handout1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Coded by CheolJeongPark: Advcaned Manufacturing Laboratory.<br>\n",
    "> Visit my github [CheolJ Github](https://github.com/CheolJ) <br>\n",
    "> This code is for new lab member who follows the study of rail temperature prediction. <br>\n",
    "> For detail information or cooperation please contact me(**pffiro@gmail.com**) or professor Seong J. Cho (**scho@cnu.ac.kr**)<br>\n",
    "# **1.Handout Part1**\n",
    "## **1.1. Introduction**\n",
    "> Kaggle에서 배웠던 기초적인 Python과 Pandas 라이브러리를 활용하여 아래 문제들을 해결해보자.<br>\n",
    "> 본 과제에서 제공하는 **data**는 **2018년 7월 20일부터 2019년 7월 20일까지 충남대학교 내 설치했었던 계측시스템에서 계측된 데이터**입니다.<br>\n",
    "> 과제를 해결하는 과정에서 질문은 **Slack**을 통해 진행할테니 많이 질문해주세요.<br>\n",
    "***\n",
    "<br><br>\n",
    "## **1.2. Objective**\n",
    "> 본 과제의 목적은 다음과 같습니다.<br>\n",
    "> 1. Python의 기본적인 문법 확인\n",
    "> 2. 엑셀 혹은 csv 파일의 입출력\n",
    "> 3. 필요한 data의 핸들링 및 시각화<br>\n",
    "***\n",
    "<br><br>\n",
    "## **1.3. Data Wrangling (^・ω・^ )**\n",
    "> **Data munging**이라고도 불리는 **Data wrangling**은 처음에 data를 받아서 우리가 필요한 형태로 변환하는 과정을 얘기합니다.<br>\n",
    "> Raw data를 받으면, 그 자체로는 데이터가 어떤걸 의미하는지 이름을 지정해줘야하고(labeling), 결측치들에 대해서도 어떻게 처리할 것인지 결정해야합니다.<br>\n",
    "> 또한 활용목적에 맞게 데이터를 정리하는 과정 모두가 data wrangling에 포함됩니다.<br>\n",
    "> 그래서 본 과제에서는 그 입문단계인 data처리를 간단하게 그리고 필수적인 내용들을 직접 해보고 검토하려고 합니다.<br>\n",
    "> 결측치 처리 방법에 대해서는 다른 handout에서 진행할테니 관련된 자료를 찾아보는 것도 좋습니다.<br>\n",
    "***\n",
    "<br><br>\n",
    "## **1.4. References (•̀ᴗ•́)و ̑̑**\n",
    "> Dataframe형태의 자료의 인덱싱 및 접근법은 [Dataframe 인덱싱](https://nittaku.tistory.com/111)에 정리가 잘 되어있으니 참고! <br>\n",
    "> Data Wrangling은 [Data Wrangling](https://bkshin.tistory.com/entry/DATA-23-Data-Wrangling?category=1042793)를 참고하면 좋을 것 같습니다. <br>\n",
    "> Jupyter notebook 하나하나 클릭하기 귀찮다면 [Jupyter Notebook 단축키 (ᐠ｡‸｡ᐟ)](https://iludaslab.tistory.com/43)를 참고하세요 <br>\n",
    "***\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **2. EXERCISE**\n",
    "## **2.1. Loading Library**\n",
    "> 우선 필요한 library를 로딩해보자!<br>\n",
    "> 필요한 library들은 pandas와 matplotlib 두가지가 필요합니다!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **2.2. Loading Data File**\n",
    "> library들을 불러왔으면, 파일을 입력해보자!<br>\n",
    "> 문제 : 파일은 총 4개의 sheet로 이루어져 있으며, 그 중에서 north, west 방향의 데이터들을 불러오자<br>\n",
    ">    - **temp_data_n 에는 north 방향 데이터를, temp_data_w 에는 west 방향 데이터를 로딩하자!**<br>\n",
    ">    - **column인덱스는 data의 첫번째 행을 적용하자.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_n = pd.read_excel(\"/Users/cj/Desktop/coders/rail_temperature_prediction_amledu/rail_data_handout1.xlsx\",sheet_name=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_w = pd.read_excel(\"/Users/cj/Desktop/coders/rail_temperature_prediction_amledu/rail_data_handout1.xlsx\",sheet_name=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **2.3. Data summary**\n",
    "> data를 로딩했다면, 로딩한 data가 원하는 데이터가 맞는지 확인하는 과정이 필요하다.<br>\n",
    "\n",
    "> 문제 : 아래 항목들을 해결하시오!\n",
    ">   - **최상위 5개의 데이터들을 출력하자**<br>\n",
    ">   - **전체적인 data의 요약(count, average, 25%, 50%)을 확인할 수 있도록 출력해보자**<br>\n",
    ">   - **결측치 확인은 isnull()을 통해 확인이 가능합니다.** \n",
    ">   - **또한 isnull().sum()을 통해 결측치가 어떤 column에 모여있는지 확인할 수 있습니다. 한번 실행해봅시다!!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              date  air_temp  humidity  wind_speed_unused  \\\n0  2018-07-20 0000      27.8        72                0.0   \n1  2018-07-20 0010      27.7        72                0.0   \n2  2018-07-20 0020      27.6        72                0.0   \n3  2018-07-20 0030      27.5        72                0.0   \n4  2018-07-20 0040      27.3        73                0.0   \n\n  wind_direction_unused  rain  solar_rad  cumulative_solar_irradiance  \\\n0                   ---   0.0          0                            0   \n1                   ---   0.0          0                            0   \n2                   ---   0.0          0                            0   \n3                   ---   0.0          0                            0   \n4                   ---   0.0          0                            0   \n\n   High_solar_rad wind_speed  ...  n_o_12  n_o_13  n_o_14  n_o_15  n_o_16  \\\n0               0        242  ...    28.6    28.8    28.5    28.7    28.9   \n1               0        197  ...    28.5    28.7    28.4    28.5    28.8   \n2               0        203  ...    28.4    28.5    28.3    28.5    28.6   \n3               0        253  ...    28.2    28.4    28.0    28.3    28.5   \n4               0        190  ...    28.2    28.2    27.9    28.2    28.4   \n\n   n_o_17  n_o_18  n_o_19  casi  rail_direction  \n0    29.1    28.9    32.6     0               0  \n1    28.9    28.9    32.5     0               0  \n2    28.7    28.7    32.4     0               0  \n3    28.6    28.5    32.3     0               0  \n4    28.6    28.5    32.2     0               0  \n\n[5 rows x 42 columns]\n           air_temp      humidity  wind_speed_unused          rain  \\\ncount  48196.000000  48196.000000       48196.000000  48196.000000   \nmean      14.543441     65.357934           0.419863      0.029474   \nstd       11.015772     18.660919           0.578189      0.420340   \nmin      -10.700000     13.000000           0.000000      0.000000   \n25%        5.400000     51.000000           0.000000      0.000000   \n50%       15.200000     67.000000           0.000000      0.000000   \n75%       23.300000     81.000000           0.900000      0.000000   \nmax       40.300000     98.000000           4.500000     25.900000   \n\n          solar_rad  cumulative_solar_irradiance  High_solar_rad  \\\ncount  48196.000000                 48196.000000    48196.000000   \nmean     161.534007                  6089.414910      181.735227   \nstd      261.562394                 10387.556118      287.785485   \nmin        0.000000                     0.000000        0.000000   \n25%        0.000000                     0.000000        0.000000   \n50%        1.000000                     1.000000        5.000000   \n75%      231.000000                  8508.000000      283.000000   \nmax     1200.000000                 47390.000000     1308.000000   \n\n       wind_direction         n_i_1         n_i_2  ...        n_o_12  \\\ncount    48196.000000  48196.000000  48196.000000  ...  48196.000000   \nmean         0.790367     18.111802     18.106542  ...     17.448126   \nstd          0.663622     14.825467     14.838975  ...     14.873554   \nmin          0.000000    -11.600000    -11.600000  ...    -12.600000   \n25%          0.320000      6.400000      6.300000  ...      5.600000   \n50%          0.600000     17.400000     17.400000  ...     17.000000   \n75%          1.070000     28.000000     28.000000  ...     27.700000   \nmax          7.340000     60.700000     60.500000  ...     61.600000   \n\n             n_o_13        n_o_14        n_o_15        n_o_16        n_o_17  \\\ncount  48196.000000  48196.000000  48196.000000  48196.000000  48196.000000   \nmean      17.501660     17.359854     17.507982     17.510833     17.578598   \nstd       14.918814     14.829734     14.871676     14.805585     14.740049   \nmin      -12.500000    -12.600000    -12.500000    -12.500000    -12.300000   \n25%        5.600000      5.600000      5.700000      5.700000      5.900000   \n50%       17.000000     16.900000     17.000000     17.100000     17.200000   \n75%       27.700000     27.500000     27.600000     27.700000     27.800000   \nmax       59.200000     62.100000     58.000000     60.200000     58.700000   \n\n             n_o_18        n_o_19          casi  rail_direction  \ncount  48196.000000  48196.000000  48196.000000         48196.0  \nmean      17.531868     17.066147   6447.731202             0.0  \nstd       14.771389     11.584737  10483.325895             0.0  \nmin      -12.300000     -4.000000      0.000000             0.0  \n25%        5.700000      7.100000      0.000000             0.0  \n50%       17.100000     17.500000     56.000000             0.0  \n75%       27.700000     25.800000   9904.250000             0.0  \nmax       58.200000     47.900000  47390.000000             0.0  \n\n[8 rows x 39 columns]\n"
     ]
    }
   ],
   "source": [
    "print(data_n.head())\n",
    "print(data_n.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "           air_temp      humidity  wind_speed_unused          rain  \\\ncount  48196.000000  48196.000000       48196.000000  48196.000000   \nmean      14.543441     65.357934           0.419863      0.029474   \nstd       11.015772     18.660919           0.578189      0.420340   \nmin      -10.700000     13.000000           0.000000      0.000000   \n25%        5.400000     51.000000           0.000000      0.000000   \n50%       15.200000     67.000000           0.000000      0.000000   \n75%       23.300000     81.000000           0.900000      0.000000   \nmax       40.300000     98.000000           4.500000     25.900000   \n\n          solar_rad  cumulative_solar_irradiance  High_solar_rad  \\\ncount  48196.000000                 48196.000000    48196.000000   \nmean     161.534007                  6089.414910      181.735227   \nstd      261.562394                 10387.556118      287.785485   \nmin        0.000000                     0.000000        0.000000   \n25%        0.000000                     0.000000        0.000000   \n50%        1.000000                     1.000000        5.000000   \n75%      231.000000                  8508.000000      283.000000   \nmax     1200.000000                 47390.000000     1308.000000   \n\n       wind_direction         w_i_1         w_i_2  ...         w_o_1  \\\ncount    48196.000000  48196.000000  48196.000000  ...  48196.000000   \nmean         0.790367     17.853417     17.794514  ...     17.786470   \nstd          0.663622     14.446427     14.448561  ...     14.791306   \nmin          0.000000    -11.600000    -11.700000  ...    -12.500000   \n25%          0.320000      6.300000      6.300000  ...      6.000000   \n50%          0.600000     17.500000     17.400000  ...     17.600000   \n75%          1.070000     27.800000     27.800000  ...     27.800000   \nmax          7.340000     58.100000     58.200000  ...     58.600000   \n\n              w_o_2         w_o_3         w_o_4         w_o_5         w_o_6  \\\ncount  48196.000000  48196.000000  48196.000000  48196.000000  48196.000000   \nmean      17.740578     17.499670     17.161555     17.595230     17.457961   \nstd       14.748261     14.708806     14.512534     14.550834     14.548300   \nmin      -12.400000    -12.600000    -12.700000    -12.300000    -12.300000   \n25%        6.000000      5.800000      5.500000      6.000000      5.900000   \n50%       17.600000     17.300000     17.100000     17.500000     17.300000   \n75%       27.700000     27.500000     27.200000     27.600000     27.500000   \nmax       58.300000     58.100000     56.900000     57.500000     57.500000   \n\n              w_o_7         w_o_8          casi  rail_direction  \ncount  48196.000000  48196.000000  48196.000000         48196.0  \nmean      17.407353     16.869238   6447.731202            90.0  \nstd       14.384688     14.386927  10483.325895             0.0  \nmin      -12.100000    -12.700000      0.000000            90.0  \n25%        5.900000      5.400000      0.000000            90.0  \n50%       17.300000     16.800000     56.000000            90.0  \n75%       27.400000     26.900000   9904.250000            90.0  \nmax       56.800000     56.400000  47390.000000            90.0  \n\n[8 rows x 26 columns]\n"
     ]
    }
   ],
   "source": [
    "data_w.head()\n",
    "print(data_w.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "date                           0\n",
       "air_temp                       0\n",
       "humidity                       0\n",
       "wind_speed_unused              0\n",
       "wind_direction_unused          0\n",
       "rain                           0\n",
       "solar_rad                      0\n",
       "cumulative_solar_irradiance    0\n",
       "High_solar_rad                 0\n",
       "wind_speed                     2\n",
       "wind_direction                 0\n",
       "n_i_1                          0\n",
       "n_i_2                          0\n",
       "n_i_3                          0\n",
       "n_i_4                          0\n",
       "n_i_5                          0\n",
       "n_i_6                          0\n",
       "n_i_7                          0\n",
       "n_i_8                          0\n",
       "n_i_9                          0\n",
       "n_i_10                         0\n",
       "n_o_1                          0\n",
       "n_o_2                          0\n",
       "n_o_3                          0\n",
       "n_o_4                          0\n",
       "n_o_5                          0\n",
       "n_o_6                          0\n",
       "n_o_7                          0\n",
       "n_o_8                          0\n",
       "n_o_9                          0\n",
       "n_o_10                         0\n",
       "n_o_11                         0\n",
       "n_o_12                         0\n",
       "n_o_13                         0\n",
       "n_o_14                         0\n",
       "n_o_15                         0\n",
       "n_o_16                         0\n",
       "n_o_17                         0\n",
       "n_o_18                         0\n",
       "n_o_19                         0\n",
       "casi                           0\n",
       "rail_direction                 0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "data_n.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "date                           0\n",
       "air_temp                       0\n",
       "humidity                       0\n",
       "wind_speed_unused              0\n",
       "wind_direction_unused          0\n",
       "rain                           0\n",
       "solar_rad                      0\n",
       "cumulative_solar_irradiance    0\n",
       "High_solar_rad                 0\n",
       "wind_speed                     2\n",
       "wind_direction                 0\n",
       "w_i_1                          0\n",
       "w_i_2                          0\n",
       "w_i_3                          0\n",
       "w_i_4                          0\n",
       "w_i_5                          0\n",
       "w_i_6                          0\n",
       "w_i_7                          0\n",
       "w_i_8                          0\n",
       "w_o_1                          0\n",
       "w_o_2                          0\n",
       "w_o_3                          0\n",
       "w_o_4                          0\n",
       "w_o_5                          0\n",
       "w_o_6                          0\n",
       "w_o_7                          0\n",
       "w_o_8                          0\n",
       "casi                           0\n",
       "rail_direction                 0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "data_w.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **2.4. Dataframe Handling ; iloc, loc**\n",
    "> data를 확인했다면 다음은 data를 핸들링해보자!<br>\n",
    "> pandas에서 제공하는 **DataFrame** 형태의 자료형은 column과 index의 접근이 가능하죠.<br>\n",
    "> 그중에서도 iloc과 loc은 잘만 사용한다면, 데이터 필터링을 정말 손쉽게 할 수 있게 사용할 수 있습니다.<br>\n",
    "> ***(사실 정말 간단한 자료들의 필터링들은 엑셀이 제일편합니다)***<br><br>ლ(●ↀωↀ●)ლ<br><br>\n",
    "> 문제 : 이번에는 iloc과 loc을 사용해서 로딩한 자료들을 두개의 변수로 나눠봅시다.(ﾟдﾟ；) \n",
    ">    - 조건 1 : 새로운 dataframe 3개를 생성할 것\n",
    ">    - 조건 2 : 기후데이터, 북쪽방향 온도 데이터(북쪽 방향 레일 온도 데이터 전부), 서쪽방향 온도 데이터(서쪽 방향 레일 온도 데이터 전부)로 데이터 슬라이싱 후 앞서 만든 변수에 저장!(loc, iloc 둘중 하나 사용)\n",
    ">    - 조건 3 : 모든 dataframe에는 date column이 제일 먼저 포함되어 있어야 함."
