.008984 109.916638 \r\nL 127.039423 109.043667 \r\nL 127.050838 109.219052 \r\nL 127.115521 107.268225 \r\nL 127.168789 107.70552 \r\nL 127.275326 108.583347 \r\nL 127.233472 106.455874 \r\nL 127.28674 108.052693 \r\nL 127.298155 107.450446 \r\nL 127.355228 109.844325 \r\nL 127.359033 109.794137 \r\nL 127.381862 110.180705 \r\nL 127.438935 108.733909 \r\nL 127.45796 108.817914 \r\nL 127.560692 109.235602 \r\nL 127.530252 108.754236 \r\nL 127.572106 108.976031 \r\nL 127.640594 108.436923 \r\nL 127.682448 108.926383 \r\nL 127.773765 111.601599 \r\nL 127.804204 110.09706 \r\nL 127.808009 110.095261 \r\nL 127.922155 112.624052 \r\nL 127.830838 109.638899 \r\nL 127.937374 111.633078 \r\nL 128.013472 108.519489 \r\nL 128.062935 110.003521 \r\nL 128.078155 110.655775 \r\nL 128.146643 109.611556 \r\nL 128.154253 109.627926 \r\nL 128.177082 109.747548 \r\nL 128.306448 108.543593 \r\nL 128.382545 111.01662 \r\nL 128.428204 109.501109 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130.376302 100.60573 \r\nL 130.452399 99.203724 \r\nL 130.471423 99.526793 \r\nL 130.528497 98.010382 \r\nL 130.539911 97.295888 \r\nL 130.623619 99.451422 \r\nL 130.699716 101.523851 \r\nL 130.745375 100.622639 \r\nL 130.825277 98.830647 \r\nL 130.867131 98.906378 \r\nL 130.920399 99.304278 \r\nL 130.935619 98.055533 \r\nL 130.947033 98.245849 \r\nL 131.042155 97.088663 \r\nL 130.977472 98.446598 \r\nL 131.04596 97.874211 \r\nL 131.144887 100.044855 \r\nL 131.160106 99.250134 \r\nL 131.171521 98.392453 \r\nL 131.220985 102.340698 \r\nL 131.259033 100.683079 \r\nL 131.319911 101.409086 \r\nL 131.323716 97.106292 \r\nL 131.445472 90.204553 \r\nL 131.453082 90.718658 \r\nL 131.50635 88.745344 \r\nL 131.612887 87.613882 \r\nL 131.593863 89.080646 \r\nL 131.616692 88.523369 \r\nL 131.681375 92.743957 \r\nL 131.628106 88.302113 \r\nL 131.738448 90.625838 \r\nL 131.746058 90.395228 \r\nL 131.803131 93.117213 \r\nL 131.83357 97.840573 \r\nL 131.924887 97.191556 \r\nL 131.99718 96.72566 \r\nL 131.955326 97.269445 \r\nL 132.016204 97.264049 \r\nL 132.122741 98.497684 \r\nL 132.065668 97.219258 \r\nL 132.13035 98.239913 \r\nL 132.297765 92.428622 \r\nL 132.335814 95.239829 \r\nL 132.339619 95.826786 \r\nL 132.358643 92.231651 \r\nL 132.44235 95.000045 \r\nL 132.495619 96.063692 \r\nL 132.453765 94.999326 \r\nL 132.571716 95.404961 \r\nL 132.586936 95.289117 \r\nL 132.59835 95.892443 \r\nL 132.602155 95.875894 \r\nL 132.704887 97.907669 \r\nL 132.727716 97.024445 \r\nL 132.803814 96.310671 \r\nL 132.765765 97.606186 \r\nL 132.830448 97.264229 \r\nL 132.898936 99.814785 \r\nL 132.841863 96.789878 \r\nL 133.009277 99.006932 \r\nL 133.134838 98.186667 \r\nL 133.146253 98.247108 \r\nL 133.283229 102.036696 \r\nL 133.363131 100.515968 \r\nL 133.40118 99.460417 \r\nL 133.446838 102.896716 \r\nL 133.469668 103.471441 \r\nL 133.503912 101.86329 \r\nL 133.54957 102.544685 \r\nL 133.55718 102.339259 \r\nL 133.583814 103.516951 \r\nL 133.644692 102.96705 \r\nL 133.659912 103.512634 \r\nL 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133.254618 \r\nL 163.927719 131.336888 \r\nL 163.980987 133.379096 \r\nL 163.992402 132.917697 \r\nL 163.996207 132.870388 \r\nL 164.011426 133.590099 \r\nL 164.148402 142.126971 \r\nL 164.152207 142.427735 \r\nL 164.235914 140.507128 \r\nL 164.239719 140.501731 \r\nL 164.296792 142.152155 \r\nL 164.346256 140.443629 \r\nL 164.350061 140.483743 \r\nL 164.551719 145.813128 \r\nL 164.418548 140.462697 \r\nL 164.555524 145.281035 \r\nL 164.597378 141.62366 \r\nL 164.681085 144.280347 \r\nL 164.68489 144.279987 \r\nL 164.696305 144.909757 \r\nL 164.783817 143.733144 \r\nL 164.806646 143.476991 \r\nL 164.829475 144.548552 \r\nL 164.837085 144.92001 \r\nL 164.875134 143.492101 \r\nL 164.932207 144.032649 \r\nL 164.985475 142.599703 \r\nL 165.046353 143.452887 \r\nL 165.092012 144.250846 \r\nL 165.076792 143.370321 \r\nL 165.099622 144.026892 \r\nL 165.149085 139.103143 \r\nL 165.213768 140.270942 \r\nL 165.29367 142.46587 \r\nL 165.362158 141.829266 \r\nL 165.46489 140.375814 \r\nL 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167.02489 133.49584 \r\nL 167.047719 132.942161 \r\nL 167.070549 133.883847 \r\nL 167.139036 133.324052 \r\nL 167.165671 135.049847 \r\nL 167.203719 132.089338 \r\nL 167.253183 134.136942 \r\nL 167.283622 132.9141 \r\nL 167.333085 137.03845 \r\nL 167.367329 136.886089 \r\nL 167.473866 138.284497 \r\nL 167.481475 138.385591 \r\nL 167.530939 137.972221 \r\nL 167.561378 137.984813 \r\nL 167.572793 137.727221 \r\nL 167.6565 138.267768 \r\nL 167.782061 141.103799 \r\nL 167.789671 140.67208 \r\nL 167.793476 140.646357 \r\nL 167.801085 141.098762 \r\nL 167.8125 142.090635 \r\nL 167.869573 139.350482 \r\nL 167.979915 134.642053 \r\nL 167.991329 134.757897 \r\nL 168.025573 133.709181 \r\nL 168.036988 138.090944 \r\nL 168.090256 135.236565 \r\nL 168.113085 136.788413 \r\nL 168.192988 135.146804 \r\nL 168.200597 135.003437 \r\nL 168.227232 136.076977 \r\nL 168.276695 135.892417 \r\nL 168.303329 136.377921 \r\nL 168.28811 135.650655 \r\nL 168.318549 135.690589 \r\nL 168.360402 134.03225 \r\nL 168.402256 136.611229 \r\nL 168.42889 135.663786 \r\nL 168.4365 135.725126 \r\nL 168.466939 134.487534 \r\nL 168.493573 133.273146 \r\nL 168.57728 134.30837 \r\nL 168.596305 135.069634 \r\nL 168.657183 133.912808 \r\nL 168.683817 134.165184 \r\nL 168.794159 132.126393 \r\nL 168.809378 132.623589 \r\nL 168.816988 132.396217 \r\nL 168.836012 134.70699 \r\nL 168.885476 134.013003 \r\nL 169.018646 138.459523 \r\nL 169.071915 137.77489 \r\nL 169.148012 135.21426 \r\nL 169.186061 135.574745 \r\nL 169.193671 135.702461 \r\nL 169.220305 135.114605 \r\nL 169.345866 132.709753 \r\nL 169.357281 133.194897 \r\nL 169.429573 134.865827 \r\nL 169.383915 133.076534 \r\nL 169.471427 133.866039 \r\nL 169.585573 136.235993 \r\nL 169.555134 132.863912 \r\nL 169.616012 135.943144 \r\nL 169.730159 134.550133 \r\nL 169.745378 134.63144 \r\nL 169.806256 136.038122 \r\nL 169.870939 134.901084 \r\nL 169.905183 134.135143 \r\nL 169.931817 136.531541 \r\nL 169.935622 137.09943 \r\nL 169.973671 135.013331 \r\nL 170.026939 135.149682 \r\nL 170.072598 127.918398 \r\nL 170.175329 130.606565 \r\nL 170.182939 130.512666 \r\nL 170.205768 131.21223 \r\nL 170.220988 132.174422 \r\nL 170.297085 130.420566 \r\nL 170.30089 130.584799 \r\nL 170.335134 129.283707 \r\nL 170.430256 130.188697 \r\nL 170.445476 130.84383 \r\nL 170.506354 129.250069 \r\nL 170.62811 126.945952 \r\nL 170.548207 130.062599 \r\nL 170.631915 127.197248 \r\nL 170.730842 129.517375 \r\nL 170.757476 128.935274 \r\nL 170.765086 128.579107 \r\nL 170.860207 129.303314 \r\nL 170.883037 129.047341 \r\nL 170.94011 129.797812 \r\nL 170.943915 129.792595 \r\nL 170.959134 129.894229 \r\nL 170.978159 129.501905 \r\nL 171.065671 128.475494 \r\nL 171.016207 130.224134 \r\nL 171.09611 128.780755 \r\nL 171.107525 129.497768 \r\nL 171.153183 125.417668 \r\nL 171.210256 129.199162 \r\nL 171.233086 128.692793 \r\nL 171.297768 129.583392 \r\nL 171.309183 129.725499 \r\nL 171.324403 130.716473 \r\nL 171.396695 128.940851 \r\nL 171.404305 128.946787 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132.723244 \r\nL 176.217476 132.86769 \r\nL 176.312598 132.587792 \r\nL 176.247915 133.374779 \r\nL 176.316403 132.872007 \r\nL 176.331623 133.457885 \r\nL 176.40772 132.060736 \r\nL 176.419135 132.1469 \r\nL 176.57133 135.326147 \r\nL 176.609379 134.649788 \r\nL 176.731135 135.713794 \r\nL 176.620793 134.453356 \r\nL 176.792013 135.417527 \r\nL 176.795818 135.416628 \r\nL 176.871915 134.914215 \r\nL 176.898549 135.883603 \r\nL 176.974647 137.165087 \r\nL 177.016501 136.373064 \r\nL 177.020306 136.36317 \r\nL 177.03172 137.320866 \r\nL 177.084989 134.956668 \r\nL 177.126842 136.052333 \r\nL 177.172501 135.302222 \r\nL 177.20294 136.147131 \r\nL 177.309476 139.788856 \r\nL 177.320891 139.49187 \r\nL 177.431232 138.594795 \r\nL 177.34372 140.046448 \r\nL 177.446452 138.902574 \r\nL 177.632891 141.499181 \r\nL 177.648111 140.790983 \r\nL 177.724208 142.35884 \r\nL 177.766062 144.779522 \r\nL 177.739428 142.008609 \r\nL 177.864989 144.562583 \r\nL 177.998159 142.938782 \r\nL 177.891623 144.576254 \r\nL 178.013379 143.464759 \r\nL 178.13133 144.939437 \r\nL 178.074257 143.23325 \r\nL 178.150354 144.108739 \r\nL 178.188403 143.155181 \r\nL 178.169379 144.162884 \r\nL 178.256891 144.161804 \r\nL 178.275915 146.113891 \r\nL 178.359623 143.473573 \r\nL 178.454745 144.846438 \r\nL 178.492794 144.30607 \r\nL 178.553672 143.608126 \r\nL 178.508013 144.573376 \r\nL 178.59172 144.212531 \r\nL 178.618355 145.089999 \r\nL 178.679233 143.497678 \r\nL 178.683037 143.614242 \r\nL 178.804794 140.560913 \r\nL 178.808598 140.720289 \r\nL 178.812403 140.823542 \r\nL 178.839037 138.766943 \r\nL 178.842842 138.23521 \r\nL 178.854257 142.319626 \r\nL 178.949379 138.662971 \r\nL 179.132013 135.908067 \r\nL 178.998842 139.740648 \r\nL 179.135818 136.187605 \r\nL 179.21572 139.44528 \r\nL 179.284208 138.208587 \r\nL 179.497281 125.466956 \r\nL 179.626647 126.620724 \r\nL 179.657086 127.180159 \r\nL 179.710355 125.989875 \r\nL 179.729379 126.600037 \r\nL 179.775038 123.572252 \r\nL 179.820696 127.471389 \r\nL 179.832111 126.972215 \r\nL 179.904403 129.276872 \r\nL 179.870159 125.181303 \r\nL 179.972891 128.749636 \r\nL 180.022355 124.929287 \r\nL 180.106062 126.204835 \r\nL 180.300111 123.701408 \r\nL 180.125086 126.379321 \r\nL 180.307721 124.371471 \r\nL 180.368599 121.469243 \r\nL 180.429477 125.653495 \r\nL 180.456111 120.121023 \r\nL 180.524599 128.884008 \r\nL 180.532208 128.431783 \r\nL 180.631135 130.960214 \r\nL 180.657769 130.255434 \r\nL 180.718647 129.495789 \r\nL 180.756696 130.382251 \r\nL 180.771916 130.103972 \r\nL 180.901282 128.150087 \r\nL 180.867038 130.346814 \r\nL 180.905086 128.306045 \r\nL 180.965964 129.612713 \r\nL 180.981184 124.895829 \r\nL 181.015428 126.099244 \r\nL 180.996403 120.96863 \r\nL 181.038257 123.746558 \r\nL 181.080111 119.613394 \r\nL 181.152404 122.646756 \r\nL 181.163818 123.480332 \r\nL 181.213282 122.373874 \r\nL 181.26655 122.906866 \r\nL 181.403525 124.806967 \r\nL 181.452989 124.317146 \r\nL 181.479623 125.341218 \r\nL 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182.822745 115.205189 \r\nL 182.849379 114.962347 \r\nL 182.853184 115.047252 \r\nL 182.921672 114.349667 \r\nL 182.955916 114.884458 \r\nL 182.959721 114.910721 \r\nL 182.978745 114.492134 \r\nL 183.077672 113.173414 \r\nL 183.096696 113.54721 \r\nL 183.207038 117.301362 \r\nL 183.222257 116.188427 \r\nL 183.237477 115.701485 \r\nL 183.29455 119.950854 \r\nL 183.332599 121.114875 \r\nL 183.30216 119.92603 \r\nL 183.412501 120.23237 \r\nL 183.500013 119.740751 \r\nL 183.469574 120.565873 \r\nL 183.522843 120.053926 \r\nL 183.538062 120.321052 \r\nL 183.587526 118.902857 \r\nL 183.610355 119.561228 \r\nL 183.61416 119.252369 \r\nL 183.690257 121.115234 \r\nL 183.713087 120.037737 \r\nL 183.77016 123.339664 \r\nL 183.842453 122.373154 \r\nL 183.857672 121.815338 \r\nL 183.91094 123.064083 \r\nL 