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6003 lines
795 KiB
6003 lines
795 KiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import sys\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"from datetime import datetime, timedelta\n",
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"from scipy import stats\n",
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"import matplotlib.pyplot as plt\n",
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"plt.rcParams['font.family'] = 'Malgun Gothic'\n",
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"import matplotlib.dates as mdates\n",
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"\n",
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"def get_hstr(path):\n",
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" hstr = pd.read_csv(path)\n",
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" hstr['datetime'] = pd.to_datetime(hstr['CLCT_UNIX_TM'], unit='s', utc=True)\n",
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" hstr['datetime'] = hstr['datetime'].dt.tz_convert('Asia/Seoul').dt.strftime('%Y-%m-%d %H:%M:%S')\n",
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" hstr['datetime'] = hstr['datetime'].astype('datetime64[ns]')\n",
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" hstr.columns = ['현장교차로ID', '수집유닉스시각', '수집일시', '제어구분코드', '제어상태코드', '주기시간', '옵셋시간',\n",
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" 'A링1현시시간', 'A링2현시시간', 'A링3현시시간', 'A링4현시시간', 'A링5현시시간', 'A링6현시시간',\n",
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" 'A링7현시시간', 'A링8현시시간', 'B링1현시시간', 'B링2현시시간', 'B링3현시시간', 'B링4현시시간',\n",
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" 'B링5현시시간', 'B링6현시시간', 'B링7현시시간', 'B링8현시시간', 'A링1현시보행시간',\n",
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" 'A링2현시보행시간', 'A링3현시보행시간', 'A링4현시보행시간', 'A링5현시보행시간', 'A링6현시보행시간',\n",
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" 'A링7현시보행시간', 'A링8현시보행시간', 'B링1현시보행시간', 'B링2현시보행시간', 'B링3현시보행시간',\n",
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" 'B링4현시보행시간', 'B링5현시보행시간', 'B링6현시보행시간', 'B링7현시보행시간', 'B링8현시보행시간',\n",
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" '수집날짜시각']\n",
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" cols = list(hstr.columns).copy()\n",
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" cols.remove('수집날짜시각')\n",
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" cols.insert(1, '수집날짜시각')\n",
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" hstr = hstr[cols]\n",
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" return hstr\n",
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"\n",
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"inter_nos = list(range(5031, 5048))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"soitdspotintsoperhstr_202312110921\n",
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"2023-12-10 09:00:00\n",
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"2023-12-11 09:21:31\n",
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"soitdspotintsoperhstr_202312120814\n",
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"2023-12-11 09:00:57\n",
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"2023-12-12 08:14:02\n",
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"soitdspotintsoperhstr_202312131102\n",
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"2023-12-12 09:00:58\n",
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"2023-12-13 11:02:54\n",
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"soitdspotintsoperhstr_202312141254\n",
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"2023-12-13 09:00:58\n",
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"2023-12-14 12:53:54\n",
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"soitdspotintsoperhstr_202312150926\n",
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"2023-12-14 09:00:58\n",
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"2023-12-15 09:26:01\n",
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"soitdspotintsoperhstr_202312151700\n",
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"2023-12-15 09:00:58\n",
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"2023-12-15 16:59:54\n",
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"32466\n",
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"2023-12-12 00:00:09\n",
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"2023-12-15 16:59:54\n"
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]
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}
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],
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"source": [
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"# 성남시 신호이력 데이터\n",
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"s_tod_his = pd.read_csv('seongnam/S_TOD_HIS_1702597659892.csv')\n",
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"\n",
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"# 인천시 신호이력 데이터\n",
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"'''SELECT * FROM soitdspotintsoperhstr WHERE SPOT_INTS_ID BETWEEN 5031 AND 5047'''\n",
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"csv_files = [f for f in os.listdir('incheon') if f.endswith('.csv')]\n",
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"\n",
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"dfs = []\n",
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"for file in csv_files:\n",
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" df_name = file.replace('.csv', '')\n",
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" print(df_name)\n",
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" globals()[df_name] = get_hstr(f'incheon/{file}')\n",