   ]
  },
  {
   "source": [],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 핸들링 편의성을 위해, date column을 datetime 형태로 변환\n",
    "data_n.date = pd.to_datetime(data_n.date)\n",
    "data_w.date = pd.to_datetime(data_w.date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                 date  air_temp  humidity  rain  solar_rad  \\\n",
       "0 2018-07-20 00:00:00      27.8        72   0.0          0   \n",
       "1 2018-07-20 00:10:00      27.7        72   0.0          0   \n",
       "2 2018-07-20 00:20:00      27.6        72   0.0          0   \n",
       "3 2018-07-20 00:30:00      27.5        72   0.0          0   \n",
       "4 2018-07-20 00:40:00      27.3        73   0.0          0   \n",
       "\n",
       "   cumulative_solar_irradiance  High_solar_rad wind_speed  wind_direction  \n",
       "0                            0               0        242            0.60  \n",
       "1                            0               0        197            0.46  \n",
       "2                            0               0        203            0.18  \n",
       "3                            0               0        253            0.49  \n",
       "4                            0               0        190            0.42  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>air_temp</th>\n      <th>humidity</th>\n      <th>rain</th>\n      <th>solar_rad</th>\n      <th>cumulative_solar_irradiance</th>\n      <th>High_solar_rad</th>\n      <th>wind_speed</th>\n      <th>wind_direction</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2018-07-20 00:00:00</td>\n      <td>27.8</td>\n      <td>72</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>242</td>\n      <td>0.60</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2018-07-20 00:10:00</td>\n      <td>27.7</td>\n      <td>72</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>197</td>\n      <td>0.46</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2018-07-20 00:20:00</td>\n      <td>27.6</td>\n      <td>72</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>203</td>\n      <td>0.18</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2018-07-20 00:30:00</td>\n      <td>27.5</td>\n      <td>72</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>253</td>\n      <td>0.49</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2018-07-20 00:40:00</td>\n      <td>27.3</td>\n      <td>73</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>190</td>\n      <td>0.42</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "climate_data = data_n.iloc[:, 0:11]\n",
    "# 필요없는 Column들을 삭제\n",
    "climate_data.drop([\"wind_speed_unused\",\"wind_direction_unused\"], axis=1, inplace=True)\n",
    "climate_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                 date  n_i_1  n_i_2  n_i_3  n_i_4  n_i_5  n_i_6  n_i_7  n_i_8  \\\n",
       "0 2018-07-20 00:00:00   28.7   28.7   28.6   28.7   28.7   28.7   28.7   28.9   \n",
       "1 2018-07-20 00:10:00   28.5   28.5   28.5   28.5   28.5   28.5   28.5   28.7   \n",
       "2 2018-07-20 00:20:00   28.4   28.4   28.3   28.4   28.4   28.4   28.4   28.6   \n",
       "3 2018-07-20 00:30:00   28.2   28.2   28.1   28.2   28.2   28.2   28.2   28.4   \n",
       "4 2018-07-20 00:40:00   28.1   28.1   28.1   28.0   28.1   28.1   28.0   28.3   \n",
       "\n",
       "   n_i_9  ...  n_o_9  n_o_10  n_o_11  n_o_12  n_o_13  n_o_14  n_o_15  n_o_16  \\\n",
       "0   28.8  ...   28.6    28.6    28.6    28.6    28.8    28.5    28.7    28.9   \n",
       "1   28.7  ...   28.4    28.5    28.5    28.5    28.7    28.4    28.5    28.8   \n",
       "2   28.5  ...   28.2    28.3    28.4    28.4    28.5    28.3    28.5    28.6   \n",
       "3   28.4  ...   28.1    28.1    28.2    28.2    28.4    28.0    28.3    28.5   \n",
       "4   28.2  ...   28.0    28.0    28.1    28.2    28.2    27.9    28.2    28.4   \n",
       "\n",
       "   n_o_17  n_o_18  \n",
       "0    29.1    28.9  \n",
       "1    28.9    28.9  \n",
       "2    28.7    28.7  \n",
       "3    28.6    28.5  \n",
       "4    28.6    28.5  \n",
       "\n",
       "[5 rows x 29 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>n_i_1</th>\n      <th>n_i_2</th>\n      <th>n_i_3</th>\n      <th>n_i_4</th>\n      <th>n_i_5</th>\n      <th>n_i_6</th>\n      <th>n_i_7</th>\n      <th>n_i_8</th>\n      <th>n_i_9</th>\n      <th>...</th>\n      <th>n_o_9</th>\n      <th>n_o_10</th>\n      <th>n_o_11</th>\n      <th>n_o_12</th>\n      <th>n_o_13</th>\n      <th>n_o_14</th>\n      <th>n_o_15</th>\n      <th>n_o_16</th>\n      <th>n_o_17</th>\n      <th>n_o_18</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2018-07-20 00:00:00</td>\n      <td>28.7</td>\n      <td>28.7</td>\n      <td>28.6</td>\n      <td>28.7</td>\n      <td>28.7</td>\n      <td>28.7</td>\n      <td>28.7</td>\n      <td>28.9</td>\n      <td>28.8</td>\n      <td>...</td>\n      <td>28.6</td>\n      <td>28.6</td>\n      <td>28.6</td>\n      <td>28.6</td>\n      <td>28.8</td>\n      <td>28.5</td>\n      <td>28.7</td>\n      <td>28.9</td>\n      <td>29.1</td>\n      <td>28.9</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2018-07-20 00:10:00</td>\n      <td>28.5</td>\n      <td>28.5</td>\n      <td>28.5</td>\n      <td>28.5</td>\n      <td>28.5</td>\n      <td>28.5</td>\n      <td>28.5</td>\n      <td>28.7</td>\n      <td>28.7</td>\n      <td>...</td>\n      <td>28.4</td>\n      <td>28.5</td>\n      <td>28.5</td>\n      <td>28.5</td>\n      <td>28.7</td>\n      <td>28.4</td>\n      <td>28.5</td>\n      <td>28.8</td>\n      <td>28.9</td>\n      <td>28.9</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2018-07-20 00:20:00</td>\n      <td>28.4</td>\n      <td>28.4</td>\n      <td>28.3</td>\n      <td>28.4</td>\n      <td>28.4</td>\n      <td>28.4</td>\n      <td>28.4</td>\n      <td>28.6</td>\n      <td>28.5</td>\n      <td>...</td>\n      <td>28.2</td>\n      <td>28.3</td>\n      <td>28.4</td>\n      <td>28.4</td>\n      <td>28.5</td>\n      <td>28.3</td>\n      <td>28.5</td>\n      <td>28.6</td>\n      <td>28.7</td>\n      <td>28.7</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2018-07-20 00:30:00</td>\n      <td>28.2</td>\n      <td>28.2</td>\n      <td>28.1</td>\n      <td>28.2</td>\n      <td>28.2</td>\n      <td>28.2</td>\n      <td>28.2</td>\n      <td>28.4</td>\n      <td>28.4</td>\n      <td>...</td>\n      <td>28.1</td>\n      <td>28.1</td>\n      <td>28.2</td>\n      <td>28.2</td>\n      <td>28.4</td>\n      <td>28.0</td>\n      <td>28.3</td>\n      <td>28.5</td>\n      <td>28.6</td>\n      <td>28.5</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2018-07-20 00:40:00</td>\n      <td>28.1</td>\n      <td>28.1</td>\n      <td>28.1</td>\n      <td>28.0</td>\n      <td>28.1</td>\n      <td>28.1</td>\n      <td>28.0</td>\n      <td>28.3</td>\n      <td>28.2</td>\n      <td>...</td>\n      <td>28.0</td>\n      <td>28.0</td>\n      <td>28.1</td>\n      <td>28.2</td>\n      <td>28.2</td>\n      <td>27.9</td>\n      <td>28.2</td>\n      <td>28.4</td>\n      <td>28.6</td>\n      <td>28.5</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 29 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "temp_data_n = pd.concat([data_n[\"date\"], data_n.loc[:, \"n_i_1\":\"n_o_18\"]], axis=1)\n",
    "temp_data_n.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                 date  w_i_1  w_i_2  w_i_3  w_i_4  w_i_5  w_i_6  w_i_7  w_i_8  \\\n",
       "0 2018-07-20 00:00:00   28.6   28.5   28.7   28.7   28.7   28.9   28.8   28.8   \n",
       "1 2018-07-20 00:10:00   28.3   28.4   28.5   28.5   28.6   28.6   28.6   28.6   \n",
       "2 2018-07-20 00:20:00   28.2   28.2   28.3   28.3   28.4   28.4   28.3   28.4   \n",
       "3 2018-07-20 00:30:00   28.1   28.1   28.3   28.3   28.3   28.4   28.3   28.4   \n",
       "4 2018-07-20 00:40:00   27.9   27.9   28.1   28.1   28.1   28.2   28.1   28.2   \n",
       "\n",
       "   w_o_1  w_o_2  w_o_3  w_o_4  w_o_5  w_o_6  w_o_7  w_o_8  \n",
       "0   28.3   28.3   28.1   28.1   28.3   28.4   28.5   27.9  \n",
       "1   28.2   28.4   28.3   27.8   28.3   28.4   28.6   27.8  \n",
       "2   28.1   28.2   27.9   27.7   28.3   28.1   28.6   27.6  \n",
       "3   27.9   27.9   27.7   27.6   27.9   28.0   27.9   27.7  \n",
       "4   27.5   27.6   27.6   27.4   27.7   27.7   27.8   27.2  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>w_i_1</th>\n      <th>w_i_2</th>\n      <th>w_i_3</th>\n      <th>w_i_4</th>\n      <th>w_i_5</th>\n      <th>w_i_6</th>\n      <th>w_i_7</th>\n      <th>w_i_8</th>\n      <th>w_o_1</th>\n      <th>w_o_2</th>\n      <th>w_o_3</th>\n      <th>w_o_4</th>\n      <th>w_o_5</th>\n      <th>w_o_6</th>\n      <th>w_o_7</th>\n      <th>w_o_8</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2018-07-20 00:00:00</td>\n      <td>28.6</td>\n      <td>28.5</td>\n      <td>28.7</td>\n      <td>28.7</td>\n      <td>28.7</td>\n      <td>28.9</td>\n      <td>28.8</td>\n      <td>28.8</td>\n      <td>28.3</td>\n      <td>28.3</td>\n      <td>28.1</td>\n      <td>28.1</td>\n      <td>28.3</td>\n      <td>28.4</td>\n      <td>28.5</td>\n      <td>27.9</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2018-07-20 00:10:00</td>\n      <td>28.3</td>\n      <td>28.4</td>\n      <td>28.5</td>\n      <td>28.5</td>\n      <td>28.6</td>\n      <td>28.6</td>\n      <td>28.6</td>\n      <td>28.6</td>\n      <td>28.2</td>\n      <td>28.4</td>\n      <td>28.3</td>\n      <td>27.8</td>\n      <td>28.3</td>\n      <td>28.4</td>\n      <td>28.6</td>\n      <td>27.8</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2018-07-20 00:20:00</td>\n      <td>28.2</td>\n      <td>28.2</td>\n      <td>28.3</td>\n      <td>28.3</td>\n      <td>28.4</td>\n      <td>28.4</td>\n      <td>28.3</td>\n      <td>28.4</td>\n      <td>28.1</td>\n      <td>28.2</td>\n      <td>27.9</td>\n      <td>27.7</td>\n      <td>28.3</td>\n      <td>28.1</td>\n      <td>28.6</td>\n      <td>27.6</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2018-07-20 00:30:00</td>\n      <td>28.1</td>\n      <td>28.1</td>\n      <td>28.3</td>\n      <td>28.3</td>\n      <td>28.3</td>\n      <td>28.4</td>\n      <td>28.3</td>\n      <td>28.4</td>\n      <td>27.9</td>\n      <td>27.9</td>\n      <td>27.7</td>\n      <td>27.6</td>\n      <td>27.9</td>\n      <td>28.0</td>\n      <td>27.9</td>\n      <td>27.7</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2018-07-20 00:40:00</td>\n      <td>27.9</td>\n      <td>27.9</td>\n      <td>28.1</td>\n      <td>28.1</td>\n      <td>28.1</td>\n      <td>28.2</td>\n      <td>28.1</td>\n      <td>28.2</td>\n      <td>27.5</td>\n      <td>27.6</td>\n      <td>27.6</td>\n      <td>27.4</td>\n      <td>27.7</td>\n      <td>27.7</td>\n      <td>27.8</td>\n      <td>27.2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "temp_data_w = pd.concat([data_w[\"date\"], data_w.loc[:, \"w_i_1\":\"w_o_8\"]], axis=1)\n",
    "temp_data_w.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **2.5. Define Function ; maximum, minimum and temperature difference**\n",
    "> 앞으로 코딩하면서 정말 정말 많이 만들게 될 함수부분을 만들어보자!<br>\n",
    "> 함수는 정말 잘 만들어 놓으면 평-생 써먹을 수 있기 때문에 이번에 한번 빡세게 만들어 보는게 좋을 것 같아서 준비했습니다!! ⊂((・▽・))⊃ <br>\n",
    ">    - 함수의 목적 : 레일 데이터 자료 한 **행**을 받아 **최대값**과 **최소값**, 그리고 **최대값과 최소값의 차이**를 구하는 함수를 만들어 보자 <br>\n",
    ">    - 함수의 조건 : 입력 받는 데이터는 레일 데이터 한행만 받음.\n",
    ">    - **함수의 리턴 값 : 데이터의 최대값, 최소값, 최대값 최소값의 차이**<br>\n",
    ">    - 함수를 생성한 후, 아래의 문제를 해결하자!\n",
    "\n",
    "\n",
    "> 문제 : 10분 간격으로 계측된 레일 온도 데이터에 대하여 최대값과 최소값 그리고 그 차이를 구해보자.<br>\n",
    ">    - 문제 조건 1 : 함수를 생성해서 사용할 것.\n",
    ">    - 문제 조건 2 : 10분 간격으로 최대값 최소값 그리고 그 차이를 구할 것."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def temp(data_row):\n",
    "    data_row_max = data_row.max()\n",
    "    data_row_min = data_row.min()\n",
    "    data_row_diff = data_row_max - data_row_min\n",
    "    return [data_row_max, data_row_min, data_row_diff]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                 date   max   min  diff\n",
       "0 2018-07-20 00:00:00  29.1  28.5   0.6\n",
       "1 2018-07-20 00:10:00  28.9  28.4   0.5\n",
       "2 2018-07-20 00:20:00  28.7  28.2   0.5\n",
       "3 2018-07-20 00:30:00  28.6  28.0   0.6\n",
       "4 2018-07-20 00:40:00  28.6  27.9   0.7"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>max</th>\n      <th>min</th>\n      <th>diff</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2018-07-20 00:00:00</td>\n      <td>29.1</td>\n      <td>28.5</td>\n      <td>0.6</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2018-07-20 00:10:00</td>\n      <td>28.9</td>\n      <td>28.4</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2018-07-20 00:20:00</td>\n      <td>28.7</td>\n      <td>28.2</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2018-07-20 00:30:00</td>\n      <td>28.6</td>\n      <td>28.0</td>\n      <td>0.6</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2018-07-20 00:40:00</td>\n      <td>28.6</td>\n      <td>27.9</td>\n      <td>0.7</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "temp_diff_n = pd.DataFrame(columns=[\"date\",\"max\",\"min\",\"diff\"])\n",
    "temp_diff_n.date = temp_data_n.date\n",
    "imsi = temp_data_n.drop([\"date\"], axis=1)\n",
    "temp_diff_n[['max', 'min', 'diff']] = imsi.apply(lambda x:pd.Series(temp(x)), axis=1 )\n",
    "temp_diff_n.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                 date   max   min  diff\n",
       "0 2018-07-20 00:00:00  28.9  27.9   1.0\n",
       "1 2018-07-20 00:10:00  28.6  27.8   0.8\n",
       "2 2018-07-20 00:20:00  28.6  27.6   1.0\n",
       "3 2018-07-20 00:30:00  28.4  27.6   0.8\n",
       "4 2018-07-20 00:40:00  28.2  27.2   1.0"
      ],
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     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "temp_diff_w = pd.DataFrame(columns=[\"date\",\"max\",\"min\",\"diff\"])\n",
    "temp_diff_w.date = temp_data_w.date\n",
    "imsi = temp_data_w.drop([\"date\"], axis=1)\n",
    "temp_diff_w[['max', 'min', 'diff']] = imsi.apply(lambda x:pd.Series(temp(x)), axis=1 )\n",
    "temp_diff_w.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **2.6. Data Visualization**\n",
    "앞에서 구한 최대값과 최소값 그리고 그 차이를 시각화 해보자!!!\n",
    "\n",
    "> 데이터가 커지다보면, 데이터를 하나하나 보는 것보다 시각화해서 보는게 훨씬 좋습니다.\n",
    "> 앞에서 구했던 최대값 최소값 그리고 그 차이값을 시각화해보도록 하겠습니다.<br>\n",
    "> matplotlib 라이브러리에서는 다양한 형태의 시각화 plot을 제공합니다.<br>\n",
    "> 인터넷 검색을 통해, 본 데이터를 가장 잘 표현할 수 있는 plot을 3가지 이상 표현하면 됩니다!<br>\n",
    "> 추천 : scatter, boxplot<br>\n",
    "\n",
    "> 문제 : 앞서 구한 데이터들을 가장 잘 표현할 수 있는 데이터 3가지를 그려보자!<br>\n",