183.952794 122.178521 \r\nL 183.994648 121.995041 \r\nL 184.097379 123.338404 \r\nL 184.177282 121.242591 \r\nL 184.124014 123.48375 \r\nL 184.196306 122.545302 \r\nL 184.337087 130.593433 \r\nL 184.455038 133.216482 \r\nL 184.474062 132.677374 \r\nL 184.584404 130.788606 \r\nL 184.607233 131.137758 \r\nL 184.702355 132.879562 \r\nL 184.618648 131.102861 \r\nL 184.770843 132.14636 \r\nL 184.824111 128.002043 \r\nL 184.919233 129.165524 \r\nL 185.00294 131.777061 \r\nL 185.044794 130.940787 \r\nL 185.216014 128.68218 \r\nL 185.063818 130.944204 \r\nL 185.231233 128.88023 \r\nL 185.307331 136.362811 \r\nL 185.254062 128.130839 \r\nL 185.432892 135.891158 \r\nL 185.474745 134.037467 \r\nL 185.547038 134.512897 \r\nL 185.566062 135.026462 \r\nL 185.638355 134.164824 \r\nL 185.64977 134.235878 \r\nL 185.653575 134.188748 \r\nL 185.672599 134.887593 \r\nL 185.695428 134.828231 \r\nL 185.744892 135.753908 \r\nL 185.794355 134.695298 \r\nL 185.80577 135.258331 \r\nL 185.897087 133.214144 \r\nL 185.935136 133.331967 \r\nL 185.94655 133.515987 \r\nL 185.980794 131.541055 \r\nL 185.984599 131.633515 \r\nL 186.441184 117.665264 \r\nL 186.007428 132.892874 \r\nL 186.467819 118.129362 \r\nL 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187.681575 105.718715 \r\nL 187.864209 102.881426 \r\nL 187.871819 103.21223 \r\nL 188.081087 106.989407 \r\nL 187.932697 102.56825 \r\nL 188.119136 105.31344 \r\nL 188.122941 104.870928 \r\nL 188.202843 107.430119 \r\nL 188.221868 110.881348 \r\nL 188.316989 108.979269 \r\nL 188.377868 108.553127 \r\nL 188.343624 109.524133 \r\nL 188.385477 109.071009 \r\nL 188.480599 110.896279 \r\nL 188.503429 110.404839 \r\nL 188.541477 108.815755 \r\nL 188.590941 110.714057 \r\nL 188.62138 109.291725 \r\nL 188.644209 109.796656 \r\nL 188.686063 110.968591 \r\nL 188.716502 107.935769 \r\nL 188.746941 105.988899 \r\nL 188.800209 108.283662 \r\nL 188.842063 109.16149 \r\nL 188.876307 108.050535 \r\nL 188.906746 108.128604 \r\nL 188.91816 108.002506 \r\nL 188.93338 108.527404 \r\nL 188.952404 108.521827 \r\nL 188.971429 109.368715 \r\nL 189.043721 111.544576 \r\nL 189.08177 109.516039 \r\nL 189.188307 107.464477 \r\nL 189.207331 108.437462 \r\nL 189.23777 110.172251 \r\nL 189.283429 108.403464 \r\nL 189.321477 109.035032 \r\nL 189.325282 109.191351 \r\nL 189.351916 108.451853 \r\nL 189.424209 108.810539 \r\nL 189.477477 107.999808 \r\nL 189.507916 108.980888 \r\nL 189.511721 108.966137 \r\nL 189.595429 109.780826 \r\nL 189.530746 108.684261 \r\nL 189.614453 108.902819 \r\nL 189.679136 105.6804 \r\nL 189.800892 106.342368 \r\nL 189.857965 109.953873 \r\nL 189.812307 105.94123 \r\nL 189.956892 109.657067 \r\nL 189.994941 108.798307 \r\nL 190.044404 110.898797 \r\nL 190.18138 115.818768 \r\nL 190.265087 114.056458 \r\nL 190.299331 111.213052 \r\nL 190.364014 116.35302 \r\nL 190.443917 121.494427 \r\nL 190.539038 121.307709 \r\nL 190.561868 121.069904 \r\nL 190.626551 122.438092 \r\nL 190.637965 121.868043 \r\nL 190.645575 121.854732 \r\nL 190.64938 121.985147 \r\nL 190.660795 122.243818 \r\nL 190.683624 120.721471 \r\nL 190.695038 120.821846 \r\nL 190.786356 117.970345 \r\nL 190.843429 119.275754 \r\nL 190.892892 120.352891 \r\nL 190.934746 118.779638 \r\nL 190.949965 117.946241 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99.625909 \r\nL 122.500203 97.314776 \r\nL 122.515423 97.526678 \r\nL 122.530642 96.170722 \r\nL 122.59152 98.256462 \r\nL 122.610545 97.926917 \r\nL 122.618154 98.112376 \r\nL 122.663813 96.638777 \r\nL 122.709472 94.615636 \r\nL 122.777959 95.778398 \r\nL 122.846447 93.272632 \r\nL 122.895911 95.003103 \r\nL 122.899715 94.91604 \r\nL 122.930155 96.646512 \r\nL 122.933959 96.979834 \r\nL 122.994837 93.715683 \r\nL 122.998642 93.810841 \r\nL 123.105179 82.377857 \r\nL 123.116594 82.46528 \r\nL 123.120398 83.152432 \r\nL 123.21552 81.06975 \r\nL 123.306837 78.836866 \r\nL 123.253569 81.951355 \r\nL 123.337276 80.412279 \r\nL 123.447618 85.533719 \r\nL 123.352496 80.067624 \r\nL 123.519911 83.665098 \r\nL 123.523716 83.101885 \r\nL 123.596008 86.86647 \r\nL 123.603618 86.438529 \r\nL 123.641667 89.760782 \r\nL 123.725374 89.232287 \r\nL 123.736789 89.289849 \r\nL 123.748203 88.915513 \r\nL 123.885179 84.558935 \r\nL 123.900398 85.072859 \r\nL 123.991716 86.117978 \r\nL 123.934642 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98.245849 \r\nL 131.042155 97.088663 \r\nL 130.977472 98.446598 \r\nL 131.04596 97.874211 \r\nL 131.144887 100.044855 \r\nL 131.160106 99.250134 \r\nL 131.171521 98.392453 \r\nL 131.220985 102.340698 \r\nL 131.259033 100.683079 \r\nL 131.319911 101.409086 \r\nL 131.323716 97.106292 \r\nL 131.445472 90.204553 \r\nL 131.453082 90.718658 \r\nL 131.50635 88.745344 \r\nL 131.612887 87.613882 \r\nL 131.593863 89.080646 \r\nL 131.616692 88.523369 \r\nL 131.681375 92.743957 \r\nL 131.628106 88.302113 \r\nL 131.738448 90.625838 \r\nL 131.746058 90.395228 \r\nL 131.803131 93.117213 \r\nL 131.83357 97.840573 \r\nL 131.924887 97.191556 \r\nL 131.99718 96.72566 \r\nL 131.955326 97.269445 \r\nL 132.016204 97.264049 \r\nL 132.122741 98.497684 \r\nL 132.065668 97.219258 \r\nL 132.13035 98.239913 \r\nL 132.297765 92.428622 \r\nL 132.335814 95.239829 \r\nL 132.339619 95.826786 \r\nL 132.358643 92.231651 \r\nL 132.44235 95.000045 \r\nL 132.495619 96.063692 \r\nL 132.453765 94.999326 \r\nL 132.571716 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137.624595 120.880667 \r\nL 137.636009 122.098473 \r\nL 137.696888 121.681865 \r\nL 137.772985 127.130691 \r\nL 137.909961 119.256327 \r\nL 137.913766 119.498988 \r\nL 137.993668 120.654015 \r\nL 137.9404 119.232942 \r\nL 138.020302 119.237439 \r\nL 138.07357 116.070244 \r\nL 138.153473 117.813667 \r\nL 138.199131 119.138144 \r\nL 138.23718 116.898065 \r\nL 138.240985 117.137129 \r\nL 138.286644 114.428095 \r\nL 138.355131 116.131764 \r\nL 138.4084 118.323815 \r\nL 138.480692 117.262507 \r\nL 138.484497 117.024522 \r\nL 138.575814 117.905228 \r\nL 138.663327 119.844183 \r\nL 138.708985 119.72726 \r\nL 138.71279 119.832311 \r\nL 138.747034 118.755713 \r\nL 138.762253 116.33755 \r\nL 138.838351 122.207842 \r\nL 138.849766 121.84232 \r\nL 138.975327 111.477839 \r\nL 139.0324 112.847646 \r\nL 139.161766 111.04738 \r\nL 139.081863 113.405103 \r\nL 139.18079 111.79821 \r\nL 139.264497 113.77746 \r\nL 139.291132 112.196651 \r\nL 139.321571 111.07886 \r\nL 139.382449 114.493932 \r\nL 139.393863 112.851963 \r\nL 139.409083 112.679456 \r\nL 139.416693 113.634453 \r\nL 139.428107 113.446655 \r\nL 139.443327 114.066531 \r\nL 139.48518 110.24996 \r\nL 139.534644 108.825649 \r\nL 139.549863 112.569008 \r\nL 139.572693 111.856672 \r\nL 139.694449 113.728352 \r\nL 139.698254 113.748319 \r\nL 139.721083 113.350598 \r\nL 139.774351 111.510578 \r\nL 139.80479 114.582615 \r\nL 139.8124 114.430254 \r\nL 139.816205 114.612115 \r\nL 139.846644 110.836378 \r\nL 139.850449 110.973628 \r\nL 139.858058 110.875952 \r\nL 139.873278 114.043686 \r\nL 139.880888 114.98843 \r\nL 139.95318 111.039825 \r\nL 139.96079 111.344366 \r\nL 139.972205 109.921494 \r\nL 140.055912 114.885537 \r\nL 140.204302 113.076277 \r\nL 140.230937 113.403484 \r\nL 140.268985 113.260477 \r\nL 140.242351 113.785554 \r\nL 140.299424 113.60873 \r\nL 140.329863 113.993859 \r\nL 140.371717 112.098794 \r\nL 140.386937 112.334801 \r\nL 140.394546 111.848578 \r\nL 140.44401 113.573293 \r\nL 140.478254 113.524725 \r\nL 140.501083 115.493541 \r\nL 140.561961 111.830949 \r\nL 140.580985 112.995869 \r\nL 140.630449 112.174885 \r\nL 140.676107 114.134347 \r\nL 140.687522 114.369094 \r\nL 140.73318 113.624919 \r\nL 140.790254 111.969279 \r\nL 140.851132 112.914203 \r\nL 140.873961 112.867253 \r\nL 140.877766 113.052712 \r\nL 140.881571 113.098942 \r\nL 140.9044 112.139988 \r\nL 141.018546 109.439049 \r\nL 140.953863 112.16661 \r\nL 141.04518 110.392427 \r\nL 141.056595 110.923981 \r\nL 141.0604 110.877931 \r\nL 141.147912 113.022132 \r\nL 141.083229 110.428943 \r\nL 141.174546 111.524249 \r\nL 141.212595 109.28363 \r\nL 141.330546 110.183943 \r\nL 141.341961 110.582383 \r\nL 141.368595 109.40631 \r\nL 141.444693 110.567453 \r\nL 141.459912 111.2922 \r\nL 141.478937 111.228522 \r\nL 141.482741 111.714745 \r\nL 141.532205 109.102309 \r\nL 141.574059 109.66732 \r\nL 141.707229 107.478148 \r\nL 141.646351 111.490071 \r\nL 141.711034 107.481745 \r\nL 141.718644 107.912385 \r\nL 141.749083 104.707774 \r\nL 141.802351 105.591898 \r\nL 141.84801 105.032643 \r\nL 141.878449 106.15655 \r\nL 141.886059 105.964075 \r\nL 141.9964 107.822623 \r\nL 142.015424 107.714514 \r\nL 142.106742 104.156794 \r\nL 142.137181 105.996094 \r\nL 142.247522 109.101949 \r\nL 142.205668 105.811894 \r\nL 142.274156 107.953758 \r\nL 142.277961 107.953758 \r\nL 142.293181 108.734448 \r\nL 142.384498 107.483005 \r\nL 142.392107 107.282435 \r\nL 142.433961 108.361732 \r\nL 142.487229 107.546143 \r\nL 142.555717 108.489988 \r\nL 142.574742 107.041752 \r\nL 142.666059 101.58591 \r\nL 142.700303 102.836635 \r\nL 142.749766 108.877095 \r\nL 142.822059 107.163353 \r\nL 142.844888 106.979513 \r\nL 142.856303 107.416988 \r\nL 142.863912 107.335501 \r\nL 143.004693 109.037551 \r\nL 143.008498 108.800106 \r\nL 143.057961 108.016177 \r\nL 143.099815 109.073887 \r\nL 143.122644 108.365329 \r\nL 143.137864 110.521763 \r\nL 143.213961 106.682527 \r\nL 143.221571 106.296498 \r\nL 143.267229 107.01423 \r\nL 143.286254 104.267242 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120.229132 \r\nL 144.720693 119.524532 \r\nL 144.732108 119.758199 \r\nL 144.785376 118.084031 \r\nL 144.81201 118.762549 \r\nL 144.956595 116.098666 \r\nL 144.895717 119.099649 \r\nL 144.990839 117.792801 \r\nL 145.025083 118.083491 \r\nL 145.063132 117.471171 \r\nL 145.104986 117.938326 \r\nL 145.143035 117.087301 \r\nL 145.188693 118.096803 \r\nL 145.29523 118.656958 \r\nL 145.203913 118.065143 \r\nL 145.306644 118.347919 \r\nL 145.386547 116.516713 \r\nL 145.420791 117.820143 \r\nL 145.523522 132.179099 \r\nL 145.561571 131.274649 \r\nL 145.565376 131.277887 \r\nL 145.611035 136.864683 \r\nL 145.728986 135.882344 \r\nL 145.862157 132.908523 \r\nL 145.865961 132.932628 \r\nL 145.873571 133.516347 \r\nL 145.942059 131.401106 \r\nL 145.964888 131.915211 \r\nL 145.987718 130.761623 \r\nL 146.071425 127.754704 \r\nL 146.109474 128.179228 \r\nL 146.132303 127.83763 \r\nL 146.147522 128.378358 \r\nL 146.242644 130.292309 \r\nL 146.269279 129.918333 \r\nL 146.451913 126.239373 \r\nL 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131.691797 \r\nL 154.35084 129.802309 \r\nL 154.385084 130.896535 \r\nL 154.598157 127.3318 \r\nL 154.628596 128.108354 \r\nL 154.776987 133.073116 \r\nL 154.799816 133.323513 \r\nL 154.853084 132.512602 \r\nL 154.872109 132.722704 \r\nL 154.879718 132.406471 \r\nL 154.902548 133.883128 \r\nL 154.96723 133.044695 \r\nL 155.050938 134.454075 \r\nL 155.073767 133.331068 \r\nL 155.077572 132.952415 \r\nL 155.127035 134.782541 \r\nL 155.180304 133.692812 \r\nL 155.30967 136.276287 \r\nL 155.237377 132.986592 \r\nL 