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" dfs.append(globals()[df_name])\n",
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" print(sorted([dt for dt in globals()[df_name]['수집날짜시각'] if dt.hour != 23])[0])\n",
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" print(sorted([dt for dt in globals()[df_name]['수집날짜시각'] if dt.hour != 23])[-1])\n",
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"hstr = pd.concat(dfs).drop_duplicates().sort_values(by='수집날짜시각').reset_index(drop=True)\n",
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"hstr = hstr[['현장교차로ID', '수집날짜시각', '주기시간', '옵셋시간',\n",
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" 'A링1현시시간', 'A링2현시시간', 'A링3현시시간', 'A링4현시시간', 'A링5현시시간', 'A링6현시시간',\n",
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" 'B링1현시시간', 'B링2현시시간', 'B링3현시시간', 'B링4현시시간', 'B링5현시시간', 'B링6현시시간']]\n",
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"hstr = hstr[hstr.수집날짜시각.dt.day >= 12]\n",
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"print(len(hstr))\n",
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"print(sorted([dt for dt in hstr['수집날짜시각'] if dt.hour != 23])[0])\n",
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"print(sorted([dt for dt in hstr['수집날짜시각'] if dt.hour != 23])[-1])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"infos = {}\n",
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"# 신호이력\n",
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"infos['hstr'] = {inter_no:hstr_temp for (inter_no, hstr_temp) in hstr.groupby('현장교차로ID')}\n",
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"# 현시 개수\n",
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"infos['number_of_phases'] = {}\n",
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"for inter_no in inter_nos:\n",
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" a_ring_columns = [col for col in infos['hstr'][inter_no].columns if 'A링' in col]\n",
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" b_ring_columns = [col for col in infos['hstr'][inter_no].columns if 'B링' in col]\n",
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" max_a_ring = max([int(col[2]) for col in a_ring_columns if infos['hstr'][inter_no][col].any() != 0])\n",
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" max_b_ring = max([int(col[2]) for col in b_ring_columns if infos['hstr'][inter_no][col].any() != 0])\n",
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" if max_a_ring == max_b_ring:\n",
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" infos['number_of_phases'][inter_no] = max_a_ring\n",
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" else:\n",
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" raise \"A링, B링 현시번호 최댓값이 서로 다름\"\n",
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"# 현시시간 목록\n",
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"infos['durations'] = {inter_no:np.unique(infos['hstr'][inter_no].iloc[:,4:].values.flatten()) for inter_no in inter_nos}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"# TOD 계획 데이터\n",
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"todplans = pd.read_csv(\"soitdtodplan_202312181440.csv\")\n",
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"todplans.columns = [\"현장교차로ID\", \"시각계획번호\", \"시각운영번호\", \"수집유닉스시각\", \"수집일시\", \"시작시\", \"시작분\", \"현시운영번호\", \"현시운영계획번호\"]\n",
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"todplans = todplans[(todplans['수집일시'] == 20231212) & (todplans['시각계획번호'] == 1) & (todplans['시작시']!=0)]\n",
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"\n",
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"# 전이시각, ID\n",
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"infos['transition_times'] = {inter_no:todplans[todplans.현장교차로ID == inter_no][['시작시', '시작분']] for inter_no in inter_nos}\n",
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"infos['ID'] = {inter_no:todplans[todplans.현장교차로ID == inter_no].현시운영계획번호.unique() for inter_no in inter_nos}\n",
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"\n",
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"# 시간계획 데이터\n",
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"timeplans = pd.read_csv(\"soitdtimeplan_202312180943.csv\")\n",
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"timeplans.columns = [\"현장교차로ID\", \"시간계획번호\", \"현시운영번호\", \"현시운영계획번호\", \"수집유닉스시각\", \"수집일시\", \"주기시간\", \"옵셋시간\",\n",
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" \"A링1현시시간\", \"A링2현시시간\", \"A링3현시시간\", \"A링4현시시간\", \"A링5현시시간\", \"A링6현시시간\", \"A링7현시시간\", \"A링8현시시간\",\n",
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" \"B링1현시시간\", \"B링2현시시간\", \"B링3현시시간\", \"B링4현시시간\", \"B링5현시시간\", \"B링6현시시간\", \"B링7현시시간\", \"B링8현시시간\"]\n",
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"timeplans = timeplans[(timeplans.시간계획번호 == 1)]\n",
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"timeplan_list = []\n",
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"for inter_no in inter_nos:\n",
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" timeplan_list.append(timeplans[(timeplans.현장교차로ID==inter_no) & (timeplans.현시운영계획번호.isin(infos['ID'][inter_no]))])\n",
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"timeplans = pd.concat(timeplan_list)\n",
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"\n",
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"# 이상치로 판단하지 않는 현시시간 / 이상치로 판단하는 현시시간\n",
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"infos['accepted_durations'] = {}\n",
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"infos['unaccepted_durations'] = {}\n",
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"\n",