    "> 그래프 그리는 조건 1 : 기본 색상 외 다른 색상을 적용할 것.<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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70.059927 \nL 86.842299 67.687855 \nL 86.865399 67.424291 \nL 86.87695 74.013382 \nL 87.055977 117.501382 \nL 87.34473 129.625309 \nL 87.379381 127.253236 \nL 87.437132 105.641018 \nL 87.529533 72.168436 \nL 87.593059 75.067636 \nL 87.610384 70.323491 \nL 88.014639 117.764945 \nL 88.147465 119.346327 \nL 88.153241 119.082764 \nL 88.159016 119.082764 \nL 88.164791 119.346327 \nL 88.210991 116.447127 \nL 88.297617 98.5248 \nL 88.390019 74.804073 \nL 88.424669 79.284655 \nL 88.580596 117.501382 \nL 88.609471 116.710691 \nL 88.655672 115.92 \nL 88.626796 116.974255 \nL 88.719198 116.183564 \nL 88.89245 123.036218 \nL 88.898225 122.509091 \nL 88.904 122.509091 \nL 88.979076 122.772655 \nL 88.932875 122.245527 \nL 89.007951 122.245527 \nL 89.013726 122.245527 \nL 89.019501 122.509091 \nL 89.048377 119.873455 \nL 89.123453 87.718691 \nL 89.273605 59.517382 \nL 89.27938 60.8352 \nL 89.29093 59.253818 \nL 89.348681 65.052218 \nL 89.371781 64.788655 \nL 89.400656 80.866036 \nL 89.556583 121.7184 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93.535608 56.354618 \nL 93.587584 93.253527 \nL 93.755061 125.144727 \nL 93.968738 134.633018 \nL 93.980289 134.633018 \nL 93.991839 135.423709 \nL 94.038039 132.524509 \nL 94.06114 129.888873 \nL 94.228617 64.261527 \nL 94.286367 62.416582 \nL 94.424969 111.702982 \nL 94.690622 133.051636 \nL 94.817674 138.059345 \nL 94.852324 135.687273 \nL 94.89275 131.733818 \nL 95.031352 75.3312 \nL 95.048677 77.703273 \nL 95.193054 101.951127 \nL 95.210379 98.5248 \nL 95.418282 129.888873 \nL 95.620409 138.322909 \nL 95.626184 138.322909 \nL 95.672385 139.377164 \nL 95.71281 136.477964 \nL 95.73591 120.927709 \nL 95.891837 67.160727 \nL 95.932263 71.377745 \nL 95.949588 63.7344 \nL 95.961138 63.997964 \nL 95.966913 63.470836 \nL 95.984239 70.850618 \nL 95.990014 68.214982 \nL 96.174816 116.447127 \nL 96.365393 123.826909 \nL 96.371168 123.826909 \nL 96.48667 126.198982 \nL 96.49822 125.671855 \nL 96.503995 125.671855 \nL 96.769648 109.858036 \nL 96.827399 91.935709 \nL 96.88515 106.958836 \nL 97.127703 125.671855 \nL 97.202779 125.144727 \nL 97.220104 126.198982 \nL 97.225879 125.935418 \nL 97.335606 126.989673 \nL 97.341381 126.462545 \nL 97.595484 113.0208 \nL 97.68211 115.92 \nL 97.687885 115.92 \nL 97.838037 121.7184 \nL 97.861137 121.191273 \nL 97.970863 122.772655 \nL 97.982414 121.981964 \nL 98.051714 123.826909 \nL 98.05749 123.563345 \nL 98.155666 124.354036 \nL 98.172991 124.090473 \nL 98.196091 124.6176 \nL 98.271167 115.656436 \nL 98.398219 105.641018 \nL 98.409769 106.958836 \nL 98.571471 114.075055 \nL 98.744723 120.400582 \nL 98.762048 119.609891 \nL 98.808249 121.981964 \nL 98.814024 121.7184 \nL 98.946851 124.354036 \nL 99.166303 94.571345 \nL 98.975726 124.6176 \nL 99.270255 104.850327 \nL 99.27603 104.850327 \nL 99.28758 104.586764 \nL 99.414632 116.183564 \nL 99.634084 126.198982 \nL 99.639859 126.198982 \nL 99.65141 125.671855 \nL 99.668735 126.462545 \nL 99.749586 125.935418 \nL 99.801561 126.989673 \nL 99.847762 124.881164 \nL 99.928613 92.7264 \nL 100.078765 63.997964 \nL 100.142291 61.362327 \nL 100.10764 68.478545 \nL 100.176941 64.788655 \nL 100.228917 93.253527 \nL 100.396394 126.989673 \nL 100.402169 126.989673 \nL 100.604296 135.687273 \nL 100.610072 135.687273 \nL 100.656272 137.005091 \nL 100.679372 133.3152 \nL 100.760223 89.8272 \nL 100.91615 56.881745 \nL 100.939251 55.827491 \nL 101.025877 66.370036 \nL 101.187579 123.299782 \nL 101.395481 132.260945 \nL 101.401256 132.260945 \nL 101.412807 132.524509 \nL 101.557183 120.137018 \nL 101.71311 90.090764 \nL 101.765086 91.672145 \nL 102.209767 122.245527 \nL 102.232867 121.981964 \nL 102.284843 123.299782 \nL 102.388794 127.253236 \nL 102.411894 125.671855 \nL 102.527396 113.811491 \nL 102.567821 115.392873 \nL 102.573596 115.392873 \nL 102.579371 115.129309 \nL 102.625572 117.501382 \nL 102.775724 124.354036 \nL 102.781499 124.090473 \nL 102.879675 126.198982 \nL 102.9201 125.671855 \nL 102.925876 125.408291 \nL 102.966301 128.571055 \nL 102.972076 128.571055 \nL 103.128003 130.679564 \nL 103.133778 130.679564 \nL 103.399431 120.137018 \nL 103.428307 122.245527 \nL 103.434082 123.563345 \nL 103.514933 119.082764 \nL 103.532258 121.191273 \nL 103.71706 128.043927 \nL 103.722836 128.043927 \nL 103.728611 127.780364 \nL 103.769036 129.625309 \nL 103.780586 129.625309 \nL 103.924963 135.423709 \nL 103.930738 135.160145 \nL 103.971164 135.687273 \nL 104.005814 133.051636 \nL 104.063565 114.075055 \nL 104.202167 77.439709 \nL 104.207942 77.966836 \nL 104.219492 74.804073 \nL 104.300343 62.416582 \nL 104.340768 69.5328 \nL 104.548671 133.3152 \nL 104.693048 140.431418 \nL 104.727698 139.640727 \nL 104.745023 139.640727 \nL 104.773899 139.904291 \nL 104.825874 138.322909 \nL 104.87785 131.206691 \nL 105.039552 82.974545 \nL 105.051102 86.137309 \nL 105.108853 84.0288 \nL 105.270555 126.462545 \nL 105.345631 134.896582 \nL 105.524658 142.539927 \nL 105.536208 142.276364 \nL 105.64016 144.384873 \nL 105.66326 143.330618 \nL 105.784536 101.160436 \nL 105.975114 74.540509 \nL 106.009764 80.075345 \nL 106.171466 138.586473 \nL 106.177241 138.586473 \nL 106.338943 148.338327 \nL 106.344718 148.338327 \nL 106.48332 152.555345 \nL 106.512195 151.237527 \nL 106.546846 141.222109 \nL 106.702773 74.013382 \nL 106.777849 66.6336 \nL 106.818274 71.377745 \nL 107.089703 137.532218 \nL 107.234079 139.1136 \nL 107.25718 139.1136 \nL 107.30338 139.640727 \nL 107.332256 138.322909 \nL 107.482408 104.059636 \nL 107.597909 72.432 \nL 107.638335 81.393164 \nL 107.823137 133.051636 \nL 108.146541 148.865455 \nL 108.192741 145.702691 \nL 108.215842 129.888873 \nL 108.360219 72.959127 \nL 108.365994 75.3312 \nL 108.371769 76.385455 \nL 108.423744 66.6336 \nL 108.458395 63.997964 \nL 108.48727 68.742109 \nL 108.695173 132.788073 \nL 108.966601 145.966255 \nL 109.001252 142.803491 \nL 109.145628 104.3232 \nL 109.255355 110.1216 \nL 109.544108 134.633018 \nL 109.798212 145.439127 \nL 109.844412 143.330618 \nL 109.873288 137.268655 \nL 110.023439 71.114182 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114.169941 81.1296 \nL 114.175716 82.183855 \nL 114.181491 82.183855 \nL 114.204592 88.772945 \nL 114.262342 79.284655 \nL 114.273893 77.703273 \nL 114.302768 94.834909 \nL 114.493345 128.043927 \nL 114.793649 137.005091 \nL 115.053527 127.780364 \nL 115.082403 128.307491 \nL 115.221004 132.260945 \nL 115.22678 132.260945 \nL 115.371156 133.842327 \nL 115.388482 133.842327 \nL 115.538633 135.950836 \nL 115.550184 135.687273 \nL 115.62526 136.477964 \nL 115.642585 136.2144 \nL 115.65991 136.2144 \nL 115.867813 121.981964 \nL 115.879363 115.656436 \nL 115.919788 123.826909 \nL 115.977539 121.454836 \nL 116.029515 128.307491 \nL 116.110366 135.423709 \nL 116.168116 134.369455 \nL 116.196992 134.369455 \nL 116.45687 144.912 \nL 116.485745 142.803491 \nL 116.526171 138.850036 \nL 116.699423 72.959127 \nL 116.728298 70.850618 \nL 116.734073 77.703273 \nL 116.924651 135.423709 \nL 117.24228 154.663855 \nL 117.248055 154.663855 \nL 117.282705 155.190982 \nL 117.317356 152.818909 \nL 117.375106 138.322909 \nL 117.588784 74.540509 \nL 117.634985 83.765236 \nL 117.877538 150.446836 \nL 117.906413 150.973964 \nL 117.981489 150.446836 \nL 118.120091 149.129018 \nL 118.125866 149.129018 \nL 118.195167 141.222109 \nL 118.345319 101.951127 \nL 118.397294 110.648727 \nL 118.610972 139.1136 \nL 118.616747 139.1136 \nL 118.784224 141.749236 \nL 118.795774 141.749236 \nL 118.911276 138.059345 \nL 118.963251 139.1136 \nL 118.997902 140.694982 \nL 119.021002 139.904291 \nL 119.148054 112.757236 \nL 119.165379 117.501382 \nL 119.171154 118.8192 \nL 119.217355 105.904582 \nL 119.22313 102.478255 \nL 119.303981 115.92 \nL 119.454133 151.501091 \nL 119.794862 163.097891 \nL 119.806412 162.834327 \nL 119.858388 152.818909 \nL 119.927688 105.641018 \nL 119.991214 111.966545 \nL 120.008539 119.082764 \nL 120.048965 100.633309 \nL 120.05474 97.734109 \nL 120.118266 116.447127 \nL 120.268418 146.229818 \nL 120.586047 165.2064 \nL 120.620697 165.997091 \nL 120.661123 163.888582 \nL 120.689998 157.299491 \nL 120.84015 92.199273 \nL 120.921001 85.346618 \nL 120.949876 91.145018 \nL 121.279055 160.989382 \nL 121.452308 166.260655 \nL 121.475408 165.2064 \nL 121.521608 158.090182 \nL 121.677535 85.610182 \nL 121.723736 78.2304 \nL 121.775712 84.819491 \nL 122.006714 150.973964 \nL 122.214617 159.935127 \nL 122.283918 161.780073 \nL 122.312793 159.935127 \nL 122.353219 153.6096 \nL 122.503371 84.819491 \nL 122.509146 85.873745 \nL 122.797899 143.067055 \nL 123.063553 157.299491 \nL 123.069328 157.299491 \nL 123.121303 158.353745 \nL 123.144404 155.981673 \nL 123.23103 120.400582 \nL 123.404282 84.555927 \nL 123.415832 84.555927 \nL 123.762336 149.656145 \nL 123.872063 153.082473 \nL 123.935589 152.555345 \nL 123.941364 152.555345 \nL 124.01644 145.175564 \nL 124.212792 117.237818 \nL 124.224342 118.292073 \nL 124.784524 160.462255 \nL 124.258993 113.811491 \nL 124.790299 160.198691 \nL 124.84805 153.346036 \nL 125.009752 86.400873 \nL 125.055953 85.083055 \nL 125.102153 91.935709 \nL 125.263855 146.229818 \nL 125.575709 164.942836 \nL 125.581484 164.942836 \nL 125.604585 165.733527 \nL 125.650785 163.361455 \nL 125.685436 151.501091 \nL 125.835587 87.191564 \nL 125.841362 87.455127 \nL 125.858688 86.400873 \nL 125.968414 118.8192 \nL 126.158991 153.082473 \nL 126.343794 160.198691 \nL 126.349569 160.198691 \nL 126.45352 162.043636 \nL 126.470845 160.989382 \nL 126.511271 156.772364 \nL 126.678748 87.455127 \nL 126.724949 84.0288 \nL 126.759599 88.509382 \nL 126.927076 147.8112 \nL 127.20428 163.361455 \nL 127.210055 163.361455 \nL 127.285131 164.942836 \nL 127.314006 163.097891 \nL 127.348656 152.555345 \nL 127.516133 81.1296 \nL 127.579659 76.649018 \nL 127.591209 81.920291 \nL 127.764462 147.020509 \nL 128.110966 165.733527 \nL 128.162942 161.252945 \nL 128.197592 135.687273 \nL 128.370844 79.284655 \nL 128.417045 84.292364 \nL 128.596072 147.020509 \nL 128.80975 157.563055 \nL 128.815525 157.299491 \nL 128.827075 157.299491 \nL 128.884826 157.563055 \nL 129.011877 145.702691 \nL 129.214005 87.191564 \nL 129.21978 88.509382 \nL 129.456558 140.431418 \nL 129.681786 156.5088 \nL 129.762637 157.826618 \nL 129.779962 156.5088 \nL 129.935889 134.369455 \nL 130.01674 101.160436 \nL 130.080266 109.594473 \nL 130.270843 148.074764 \nL 130.617347 161.516509 \nL 130.640448 159.935127 \nL 130.675098 153.082473 \nL 130.831025 88.509382 \nL 130.883001 82.710982 \nL 130.911876 92.199273 \nL 131.085128 145.966255 \nL 131.379657 163.097891 \nL 131.385432 163.097891 \nL 131.408532 163.625018 \nL 131.443183 160.725818 \nL 131.506708 149.392582 \nL 131.66841 84.0288 \nL 131.674186 85.346618 \nL 131.720386 78.493964 \nL 131.986039 151.237527 \nL 131.99759 151.237527 \nL 132.089991 152.028218 \nL 132.113091 151.501091 \nL 132.118866 151.501091 \nL 132.211267 150.446836 \nL 132.228592 151.237527 \nL 132.286343 152.028218 \nL 132.320994 150.7104 \nL 132.326769 150.7104 \nL 132.482696 146.493382 \nL 132.500021 146.756945 \nL 132.557772 144.912 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134.758074 162.834327 \nL 134.763849 162.834327 \nL 134.83315 156.245236 \nL 134.977527 139.904291 \nL 135.006402 126.726109 \nL 135.093028 139.1136 \nL 135.266281 159.671564 \nL 135.283606 158.880873 \nL 135.300931 158.353745 \nL 135.364457 161.252945 \nL 135.60701 175.221818 \nL 135.61856 174.694691 \nL 135.66476 169.950545 \nL 135.803362 109.594473 \nL 135.820687 114.865745 \nL 135.832238 111.966545 \nL 135.861113 121.454836 \nL 135.889988 120.927709 \nL 136.05169 162.043636 \nL 136.230718 169.423418 \nL 136.276918 167.314909 \nL 136.502146 154.927418 \nL 136.658073 103.796073 \nL 136.692723 101.424 \nL 136.710049 102.741818 \nL 136.871751 158.353745 \nL 137.137404 172.586182 \nL 137.264456 175.748945 \nL 137.287556 174.431127 \nL 137.327981 169.686982 \nL 137.507009 99.579055 \nL 137.518559 99.579055 \nL 137.535884 98.788364 \nL 137.547434 105.904582 \nL 137.726461 159.408 \nL 137.974789 172.322618 \nL 137.980564 172.322618 \nL 138.096066 174.958255 \nL 138.119166 173.904 \nL 138.165367 164.152145 \nL 138.338619 89.563636 \nL 138.361719 87.455127 \nL 138.38482 101.951127 \nL 138.569622 156.5088 \nL 138.8295 170.477673 \nL 138.835275 170.477673 \nL 138.933451 172.586182 \nL 138.950777 171.268364 \nL 138.996977 162.3072 \nL 139.170229 91.672145 \nL 139.187555 90.354327 \nL 139.210655 100.369745 \nL 139.401232 155.454545 \nL 139.661111 169.159855 \nL 139.666886 169.159855 \nL 139.765062 171.0048 \nL 139.788162 169.686982 \nL 139.834363 155.454545 \nL 140.007615 91.935709 \nL 140.01339 92.199273 \nL 140.019165 91.935709 \nL 140.030715 95.098473 \nL 140.203967 153.082473 \nL 140.331019 163.097891 \nL 141.174179 160.725818 \nL 141.179955 160.989382 \nL 141.347432 166.260655 \nL 141.353207 166.260655 \nL 141.410957 167.578473 \nL 141.434058 165.469964 \nL 141.543784 135.687273 \nL 141.647735 101.160436 \nL 141.717036 114.865745 \nL 141.901839 150.183273 \nL 142.01734 151.501091 \nL 142.023115 151.237527 \nL 142.046215 151.237527 \nL 142.086641 150.973964 \nL 142.138617 151.501091 \nL 142.150167 151.501091 \nL 142.265668 152.291782 \nL 142.271443 152.028218 \nL 142.277218 152.028218 \nL 142.462021 139.904291 \nL 142.577522 143.067055 \nL 142.77965 147.284073 \nL 142.808525 147.284073 \nL 142.947127 148.601891 \nL 142.958677 148.601891 \nL 143.085728 147.8112 \nL 143.091503 147.8112 \nL 143.293631 142.0128 \nL 143.322506 142.276364 \nL 143.345607 142.276364 \nL 143.380257 142.0128 \nL 143.420683 142.539927 \nL 143.449558 142.539927 \nL 143.455333 142.803491 \nL 143.501534 140.167855 \nL 143.518859 139.904291 \nL 143.559284 142.539927 \nL 143.761412 150.973964 \nL 143.772962 150.973964 \nL 143.923114 152.818909 \nL 143.928889 152.818909 \nL 144.107916 139.904291 \nL 144.165667 121.454836 \nL 144.223418 138.059345 \nL 144.460196 161.516509 \nL 144.621898 165.469964 \nL 144.644998 164.942836 \nL 144.679648 164.942836 \nL 144.743174 165.733527 \nL 144.77205 164.415709 \nL 144.8298 149.129018 \nL 144.945302 112.493673 \nL 144.985727 120.137018 \nL 145.222505 154.663855 \nL 145.447733 164.415709 \nL 145.482383 161.780073 \nL 145.748037 144.648436 \nL 145.765362 143.067055 \nL 145.851988 145.175564 \nL 145.880863 144.121309 \nL 146.169617 154.663855 \nL 146.175392 154.400291 \nL 146.221593 156.772364 \nL 146.42372 168.369164 \nL 146.446821 167.314909 \nL 146.481471 163.361455 \nL 146.660498 103.796073 \nL 146.666273 106.431709 \nL 146.827975 156.245236 \nL 147.093629 170.477673 \nL 147.191805 172.586182 \nL 147.22068 171.268364 \nL 147.23223 171.268364 \nL 147.295756 163.888582 \nL 147.365057 131.997382 \nL 147.492109 104.850327 \nL 147.503659 112.493673 \nL 147.676911 156.245236 \nL 147.861713 166.787782 \nL 147.867488 166.787782 \nL 148.086941 172.322618 \nL 148.092716 172.059055 \nL 148.156242 157.035927 \nL 148.306394 97.997673 \nL 148.329494 104.850327 \nL 148.508521 154.663855 \nL 148.77995 169.423418 \nL 148.785725 169.423418 \nL 148.912776 172.322618 \nL 148.918551 172.059055 \nL 148.976302 164.415709 \nL 149.120679 110.648727 \nL 149.20153 125.408291 \nL 149.444083 162.3072 \nL 149.524934 164.415709 \nL 149.571135 163.097891 \nL 149.582685 163.097891 \nL 149.698186 164.415709 \nL 149.60001 162.834327 \nL 149.715511 164.152145 \nL 149.819463 154.400291 \nL 149.969615 126.989673 \nL 150.015815 130.416 \nL 150.258368 154.927418 \nL 150.581772 174.431127 \nL 150.587547 174.431127 \nL 150.645298 168.632727 \nL 150.789675 109.330909 \nL 150.801225 110.912291 \nL 151.147729 170.477673 \nL 151.17083 169.950545 \nL 151.413383 177.066764 \nL 151.419158 176.539636 \nL 151.661711 139.904291 \nL 151.725237 148.074764 \nL 151.915814 166.260655 \nL 151.996665 169.686982 \nL 152.048641 168.632727 \nL 152.123717 167.051345 \nL 152.146817 168.632727 \nL 152.250768 173.640436 \nL 152.279644 171.795491 \nL 152.331619 149.919709 \nL 152.452896 109.858036 \nL 152.481771 119.082764 \nL 152.689674 155.454545 \nL 152.695449 155.454545 \nL 152.886026 169.159855 \nL 153.082379 179.7024 \nL 153.099704 178.648145 \nL 153.145904 168.1056 \nL 153.284506 110.1216 \nL 153.313382 119.346327 \nL 153.498184 163.888582 \nL 153.717637 176.276073 \nL 153.740737 176.012509 \nL 153.99484 159.408 \nL 154.093016 135.423709 \nL 154.156542 149.919709 \nL 154.37022 169.423418 \nL 154.375995 169.423418 \nL 154.751375 183.392291 \nL 154.7687 182.6016 \nL 154.809125 176.012509 \nL 154.941952 122.772655 \nL 154.965052 131.470255 \nL 155.132529 167.578473 \nL 155.398183 182.074473 \nL 155.403958 182.074473 \nL 155.582985 187.345745 \nL 155.60031 186.027927 \nL 155.646511 173.640436 \nL 155.773562 123.826909 \nL 155.796663 131.470255 \nL 155.98724 172.322618 \nL 156.154717 179.965964 \nL 156.177817 179.7024 \nL 156.426145 173.376873 \nL 156.437696 173.376873 \nL 156.645598 158.880873 \nL 156.686024 157.035927 \nL 156.726449 160.462255 \nL 156.859276 165.469964 \nL 156.888151 165.469964 \nL 157.009428 165.997091 \nL 157.096054 165.997091 \nL 157.21733 165.469964 \nL 157.234656 165.469964 \nL 157.384807 142.276364 \nL 157.431008 124.881164 \nL 157.506084 135.950836 \nL 157.737087 169.159855 \nL 157.933439 177.066764 \nL 157.996965 178.648145 \nL 158.031616 177.066764 \nL 158.164442 156.772364 \nL 158.274169 116.183564 \nL 158.314594 132.260945 \nL 158.562922 170.214109 \nL 158.892101 180.229527 \nL 158.961402 