155.328694 135.810931 \r\nL 155.340109 133.560058 \r\nL 155.45045 133.803979 \r\nL 155.454255 133.821608 \r\nL 155.488499 133.473715 \r\nL 155.507523 133.584702 \r\nL 155.640694 130.394663 \r\nL 155.655913 130.946183 \r\nL 155.663523 131.497344 \r\nL 155.712987 127.11684 \r\nL 155.720596 126.554167 \r\nL 155.789084 128.211786 \r\nL 155.838548 129.622246 \r\nL 155.888011 127.225669 \r\nL 155.891816 127.450703 \r\nL 155.975523 129.227944 \r\nL 155.907035 127.275317 \r\nL 156.021182 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124.015303 \r\nL 157.828499 124.852657 \r\nL 157.832304 124.985051 \r\nL 157.885572 123.541492 \r\nL 157.995914 122.051883 \r\nL 158.003523 122.053682 \r\nL 158.170938 124.800131 \r\nL 158.174743 124.696699 \r\nL 158.231816 124.175938 \r\nL 158.262255 125.240484 \r\nL 158.269865 125.054126 \r\nL 158.296499 125.515705 \r\nL 158.361182 124.77189 \r\nL 158.372597 124.783942 \r\nL 158.501962 122.334119 \r\nL 158.520987 122.561851 \r\nL 158.543816 128.265931 \r\nL 158.631328 122.80847 \r\nL 158.680792 122.741014 \r\nL 158.661767 123.49814 \r\nL 158.703621 123.317538 \r\nL 158.711231 123.388232 \r\nL 158.764499 122.676796 \r\nL 158.775914 122.765118 \r\nL 158.863426 122.994829 \r\nL 158.901475 122.228169 \r\nL 158.958548 121.565301 \r\nL 158.977572 122.47119 \r\nL 159.008011 122.25623 \r\nL 159.186841 126.255922 \r\nL 159.034645 121.933341 \r\nL 159.213475 125.739838 \r\nL 159.262938 126.112016 \r\nL 159.540694 118.028627 \r\nL 159.632011 115.93911 \r\nL 159.552109 118.067662 \r\nL 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167.481475 138.385591 \r\nL 167.530939 137.972221 \r\nL 167.561378 137.984813 \r\nL 167.572793 137.727221 \r\nL 167.6565 138.267768 \r\nL 167.782061 141.103799 \r\nL 167.789671 140.67208 \r\nL 167.793476 140.646357 \r\nL 167.801085 141.098762 \r\nL 167.8125 142.090635 \r\nL 167.869573 139.350482 \r\nL 167.979915 134.642053 \r\nL 167.991329 134.757897 \r\nL 168.025573 133.709181 \r\nL 168.036988 138.090944 \r\nL 168.090256 135.236565 \r\nL 168.113085 136.788413 \r\nL 168.192988 135.146804 \r\nL 168.200597 135.003437 \r\nL 168.227232 136.076977 \r\nL 168.276695 135.892417 \r\nL 168.303329 136.377921 \r\nL 168.28811 135.650655 \r\nL 168.318549 135.690589 \r\nL 168.360402 134.03225 \r\nL 168.402256 136.611229 \r\nL 168.42889 135.663786 \r\nL 168.4365 135.725126 \r\nL 168.466939 134.487534 \r\nL 168.493573 133.273146 \r\nL 168.57728 134.30837 \r\nL 168.596305 135.069634 \r\nL 168.657183 133.912808 \r\nL 168.683817 134.165184 \r\nL 168.794159 132.126393 \r\nL 168.809378 132.623589 \r\nL 168.816988 132.396217 \r\nL 168.836012 134.70699 \r\nL 168.885476 134.013003 \r\nL 169.018646 138.459523 \r\nL 169.071915 137.77489 \r\nL 169.148012 135.21426 \r\nL 169.186061 135.574745 \r\nL 169.193671 135.702461 \r\nL 169.220305 135.114605 \r\nL 169.345866 132.709753 \r\nL 169.357281 133.194897 \r\nL 169.429573 134.865827 \r\nL 169.383915 133.076534 \r\nL 169.471427 133.866039 \r\nL 169.585573 136.235993 \r\nL 169.555134 132.863912 \r\nL 169.616012 135.943144 \r\nL 169.730159 134.550133 \r\nL 169.745378 134.63144 \r\nL 169.806256 136.038122 \r\nL 169.870939 134.901084 \r\nL 169.905183 134.135143 \r\nL 169.931817 136.531541 \r\nL 169.935622 137.09943 \r\nL 169.973671 135.013331 \r\nL 170.026939 135.149682 \r\nL 170.072598 127.918398 \r\nL 170.175329 130.606565 \r\nL 170.182939 130.512666 \r\nL 170.205768 131.21223 \r\nL 170.220988 132.174422 \r\nL 170.297085 130.420566 \r\nL 170.30089 130.584799 \r\nL 170.335134 129.283707 \r\nL 170.430256 130.188697 \r\nL 170.445476 130.84383 \r\nL 170.506354 129.250069 \r\nL 170.62811 126.945952 \r\nL 170.548207 130.062599 \r\nL 170.631915 127.197248 \r\nL 170.730842 129.517375 \r\nL 170.757476 128.935274 \r\nL 170.765086 128.579107 \r\nL 170.860207 129.303314 \r\nL 170.883037 129.047341 \r\nL 170.94011 129.797812 \r\nL 170.943915 129.792595 \r\nL 170.959134 129.894229 \r\nL 170.978159 129.501905 \r\nL 171.065671 128.475494 \r\nL 171.016207 130.224134 \r\nL 171.09611 128.780755 \r\nL 171.107525 129.497768 \r\nL 171.153183 125.417668 \r\nL 171.210256 129.199162 \r\nL 171.233086 128.692793 \r\nL 171.297768 129.583392 \r\nL 171.309183 129.725499 \r\nL 171.324403 130.716473 \r\nL 171.396695 128.940851 \r\nL 171.404305 128.946787 \r\nL 171.503232 128.138934 \r\nL 171.438549 129.293241 \r\nL 171.526061 128.399404 \r\nL 171.628793 129.841344 \r\nL 171.590744 127.871628 \r\nL 171.647817 129.372929 \r\nL 171.655427 128.909551 \r\nL 171.7125 132.981915 \r\nL 171.731525 132.435252 \r\nL 171.803817 131.9323 \r\nL 171.857086 133.574989 \r\nL 171.948403 133.911729 \r\nL 171.902744 132.982095 \r\nL 171.963622 133.538832 \r\nL 172.070159 132.408629 \r\nL 172.089183 133.109812 \r\nL 172.134842 132.041489 \r\nL 172.100598 133.538113 \r\nL 172.157671 133.490624 \r\nL 172.210939 136.930161 \r\nL 172.260403 133.344379 \r\nL 172.268012 132.188273 \r\nL 172.366939 133.688675 \r\nL 172.378354 134.116256 \r\nL 172.458256 133.123303 \r\nL 172.469671 133.270447 \r\nL 172.587622 132.798255 \r\nL 172.591427 132.824158 \r\nL 172.690354 131.225901 \r\nL 172.6485 133.282499 \r\nL 172.709378 132.212737 \r\nL 172.838744 137.91268 \r\nL 172.861574 137.315289 \r\nL 172.926256 138.763705 \r\nL 173.013769 138.181065 \r\nL 173.1165 137.388502 \r\nL 173.044208 138.312559 \r\nL 173.158354 137.848462 \r\nL 173.253476 139.831848 \r\nL 173.192598 137.058597 \r\nL 173.283915 139.041803 \r\nL 173.375232 140.296665 \r\nL 173.325769 137.837669 \r\nL 173.398061 139.317384 \r\nL 