172.586182 \nL 159.082679 121.7184 \nL 159.203955 140.431418 \nL 159.434958 162.834327 \nL 159.440733 162.834327 \nL 159.717937 175.221818 \nL 159.752587 172.849745 \nL 159.937389 139.377164 \nL 160.01824 150.183273 \nL 160.34742 180.493091 \nL 160.503347 182.6016 \nL 160.509122 182.6016 \nL 160.566872 183.128727 \nL 160.584198 181.547345 \nL 160.641948 168.1056 \nL 160.728574 142.539927 \nL 160.7921 147.8112 \nL 161.028878 165.469964 \nL 161.121279 169.686982 \nL 161.184805 167.578473 \nL 161.19058 167.578473 \nL 161.21368 167.314909 \nL 161.254106 169.686982 \nL 161.410033 178.384582 \nL 161.456233 173.904 \nL 161.485109 154.927418 \nL 161.58906 121.981964 \nL 161.623711 134.896582 \nL 161.837388 169.159855 \nL 162.126142 177.593891 \nL 162.195443 178.121018 \nL 162.305169 165.997091 \nL 162.426446 133.3152 \nL 162.484196 139.377164 \nL 162.790275 175.485382 \nL 163.015503 180.756655 \nL 163.021278 180.756655 \nL 163.119454 171.531927 \nL 163.252281 127.253236 \nL 163.362007 141.222109 \nL 163.55836 157.035927 \nL 163.56991 157.035927 \nL 163.714287 162.043636 \nL 163.748937 160.462255 \nL 164.118542 141.222109 \nL 164.199393 144.912 \nL 164.205168 144.912 \nL 164.251368 145.966255 \nL 164.291794 143.857745 \nL 164.297569 144.121309 \nL 164.314894 143.594182 \nL 164.36687 144.384873 \nL 164.407295 144.121309 \nL 164.41307 144.121309 \nL 164.586323 147.020509 \nL 164.597873 147.020509 \nL 164.7538 152.291782 \nL 164.7769 150.183273 \nL 164.932827 138.586473 \nL 165.158055 164.942836 \nL 165.56231 184.973673 \nL 165.60851 181.020218 \nL 165.637386 168.896291 \nL 165.724012 136.2144 \nL 165.775988 146.493382 \nL 165.97234 173.904 \nL 166.041641 175.485382 \nL 166.087841 174.167564 \nL 166.232218 172.586182 \nL 166.237993 172.586182 \nL 166.278419 172.059055 \nL 166.301519 174.167564 \nL 166.341945 178.648145 \nL 166.417021 176.012509 \nL 166.590273 152.028218 \nL 166.688449 160.198691 \nL 166.832826 174.167564 \nL 166.861701 172.586182 \nL 166.884801 171.795491 \nL 166.931002 173.376873 \nL 166.959877 173.376873 \nL 167.208205 189.454255 \nL 167.225531 190.244945 \nL 167.289057 186.291491 \nL 167.398783 164.415709 \nL 167.421883 169.159855 \nL 167.606685 193.407709 \nL 167.612461 193.407709 \nL 167.745287 195.516218 \nL 167.99939 202.105309 \nL 168.010941 202.105309 \nL 168.051366 202.896 \nL 168.086016 201.051055 \nL 168.132217 189.190691 \nL 168.195743 168.632727 \nL 168.253494 176.8032 \nL 168.513372 197.361164 \nL 168.519147 197.361164 \nL 168.744375 202.632436 \nL 168.75015 202.632436 \nL 168.888752 206.058764 \nL 168.911852 205.004509 \nL 168.963827 192.617018 \nL 169.056229 152.291782 \nL 169.096654 170.741236 \nL 169.252581 187.872873 \nL 169.264131 187.872873 \nL 169.500909 199.733236 \nL 169.506684 199.733236 \nL 169.691487 203.159564 \nL 169.714587 203.159564 \nL 169.783888 195.779782 \nL 169.887839 145.966255 \nL 169.974465 168.1056 \nL 170.257444 192.353455 \nL 170.268994 192.353455 \nL 170.309419 193.144145 \nL 170.34407 191.2992 \nL 170.511547 185.764364 \nL 170.517322 185.764364 \nL 170.638598 178.384582 \nL 170.7772 178.911709 \nL 170.846501 177.593891 \nL 170.898477 179.438836 \nL 170.910027 179.438836 \nL 171.037078 180.229527 \nL 171.042854 180.229527 \nL 171.400908 192.880582 \nL 171.412458 192.353455 \nL 171.452884 187.872873 \nL 171.55106 148.865455 \nL 171.59726 165.2064 \nL 171.810938 189.454255 \nL 172.047716 196.306909 \nL 172.053491 195.779782 \nL 172.342245 159.408 \nL 172.376895 148.601891 \nL 172.440421 162.570764 \nL 172.711849 188.4 \nL 172.729175 187.872873 \nL 172.763825 187.082182 \nL 172.810026 188.663564 \nL 173.023703 198.415418 \nL 173.116104 191.035636 \nL 173.208506 149.129018 \nL 173.277807 169.686982 \nL 173.572335 194.989091 \nL 173.57811 194.989091 \nL 173.849539 202.368873 \nL 173.861089 202.368873 \nL 173.872639 202.632436 \nL 173.913064 199.9968 \nL 173.959265 188.4 \nL 174.040116 148.601891 \nL 174.097867 162.834327 \nL 174.427046 193.407709 \nL 174.438596 193.407709 \nL 174.444371 193.671273 \nL 174.513672 192.089891 \nL 174.675374 189.717818 \nL 174.681149 189.981382 \nL 174.72735 187.082182 \nL 174.813976 178.121018 \nL 174.860176 165.733527 \nL 174.952577 157.563055 \nL 174.981453 159.408 \nL 175.224006 182.865164 \nL 175.345282 182.6016 \nL 175.408808 185.237236 \nL 175.443459 182.338036 \nL 175.651361 153.6096 \nL 175.697562 141.222109 \nL 175.755313 155.190982 \nL 176.038291 178.911709 \nL 176.361695 189.190691 \nL 176.36747 188.927127 \nL 176.384795 188.927127 \nL 176.454096 173.376873 \nL 176.529172 136.477964 \nL 176.592698 149.919709 \nL 176.968078 182.6016 \nL 177.025828 179.438836 \nL 177.031604 179.438836 \nL 177.285707 163.888582 \nL 177.360783 135.160145 \nL 177.424308 144.912 \nL 177.863214 175.221818 \nL 177.874764 175.221818 \nL 178.042241 177.857455 \nL 178.105767 170.214109 \nL 178.238594 147.547636 \nL 178.302119 153.082473 \nL 178.810326 189.717818 \nL 178.839201 189.717818 \nL 178.931602 181.283782 \nL 179.029779 140.431418 \nL 179.151055 150.7104 \nL 179.249231 160.198691 \nL 179.53221 178.911709 \nL 179.55531 178.384582 \nL 179.561085 178.384582 \nL 179.705462 182.338036 \nL 179.722787 181.283782 \nL 179.786313 167.314909 \nL 179.861389 131.997382 \nL 179.93069 141.222109 \nL 179.999991 149.129018 \nL 180.190568 164.415709 \nL 180.358045 167.051345 \nL 180.36382 167.051345 \nL 180.490872 167.842036 \nL 180.496647 167.842036 \nL 180.548623 170.741236 \nL 180.600598 167.578473 \nL 180.681449 133.578764 \nL 180.779625 153.873164 \nL 180.837376 160.989382 \nL 181.051054 180.229527 \nL 181.310932 194.1984 \nL 181.316707 194.1984 \nL 181.374458 195.252655 \nL 181.397558 193.144145 \nL 181.449534 178.648145 \nL 181.518835 145.702691 \nL 181.57081 163.361455 \nL 181.796038 184.973673 \nL 182.125217 197.0976 \nL 182.159868 196.570473 \nL 182.171418 196.570473 \nL 182.246494 188.136436 \nL 182.35622 159.144436 \nL 182.448621 162.834327 \nL 182.460171 162.570764 \nL 182.541023 171.795491 \nL 182.702725 179.438836 \nL 182.74315 178.121018 \nL 183.597861 184.973673 \nL 183.869289 201.051055 \nL 183.880839 200.787491 \nL 183.93859 194.989091 \nL 184.019441 162.834327 \nL 184.065642 178.648145 \nL 184.261994 198.942545 \nL 184.481447 203.950255 \nL 184.521872 203.423127 \nL 184.556523 205.268073 \nL 184.562298 205.268073 \nL 184.625824 207.113018 \nL 184.677799 206.058764 \nL 184.7702 197.624727 \nL 184.856827 173.904 \nL 184.903027 183.128727 \nL 185.180231 206.058764 \nL 185.307282 208.6944 \nL 185.313057 208.430836 \nL 185.318832 208.430836 \nL 185.347708 207.903709 \nL 185.417009 208.6944 \nL 185.422784 208.6944 \nL 185.428559 208.6944 \nL 185.515185 210.275782 \nL 185.54406 208.957964 \nL 185.601811 197.888291 \nL 185.688437 160.198691 \nL 185.728862 174.167564 \nL 185.959865 196.306909 \nL 186.023391 198.678982 \nL 186.075367 196.834036 \nL 186.081142 196.834036 \nL 186.35257 209.221527 \nL 186.398771 206.322327 \nL 186.433421 197.888291 \nL 186.520047 156.5088 \nL 186.566248 173.376873 \nL 186.791476 194.725527 \nL 186.797251 194.725527 \nL 187.184181 210.539345 \nL 187.213056 208.957964 \nL 187.265032 197.624727 \nL 187.351658 153.346036 \nL 187.403633 168.1056 \nL 187.709712 197.361164 \nL 187.715487 197.0976 \nL 187.802113 191.826327 \nL 187.848314 193.934836 \nL 187.92339 197.888291 \nL 187.981141 197.0976 \nL 188.021566 198.415418 \nL 188.044666 194.989091 \nL 188.200593 166.787782 \nL 188.269894 172.586182 \nL 188.431596 187.345745 \nL 188.656824 197.888291 \nL 188.858952 204.740945 \nL 188.876277 203.423127 \nL 188.928252 189.981382 \nL 189.020654 147.8112 \nL 189.061079 162.3072 \nL 189.251657 188.663564 \nL 189.50576 201.841745 \nL 189.684787 205.7952 \nL 189.690562 205.531636 \nL 189.748313 198.678982 \nL 189.794513 163.625018 \nL 189.858039 145.439127 \nL 189.90424 161.780073 \nL 190.146793 192.353455 \nL 190.510622 203.950255 \nL 190.516397 203.950255 \nL 190.591473 188.663564 \nL 190.730075 159.144436 \nL 190.741625 158.617309 \nL 190.799376 162.043636 \nL 191.070804 188.927127 \nL 191.163205 185.5008 \nL 191.168981 185.5008 \nL 191.180531 185.237236 \nL 191.226731 186.818618 \nL 191.238281 186.818618 \nL 191.365333 189.190691 \nL 191.371108 188.927127 \nL 191.417309 182.074473 \nL 191.527035 152.555345 \nL 191.561685 160.725818 \nL 191.810014 192.353455 \nL 192.092992 200.260364 \nL 192.104542 200.260364 \nL 192.133418 200.523927 \nL 192.168068 198.678982 \nL 192.173843 198.678982 \nL 192.248919 191.2992 \nL 192.358645 141.485673 \nL 192.399071 156.5088 \nL 192.664724 192.617018 \nL 192.982353 202.105309 \nL 192.988128 202.105309 \nL 193.005454 202.368873 \nL 193.040104 199.9968 \nL 193.097855 178.384582 \nL 193.196031 146.229818 \nL 193.242232 156.5088 \nL 193.519435 187.609309 \nL 193.785088 194.725527 \nL 193.790863 194.725527 \nL 193.848614 196.043345 \nL 193.91214 186.291491 \nL 194.021866 147.547636 \nL 194.068067 160.725818 \nL 194.275969 188.4 \nL 194.28752 188.4 \nL 194.674449 204.740945 \nL 194.703325 202.368873 \nL 194.761076 179.965964 \nL 194.888127 158.090182 \nL 194.905452 159.671564 \nL 195.159556 192.353455 \nL 195.361683 188.136436 \nL 195.367458 188.136436 \nL 195.610011 172.059055 \nL 195.667762 164.942836 \nL 195.737063 167.842036 \nL 196.048917 192.353455 \nL 196.083567 190.244945 \nL 196.251044 184.710109 \nL 196.256819 184.710109 \nL 196.418521 172.322618 \nL 196.528248 136.741527 \nL 196.609099 153.346036 \nL 196.944053 174.431127 \nL 197.019129 173.640436 \nL 197.042229 174.694691 \nL 197.048004 174.694691 \nL 197.175056 176.539636 \nL 197.186606 176.276073 \nL 197.250132 169.159855 \nL 197.365633 138.586473 \nL 197.400284 144.384873 \nL 197.642837 168.632727 \nL 197.769888 179.438836 \nL 198.000891 191.562764 \nL 198.006666 191.2992 \nL 198.064417 183.919418 \nL 198.197244 131.997382 \nL 198.272319 151.237527 \nL 198.584173 188.927127 \nL 198.630374 186.027927 \nL 198.763201 183.128727 \nL 198.786301 183.655855 \nL 198.815176 184.182982 \nL 198.855602 182.338036 \nL 198.971103 151.501091 \nL 199.040404 146.493382 \nL 199.075055 150.7104 \nL 199.311833 179.438836 \nL 199.444659 184.710109 \nL 199.485085 182.338036 \nL 199.571711 181.020218 \nL 199.600586 182.074473 \nL 199.606361 182.074473 \nL 199.669887 183.392291 \nL 199.739188 171.268364 \nL 199.802714 147.020509 \nL 199.866239 160.198691 \nL 200.056817 183.128727 \nL 200.489947 201.578182 \nL 200.518823 199.9968 \nL 200.582348 178.121018 \nL 200.69785 144.121309 \nL 200.715175 153.873164 \nL 200.871102 186.555055 \nL 200.917303 189.717818 \nL 200.998154 188.136436 \nL 201.038579 189.981382 \nL 201.090555 187.345745 \nL 201.252257 184.973673 \nL 201.258032 184.973673 \nL 201.431284 172.586182 \nL 201.52946 150.973964 \nL 201.598761 159.671564 \nL 201.93949 191.562764 \nL 201.945265 191.562764 \nL 202.135843 195.252655 \nL 202.158943 195.252655 \nL 202.216694 187.609309 \nL 202.366846 134.633018 \nL 202.430372 152.555345 \nL 202.696025 183.128727 \nL 202.759551 182.865164 \nL 202.771101 182.865164 \nL 203.002104 188.4 \nL 203.013654 188.4 \nL 203.054079 182.338036 \nL 203.140705 136.477964 \nL 203.261982 150.973964 \nL 203.469885 169.423418 \nL 203.47566 169.423418 \nL 203.654687 172.586182 \nL 203.689337 172.586182 \nL 203.712438 172.322618 \nL 203.758638 174.167564 \nL 203.851039 178.384582 \nL 203.88569 176.276073 \nL 204.030067 137.005091 \nL 204.053167 148.865455 \nL 204.220644 183.392291 \nL 204.428547 192.353455 \nL 204.659549 198.678982 \nL 204.665325 198.415418 \nL 204.734625 182.338036 \nL 204.867452 140.167855 \nL 204.913653 154.400291 \nL 205.133105 180.229527 \nL 205.13888 180.229527 \nL 205.450734 190.772073 \nL 205.46806 190.244945 \nL 205.52581 183.128727 \nL 205.687512 135.160145 \nL 205.745263 147.284073 \nL 206.137968 186.027927 \nL 206.143743 186.027927 \nL 206.305445 190.244945 \nL 206.32277 189.454255 \nL 206.368971 182.338036 \nL 206.524898 127.5168 \nL 206.588424 147.020509 \nL 206.859852 183.919418 \nL 207.102405 196.043345 \nL 207.154381 197.361164 \nL 207.183256 194.989091 \nL 207.223682 178.648145 \nL 207.373833 122.509091 \nL 207.379609 125.935418 \nL 207.547086 174.167564 \nL 207.789639 192.353455 \nL 207.847389 191.2992 \nL 207.939791 194.1984 \nL 207.945566 194.1984 \nL 207.980216 194.725527 \nL 208.009091 192.089891 \nL 208.066842 172.059055 \nL 208.182344 138.586473 \nL 208.216994 144.384873 \nL 208.378696 182.338036 \nL 208.471097 188.136436 \nL 211.023679 189.190691 \nL 211.220032 197.361164 \nL 211.225807 197.361164 \nL 211.283557 199.206109 \nL 211.329758 197.0976 \nL 211.375959 179.7024 \nL 211.537661 120.137018 \nL 211.543436 121.7184 \nL 211.710913 175.485382 \nL 211.91304 189.190691 \nL 211.918815 188.927127 \nL 211.930366 188.927127 \nL 212.011217 187.082182 \nL 212.040092 188.927127 \nL 212.132493 192.880582 \nL 212.167143 190.244945 \nL 212.242219 164.942836 \nL 212.369271 125.671855 \nL 212.421247 145.439127 \nL 212.617599 169.423418 \nL 212.796626 172.586182 \nL 212.808177 172.586182 \nL 212.958328 174.431127 \nL 212.975654 174.431127 \nL 213.189331 160.198691 \nL 213.212432 156.772364 \nL 213.258632 167.842036 \nL 213.47231 191.826327 \nL 213.610912 194.725527 \nL 213.628237 193.934836 \nL 213.651337 192.880582 \nL 213.709088 195.779782 \nL 213.795714 199.469673 \nL 213.824589 197.0976 \nL 213.88234 168.1056 \nL 213.974741 137.532218 \nL 214.015167 148.074764 \nL 214.026717 147.020509 \nL 214.454072 193.144145 \nL 214.575349 195.252655 \nL 214.586899 194.725527 \nL 214.742826 170.741236 \nL 214.858327 126.989673 \nL 214.939178 148.865455 \nL 215.10088 169.686982 \nL 215.147081 169.686982 \nL 215.297233 171.531927 \nL 215.326108 171.531927 \nL 215.418509 171.795491 \nL 215.435834 171.531927 \nL 215.441609 171.531927 \nL 215.666837 158.090182 \nL 215.747688 162.3072 \nL 216.290545 189.190691 \nL 216.336746 184.973673 \nL 216.371396 165.469964 \nL 216.533098 128.834618 \nL 216.544648 128.834618 \nL 216.7179 178.648145 \nL 216.937353 188.927127 \nL 216.948903 188.927127 \nL 217.11638 193.144145 \nL 217.127931 192.353455 \nL 217.179906 185.237236 \nL 217.358934 117.764945 \nL 217.422459 135.423709 \nL 217.659237 176.8032 \nL 217.861365 185.5008 \nL 218.017292 175.485382 \nL 218.144343 118.028509 \nL 218.173219 124.881164 \nL 218.456197 164.152145 \nL 218.467747 164.152145 \nL 218.496623 164.679273 \nL 218.554373 163.625018 \nL 218.560149 163.625018 \nL 218.629449 162.570764 \nL 218.67565 163.361455 \nL 218.831577 170.741236 \nL 218.866227 169.159855 \nL 218.952853 137.532218 \nL 219.033704 142.803491 \nL 219.328233 193.144145 \nL 219.49571 197.0976 \nL 219.518811 196.834036 \nL 219.536136 196.834036 \nL 219.680513 189.717818 \nL 219.83644 139.1136 \nL 219.842215 139.1136 \nL 219.87109 137.268655 \nL 219.899965 151.237527 \nL 220.084768 183.655855 \nL 220.090543 183.655855 \nL 220.217594 187.609309 \nL 220.26957 186.818618 \nL 220.30422 186.818618 \nL 220.454372 191.826327 \nL 220.477473 189.981382 \nL 220.512123 183.919418 \nL 220.662275 140.958545 \nL 220.737351 157.826618 \nL 220.904828 189.981382 \nL 220.910603 189.981382 \nL 221.026104 192.880582 \nL 221.06653 192.089891 \nL 221.07808 192.089891 \nL 221.234007 196.306909 \nL 221.262882 197.0976 \nL 221.297533 194.1984 \nL 221.49966 146.229818 \nL 221.580511 161.516509 \nL 221.776864 182.074473 \nL 221.782639 182.074473 \nL 222.094493 196.043345 \nL 222.134918 193.407709 \nL 222.181119 176.276073 \nL 222.337046 132.524509 \nL 222.377471 130.416 \nL 222.389021 137.268655 \nL 222.568049 184.973673 \nL 222.793277 195.516218 \nL 222.799052 195.516218 \nL 222.885678 196.834036 \nL 222.908778 196.306909 \nL 223.076255 160.725818 \nL 223.180206 130.152436 \nL 223.220632 137.795782 \nL 223.411209 174.958255 \nL 223.769264 193.407709 \nL 223.809689 188.927127 \nL 223.873215 157.826618 \nL 224.006042 134.633018 \nL 224.023367 136.477964 \nL 224.283245 184.182982 \nL 224.28902 184.182982 \nL 224.439172 186.027927 \nL 224.462272 186.027927 \nL 224.595099 188.927127 \nL 224.612424 187.872873 \nL 224.693275 154.400291 \nL 224.860752 117.764945 \nL 224.866528 118.028509 \nL 224.872303 117.237818 \nL 224.895403 127.5168 \nL 225.068655 176.539636 \nL 225.07443 176.539636 \nL 225.265008 181.810909 \nL 225.288108 181.283782 \nL 225.536436 165.2064 \nL 225.640387 152.028218 \nL 225.692363 154.663855 \nL 225.89449 176.012509 \nL 225.900265 176.012509 \nL 225.929141 175.748945 \nL 225.969566 177.857455 \nL 226.090843 178.911709 \nL 226.102393 178.911709 \nL 226.29297 183.392291 \nL 226.298745 183.128727 \nL 226.391147 167.314909 \nL 226.541298 141.222109 \nL 226.570174 153.346036 \nL 226.754976 187.872873 \nL 227.08993 198.678982 \nL 227.095705 198.415418 \nL 227.153456 191.2992 \nL 227.361359 117.764945 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188.663564 \nL 247.897516 186.027927 \nL 247.937941 167.842036 \nL 248.088093 103.796073 \nL 248.099643 104.586764 \nL 248.105419 104.586764 \nL 248.365297 160.198691 \nL 248.376847 160.198691 \nL 248.706026 171.0048 \nL 248.711801 170.741236 \nL 248.769552 155.718109 \nL 248.879278 129.625309 \nL 248.896603 139.377164 \nL 248.931254 147.8112 \nL 248.977454 126.989673 \nL 249.000555 138.586473 \nL 249.00633 135.950836 \nL 249.075631 154.136727 \nL 249.266208 174.167564 \nL 249.271983 173.904 \nL 249.289308 173.904 \nL 249.433685 172.322618 \nL 249.468336 172.322618 \nL 249.537636 174.167564 \nL 249.566512 172.059055 \nL 249.641588 148.338327 \nL 249.705114 121.981964 \nL 249.768639 133.578764 \nL 249.785965 136.741527 \nL 249.84949 124.6176 \nL 249.918791 148.601891 \nL 250.074718 169.950545 \nL 250.092043 169.423418 \nL 250.138244 168.1056 \nL 250.184445 169.950545 \nL 250.19022 169.950545 \nL 250.357697 174.694691 \nL 250.375022 173.640436 \nL 250.421223 167.578473 \nL 250.60025 105.641018 \nL 250.681101 99.315491 \nL 250.709976 105.641018 \nL 250.981405 173.376873 \nL 251.177757 182.338036 \nL 251.212407 179.175273 \nL 251.287483 155.190982 \nL 251.42031 109.330909 \nL 251.472286 119.082764 \nL 252.009367 177.857455 \nL 252.078668 169.950545 \nL 252.275021 91.145018 \nL 252.367422 110.385164 \nL 252.59265 157.563055 \nL 252.852528 165.2064 \nL 252.858303 164.679273 \nL 252.933379 148.074764 \nL 253.03733 123.826909 \nL 253.100856 133.842327 \nL 253.268333 152.555345 \nL 253.274108 152.555345 \nL 253.41271 153.873164 \nL 253.493561 146.756945 \nL 253.568637 151.501091 \nL 253.689913 158.090182 \nL 253.741889 157.299491 \nL 253.753439 157.299491 \nL 253.84584 149.919709 \nL 253.897816 137.005091 \nL 253.967117 144.384873 \nL 253.972892 144.648436 \nL 253.990217 141.485673 \nL 254.007542 137.268655 \nL 254.065293 151.237527 \nL 254.24432 172.059055 \nL 254.250095 171.795491 \nL 254.307846 171.0048 \nL 254.342497 172.322618 \nL 254.504199 178.384582 \nL 254.538849 176.276073 \nL 254.63125 129.888873 \nL 254.798727 103.005382 \nL 254.810277 103.268945 \nL 254.816053 103.268945 \nL 254.873803 117.764945 \nL 255.081706 173.376873 \nL 255.335809 183.128727 \nL 255.37046 181.547345 \nL 255.410885 170.214109 \nL 255.503286 118.8192 \nL 255.549487 126.726109 \nL 255.768939 167.578473 \nL 256.046143 182.865164 \nL 256.173195 186.555055 \nL 256.20207 184.973673 \nL 256.24827 163.625018 \nL 256.404197 106.431709 \nL 256.415748 106.168145 \nL 256.531249 117.237818 \nL 256.583225 153.873164 \nL 256.739152 173.376873 \nL 256.99903 180.229527 \nL 257.056781 173.376873 \nL 257.137632 129.888873 \nL 257.293559 108.803782 \nL 257.726689 167.842036 \nL 257.899941 156.245236 \nL 258.136719 84.819491 \nL 258.223345 110.385164 \nL 258.402372 152.291782 \nL 258.552524 157.826618 \nL 258.587175 155.718109 \nL 258.731552 144.121309 \nL 258.927904 100.106182 \nL 258.962554 103.532509 \nL 259.049181 130.416 \nL 259.193557 146.493382 \nL 259.488086 162.834327 \nL 259.540062 157.563055 \nL 259.592037 137.268655 \nL 259.753739 96.679855 \nL 259.794165 95.6256 \nL 259.817265 100.896873 \nL 259.875016 123.299782 \nL 260.059818 153.6096 \nL 260.267721 162.570764 \nL 260.273496 162.570764 \nL 260.400547 152.028218 \nL 260.602675 85.083055 \nL 260.683526 102.741818 \nL 260.885654 150.183273 \nL 260.98383 159.671564 \nL 261.139757 166.524218 \nL 261.145532 166.524218 \nL 261.220608 160.725818 \nL 261.266808 148.074764 \nL 261.341884 153.082473 \nL 261.353434 153.6096 \nL 261.388085 150.7104 \nL 261.445836 141.485673 \nL 261.480486 153.6096 \nL 261.497811 150.183273 \nL 261.676838 169.950545 \nL 261.699939 169.950545 \nL 261.728814 170.477673 \nL 261.775015 169.686982 \nL 261.798115 169.686982 \nL 261.913616 169.423418 \nL 261.919392 169.423418 \nL 261.994467 169.950545 \nL 262.075318 159.408 \nL 262.231245 130.943127 \nL 262.242796 133.578764 \nL 262.254346 135.160145 \nL 262.312096 118.028509 \nL 262.340972 111.439418 \nL 262.381397 138.850036 \nL 262.531549 172.586182 \nL 262.739452 177.066764 \nL 262.745227 177.066764 \nL 262.808753 179.438836 \nL 262.854953 177.066764 \nL 262.889604 164.679273 \nL 262.96468 123.299782 \nL 263.028205 126.462545 \nL 263.03398 126.462545 \nL 263.143707 114.602182 \nL 263.080181 127.780364 \nL 263.155257 122.509091 \nL 263.334284 164.942836 \nL 263.640363 183.128727 \nL 263.651913 183.128727 \nL 263.715439 177.066764 \nL 263.865591 101.687564 \nL 263.917567 96.416291 \nL 263.981092 100.369745 \nL 264.431548 178.384582 \nL 264.483524 179.965964 \nL 264.518174 177.857455 \nL 264.547049 172.586182 \nL 264.702976 97.470545 \nL 264.743402 92.199273 \nL 264.789602 99.579055 \nL 264.876229 124.090473 \nL 265.037931 164.415709 \nL 265.297809 178.121018 \nL 265.315134 178.121018 \nL 265.37866 168.369164 \nL 265.534587 97.734109 \nL 265.603888 90.617891 \nL 265.661638 96.679855 \nL 265.702064 115.92 \nL 265.852216 158.090182 \nL 266.019693 163.625018 \nL 266.042793 163.625018 \nL 266.296896 147.547636 \nL 266.337322 120.664145 \nL 266.441273 95.098473 \nL 266.487474 99.315491 \nL 266.909054 170.477673 \nL 266.978355 172.322618 \nL 267.001455 170.214109 \nL 267.053431 151.501091 \nL 267.209358 83.765236 \nL 267.232458 95.6256 \nL 267.38261 125.671855 \nL 267.584737 147.284073 \nL 267.75799 154.927418 \nL 267.82729 153.6096 \nL 267.896591 139.904291 \nL 268.046743 88.772945 \nL 268.075619 90.881455 \nL 268.497199 161.780073 \nL 268.647351 167.314909 \nL 268.676226 164.942836 \nL 268.710877 149.392582 \nL 268.878354 87.191564 \nL 268.930329 85.610182 \nL 269.057381 126.462545 \nL 269.247958 153.082473 \nL 269.334584 156.772364 \nL 269.392335 156.245236 \nL 269.536712 145.175564 \nL 269.606013 109.858036 \nL 269.698414 124.6176 \nL 270.114219 167.578473 \nL 270.119994 167.314909 \nL 270.154645 167.842036 \nL 270.20662 167.051345 \nL 270.223946 167.051345 \nL 270.449173 151.764655 \nL 270.633976 131.997382 \nL 270.651301 134.633018 \nL 270.899629 159.935127 \nL 271.119082 169.950545 \nL 271.147957 167.314909 \nL 271.286559 122.509091 \nL 271.41361 101.424 \nL 271.425161 102.214691 \nL 271.598413 146.756945 \nL 271.944917 168.369164 \nL 271.962242 168.896291 \nL 271.996893 165.997091 \nL 272.048868 140.431418 \nL 272.222121 82.183855 \nL 272.239446 79.811782 \nL 272.268321 89.8272 \nL 272.291421 87.982255 \nL 272.464674 146.756945 \nL 272.666801 163.888582 \nL 272.788078 167.578473 \nL 272.811178 165.733527 \nL 272.886254 140.694982 \nL 273.047956 80.075345 \nL 273.053731 80.602473 \nL 273.094156 79.284655 \nL 273.111482 82.447418 \nL 273.180783 114.865745 \nL 273.400235 155.718109 \nL 273.463761 157.826618 \nL 273.544612 157.299491 \nL 273.585038 157.299491 \nL 273.608138 157.563055 \nL 273.648563 155.190982 \nL 273.654338 155.190982 \nL 273.706314 148.338327 \nL 273.75829 135.687273 \nL 273.81604 147.8112 \nL 273.821816 147.8112 \nL 273.833366 149.392582 \nL 273.856466 137.795782 \nL 273.960417 107.2224 \nL 274.012393 114.075055 \nL 274.185645 160.198691 \nL 274.433973 173.113309 \nL 274.451298 173.113309 \nL 274.514824 166.524218 \nL 274.751602 78.2304 \nL 274.792028 82.183855 \nL 275.207833 166.787782 \nL 275.288684 169.423418 \nL 275.317559 167.314909 \nL 275.36376 149.392582 \nL 275.531237 78.757527 \nL 275.600538 78.2304 \nL 275.652513 99.842618 \nL 275.848866 144.121309 \nL 276.056769 153.082473 \nL 276.062544 153.082473 \nL 276.120294 153.6096 \nL 276.14917 152.028218 \nL 276.18382 146.229818 \nL 276.420598 61.889455 \nL 276.495674 76.385455 \nL 276.732452 145.439127 \nL 276.93458 155.190982 \nL 276.94613 154.927418 \nL 277.009655 147.020509 \nL 277.200233 87.982255 \nL 277.223333 98.261236 \nL 277.37926 128.571055 \nL 277.535187 141.222109 \nL 277.540962 141.222109 \nL 277.696889 143.594182 \nL 277.702664 143.330618 \nL 277.771965 144.648436 \nL 277.77774 144.648436 \nL 277.812391 144.912 \nL 277.847041 143.594182 \nL 277.991418 109.330909 \nL 278.083819 100.369745 \nL 278.106919 105.113891 \nL 278.326372 148.865455 \nL 278.349472 148.338327 \nL 278.355247 148.338327 \nL 278.436098 147.8112 \nL 278.459199 148.601891 \nL 278.464974 148.601891 \nL 278.592025 149.392582 \nL 278.609351 149.392582 \nL 278.655551 143.067055 \nL 278.782603 88.772945 \nL 278.915429 70.587055 \nL 278.93853 74.276945 \nL 278.98473 86.400873 \nL 279.181083 133.842327 \nL 279.186858 133.842327 \nL 279.36011 137.005091 \nL 279.365885 136.741527 \nL 279.429411 138.322909 \nL 279.435186 138.850036 \nL 279.487162 133.842327 \nL 279.712389 93.517091 \nL 279.73549 100.896873 \nL 279.908742 126.462545 \nL 280.15707 145.702691 \nL 280.266796 151.237527 \nL 280.307222 148.074764 \nL 280.347647 136.741527 \nL 280.549775 52.664727 \nL 280.584425 48.711273 \nL 280.636401 55.563927 \nL 280.676826 75.858327 \nL 280.867404 127.253236 \nL 281.098407 138.850036 \nL 281.133057 134.896582 \nL 281.294759 104.850327 \nL 281.38716 107.485964 \nL 281.444911 114.075055 \nL 281.629713 138.059345 \nL 281.635488 138.059345 \nL 281.79719 140.431418 \nL 281.802966 140.431418 \nL 281.826066 140.167855 \nL 281.912692 140.431418 \nL 281.918467 140.431418 \nL 282.068619 128.571055 \nL 282.316947 106.695273 \nL 282.345822 113.284364 \nL 282.513299 137.268655 \nL 282.646126 141.749236 \nL 282.686552 140.167855 \nL 282.894454 131.997382 \nL 282.917554 134.369455 \nL 282.95798 133.3152 \nL 283.056156 137.005091 \nL 283.073481 136.741527 \nL 283.599013 156.772364 \nL 283.604788 156.772364 \nL 283.72029 137.795782 \nL 283.74339 129.888873 \nL 283.801141 139.904291 \nL 283.824241 139.904291 \nL 284.009043 150.183273 \nL 284.014818 150.183273 \nL 284.424848 167.051345 \nL 284.453724 165.469964 \nL 284.511474 146.229818 \nL 284.673176 84.0288 \nL 284.696277 82.447418 \nL 284.730927 92.7264 \nL 285.077432 147.284073 \nL 285.083207 147.284073 \nL 285.285334 151.501091 \nL 285.314209 151.501091 \nL 285.389285 144.384873 \nL 285.545212 87.455127 \nL 285.597188 108.276655 \nL 285.868616 153.346036 \nL 286.076519 159.408 \nL 286.093844 158.617309 \nL 286.128495 152.818909 \nL 286.215121 119.609891 \nL 286.284422 142.276364 \nL 286.434573 145.966255 \nL 286.451899 145.966255 \nL 286.706002 153.346036 \nL 286.717552 153.082473 \nL 286.734877 153.082473 \nL 286.809953 152.291782 \nL 286.827278 153.346036 \nL 286.913905 156.5088 \nL 286.94278 153.873164 \nL 287.046731 115.129309 \nL 287.191108 73.486255 \nL 287.196883 75.594764 \nL 287.35281 115.129309 \nL 287.537612 143.067055 \nL 287.72819 153.082473 \nL 287.75129 152.555345 \nL 287.809041 145.439127 \nL 288.080469 74.540509 \nL 288.143995 83.765236 \nL 288.340347 148.074764 \nL 288.57135 159.144436 \nL 288.5829 159.144436 \nL 288.640651 153.082473 \nL 288.91208 62.943709 \nL 288.95828 75.3312 \nL 289.166183 144.912 \nL 289.171958 144.912 \nL 289.385635 159.144436 \nL 289.414511 159.671564 \nL 289.449161 157.299491 \nL 289.495362 143.594182 \nL 289.680164 53.718982 \nL 289.726365 51.610473 \nL 289.761015 56.091055 \nL 289.836091 97.997673 \nL 290.026668 139.377164 \nL 290.234571 153.082473 \nL 290.246121 153.082473 \nL 290.303872 145.702691 \nL 290.494449 64.525091 \nL 290.569525 81.656727 \nL 290.604176 79.284655 \nL 290.656151 90.090764 \nL 290.869829 137.268655 \nL 291.083507 147.8112 \nL 291.118157 144.384873 \nL 291.216333 98.5248 \nL 291.37226 60.308073 \nL 291.378035 60.044509 \nL 291.401136 63.7344 \nL 291.470437 79.284655 \nL 291.666789 136.741527 \nL 291.926667 150.446836 \nL 291.932442 150.7104 \nL 291.967093 149.129018 \nL 292.221196 76.385455 \nL 292.226971 75.594764 \nL 292.278947 82.447418 \nL 292.307822 91.935709 \nL 292.463749 146.493382 \nL 292.677427 160.989382 \nL 292.740952 162.834327 \nL 292.781378 160.725818 \nL 292.816028 148.865455 \nL 293.035481 65.842909 \nL 293.075907 66.897164 \nL 293.116332 73.749818 \nL 293.532137 157.826618 \nL 293.561013 158.617309 \nL 293.595663 155.981673 \nL 293.653414 138.586473 \nL 293.809341 82.183855 \nL 293.815116 82.183855 \nL 293.930617 101.160436 \nL 293.855541 81.656727 \nL 293.959493 100.106182 \nL 294.230921 144.121309 \nL 294.404173 150.7104 \nL 294.427274 148.338327 \nL 294.490799 124.881164 \nL 294.710252 64.788655 \nL 294.716027 65.052218 \nL 294.744903 64.261527 \nL 294.768003 66.897164 \nL 294.814203 86.928 \nL 294.993231 139.640727 \nL 295.241559 155.981673 \nL 295.264659 154.927418 \nL 295.305085 142.276364 \nL 295.489887 56.618182 \nL 295.588063 60.308073 \nL 295.663139 91.935709 \nL 295.836391 131.997382 \nL 296.061619 147.8112 \nL 296.11937 142.276364 \nL 296.390798 51.610473 \nL 296.419674 54.773236 \nL 296.477424 77.703273 \nL 296.650676 128.043927 \nL 296.870129 143.067055 \nL 296.893229 143.594182 \nL 296.933655 139.377164 \nL 296.985631 118.555636 \nL 297.176208 54.509673 \nL 297.181983 54.773236 \nL 297.210858 52.401164 \nL 297.291709 64.525091 \nL 297.592013 135.160145 \nL 297.72484 143.067055 \nL 297.776816 138.586473 \nL 297.799916 126.989673 \nL 297.978943 55.0368 \nL 298.036694 75.3312 \nL 298.042469 74.804073 \nL 298.054019 81.393164 \nL 298.238821 125.935418 \nL 298.504475 140.167855 \nL 298.539125 141.485673 \nL 298.585326 138.850036 \nL 298.660402 106.958836 \nL 298.839429 61.362327 \nL 298.845204 61.362327 \nL 298.89718 57.408873 \nL 298.93183 63.997964 \nL 299.29566 134.633018 \nL 299.301435 134.633018 \nL 299.388061 136.741527 \nL 299.411161 135.160145 \nL 299.492012 109.858036 \nL 299.636389 60.044509 \nL 299.676814 62.680145 \nL 299.734565 61.362327 \nL 299.71724 65.842909 \nL 299.76344 63.470836 \nL 300.121495 131.997382 \nL 300.219671 134.369455 \nL 300.242771 132.788073 \nL 300.306297 117.764945 \nL 300.508425 46.866327 \nL 300.589276 51.874036 \nL 300.918455 122.509091 \nL 301.057057 129.098182 \nL 301.080157 127.253236 \nL 301.143683 104.586764 \nL 300.837604 101.687564 \nL 300.837604 84.0288 \nL 301.201433 83.501673 \nL 301.305385 80.075345 \nL 301.264959 84.819491 \nL 301.316935 82.183855 \nL 301.813591 135.423709 \nL 301.819366 135.423709 \nL 301.882892 138.322909 \nL 301.940643 136.477964 \nL 301.946418 136.477964 \nL 302.044594 117.501382 \nL 302.148545 89.8272 \nL 302.177421 108.276655 \nL 302.304472 129.888873 \nL 302.419974 132.524509 \nL 302.437299 131.997382 \nL 302.454624 131.997382 \nL 302.471949 131.470255 \nL 302.56435 131.997382 \nL 302.575901 131.997382 \nL 302.656752 130.943127 \nL 302.685627 131.997382 \nL 302.708727 133.842327 \nL 302.766478 129.361745 \nL 302.876204 101.424 \nL 302.91663 88.772945 \nL 302.980156 107.749527 \nL 303.055232 106.168145 \nL 303.107207 115.392873 \nL 303.124532 115.129309 \nL 303.31511 129.098182 \nL 303.332435 129.098182 \nL 303.459487 130.943127 \nL 303.505687 130.679564 \nL 303.528788 131.470255 \nL 303.592313 136.477964 \nL 303.626964 130.943127 \nL 303.857967 78.757527 \nL 303.863742 79.284655 \nL 303.898392 77.703273 \nL 303.933043 82.710982 \nL 303.996568 121.454836 \nL 304.14672 147.547636 \nL 304.343073 151.501091 \nL 304.366173 151.501091 \nL 304.435474 143.330618 \nL 304.602951 77.439709 \nL 304.614501 77.703273 \nL 304.730003 72.695564 \nL 304.741553 74.804073 \nL 304.787753 85.873745 \nL 304.966781 137.532218 \nL 305.209334 147.284073 \nL 305.238209 145.439127 \nL 305.278634 127.253236 \nL 305.480762 68.214982 \nL 305.486537 68.214982 \nL 305.538513 64.788655 \nL 305.578938 68.478545 \nL 305.861917 132.524509 \nL 306.000519 137.795782 \nL 306.046719 136.477964 \nL 306.10447 122.509091 \nL 306.329698 53.191855 \nL 306.352798 51.083345 \nL 306.404774 56.354618 \nL 306.485625 88.772945 \nL 306.653102 126.989673 \nL 306.866779 137.532218 \nL 306.91298 134.105891 \nL 306.964956 