173.409476 139.091991 \r\nL 173.458939 139.929704 \r\nL 173.496988 140.708596 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125.653495 \r\nL 180.456111 120.121023 \r\nL 180.524599 128.884008 \r\nL 180.532208 128.431783 \r\nL 180.631135 130.960214 \r\nL 180.657769 130.255434 \r\nL 180.718647 129.495789 \r\nL 180.756696 130.382251 \r\nL 180.771916 130.103972 \r\nL 180.901282 128.150087 \r\nL 180.867038 130.346814 \r\nL 180.905086 128.306045 \r\nL 180.965964 129.612713 \r\nL 180.981184 124.895829 \r\nL 181.015428 126.099244 \r\nL 180.996403 120.96863 \r\nL 181.038257 123.746558 \r\nL 181.080111 119.613394 \r\nL 181.152404 122.646756 \r\nL 181.163818 123.480332 \r\nL 181.213282 122.373874 \r\nL 181.26655 122.906866 \r\nL 181.403525 124.806967 \r\nL 181.452989 124.317146 \r\nL 181.479623 125.341218 \r\nL 181.574745 114.771851 \r\nL 181.597574 112.670102 \r\nL 181.612794 114.800992 \r\nL 181.685086 113.76091 \r\nL 181.780208 116.500884 \r\nL 181.806843 115.898277 \r\nL 181.810647 115.736922 \r\nL 181.88294 117.291828 \r\nL 181.981867 119.997623 \r\nL 182.03133 118.191421 \r\nL 182.145477 116.79823 \r\nL 182.118843 119.337993 \r\nL 182.153087 117.321868 \r\nL 182.164501 117.554277 \r\nL 182.172111 116.910836 \r\nL 182.179721 116.976493 \r\nL 182.21016 116.347624 \r\nL 182.274843 117.329243 \r\nL 182.282452 117.151339 \r\nL 182.354745 117.464875 \r\nL 182.34333 116.495667 \r\nL 182.373769 116.768189 \r\nL 182.377574 116.734191 \r\nL 182.388989 117.479805 \r\nL 182.392794 117.648715 \r\nL 182.461282 116.540098 \r\nL 182.476501 116.78312 \r\nL 182.51455 115.062901 \r\nL 182.548794 116.997899 \r\nL 182.586843 116.119892 \r\nL 182.632501 117.655011 \r\nL 182.651526 116.036786 \r\nL 182.704794 116.910297 \r\nL 182.708599 117.037833 \r\nL 182.742843 114.676873 \r\nL 182.769477 113.813796 \r\nL 182.822745 115.205189 \r\nL 182.849379 114.962347 \r\nL 182.853184 115.047252 \r\nL 182.921672 114.349667 \r\nL 182.955916 114.884458 \r\nL 182.959721 114.910721 \r\nL 182.978745 114.492134 \r\nL 183.077672 113.173414 \r\nL 183.096696 113.54721 \r\nL 183.207038 117.301362 \r\nL 183.222257 116.188427 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132.099951 \r\nL 193.1606 131.258639 \r\nL 193.217673 132.712451 \r\nL 193.248112 131.900641 \r\nL 193.312795 131.51803 \r\nL 193.339429 132.39244 \r\nL 193.350844 132.173882 \r\nL 193.396502 132.385424 \r\nL 193.404112 132.006412 \r\nL 193.423136 131.558684 \r\nL 193.487819 133.112151 \r\nL 193.514453 134.572978 \r\nL 193.598161 133.463102 \r\nL 193.704697 132.387223 \r\nL 193.640014 133.564376 \r\nL 193.719917 132.577 \r\nL 193.723722 132.571783 \r\nL 193.727527 132.715869 \r\nL 193.731331 132.641577 \r\nL 193.860697 133.601431 \r\nL 193.868307 133.668708 \r\nL 193.91777 132.861214 \r\nL 193.921575 132.857976 \r\nL 193.93299 132.956192 \r\nL 193.997673 133.090205 \r\nL 193.990063 132.705975 \r\nL 194.001478 132.98983 \r\nL 194.069966 130.155059 \r\nL 194.115624 131.816815 \r\nL 194.41621 124.565924 \r\nL 194.134649 131.934099 \r\nL 194.420014 124.729797 \r\nL 194.423819 125.028402 \r\nL 194.488502 122.520478 \r\nL 194.503722 122.697842 \r\nL 194.515136 122.530371 \r\nL 194.545575 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130.735361 \r\nL 198.529283 131.567498 \r\nL 198.533088 131.568757 \r\nL 198.552112 130.749751 \r\nL 198.605381 132.087179 \r\nL 198.643429 131.386716 \r\nL 198.79182 133.339882 \r\nL 198.807039 133.945367 \r\nL 198.92499 135.687891 \r\nL 198.928795 135.564671 \r\nL 198.936405 135.853922 \r\nL 198.94021 135.837193 \r\nL 199.122844 141.419853 \r\nL 198.959234 135.212101 \r\nL 199.141868 141.175032 \r\nL 199.187527 140.904669 \r\nL 199.240795 141.888447 \r\nL 199.297869 141.271809 \r\nL 199.370161 142.482959 \r\nL 199.488112 141.404742 \r\nL 199.453869 142.827794 \r\nL 199.495722 141.866501 \r\nL 199.507137 143.622516 \r\nL 199.57943 141.548109 \r\nL 199.613673 142.899927 \r\nL 199.651722 141.399706 \r\nL 199.697381 142.951554 \r\nL 199.72782 142.700258 \r\nL 199.73543 142.642156 \r\nL 199.739234 142.889134 \r\nL 199.781088 144.112157 \r\nL 199.838161 142.80441 \r\nL 199.849576 142.839667 \r\nL 199.853381 142.838408 \r\nL 199.857186 142.904964 \r\nL 199.872405 142.863951 \r\nL 199.997966 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150.53217 \r\nL 203.15982 152.314448 \r\nL 203.163625 152.351863 \r\nL 203.17504 151.46738 \r\nL 203.216893 150.589553 \r\nL 203.251137 153.286714 \r\nL 203.281576 152.121254 \r\nL 203.289186 152.463031 \r\nL 203.342454 150.227089 \r\nL 203.346259 148.554 \r\nL 203.399527 150.273679 \r\nL 203.452796 149.405565 \r\nL 203.460405 151.121106 \r\nL 203.555527 147.109002 \r\nL 203.559332 147.067629 \r\nL 203.574552 147.993306 \r\nL 203.601186 147.719165 \r\nL 203.64304 148.393545 \r\nL 203.662064 147.665739 \r\nL 203.711527 148.084686 \r\nL 203.818064 144.711706 \r\nL 203.837088 144.821434 \r\nL 203.875137 146.25474 \r\nL 203.909381 143.396943 \r\nL 204.118649 132.884239 \r\nL 204.130064 133.468678 \r\nL 204.164308 134.683066 \r\nL 204.213771 132.952235 \r\nL 204.278454 127.242039 \r\nL 204.335527 130.226113 \r\nL 204.57143 138.076553 \r\nL 204.586649 137.126233 \r\nL 204.849186 131.796309 \r\nL 204.594259 137.272657 \r\nL 204.921479 132.197447 \r\nL 205.050845 136.82313 \r\nL 205.073674 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130.630489 \r\nL 206.873381 131.971155 \r\nL 206.880991 131.241371 \r\nL 206.915235 132.275876 \r\nL 206.987528 131.888409 \r\nL 206.998942 131.777781 \r\nL 207.017967 