105.113891 \nL 307.138208 51.610473 \nL 307.155533 52.401164 \nL 307.213284 48.974836 \nL 307.242159 55.300364 \nL 307.317235 87.982255 \nL 307.490487 123.036218 \nL 307.692615 131.206691 \nL 307.72149 128.571055 \nL 307.865867 84.0288 \nL 307.969818 58.726691 \nL 307.998694 66.897164 \nL 308.073769 63.7344 \nL 308.241247 115.392873 \nL 308.51845 133.578764 \nL 308.535775 132.788073 \nL 308.599301 120.137018 \nL 308.755228 46.602764 \nL 308.795654 49.765527 \nL 309.361611 133.3152 \nL 309.367386 133.578764 \nL 309.436687 131.733818 \nL 309.615714 122.509091 \nL 309.621489 124.354036 \nL 309.771641 133.842327 \nL 310.181671 149.392582 \nL 310.198996 148.338327 \nL 310.250972 141.222109 \nL 310.51085 70.059927 \nL 310.591701 76.912582 \nL 310.811154 139.377164 \nL 311.648539 135.160145 \nL 311.654314 135.687273 \nL 311.833342 142.0128 \nL 311.839117 142.0128 \nL 311.850667 142.539927 \nL 311.879542 139.377164 \nL 311.925743 126.726109 \nL 312.11632 65.052218 \nL 312.133645 73.486255 \nL 312.301122 117.764945 \nL 312.636077 140.694982 \nL 312.659177 140.431418 \nL 312.664952 140.694982 \nL 312.722703 138.850036 \nL 312.780453 116.447127 \nL 312.947931 74.804073 \nL 312.953706 76.385455 \nL 312.959481 75.858327 \nL 312.976806 80.602473 \nL 313.034557 85.083055 \nL 313.017231 74.540509 \nL 313.086532 79.811782 \nL 313.092307 79.811782 \nL 313.121183 89.563636 \nL 313.282885 135.160145 \nL 313.508113 145.966255 \nL 313.513888 145.966255 \nL 313.588964 129.625309 \nL 313.756441 79.284655 \nL 313.785316 83.501673 \nL 313.935468 95.362036 \nL 313.831517 79.021091 \nL 313.941243 95.098473 \nL 313.947018 93.780655 \nL 313.987444 108.276655 \nL 314.172246 133.3152 \nL 314.357048 141.485673 \nL 314.397474 138.586473 \nL 314.460999 101.951127 \nL 314.622701 52.928291 \nL 314.628477 53.191855 \nL 314.686227 50.556218 \nL 314.720878 53.982545 \nL 314.784404 73.749818 \nL 314.951881 124.090473 \nL 315.171333 139.1136 \nL 315.240634 132.524509 \nL 315.448537 50.029091 \nL 315.517838 46.866327 \nL 315.558263 48.974836 \nL 316.002944 131.733818 \nL 316.026044 130.943127 \nL 316.072245 126.198982 \nL 316.262822 46.866327 \nL 316.366773 43.176436 \nL 316.378323 44.230691 \nL 316.430299 63.207273 \nL 316.603551 118.555636 \nL 316.823004 130.679564 \nL 316.880755 126.198982 \nL 317.105983 47.129891 \nL 317.233034 57.936 \nL 317.591089 125.935418 \nL 317.596864 125.935418 \nL 317.677715 127.253236 \nL 317.689265 125.408291 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109.067345 \nL 79.502182 109.067345 \nL 79.554158 108.013091 \nL 79.623459 113.811491 \nL 79.733185 115.392873 \nL 79.756285 115.129309 \nL 79.866012 114.338618 \nL 79.773611 115.392873 \nL 79.871787 114.602182 \nL 79.883337 114.865745 \nL 79.923762 113.0208 \nL 79.946863 113.811491 \nL 80.097015 106.695273 \nL 80.10279 106.958836 \nL 80.12589 107.485964 \nL 80.143215 105.377455 \nL 80.14899 103.796073 \nL 80.195191 111.439418 \nL 80.241391 106.958836 \nL 80.48972 119.873455 \nL 80.616771 118.292073 \nL 80.680297 119.082764 \nL 80.697622 117.764945 \nL 80.870874 92.199273 \nL 80.963276 95.098473 \nL 80.997926 94.571345 \nL 81.171178 113.547927 \nL 81.483032 124.6176 \nL 81.511907 124.354036 \nL 81.644734 105.641018 \nL 81.673609 119.082764 \nL 81.690935 122.772655 \nL 81.75446 119.082764 \nL 81.766011 116.710691 \nL 81.841086 125.671855 \nL 81.921938 125.144727 \nL 81.875737 126.726109 \nL 81.933488 126.198982 \nL 82.08364 128.307491 \nL 82.274217 123.563345 \nL 82.291542 123.826909 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134.633018 \nL 93.893662 134.369455 \nL 93.951413 136.741527 \nL 93.957188 136.2144 \nL 93.991839 138.059345 \nL 94.043814 134.369455 \nL 94.049589 134.369455 \nL 94.08424 110.648727 \nL 94.228617 69.269236 \nL 94.234392 72.695564 \nL 94.292142 67.424291 \nL 94.442294 116.974255 \nL 94.448069 116.974255 \nL 94.644422 132.788073 \nL 94.806124 140.167855 \nL 94.811899 140.167855 \nL 94.817674 140.431418 \nL 94.852324 137.795782 \nL 94.8581 137.795782 \nL 94.91585 115.92 \nL 95.031352 79.284655 \nL 95.066002 86.664436 \nL 95.360531 129.098182 \nL 95.551108 137.795782 \nL 95.556883 136.741527 \nL 95.637734 141.485673 \nL 95.672385 142.276364 \nL 95.707035 139.904291 \nL 95.741686 121.191273 \nL 95.897613 71.377745 \nL 95.966913 67.951418 \nL 96.06509 97.206982 \nL 96.267217 123.036218 \nL 96.49822 128.834618 \nL 96.671472 115.92 \nL 96.688797 116.710691 \nL 96.734998 118.555636 \nL 96.763873 114.075055 \nL 96.833174 95.6256 \nL 96.88515 108.803782 \nL 97.041077 126.726109 \nL 97.058402 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125.671855 \nL 102.86235 127.5168 \nL 102.868125 126.989673 \nL 103.012502 131.470255 \nL 102.90855 126.726109 \nL 103.024052 131.206691 \nL 103.029827 131.206691 \nL 103.035602 130.943127 \nL 103.093353 133.051636 \nL 103.110678 132.260945 \nL 103.145328 133.3152 \nL 103.203079 131.470255 \nL 103.214629 131.733818 \nL 103.497608 121.454836 \nL 103.520708 121.981964 \nL 103.676635 129.098182 \nL 103.688185 128.307491 \nL 103.740161 130.943127 \nL 103.745936 130.943127 \nL 103.751711 130.943127 \nL 103.942288 137.795782 \nL 103.763261 130.679564 \nL 103.988489 136.741527 \nL 104.300343 69.5328 \nL 104.358093 87.455127 \nL 104.525571 134.896582 \nL 104.537121 134.633018 \nL 104.542896 134.633018 \nL 104.681498 142.803491 \nL 104.762349 143.067055 \nL 104.802774 141.222109 \nL 104.808549 141.749236 \nL 104.860525 136.741527 \nL 104.918275 106.695273 \nL 105.045327 87.982255 \nL 105.056877 91.408582 \nL 105.108853 89.036509 \nL 105.212804 114.075055 \nL 105.386056 139.904291 \nL 105.397606 139.904291 \nL 105.518883 145.439127 \nL 105.530433 144.648436 \nL 105.536208 144.121309 \nL 105.611284 147.020509 \nL 105.617059 145.966255 \nL 105.64016 147.020509 \nL 105.68636 141.749236 \nL 105.755661 118.292073 \nL 105.853837 87.982255 \nL 105.905813 88.509382 \nL 105.980889 81.656727 \nL 106.009764 87.455127 \nL 106.183016 142.0128 \nL 106.338943 151.237527 \nL 106.344718 150.446836 \nL 106.419794 154.136727 \nL 106.442895 155.981673 \nL 106.517971 153.346036 \nL 106.552621 139.1136 \nL 106.714323 77.966836 \nL 106.720098 77.966836 \nL 106.777849 72.959127 \nL 106.824049 79.548218 \nL 107.037727 138.059345 \nL 107.210979 140.958545 \nL 107.216754 140.958545 \nL 107.297605 141.749236 \nL 107.326481 140.958545 \nL 107.493958 103.005382 \nL 107.597909 77.966836 \nL 107.64411 89.300073 \nL 107.892438 139.904291 \nL 108.146541 151.501091 \nL 108.186966 148.601891 \nL 108.215842 135.950836 \nL 108.360219 77.176145 \nL 108.365994 79.548218 \nL 108.371769 81.656727 \nL 108.45262 71.641309 \nL 108.46417 71.114182 \nL 108.48727 75.3312 \nL 108.718273 135.687273 \nL 108.955051 148.601891 \nL 108.966601 148.338327 \nL 109.018577 141.222109 \nL 109.151403 107.2224 \nL 109.255355 112.493673 \nL 109.728911 145.702691 \nL 109.798212 147.8112 \nL 109.838637 145.702691 \nL 109.879063 133.578764 \nL 110.03499 75.067636 \nL 110.10429 67.951418 \nL 110.150491 74.540509 \nL 110.43347 130.416 \nL 110.641372 141.749236 \nL 110.658697 141.222109 \nL 110.704898 135.423709 \nL 110.8435 72.168436 \nL 110.8666 78.493964 \nL 111.363256 149.392582 \nL 111.455657 152.291782 \nL 111.484533 150.446836 \nL 111.565384 136.741527 \nL 111.68666 104.3232 \nL 111.703985 110.648727 \nL 111.744411 97.997673 \nL 111.952314 142.0128 \nL 111.963864 142.0128 \nL 112.310368 155.190982 \nL 112.327693 154.136727 \nL 112.379669 136.477964 \nL 112.489395 87.191564 \nL 112.529821 88.509382 \nL 112.547146 96.679855 \nL 112.610672 79.548218 \nL 112.616447 79.548218 \nL 112.627997 82.710982 \nL 112.824349 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124.090473 \nL 116.03529 130.943127 \nL 116.104591 137.005091 \nL 116.179666 135.687273 \nL 116.191217 135.687273 \nL 116.462645 147.020509 \nL 116.49152 144.648436 \nL 116.537721 131.997382 \nL 116.710973 78.493964 \nL 116.728298 77.703273 \nL 116.734073 82.447418 \nL 116.924651 137.005091 \nL 117.259605 157.563055 \nL 117.282705 158.090182 \nL 117.317356 155.454545 \nL 117.403982 129.625309 \nL 117.588784 81.920291 \nL 117.623435 87.455127 \nL 117.912188 153.6096 \nL 117.969939 153.346036 \nL 117.975714 153.346036 \nL 118.206717 139.1136 \nL 118.339544 105.377455 \nL 118.397294 113.547927 \nL 118.628297 141.485673 \nL 118.651397 141.222109 \nL 118.697598 142.0128 \nL 118.726473 142.0128 \nL 118.743799 142.0128 \nL 118.778449 143.857745 \nL 118.841975 141.485673 \nL 118.917051 139.640727 \nL 118.957476 140.431418 \nL 119.003677 142.276364 \nL 119.026777 139.904291 \nL 119.148054 115.656436 \nL 119.159604 119.609891 \nL 119.171154 121.454836 \nL 119.182704 117.501382 \nL 119.22313 108.276655 \nL 119.286655 118.028509 \nL 119.29243 117.237818 \nL 119.298206 116.974255 \nL 119.303981 119.873455 \nL 119.459908 154.136727 \nL 119.69091 165.2064 \nL 119.714011 165.2064 \nL 119.789087 166.260655 \nL 119.858388 157.299491 \nL 119.973889 111.175855 \nL 119.996989 116.447127 \nL 120.008539 121.981964 \nL 120.05474 104.059636 \nL 120.083615 106.431709 \nL 120.08939 106.431709 \nL 120.106716 110.648727 \nL 120.268418 148.338327 \nL 120.401244 159.408 \nL 120.620697 169.423418 \nL 120.689998 161.780073 \nL 120.8517 96.943418 \nL 120.915226 92.462836 \nL 120.949876 97.734109 \nL 121.302156 164.942836 \nL 121.307931 164.942836 \nL 121.458083 169.686982 \nL 121.463858 169.159855 \nL 121.469633 169.159855 \nL 121.533159 149.129018 \nL 121.706411 86.137309 \nL 121.723736 84.819491 \nL 121.769937 89.563636 \nL 122.02404 154.927418 \nL 122.278143 164.942836 \nL 122.307018 163.361455 \nL 122.370544 142.0128 \nL 122.503371 89.563636 \nL 122.538021 93.253527 \nL 122.786349 143.857745 \nL 122.959601 155.718109 \nL 123.121303 161.252945 \nL 123.138629 159.408 \nL 123.190604 147.020509 \nL 123.404282 90.354327 \nL 123.410057 90.354327 \nL 123.450483 102.478255 \nL 123.646835 145.702691 \nL 123.860513 155.190982 \nL 123.872063 155.190982 \nL 123.877838 155.718109 \nL 123.964464 154.136727 \nL 124.079965 133.578764 \nL 124.212792 120.137018 \nL 124.235892 121.191273 \nL 124.258993 117.501382 \nL 124.305193 127.5168 \nL 124.536196 155.190982 \nL 124.784524 163.361455 \nL 124.801849 162.3072 \nL 124.853825 151.237527 \nL 125.032852 91.145018 \nL 125.102153 98.261236 \nL 125.28118 150.446836 \nL 125.494858 164.942836 \nL 125.616135 168.896291 \nL 125.64501 167.051345 \nL 125.691211 150.7104 \nL 125.841362 92.199273 \nL 125.852913 94.044218 \nL 125.858688 91.408582 \nL 125.933764 105.904582 \nL 126.124341 153.346036 \nL 126.45352 164.942836 \nL 126.46507 164.152145 \nL 126.522821 149.129018 \nL 126.690298 92.462836 \nL 126.719173 90.617891 \nL 126.759599 95.889164 \nL 126.955951 153.346036 \nL 127.27358 168.369164 \nL 127.319781 165.469964 \nL 127.360207 145.966255 \nL 127.527684 85.610182 \nL 127.573884 83.765236 \nL 127.591209 88.509382 \nL 127.781787 151.237527 \nL 128.076315 168.1056 \nL 128.082091 168.1056 \nL 128.116741 168.896291 \nL 128.157166 164.942836 \nL 128.191817 146.756945 \nL 128.365069 85.610182 \nL 128.370844 85.610182 \nL 128.417045 90.354327 \nL 128.613397 151.237527 \nL 128.80975 160.462255 \nL 128.815525 159.935127 \nL 128.8444 159.935127 \nL 128.913701 160.462255 \nL 129.040753 134.369455 \nL 129.214005 92.989964 \nL 129.21978 93.780655 \nL 129.462333 143.330618 \nL 129.756862 160.462255 \nL 129.762637 160.462255 \nL 129.901238 149.392582 \nL 130.01674 105.904582 \nL 130.097591 120.137018 \nL 130.311268 153.6096 \nL 130.524946 163.097891 \nL 130.611572 164.415709 \nL 130.559597 162.570764 \nL 130.634673 163.097891 \nL 130.680873 150.7104 \nL 130.84835 91.408582 \nL 130.854125 91.408582 \nL 130.888776 89.8272 \nL 130.906101 92.989964 \nL 131.096678 149.392582 \nL 131.310356 163.625018 \nL 131.316131 163.625018 \nL 131.408532 166.524218 \nL 131.431632 164.679273 \nL 131.512484 147.547636 \nL 131.66841 89.300073 \nL 131.685736 90.881455 \nL 131.720386 85.610182 \nL 131.737711 92.462836 \nL 131.962939 152.291782 \nL 132.095766 154.400291 \nL 132.107316 154.136727 \nL 132.217042 152.818909 \nL 132.222817 153.346036 \nL 132.263243 154.663855 \nL 132.320994 153.346036 \nL 132.482696 148.601891 \nL 132.500021 148.601891 \nL 132.621297 146.493382 \nL 132.627072 147.020509 \nL 132.696373 155.718109 \nL 132.777224 154.927418 \nL 132.921601 159.671564 \nL 132.973577 158.880873 \nL 132.990902 158.880873 \nL 133.094853 162.3072 \nL 133.123729 161.516509 \nL 133.181479 148.338327 \nL 133.279656 119.873455 \nL 133.325856 127.780364 \nL 133.377832 115.392873 \nL 133.499108 154.136727 \nL 133.672361 166.524218 \nL 133.787862 163.625018 \nL 133.822512 164.415709 \nL 133.909139 168.1056 \nL 133.955339 165.469964 \nL 134.018865 142.539927 \nL 134.03619 134.369455 \nL 134.070841 151.237527 \nL 134.128591 143.067055 \nL 134.157467 138.322909 \nL 134.353819 159.408 \nL 134.40002 158.090182 \nL 134.43467 160.462255 \nL 134.440445 160.462255 \nL 134.596372 167.842036 \nL 134.659898 165.469964 \nL 134.682998 164.679273 \nL 134.717649 166.787782 \nL 134.769624 165.469964 \nL 134.83315 158.617309 \nL 134.977527 142.803491 \nL 135.006402 131.206691 \nL 135.093028 142.0128 \nL 135.277831 161.780073 \nL 135.300931 160.462255 \nL 135.485733 173.904 \nL 135.60701 179.175273 \nL 135.63011 177.593891 \nL 135.670536 168.896291 \nL 135.797587 114.865745 \nL 135.826463 119.082764 \nL 135.832238 117.764945 \nL 135.861113 125.935418 \nL 135.889988 125.935418 \nL 136.074791 166.524218 \nL 136.230718 172.849745 \nL 136.502146 159.935127 \nL 136.663848 109.067345 \nL 136.669623 109.067345 \nL 136.692723 108.276655 \nL 136.715824 114.338618 \nL 136.917951 165.469964 \nL 137.148954 176.539636 \nL 137.154729 176.539636 \nL 137.252905 179.7024 \nL 137.281781 178.648145 \nL 137.327981 172.849745 \nL 137.535884 106.168145 \nL 137.547434 111.702982 \nL 137.749562 164.152145 \nL 137.992115 176.539636 \nL 137.99789 176.539636 \nL 138.101841 178.648145 \nL 138.119166 177.593891 \nL 138.176917 157.826618 \nL 138.350169 96.679855 \nL 138.367494 95.6256 \nL 138.379044 103.268945 \nL 138.586947 160.725818 \nL 138.777524 172.059055 \nL 138.927676 176.276073 \nL 138.945002 175.221818 \nL 139.002752 161.252945 \nL 139.176004 97.734109 \nL 139.187555 96.679855 \nL 139.21643 108.803782 \nL 139.407007 158.353745 \nL 139.60336 170.741236 \nL 139.609135 170.741236 \nL 139.753512 174.694691 \nL 139.782387 173.640436 \nL 139.834363 160.989382 \nL 140.007615 97.997673 \nL 140.04804 111.175855 \nL 140.267493 162.043636 \nL 140.331019 165.997091 \nL 141.168404 163.625018 \nL 141.179955 163.888582 \nL 141.410957 171.0048 \nL 141.422508 169.950545 \nL 141.497584 155.718109 \nL 141.65351 106.695273 \nL 141.665061 109.330909 \nL 141.670836 109.330909 \nL 141.676611 108.803782 \nL 141.705486 114.602182 \nL 141.936489 153.346036 \nL 141.965364 153.346036 \nL 142.000015 153.873164 \nL 142.05199 153.082473 \nL 142.075091 153.346036 \nL 142.080866 153.082473 \nL 142.150167 153.6096 \nL 142.167492 153.6096 \nL 142.242568 154.663855 \nL 142.277218 154.136727 \nL 142.467796 142.539927 \nL 142.583297 145.175564 \nL 142.773875 148.865455 \nL 142.77965 148.865455 \nL 142.924026 150.446836 \nL 142.976002 150.446836 \nL 143.149254 147.547636 \nL 143.155029 147.547636 \nL 143.322506 143.857745 \nL 143.339832 144.121309 \nL 143.345607 144.121309 \nL 143.403357 143.857745 \nL 143.426458 144.384873 \nL 143.449558 144.384873 \nL 143.461108 144.384873 \nL 143.513084 141.485673 \nL 143.559284 144.121309 \nL 143.732537 152.818909 \nL 143.755637 152.818909 \nL 143.865363 153.873164 \nL 143.882688 153.6096 \nL 143.888463 153.6096 \nL 143.934664 154.663855 \nL 144.003965 154.136727 \nL 144.102141 145.439127 \nL 144.165667 126.726109 \nL 144.223418 140.694982 \nL 144.460196 164.415709 \nL 144.616123 168.896291 \nL 144.650773 168.1056 \nL 144.668098 167.842036 \nL 144.714299 169.159855 \nL 144.754724 168.369164 \nL 144.760499 168.369164 \nL 144.8298 153.082473 \nL 144.945302 117.237818 \nL 144.985727 123.826909 \nL 145.303356 162.570764 \nL 145.309131 162.570764 \nL 145.447733 167.578473 \nL 145.459283 167.051345 \nL 145.765362 145.439127 \nL 145.794237 146.493382 \nL 145.817338 145.439127 \nL 145.97904 153.6096 \nL 145.984815 153.6096 \nL 146.123416 156.5088 \nL 146.175392 156.5088 \nL 146.42372 171.795491 \nL 146.481471 166.260655 \nL 146.654723 111.966545 \nL 146.683598 123.299782 \nL 146.862626 162.570764 \nL 147.099404 174.431127 \nL 147.116729 174.431127 \nL 147.191805 176.276073 \nL 147.23223 174.694691 \nL 147.289981 167.842036 \nL 147.492109 112.493673 \nL 147.544084 132.788073 \nL 147.775087 166.787782 \nL 148.081166 176.012509 \nL 148.144692 168.369164 \nL 148.306394 105.641018 \nL 148.387245 131.470255 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\nL 152.487546 125.408291 \nL 152.747424 159.935127 \nL 152.753199 159.935127 \nL 152.857151 169.686982 \nL 153.076604 184.182982 \nL 153.099704 182.6016 \nL 153.151679 168.632727 \nL 153.290281 116.974255 \nL 153.313382 123.563345 \nL 153.503959 167.578473 \nL 153.706086 180.229527 \nL 153.711861 180.229527 \nL 153.989065 163.625018 \nL 154.098791 139.1136 \nL 154.144992 149.656145 \nL 154.352895 172.059055 \nL 154.364445 172.059055 \nL 154.543472 178.384582 \nL 154.739824 187.872873 \nL 154.786025 185.237236 \nL 154.82645 165.733527 \nL 154.941952 128.834618 \nL 154.965052 135.950836 \nL 155.149854 171.531927 \nL 155.438608 187.872873 \nL 155.444383 187.872873 \nL 155.57721 191.826327 \nL 155.60031 189.981382 \nL 155.646511 178.911709 \nL 155.773562 129.625309 \nL 155.796663 136.2144 \nL 156.02189 179.175273 \nL 156.143167 184.182982 \nL 156.177817 183.655855 \nL 156.449246 176.276073 \nL 156.455021 176.276073 \nL 156.634048 163.097891 \nL 156.686024 160.198691 \nL 156.732224 164.152145 \nL 156.882376 168.1056 \nL 156.899701 168.1056 \nL 157.020978 168.632727 \nL 157.032528 168.896291 \nL 157.142254 168.1056 \nL 157.20578 168.1056 \nL 157.338607 157.299491 \nL 157.431008 130.416 \nL 157.534959 144.384873 \nL 157.910339 180.756655 \nL 157.916114 180.756655 \nL 158.00274 182.6016 \nL 158.025841 181.020218 \nL 158.141342 170.741236 \nL 158.274169 122.509091 \nL 158.343469 141.485673 \nL 158.441646 160.462255 \nL 158.643773 177.593891 \nL 158.892101 184.182982 \nL 158.955627 176.539636 \nL 158.996053 157.299491 \nL 159.082679 126.462545 \nL 159.15198 137.532218 \nL 159.469609 166.260655 \nL 159.712162 178.648145 \nL 159.758362 174.958255 \nL 159.937389 144.121309 \nL 159.99514 150.973964 \nL 160.278119 182.6016 \nL 160.283894 182.6016 \nL 160.474471 186.291491 \nL 160.480246 186.291491 \nL 160.566872 187.082182 \nL 160.578422 186.291491 \nL 160.641948 171.795491 \nL 160.7459 146.493382 \nL 160.80365 152.028218 \nL 161.017328 166.787782 \nL 161.121279 172.849745 \nL 161.16748 170.741236 \nL 161.173255 170.741236 \nL 161.207905 169.686982 \nL 161.242556 171.531927 \nL 161.248331 171.531927 \nL 161.410033 182.338036 \nL 161.427358 181.283782 \nL 161.485109 160.462255 \nL 161.58906 127.253236 \nL 161.623711 138.322909 \nL 161.837388 172.059055 \nL 162.01064 180.756655 \nL 162.068391 181.283782 \nL 162.126142 181.020218 \nL 162.149242 181.020218 \nL 162.201218 182.074473 \nL 162.305169 168.896291 \nL 162.420671 138.059345 \nL 162.489971 143.594182 \nL 162.876901 181.810909 \nL 163.027053 184.710109 \nL 163.044378 184.446545 \nL 163.050153 184.446545 \nL 163.055929 184.710109 \nL 163.084804 182.338036 \nL 163.252281 132.524509 \nL 163.362007 143.857745 \nL 163.53526 158.617309 \nL 163.714287 164.415709 \nL 163.737387 163.097891 \nL 164.124317 144.121309 \nL 164.205168 146.756945 \nL 164.257144 147.8112 \nL 164.291794 145.966255 \nL 164.303344 145.966255 \nL 164.332219 145.966255 \nL 164.476596 147.547636 \nL 164.499697 147.284073 \nL 164.563222 148.601891 \nL 164.568997 148.601891 \nL 164.759575 154.136727 \nL 164.771125 153.346036 \nL 164.932827 141.749236 \nL 164.938602 142.803491 \nL 165.192705 169.423418 \nL 165.55076 189.454255 \nL 165.614286 184.182982 \nL 165.660486 163.361455 \nL 165.724012 140.167855 \nL 165.793313 154.400291 \nL 165.955015 176.276073 \nL 166.041641 178.648145 \nL 166.076291 177.330327 \nL 166.226443 175.485382 \nL 166.237993 175.485382 \nL 166.284194 174.958255 \nL 166.301519 177.066764 \nL 166.341945 182.6016 \nL 166.417021 179.175273 \nL 166.613373 155.981673 \nL 166.688449 163.361455 \nL 166.832826 177.593891 \nL 166.879026 174.694691 \nL 166.884801 174.694691 \nL 167.225531 194.461964 \nL 167.289057 189.981382 \nL 167.398783 168.1056 \nL 167.427658 174.431127 \nL 167.606685 197.361164 \nL 167.855014 203.686691 \nL 167.860789 203.686691 \nL 168.045591 207.376582 \nL 168.126442 199.206109 \nL 168.195743 172.586182 \nL 168.259269 181.547345 \nL 168.461396 199.206109 \nL 168.467171 199.206109 \nL 168.628873 204.213818 \nL 168.634648 204.213818 \nL 168.882976 210.539345 \nL 168.888752 210.539345 \nL 168.958052 203.159564 \nL 169.056229 157.299491 \nL 169.102429 176.539636 \nL 169.264131 191.562764 \nL 169.703037 207.640145 \nL 169.720362 207.376582 \nL 169.783888 199.469673 \nL 169.887839 150.7104 \nL 169.986015 172.849745 \nL 170.043766 180.229527 \nL 170.217018 195.252655 \nL 170.228568 195.252655 \nL 170.309419 197.0976 \nL 170.338295 195.252655 \nL 170.644374 182.338036 \nL 170.713674 182.865164 \nL 170.794525 183.128727 \nL 170.731 182.338036 \nL 170.811851 182.338036 \nL 170.858051 181.283782 \nL 170.904252 182.865164 \nL 171.019753 183.919418 \nL 171.031303 183.655855 \nL 171.042854 183.655855 \nL 171.395133 197.624727 \nL 171.418233 196.306909 \nL 171.470209 181.810909 \nL 171.545285 155.190982 \nL 171.59726 169.423418 \nL 171.845589 194.725527 \nL 172.047716 200.523927 \nL 172.059266 199.469673 \nL 172.284494 184.182982 \nL 172.376895 153.6096 \nL 172.475071 167.842036 \nL 172.659874 188.927127 \nL 172.711849 192.089891 \nL 172.7927 191.2992 \nL 172.798476 191.2992 \nL 172.983278 201.841745 \nL 173.023703 202.896 \nL 173.069904 201.314618 \nL 173.14498 176.539636 \nL 173.208506 154.400291 \nL 173.260481 169.950545 \nL 173.514584 196.306909 \nL 173.52036 196.306909 \nL 173.710937 203.686691 \nL 173.722487 203.686691 \nL 173.866864 207.113018 \nL 173.872639 207.113018 \nL 173.982365 177.593891 \nL 174.040116 154.136727 \nL 174.103642 167.578473 \nL 174.455921 197.361164 \nL 174.513672 195.779782 \nL 174.571423 193.144145 \nL 174.646499 193.671273 \nL 174.825526 180.493091 \nL 174.952577 161.516509 \nL 174.975678 162.3072 \nL 175.079629 172.059055 \nL 175.229781 186.291491 \nL 175.247106 186.027927 \nL 175.316407 184.710109 \nL 175.351057 186.291491 \nL 175.356833 186.291491 \nL 175.408808 188.663564 \nL 175.449234 185.237236 \nL 175.697562 145.966255 \nL 175.749537 157.826618 \nL 176.032516 181.547345 \nL 176.211543 190.772073 \nL 176.240419 190.508509 \nL 176.246194 190.508509 \nL 176.384795 192.617018 \nL 176.471421 165.733527 \nL 176.529172 142.0128 \nL 176.598473 153.6096 \nL 176.690874 162.834327 \nL 176.869901 182.338036 \nL 176.968078 186.027927 \nL 176.996953 183.919418 \nL 177.285707 167.842036 \nL 177.360783 140.431418 \nL 177.435859 149.656145 \nL 177.851664 177.857455 \nL 177.857439 177.857455 \nL 178.036466 181.020218 \nL 178.042241 181.020218 \nL 178.105767 173.376873 \nL 178.238594 151.237527 \nL 178.302119 156.245236 \nL 178.821876 193.671273 \nL 178.937377 183.919418 \nL 179.029779 144.912 \nL 179.15683 153.873164 \nL 179.526435 182.6016 \nL 179.57841 182.338036 \nL 179.699687 186.027927 \nL 179.728562 184.182982 \nL 179.786313 172.059055 \nL 179.861389 137.795782 \nL 179.936465 144.912 \nL 180.30607 168.896291 \nL 180.311845 168.896291 \nL 180.438896 170.477673 \nL 180.444671 169.950545 \nL 180.467772 169.950545 \nL 180.560173 173.376873 \nL 180.594823 171.0048 \nL 180.623698 160.725818 \nL 180.681449 138.059345 \nL 180.7392 153.082473 \nL 181.091479 186.291491 \nL 181.339807 198.942545 \nL 181.368683 199.733236 \nL 181.397558 197.0976 \nL 181.484184 161.252945 \nL 181.518835 150.973964 \nL 181.576585 167.842036 \nL 181.876889 194.1984 \nL 182.136767 201.051055 \nL 182.246494 191.562764 \nL 182.361995 163.361455 \nL 182.465947 165.997091 \nL 182.477497 165.733527 \nL 182.506372 168.896291 \nL 182.696949 182.865164 \nL 182.714275 182.338036 \nL 182.72005 182.338036 \nL 182.74315 181.547345 \nL 183.592086 188.136436 \nL 183.863514 205.268073 \nL 183.880839 204.477382 \nL 183.93859 199.469673 \nL 184.013666 167.842036 \nL 184.071417 183.919418 \nL 184.290869 204.477382 \nL 184.296644 204.477382 \nL 184.475672 207.903709 \nL 184.521872 207.376582 \nL 184.556523 209.221527 \nL 184.625824 211.330036 \nL 184.683574 210.275782 \nL 184.7702 201.578182 \nL 184.851051 179.175273 \nL 184.908802 188.136436 \nL 185.289957 213.174982 \nL 185.295732 213.174982 \nL 185.370808 212.120727 \nL 185.411233 212.647855 \nL 185.526735 214.4928 \nL 185.538285 213.965673 \nL 185.619136 189.454255 \nL 185.688437 165.733527 \nL 185.740413 180.756655 \nL 186.023391 202.632436 \nL 186.063817 200.523927 \nL 186.081142 200.260364 \nL 186.127342 202.105309 \nL 186.35257 213.438545 \nL 186.358345 213.174982 \nL 186.36412 213.174982 \nL 186.427646 205.531636 \nL 186.514272 161.516509 \nL 186.600898 181.283782 \nL 186.999378 207.640145 \nL 187.189956 214.756364 \nL 187.218831 212.647855 \nL 187.293907 180.493091 \nL 187.351658 158.090182 \nL 187.409408 173.113309 \nL 187.703937 201.314618 \nL 187.773238 196.306909 \nL 187.807888 195.252655 \nL 187.848314 197.361164 \nL 187.917615 201.841745 \nL 187.981141 200.523927 \nL 188.015791 202.105309 \nL 188.044666 198.415418 \nL 188.200593 171.0048 \nL 188.287219 179.965964 \nL 188.495122 193.407709 \nL 188.500897 193.407709 \nL 188.858952 208.957964 \nL 188.887827 206.058764 \nL 188.939803 186.818618 \nL 189.014879 153.6096 \nL 189.072629 169.159855 \nL 189.430684 203.159564 \nL 189.679012 210.012218 \nL 189.684787 209.748655 \nL 189.748313 202.368873 \nL 189.782963 178.911709 \nL 189.852264 150.7104 \nL 189.910015 167.051345 \nL 190.192993 198.678982 \nL 190.198768 198.678982 \nL 190.504847 208.167273 \nL 190.510622 207.903709 \nL 190.516397 207.903709 \nL 190.591473 194.1984 \nL 190.741625 162.834327 \nL 190.7474 162.834327 \nL 191.070804 192.353455 \nL 191.14588 189.190691 \nL 191.284482 191.035636 \nL 191.174756 188.663564 \nL 191.290257 190.772073 \nL 191.296032 190.772073 \nL 191.353783 192.880582 \nL 191.428859 180.756655 \nL 191.527035 157.563055 \nL 191.561685 164.415709 \nL 191.775363 193.407709 \nL 192.121867 204.213818 \nL 192.133418 204.213818 \nL 192.266244 184.182982 \nL 192.35287 146.756945 \nL 192.404846 162.043636 \nL 192.664724 196.306909 \nL 192.988128 206.058764 \nL 193.005454 206.058764 \nL 193.011229 206.322327 \nL 193.045879 202.896 \nL 193.10363 181.283782 \nL 193.190256 150.973964 \nL 193.242232 160.462255 \nL 193.519435 191.035636 \nL 193.785088 198.151855 \nL 193.796638 198.151855 \nL 193.842839 199.469673 \nL 193.917915 187.872873 \nL 194.021866 152.028218 \nL 194.085392 168.896291 \nL 194.32217 192.353455 \nL 194.674449 208.957964 \nL 194.7091 205.268073 \nL 194.766851 183.128727 \nL 194.882352 163.097891 \nL 194.911227 165.469964 \nL 195.15378 195.779782 \nL 195.165331 195.779782 \nL 195.303932 193.144145 \nL 195.471409 190.508509 \nL 195.500285 190.508509 \nL 195.569586 182.338036 \nL 195.679312 168.896291 \nL 195.771713 173.640436 \nL 195.869889 182.6016 \nL 196.043142 196.043345 \nL 196.048917 195.779782 \nL 196.430071 173.376873 \nL 196.528248 142.539927 \nL 196.597549 155.718109 \nL 196.857427 176.276073 \nL 196.874752 176.276073 \nL 196.972928 177.593891 \nL 197.001804 177.066764 \nL 197.013354 177.066764 \nL 197.019129 176.8032 \nL 197.08843 178.648145 \nL 197.094205 178.648145 \nL 197.175056 179.7024 \nL 197.203931 178.648145 \nL 197.267457 164.415709 \nL 197.359858 143.594182 \nL 197.406059 149.129018 \nL 197.631286 171.0048 \nL 197.637062 171.0048 \nL 197.746788 180.756655 \nL 197.954691 194.1984 \nL 197.960466 194.1984 \nL 198.000891 195.252655 \nL 198.029766 193.144145 \nL 198.093292 171.268364 \nL 198.197244 138.059345 \nL 198.237669 148.601891 \nL 198.584173 192.617018 \nL 198.774751 186.818618 \nL 198.809401 187.345745 \nL 198.826726 187.345745 \nL 198.896027 179.175273 \nL 199.040404 150.7104 \nL 199.098155 158.353745 \nL 199.421559 187.872873 \nL 199.427334 187.872873 \nL 199.438884 188.4 \nL 199.485085 185.764364 \nL 199.577486 184.446545 \nL 199.600586 185.237236 \nL 199.681437 186.555055 \nL 199.629461 184.446545 \nL 199.704537 184.710109 \nL 199.744963 172.586182 \nL 199.802714 150.973964 \nL 199.872015 164.942836 \nL 200.079917 187.609309 \nL 200.351346 202.368873 \nL 200.495722 205.531636 \nL 200.507272 204.740945 \nL 200.553473 197.888291 \nL 200.69785 150.7104 \nL 200.761376 174.167564 \nL 200.911528 193.671273 \nL 200.963503 191.035636 \nL 200.969278 191.035636 \nL 201.038579 193.407709 \nL 201.08478 191.2992 \nL 201.177181 188.927127 \nL 201.217606 189.190691 \nL 201.437059 174.958255 \nL 201.52946 155.454545 \nL 201.598761 163.361455 \nL 201.93949 195.252655 \nL 201.951041 195.252655 \nL 202.095417 198.942545 \nL 202.222469 189.454255 \nL 202.366846 140.958545 \nL 202.424596 155.190982 \nL 202.672925 187.082182 \nL 202.7018 186.555055 \nL 202.71335 186.555055 \nL 202.748001 186.027927 \nL 202.799976 187.345745 \nL 202.805751 187.345745 \nL 203.002104 192.089891 \nL 203.013654 191.826327 \nL 203.019429 191.826327 \nL 203.071405 177.330327 \nL 203.146481 142.0128 \nL 203.215781 152.291782 \nL 203.244657 152.028218 \nL 203.261982 154.663855 \nL 203.504535 172.849745 \nL 203.516085 172.849745 \nL 203.654687 175.485382 \nL 203.660462 175.485382 \nL 203.839489 181.810909 \nL 203.695112 175.221818 \nL 203.87414 180.493091 \nL 203.89724 175.485382 \nL 204.030067 143.067055 \nL 204.041617 146.493382 \nL 204.214869 186.555055 \nL 204.422772 195.779782 \nL 204.428547 195.779782 \nL 204.653774 202.632436 \nL 204.6711 201.841745 \nL 204.734625 186.291491 \nL 204.873227 146.229818 \nL 204.925203 160.462255 \nL 205.277482 189.190691 \nL 205.346783 188.927127 \nL 205.352558 188.927127 \nL 205.456509 194.461964 \nL 205.50271 190.772073 \nL 205.681737 140.167855 \nL 205.768363 155.190982 \nL 205.970491 180.756655 \nL 206.143743 189.717818 \nL 206.305445 193.671273 \nL 206.32277 192.880582 \nL 206.368971 186.027927 \nL 206.524898 133.578764 \nL 206.599974 152.291782 \nL 206.929153 192.617018 \nL 207.13128 201.051055 \nL 207.142831 201.578182 \nL 207.183256 198.678982 \nL 207.229457 179.7024 \nL 207.362283 129.888873 \nL 207.385384 136.2144 \nL 207.570186 181.020218 \nL 207.806964 196.570473 \nL 207.835839 195.516218 \nL 207.864715 194.989091 \nL 207.910915 197.0976 \nL 207.968666 198.415418 \nL 208.003316 196.306909 \nL 208.055292 183.655855 \nL 208.182344 144.384873 \nL 208.211219 145.966255 \nL 208.396021 187.872873 \nL 208.471097 192.089891 \nL 211.023679 193.144145 \nL 211.289333 202.896 \nL 211.318208 201.841745 \nL 211.375959 186.555055 \nL 211.537661 128.834618 \nL 211.543436 129.888873 \nL 211.751338 184.182982 \nL 211.757113 184.182982 \nL 211.92459 192.617018 \nL 211.930366 192.617018 \nL 212.011217 190.772073 \nL 212.040092 192.617018 \nL 212.138268 196.306909 \nL 212.167143 194.1984 \nL 212.282645 153.6096 \nL 212.369271 132.524509 \nL 212.403921 144.648436 \nL 212.611824 172.059055 \nL 212.796626 175.485382 \nL 212.808177 175.485382 \nL 212.952553 177.330327 \nL 213.212432 161.252945 \nL 212.964103 177.593891 \nL 213.241307 168.369164 \nL 213.501185 196.306909 \nL 213.524285 196.306909 \nL 213.605136 198.415418 \nL 213.639787 196.834036 \nL 213.651337 196.306909 \nL 213.697538 198.942545 \nL 213.795714 203.423127 \nL 213.824589 200.787491 \nL 213.876565 180.229527 \nL 213.974741 142.539927 \nL 214.015167 153.082473 \nL 214.032492 151.764655 \nL 214.067142 160.462255 \nL 214.30392 189.981382 \nL 214.575349 198.942545 \nL 214.586899 198.415418 \nL 214.748601 173.640436 \nL 214.858327 134.369455 \nL 214.939178 152.555345 \nL 215.095105 172.322618 \nL 215.10088 172.059055 \nL 215.175956 173.376873 \nL 215.181731 173.376873 \nL 215.314558 174.431127 \nL 215.343433 174.431127 \nL 215.412734 174.694691 \nL 215.441609 174.167564 \nL 215.447385 174.167564 \nL 215.458935 174.167564 \nL 215.655287 160.989382 \nL 215.759238 165.2064 \nL 215.926716 170.477673 \nL 216.128843 184.446545 \nL 216.28477 193.407709 \nL 216.29632 193.144145 \nL 