132.748967 \r\nL 207.08265 136.160622 \r\nL 207.177772 135.373096 \r\nL 207.284308 130.922439 \r\nL 207.196796 135.846727 \r\nL 207.310942 132.327862 \r\nL 207.436503 138.115228 \r\nL 207.512601 138.482368 \r\nL 207.531625 137.972041 \r\nL 207.573479 137.459735 \r\nL 207.630552 138.420848 \r\nL 207.782747 136.545931 \r\nL 207.68382 139.152791 \r\nL 207.797967 136.924764 \r\nL 207.923528 143.307002 \r\nL 207.832211 136.300931 \r\nL 207.938747 142.588371 \r\nL 207.942552 142.507963 \r\nL 207.984406 143.883706 \r\nL 207.99582 143.508471 \r\nL 208.01104 144.162884 \r\nL 208.083333 142.588191 \r\nL 208.087138 142.6659 \r\nL 208.208894 141.596138 \r\nL 208.148016 142.924212 \r\nL 208.224113 141.805341 \r\nL 208.262162 142.749366 \r\nL 208.284991 141.551707 \r\nL 208.32304 141.864163 \r\nL 208.349674 140.87139 \r\nL 208.406747 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171.135034 \r\nL 316.271245 170.784982 \r\nL 316.412025 163.710915 \r\nL 316.427245 166.932075 \r\nL 316.587049 172.560604 \r\nL 316.438659 166.436138 \r\nL 316.666952 171.935692 \r\nL 316.670757 171.941448 \r\nL 316.678367 171.76894 \r\nL 316.682171 171.588518 \r\nL 316.731635 173.460197 \r\nL 316.746854 173.304239 \r\nL 316.89905 180.091033 \r\nL 316.914269 179.903775 \r\nL 316.933293 178.159633 \r\nL 317.017001 180.497928 \r\nL 317.020806 180.094811 \r\nL 317.058854 180.903743 \r\nL 317.100708 179.653379 \r\nL 317.104513 179.706084 \r\nL 317.218659 178.345452 \r\nL 317.173001 179.984183 \r\nL 317.222464 178.606281 \r\nL 317.271928 180.306712 \r\nL 317.233879 178.458957 \r\nL 317.279537 180.212814 \r\nL 317.386074 183.310214 \r\nL 317.408903 182.38094 \r\nL 317.465976 178.99141 \r\nL 317.488806 179.523143 \r\nL 317.496415 178.02562 \r\nL 317.542074 180.389818 \r\nL 317.595342 180.135104 \r\nL 317.602952 180.083118 \r\nL 317.606757 180.291422 \r\nL 317.610562 180.313008 \r\nL 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123.150427 \r\nL 322.99066 119.520215 \r\nL 323.085782 120.621996 \r\nL 323.017294 117.562371 \r\nL 323.104806 119.719165 \r\nL 323.123831 118.338565 \r\nL 323.199928 120.498237 \r\nL 323.211343 120.065079 \r\nL 323.245587 118.635012 \r\nL 323.253196 117.64152 \r\nL 323.344513 121.194922 \r\nL 323.348318 121.248348 \r\nL 323.382562 120.265469 \r\nL 323.386367 120.18668 \r\nL 323.397782 121.555048 \r\nL 323.44344 126.237034 \r\nL 323.511928 122.985295 \r\nL 323.603245 110.351234 \r\nL 323.694562 112.922657 \r\nL 323.698367 112.90251 \r\nL 323.702172 113.222881 \r\nL 323.793489 114.092434 \r\nL 323.808709 112.830557 \r\nL 323.938075 109.412966 \r\nL 323.854367 113.094265 \r\nL 323.968514 111.100985 \r\nL 323.972318 111.456613 \r\nL 323.991343 108.450773 \r\nL 324.048416 108.996358 \r\nL 324.06744 106.472604 \r\nL 324.143538 111.797491 \r\nL 324.147343 111.98331 \r\nL 324.200611 109.645195 \r\nL 324.219636 103.309187 \r\nL 324.333782 107.764521 \r\nL 324.341392 108.619324 \r\nL 324.383245 105.506634 \r\nL 324.40227 105.789769 \r\nL 324.406075 105.351035 \r\nL 324.455538 111.551951 \r\nL 324.466953 113.765587 \r\nL 324.539245 107.001459 \r\nL 324.54305 107.420765 \r\nL 324.562075 105.324592 \r\nL 324.630562 112.683953 \r\nL 324.645782 109.229306 \r\nL 324.672416 105.394387 \r\nL 324.657197 110.23539 \r\nL 324.729489 108.527404 \r\nL 324.85505 120.553641 \r\nL 324.858855 120.460461 \r\nL 324.866465 119.699917 \r\nL 324.915928 121.916432 \r\nL 324.965392 120.403978 \r\nL 324.969197 120.580623 \r\nL 325.022465 118.456029 \r\nL 325.090953 113.835562 \r\nL 325.052904 118.667391 \r\nL 325.136611 117.593131 \r\nL 325.296416 131.215647 \r\nL 325.205099 117.078667 \r\nL 325.307831 130.553499 \r\nL 325.49427 125.312257 \r\nL 325.50188 125.511747 \r\nL 325.597001 129.037448 \r\nL 325.543733 124.217851 \r\nL 325.619831 127.508265 \r\nL 325.64266 124.902844 \r\nL 325.733977 126.64213 \r\nL 325.741587 126.747541 \r\nL 325.753002 125.601509 \r\nL 325.859538 115.885325 \r\nL 325.882367 116.304632 \r\nL 325.889977 117.335 \r\nL 325.981294 114.08416 \r\nL 326.072611 112.271302 \r\nL 326.026953 114.637839 \r\nL 326.095441 113.779618 \r\nL 326.137294 114.071388 \r\nL 326.114465 112.523497 \r\nL 326.144904 112.971405 \r\nL 326.148709 112.38175 \r\nL 326.232416 116.479657 \r\nL 326.285684 119.932506 \r\nL 326.331343 127.928291 \r\nL 326.407441 124.616111 \r\nL 326.418855 123.724433 \r\nL 326.502563 125.750811 \r\nL 326.513977 125.06294 \r\nL 326.708026 120.795943 \r\nL 326.544416 125.110249 \r\nL 326.719441 123.157982 \r\nL 326.864026 131.342105 \r\nL 326.867831 131.137938 \r\nL 326.940124 128.443295 \r\nL 326.894465 131.905318 \r\nL 326.989587 128.600333 \r\nL 326.993392 128.594217 \r\nL 326.997197 128.665271 \r\nL 327.111343 127.146881 \r\nL 327.122758 129.09501 \r\nL 327.198855 125.841832 \r\nL 327.21788 126.542475 \r\nL 327.271148 128.77302 \r\nL 327.248319 126.067045 \r\nL 327.328221 126.706708 \r\nL 327.332026 126.39659 \r\nL 327.389099 130.451506 \r\nL 327.396709 130.047489 \r\nL 327.480416 135.094997 \r\nL 327.51466 133.246163 \r\nL 327.518465 133.260554 \r\nL 327.526075 132.954393 \r\nL 327.621197 132.572862 \r\nL 327.579343 133.839776 \r\nL 327.632611 133.319375 \r\nL 327.647831 133.944288 \r\nL 327.727733 132.180898 \r\nL 327.731538 132.245835 \r\nL 327.784807 131.496264 \r\nL 327.81905 133.167015 \r\nL 327.845685 131.975292 \r\nL 327.857099 131.808361 \r\nL 327.860904 132.326063 \r\nL 327.872319 134.065889 \r\nL 327.921782 127.996467 \r\nL 327.952221 130.238165 \r\nL 327.956026 129.845661 \r\nL 328.001685 136.28654 \r\nL 328.009294 135.517362 \r\nL 328.016904 135.245739 \r\nL 328.043538 136.137418 \r\nL 328.104416 137.931927 \r\nL 328.073977 136.051254 \r\nL 328.157685 137.186134 \r\nL 328.169099 138.355011 \r\nL 328.241392 135.2139 \r\nL 328.252807 135.369678 \r\nL 328.385977 130.757846 \r\nL 328.424026 131.485112 \r\nL 328.443051 133.027606 \r\nL 328.492514 128.832382 \r\nL 328.602855 124.971919 \r\nL 328.62188 125.527037 \r\nL 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330.387343 132.45342 \r\nL 330.471051 130.4605 \r\nL 330.47866 130.533532 \r\nL 330.48627 130.52076 \r\nL 330.509099 131.259898 \r\nL 330.535734 132.782605 \r\nL 330.573782 129.1112 \r\nL 330.608026 130.207225 \r\nL 330.615636 129.673153 \r\nL 330.64988 132.194749 \r\nL 330.680319 130.933232 \r\nL 330.710758 133.792647 \r\nL 330.771636 128.893002 \r\nL 330.783051 129.497948 \r\nL 330.847734 131.110056 \r\nL 330.904807 130.879806 \r\nL 331.068417 124.763615 \r\nL 331.083636 125.776534 \r\nL 331.102661 127.428577 \r\nL 331.163539 122.228169 \r\nL 331.213002 121.392254 \r\nL 331.243441 126.399828 \r\nL 331.308124 130.173767 \r\nL 331.365197 128.769063 \r\nL 331.369002 128.794067 \r\nL 331.384222 128.348677 \r\nL 331.388026 128.356952 \r\nL 331.460319 126.67217 \r\nL 331.403246 128.521904 \r\nL 331.513587 127.169366 \r\nL 331.589685 128.85001 \r\nL 331.700026 133.835099 \r\nL 331.719051 132.803652 \r\nL 331.863636 126.817156 \r\nL 331.74188 134.66238 \r\nL 331.89788 128.869977 \r\nL 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# shift train predictions for plotting\n",
    "trainPredictPlot = np.empty_like(dataset)\n",
    "trainPredictPlot[:, :] = np.nan\n",
    "trainPredictPlot[(0+look_back+1):train.shape[0], :] = trainPredict\n",
    "\n",
    "# shift train predictions for plotting\n",
    "valPredictPlot = np.empty_like(dataset)\n",
    "valPredictPlot[:, :] = np.nan\n",
    "valPredictPlot[(train.shape[0]+look_back+1):(train.shape[0] + val.shape[0]), :] = valPredict\n",
    "\n",
    "# shift test predictions for plotting\n",
    "testPredictPlot = np.empty_like(dataset)\n",
    "testPredictPlot[:, :] = np.nan\n",
    "testPredictPlot[(train.shape[0]+val.shape[0]+look_back+1):len(dataset), :] = testPredict\n",
    "\n",
    "# plot baseline and predictions\n",
    "#plt.plot(scaler.inverse_transform(dataset))\n",
    "plt.plot(trainPredictPlot)\n",
    "plt.plot(valPredictPlot)\n",
    "plt.plot(testPredictPlot)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_pred = np.vstack((dataset[(train.shape[0]+look_back+1):(train.shape[0] + val.shape[0]),3], valPredict[:,0])).T\n",
    "test_pred = np.vstack((dataset[(train.shape[0]+val.shape[0]+look_back+1):len(dataset),3], testPredict[:,0])).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_pred_df = pd.DataFrame(val_pred,columns=('y', 'y_pred'))\n",
    "test_pred_df = pd.DataFrame(test_pred,columns=('y', 'y_pred'))\n",
    "\n",
    "val_pred_df['diff'] = val_pred_df['y'] - val_pred_df['y_pred']\n",
    "test_pred_df['diff'] = test_pred_df['y'] - test_pred_df['y_pred']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(array([1.000e+00, 3.000e+00, 1.400e+01, 6.900e+01, 7.090e+02, 2.758e+03,\n        3.940e+02, 4.200e+01, 3.000e+00, 1.000e+00]),\n array([-0.00477171, -0.00353382, -0.00229592, -0.00105803,  0.00017986,\n         0.00141776,  0.00265565,  0.00389354,  0.00513144,  0.00636933,\n         0.00760722]),\n <BarContainer object of 10 artists>)"
     },
     "metadata": {},
     "execution_count": 160
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.hist(val_pred_df['diff'])\n",
    "plt.hist(test_pred_df['diff'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "ValueError",
     "evalue": "could not broadcast input array from shape (2494,1) into shape (7494,4)",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-54-964a2a9fcd0d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mtestPredictPlot\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mempty_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0mtestPredictPlot\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mtestPredictPlot\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrainPredict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlook_back\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtestPredict\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;31m# plot baseline and predictions\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not broadcast input array from shape (2494,1) into shape (7494,4)"
     ]
    }
   ],
   "source": [
    "\"\"\" # shift train predictions for plotting\n",
    "trainPredictPlot = np.empty_like(dataset)\n",
    "trainPredictPlot[:, 2:3] = np.nan\n",
    "trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict\n",
    "\n",
    "# shift train predictions for plotting\n",
    "valPredictPlot = np.empty_like(dataset)\n",
    "valPredictPlot[:, 2:3] = np.nan\n",
    "trainPredictPlot[len(trainPredict)+(look_back*2):len(trainPredict)+look_back, :] = valPredict\n",
    "\n",
    "# shift test predictions for plotting\n",
    "testPredictPlot = np.empty_like(dataset)\n",
    "testPredictPlot[:, 2:3] = np.nan\n",
    "testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict\n",
    "\n",
    "# plot baseline and predictions\n",
    "plt.plot(scaler.inverse_transform(dataset))\n",
    "plt.plot(trainPredictPlot)\n",
    "plt.plot(testPredictPlot)\n",
    "plt.show() \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}