216.359846 181.020218 \nL 216.509998 136.2144 \nL 216.527323 136.741527 \nL 216.538873 136.741527 \nL 216.550423 139.904291 \nL 216.735226 183.919418 \nL 217.10483 197.0976 \nL 217.122156 196.570473 \nL 217.179906 188.927127 \nL 217.353158 124.881164 \nL 217.44556 146.229818 \nL 217.641912 178.648145 \nL 217.85559 189.190691 \nL 217.861365 188.927127 \nL 217.86714 188.927127 \nL 218.017292 180.756655 \nL 218.144343 123.826909 \nL 218.178994 131.206691 \nL 218.438872 165.469964 \nL 218.508173 167.051345 \nL 218.554373 166.260655 \nL 218.658325 165.2064 \nL 218.67565 165.733527 \nL 218.825802 173.904 \nL 218.860452 172.586182 \nL 218.889328 162.3072 \nL 218.952853 141.749236 \nL 219.022154 150.183273 \nL 219.033704 148.074764 \nL 219.079905 167.051345 \nL 219.293583 196.306909 \nL 219.299358 196.306909 \nL 219.489935 201.051055 \nL 219.50726 200.787491 \nL 219.536136 200.787491 \nL 219.686288 189.981382 \nL 219.853765 144.384873 \nL 219.888415 149.392582 \nL 220.078993 187.345745 \nL 220.23492 191.035636 \nL 220.25802 190.772073 \nL 220.29267 190.244945 \nL 220.367746 190.772073 \nL 220.379296 190.772073 \nL 220.454372 195.252655 \nL 220.500573 192.353455 \nL 220.523673 180.229527 \nL 220.6796 147.284073 \nL 220.737351 161.516509 \nL 220.910603 193.934836 \nL 220.916378 193.671273 \nL 220.979904 195.779782 \nL 220.985679 195.779782 \nL 221.05498 195.516218 \nL 221.112731 197.361164 \nL 221.124281 197.361164 \nL 221.257107 201.051055 \nL 221.268657 200.787491 \nL 221.320633 193.407709 \nL 221.49966 150.973964 \nL 221.551636 159.935127 \nL 222.094493 199.733236 \nL 222.100268 199.733236 \nL 222.169569 192.089891 \nL 222.360146 138.850036 \nL 222.377471 138.322909 \nL 222.383246 139.1136 \nL 222.562274 188.4 \nL 222.787501 199.206109 \nL 222.793277 199.206109 \nL 222.897228 200.523927 \nL 222.908778 199.9968 \nL 223.030055 179.438836 \nL 223.180206 137.005091 \nL 223.249507 152.291782 \nL 223.428535 178.911709 \nL 223.769264 197.0976 \nL 223.809689 192.617018 \nL 224.006042 140.694982 \nL 224.069568 155.454545 \nL 224.248595 187.609309 \nL 224.25437 187.345745 \nL 224.398747 188.927127 \nL 224.404522 188.927127 \nL 224.595099 192.617018 \nL 224.670175 178.121018 \nL 224.837652 127.5168 \nL 224.843427 127.780364 \nL 224.878078 125.935418 \nL 224.901178 137.005091 \nL 225.09753 181.020218 \nL 225.114856 181.020218 \nL 225.161056 180.756655 \nL 225.195707 182.338036 \nL 225.201482 182.338036 \nL 225.270783 185.237236 \nL 225.328533 184.182982 \nL 225.524886 170.214109 \nL 225.640387 155.981673 \nL 225.703913 160.462255 \nL 225.89449 178.911709 \nL 225.906041 178.911709 \nL 225.923366 178.648145 \nL 225.958016 180.756655 \nL 225.963791 180.756655 \nL 226.090843 182.338036 \nL 226.108168 182.338036 \nL 226.29297 187.082182 \nL 226.304521 186.291491 \nL 226.333396 179.965964 \nL 226.460447 147.8112 \nL 226.483548 150.973964 \nL 226.541298 148.338327 \nL 226.69145 186.555055 \nL 226.922453 197.888291 \nL 226.928228 197.888291 \nL 227.084155 202.896 \nL 227.113031 201.314618 \nL 227.159231 192.880582 \nL 227.326708 127.780364 \nL 227.361359 126.726109 \nL 227.396009 135.160145 \nL 227.580812 186.291491 \nL 227.759839 196.043345 \nL 227.765614 196.043345 \nL 227.881115 196.834036 \nL 227.88689 196.570473 \nL 227.985067 187.609309 \nL 228.100568 140.694982 \nL 228.192969 150.973964 \nL 228.412422 171.0048 \nL 228.418197 171.0048 \nL 228.574124 180.229527 \nL 228.706951 179.438836 \nL 229.001479 167.842036 \nL 229.041905 169.159855 \nL 229.203607 181.547345 \nL 229.313333 186.818618 \nL 246.788702 185.764364 \nL 246.967729 188.927127 \nL 246.996605 188.927127 \nL 247.03703 189.454255 \nL 247.06013 185.237236 \nL 247.112106 164.679273 \nL 247.233383 133.842327 \nL 247.250708 138.322909 \nL 247.279583 134.896582 \nL 247.325784 140.431418 \nL 247.383534 137.268655 \nL 247.551012 177.330327 \nL 247.85709 191.562764 \nL 247.897516 188.663564 \nL 247.937941 173.904 \nL 248.088093 110.385164 \nL 248.099643 111.175855 \nL 248.105419 111.175855 \nL 248.446148 163.888582 \nL 248.700251 173.113309 \nL 248.723351 171.795491 \nL 248.769552 159.935127 \nL 248.879278 133.051636 \nL 248.896603 142.539927 \nL 248.931254 150.183273 \nL 248.977454 131.733818 \nL 249.000555 141.222109 \nL 249.012105 141.222109 \nL 249.185357 174.958255 \nL 249.196907 174.958255 \nL 249.248883 176.539636 \nL 249.318184 176.012509 \nL 249.323959 176.012509 \nL 249.433685 174.167564 \nL 249.45101 174.431127 \nL 249.456785 174.167564 \nL 249.520311 176.012509 \nL 249.543412 176.276073 \nL 249.572287 173.376873 \nL 249.647363 148.865455 \nL 249.705114 127.253236 \nL 249.774414 138.322909 \nL 249.785965 139.377164 \nL 249.81484 133.578764 \nL 249.820615 133.842327 \nL 249.84949 130.152436 \nL 249.889916 137.532218 \nL 250.068943 172.059055 \nL 250.155569 170.214109 \nL 250.184445 171.531927 \nL 250.363472 176.8032 \nL 250.380797 175.485382 \nL 250.426998 166.524218 \nL 250.571374 114.338618 \nL 250.582925 115.656436 \nL 250.606025 110.648727 \nL 250.663776 106.695273 \nL 250.709976 112.230109 \nL 251.027605 179.175273 \nL 251.03338 179.175273 \nL 251.183532 185.237236 \nL 251.195082 184.446545 \nL 251.258608 170.477673 \nL 251.42031 114.865745 \nL 251.460736 121.7184 \nL 251.512711 129.098182 \nL 251.749489 163.625018 \nL 252.015142 180.493091 \nL 252.032468 179.965964 \nL 252.101769 156.245236 \nL 252.275021 97.470545 \nL 252.280796 98.261236 \nL 252.413622 131.206691 \nL 252.61575 160.725818 \nL 252.789002 166.787782 \nL 252.835203 166.787782 \nL 252.846753 167.051345 \nL 252.875628 164.152145 \nL 253.03733 126.726109 \nL 253.112406 139.1136 \nL 253.256783 153.346036 \nL 253.262558 153.346036 \nL 253.41271 155.190982 \nL 253.493561 148.338327 \nL 253.568637 152.818909 \nL 253.689913 159.671564 \nL 253.741889 158.880873 \nL 253.747664 158.880873 \nL 253.868941 148.338327 \nL 253.897816 140.167855 \nL 253.990217 144.384873 \nL 254.007542 140.958545 \nL 254.065293 153.082473 \nL 254.250095 174.167564 \nL 254.261646 173.640436 \nL 254.267421 173.640436 \nL 254.307846 172.849745 \nL 254.348272 174.958255 \nL 254.509974 181.020218 \nL 254.538849 178.911709 \nL 254.804502 110.1216 \nL 254.862253 118.8192 \nL 255.099031 176.8032 \nL 255.335809 186.291491 \nL 255.37046 184.446545 \nL 255.41666 168.1056 \nL 255.537937 124.881164 \nL 255.555262 132.260945 \nL 255.607237 167.051345 \nL 255.688088 148.074764 \nL 255.855566 177.593891 \nL 256.173195 189.454255 \nL 256.196295 188.136436 \nL 256.242495 177.593891 \nL 256.398422 114.075055 \nL 256.404197 114.075055 \nL 256.421523 113.0208 \nL 256.571675 149.129018 \nL 256.716051 174.694691 \nL 257.004805 182.865164 \nL 257.056781 175.485382 \nL 257.120306 144.384873 \nL 257.287784 114.338618 \nL 257.293559 114.338618 \nL 257.703589 169.423418 \nL 257.801765 169.686982 \nL 257.755564 168.896291 \nL 257.813315 169.423418 \nL 257.888391 161.516509 \nL 257.969242 118.8192 \nL 258.113619 91.408582 \nL 258.125169 91.672145 \nL 258.130944 91.408582 \nL 258.154044 94.571345 \nL 258.234895 119.609891 \nL 258.408148 153.873164 \nL 258.546749 159.408 \nL 258.587175 157.299491 \nL 258.731552 145.702691 \nL 258.945229 105.377455 \nL 258.96833 109.067345 \nL 259.326384 154.927418 \nL 259.476536 164.679273 \nL 259.534287 159.408 \nL 259.568937 153.346036 \nL 259.759514 100.633309 \nL 259.83459 109.594473 \nL 260.054043 154.663855 \nL 260.267721 164.415709 \nL 260.273496 164.415709 \nL 260.394772 154.663855 \nL 260.475623 111.439418 \nL 260.602675 91.935709 \nL 260.63155 94.834909 \nL 260.637325 94.834909 \nL 260.660426 100.369745 \nL 260.914529 152.818909 \nL 261.105106 168.896291 \nL 261.180182 167.051345 \nL 261.266808 150.7104 \nL 261.353434 155.454545 \nL 261.359209 155.454545 \nL 261.445836 144.384873 \nL 261.474711 150.7104 \nL 261.607538 168.632727 \nL 261.728814 172.322618 \nL 261.78079 171.531927 \nL 261.82699 171.531927 \nL 261.942492 171.0048 \nL 261.994467 171.795491 \nL 262.023343 170.214109 \nL 262.086869 155.718109 \nL 262.23702 135.423709 \nL 262.242796 137.268655 \nL 262.340972 118.8192 \nL 262.254346 138.322909 \nL 262.369847 131.206691 \nL 262.525774 174.431127 \nL 262.681701 178.648145 \nL 262.716351 178.648145 \nL 262.820303 182.074473 \nL 262.849178 180.229527 \nL 262.889604 170.477673 \nL 262.96468 127.780364 \nL 263.028205 129.361745 \nL 263.080181 130.679564 \nL 263.103281 126.198982 \nL 263.120607 120.137018 \nL 263.184132 140.694982 \nL 263.36316 169.950545 \nL 263.571062 183.655855 \nL 263.651913 186.291491 \nL 263.686564 183.919418 \nL 263.732764 159.408 \nL 263.882916 104.586764 \nL 263.894466 105.641018 \nL 263.940667 103.532509 \nL 263.986867 108.013091 \nL 264.061943 140.167855 \nL 264.22942 168.632727 \nL 264.489299 182.865164 \nL 264.535499 178.384582 \nL 264.564375 156.245236 \nL 264.726077 101.160436 \nL 264.737627 99.051927 \nL 264.795378 106.695273 \nL 264.847353 113.547927 \nL 264.905104 143.330618 \nL 265.078356 169.686982 \nL 265.315134 181.020218 \nL 265.349784 177.330327 \nL 265.384435 165.733527 \nL 265.557687 101.424 \nL 265.563462 101.424 \nL 265.609663 97.206982 \nL 265.661638 103.005382 \nL 265.702064 119.609891 \nL 265.852216 159.408 \nL 266.025468 165.2064 \nL 266.037018 165.2064 \nL 266.302671 148.074764 \nL 266.447048 100.633309 \nL 266.493249 105.904582 \nL 266.556775 129.098182 \nL 266.730027 161.252945 \nL 266.932154 173.904 \nL 266.97258 174.958255 \nL 267.001455 172.322618 \nL 267.059206 151.237527 \nL 267.209358 89.563636 \nL 267.226683 95.6256 \nL 267.38261 126.989673 \nL 267.607838 148.865455 \nL 267.613613 148.865455 \nL 267.75799 156.245236 \nL 267.833066 154.663855 \nL 267.988993 107.485964 \nL 268.040968 93.253527 \nL 268.104494 103.532509 \nL 268.364372 151.764655 \nL 268.55495 166.524218 \nL 268.641576 169.686982 \nL 268.670451 167.314909 \nL 268.710877 154.136727 \nL 268.878354 92.989964 \nL 268.884129 93.517091 \nL 268.936104 91.408582 \nL 268.959205 94.307782 \nL 269.034281 117.764945 \nL 269.207533 151.501091 \nL 269.334584 158.353745 \nL 269.432761 157.035927 \nL 269.530937 148.074764 \nL 269.606013 115.392873 \nL 269.715739 130.152436 \nL 270.033368 168.896291 \nL 270.044918 168.896291 \nL 270.16042 169.686982 \nL 270.166195 169.423418 \nL 270.177745 169.423418 \nL 270.460723 151.501091 \nL 270.639751 136.2144 \nL 270.674401 142.0128 \nL 270.841878 158.880873 \nL 270.847653 158.880873 \nL 271.101756 172.322618 \nL 271.171057 164.415709 \nL 271.419385 106.695273 \nL 271.465586 121.7184 \nL 271.725464 155.981673 \nL 271.962242 171.268364 \nL 271.985343 169.686982 \nL 272.043093 150.183273 \nL 272.233671 87.718691 \nL 272.245221 86.928 \nL 272.262546 93.253527 \nL 272.632151 164.679273 \nL 272.782303 169.950545 \nL 272.811178 167.842036 \nL 272.886254 144.384873 \nL 273.059506 87.455127 \nL 273.065281 88.245818 \nL 273.088381 86.400873 \nL 273.123032 90.617891 \nL 273.377135 155.981673 \nL 273.463761 159.408 \nL 273.515737 158.617309 \nL 273.527287 158.617309 \nL 273.538837 158.880873 \nL 273.723639 144.912 \nL 273.75829 137.795782 \nL 273.81604 149.129018 \nL 273.821816 149.129018 \nL 273.833366 150.7104 \nL 273.856466 140.431418 \nL 273.960417 114.075055 \nL 274.012393 119.082764 \nL 274.20297 163.361455 \nL 274.387773 173.640436 \nL 274.445523 176.012509 \nL 274.485949 173.640436 \nL 274.5437 147.284073 \nL 274.711177 87.718691 \nL 274.716952 87.718691 \nL 274.751602 85.610182 \nL 274.797803 90.354327 \nL 275.144307 166.260655 \nL 275.277134 172.059055 \nL 275.317559 169.423418 \nL 275.357985 158.617309 \nL 275.531237 85.346618 \nL 275.554337 86.928 \nL 275.600538 85.346618 \nL 275.635188 94.571345 \nL 275.854641 146.229818 \nL 276.114519 155.190982 \nL 276.131844 154.136727 \nL 276.19537 140.694982 \nL 276.380173 72.959127 \nL 276.420598 69.796364 \nL 276.472574 74.804073 \nL 276.512999 92.462836 \nL 276.686251 143.857745 \nL 276.940355 156.772364 \nL 276.986555 152.818909 \nL 277.038531 131.997382 \nL 277.200233 92.7264 \nL 277.206008 93.253527 \nL 277.425461 138.322909 \nL 277.569837 143.067055 \nL 277.587163 143.067055 \nL 277.737315 144.912 \nL 277.74309 144.912 \nL 277.812391 146.229818 \nL 277.841266 144.912 \nL 277.968317 123.826909 \nL 278.083819 104.3232 \nL 278.101144 106.695273 \nL 278.337922 149.919709 \nL 278.343697 149.656145 \nL 278.412998 148.601891 \nL 278.453424 149.392582 \nL 278.592025 150.7104 \nL 278.5978 150.7104 \nL 278.661326 143.067055 \nL 278.909654 77.703273 \nL 278.97318 86.928 \nL 279.233058 136.2144 \nL 279.238833 136.2144 \nL 279.400535 138.586473 \nL 279.412086 138.586473 \nL 279.440961 139.640727 \nL 279.481386 135.950836 \nL 279.712389 97.206982 \nL 279.74704 107.2224 \nL 279.943392 131.206691 \nL 280.18017 148.601891 \nL 280.266796 152.818909 \nL 280.307222 149.392582 \nL 280.359197 131.206691 \nL 280.544 61.362327 \nL 280.584425 57.145309 \nL 280.630626 61.362327 \nL 280.694152 91.408582 \nL 280.867404 128.571055 \nL 280.873179 128.571055 \nL 281.098407 139.904291 \nL 281.202358 120.664145 \nL 281.294759 107.749527 \nL 281.36406 108.013091 \nL 281.369835 108.013091 \nL 281.473786 127.5168 \nL 281.606613 138.586473 \nL 281.612388 138.586473 \nL 281.745215 140.958545 \nL 281.76254 140.958545 \nL 281.860716 141.749236 \nL 281.878041 141.485673 \nL 281.918467 141.485673 \nL 282.097494 128.307491 \nL 282.241871 115.392873 \nL 282.247646 116.183564 \nL 282.316947 111.702982 \nL 282.449774 133.578764 \nL 282.651901 143.067055 \nL 282.709652 140.431418 \nL 282.726977 140.431418 \nL 282.894454 133.578764 \nL 282.96953 134.369455 \nL 283.131232 141.485673 \nL 283.599013 158.353745 \nL 283.604788 158.090182 \nL 283.72029 140.167855 \nL 283.74339 133.3152 \nL 283.801141 141.749236 \nL 283.824241 141.485673 \nL 284.003268 151.764655 \nL 284.014818 151.501091 \nL 284.055244 154.400291 \nL 284.424848 169.686982 \nL 284.453724 167.842036 \nL 284.517249 146.756945 \nL 284.684727 90.354327 \nL 284.690502 90.354327 \nL 284.696277 89.8272 \nL 284.725152 96.416291 \nL 285.123632 149.656145 \nL 285.302659 152.818909 \nL 285.319985 152.818909 \nL 285.389285 145.966255 \nL 285.550987 93.517091 \nL 285.591413 110.1216 \nL 285.862841 154.663855 \nL 286.036094 160.725818 \nL 286.041869 160.725818 \nL 286.076519 161.252945 \nL 286.105394 158.880873 \nL 286.215121 122.772655 \nL 286.284422 143.330618 \nL 286.411473 147.020509 \nL 286.417248 146.493382 \nL 286.706002 154.663855 \nL 286.711777 154.663855 \nL 286.809953 153.6096 \nL 286.821503 154.136727 \nL 286.908129 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129.361745 \nL 303.413286 131.470255 \nL 303.419061 131.470255 \nL 303.586538 137.532218 \nL 303.609639 135.423709 \nL 303.771341 98.261236 \nL 303.904167 84.819491 \nL 303.909942 85.610182 \nL 303.938818 90.617891 \nL 304.216021 151.764655 \nL 304.221796 151.764655 \nL 304.348848 153.082473 \nL 304.354623 152.818909 \nL 304.377723 152.818909 \nL 304.447024 137.532218 \nL 304.631826 80.866036 \nL 304.654927 81.920291 \nL 304.660702 81.920291 \nL 304.730003 79.284655 \nL 304.753103 83.238109 \nL 305.140033 146.229818 \nL 305.145808 146.229818 \nL 305.203559 148.865455 \nL 305.243984 145.702691 \nL 305.290185 125.144727 \nL 305.515412 72.959127 \nL 305.538513 71.114182 \nL 305.578938 74.804073 \nL 305.960093 136.741527 \nL 305.965868 136.741527 \nL 306.000519 139.1136 \nL 306.064044 135.950836 \nL 306.133345 112.757236 \nL 306.312372 63.997964 \nL 306.352798 58.199564 \nL 306.416324 64.261527 \nL 306.462524 77.176145 \nL 306.687752 131.470255 \nL 306.866779 139.1136 \nL 306.90143 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "x = temp_diff_n.date\n",
    "y1 = temp_diff_n[\"max\"]\n",
    "y2 = temp_diff_n[\"min\"]\n",
    "y3 = temp_diff_n[\"diff\"]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "line1, = ax.plot(x, y1, label=\"max_temperature\",alpha=0.3)\n",
    "line1.set_dashes([2,2,10,2])\n",
    "\n",
    "line2, = ax.plot(x, y2, label=\"min_temperature\", alpha=0.3)\n",
    "line2.set_dashes([6,2])\n",
    "\n",
    "#line3, = ax.plot(x[2300:2500], y3[2300:2500], label=\"max_temp - min_temp\")\n",
    "#line4, = ax.plot(x, y3, label=\"max_temp - min_temp\")\n",
    "\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}