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"# 현시시간 빈도, 옵셋\n",
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"infos['unique_durations'] = {}\n",
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"infos['duration_frequencies'] = {}\n",
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"infos['offsets'] = {}\n",
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"infos['cycles'] = {}\n",
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"\n",
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"for inter_no in inter_nos:\n",
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" phase_times = np.unique(timeplans[timeplans.현장교차로ID == inter_no].iloc[:, 8:].values.flatten())\n",
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" accepted_phase_times = np.unique(np.concatenate([phase_times - 1, phase_times, phase_times + 1]))\n",
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" infos['accepted_durations'][inter_no] = accepted_phase_times\n",
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" unaccepted_phase_times = sorted(set(infos['durations'][inter_no]) - set(accepted_phase_times))\n",
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" infos['unaccepted_durations'][inter_no] = unaccepted_phase_times\n",
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"\n",
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" unique_durations, frequencies = np.unique(infos['hstr'][inter_no].iloc[:,4:].values, return_counts=True)\n",
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" sorted_indices = np.argsort(frequencies)[::-1]\n",
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" infos['unique_durations'][inter_no] = unique_durations[sorted_indices]\n",
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" infos['duration_frequencies'][inter_no] = frequencies[sorted_indices]\n",
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"\n",
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" infos['offsets'][inter_no] = {}\n",
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" infos['cycles'][inter_no] = {}\n",
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" for _, row in infos['transition_times'][inter_no].iterrows():\n",
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" hour = row.시작시\n",
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" minute = row.시작분\n",
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" ID = todplans[(todplans.현장교차로ID==inter_no) & (todplans.시작시==hour) & (todplans.시작분==minute)].iloc[0].현시운영계획번호\n",
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" offset = timeplans[(timeplans.현장교차로ID==inter_no)&(timeplans.현시운영계획번호==ID)].iloc[0].옵셋시간\n",
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" cycle = timeplans[(timeplans.현장교차로ID==inter_no)&(timeplans.현시운영계획번호==ID)].iloc[0].주기시간\n",
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" infos['offsets'][inter_no][(hour, minute)] = offset\n",
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" infos['cycles'][inter_no][(hour, minute)] = cycle\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[ 35 27 0 20 25 49 16 37 29 33 65 31 34 58 19 28 17 44\n",
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" 18 51 30 43 40 39 50 32 66 36 48 26 56 21 54 38 22 52\n",
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" 41 46 42 70 72 76 59 80 67 23 126 156 90 45 69 78 71 47\n",
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" 161 179]\n",
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"[4186 3992 3948 1586 1392 1384 1306 1306 1170 404 394 350 322 280\n",
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" 276 162 142 140 130 120 98 94 92 82 50 44 20 20\n",
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" 18 16 16 16 14 14 14 10 10 8 8 8 6 4\n",
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" 4 4 4 4 2 2 2 2 2 2 2 2 2 2]\n"
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]
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}
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],
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"source": [
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"print(infos['unique_durations'][5031])\n",
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"print(infos['duration_frequencies'][5031])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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"text/plain": [
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"<Figure size 2000x4500 with 18 Axes>"
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]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
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}
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],
|
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"source": [
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"\"\"\"\n",
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"1. 이상치 시각화 (1)\n",
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"\n",
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"각 교차로ID별로 현시시간의 빈도를 그렸습니다.\n",
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"예를 들어, 교차로ID가 5031인 경우에 대해서는 현시시간이 35인 경우가 4186번 존재했으므로 (35,4186)에 파란 점이 찍혔습니다.\n",
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"\n",
|
|
"그림에서 빨간 점선은 이상치가 아닌 정상 현시시간을 가지는 부분을 의미합니다.\n",
|
|
"시간계획 데이터에서 규정된 현시시간들에서 1의 오차를 허용한 값들은 이상치가 아니라고 정하고 (정상 현시시간), 1보다 큰 오차를 가진다면 이상치라고 정했습니다 (이상 현시시간).\n",
|
|
"그러니까, 아래 그림에서 빨간 점선에 해당하지 않는 파란 점들이 이상 현시시간 값들과 그 빈도를 나타냅니다.\n",
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"\n",
|
|
"해석 : \n",
|
|
"정상 현시시간들은 높은 빈도를 보이는 경우가 많고, 이상 현시시간들은 낮은 (주로 2회) 빈도를 보입니다.\n",
|
|
"정상 현시시간들은 convex하지 않고 안에 구멍이 있는 경우가 있습니다.\n",
|
|
"정상 현시시간에서 많이 벗어나서 그 값이 비이상적으로 커지는 경우도 많이 보입니다.\n",
|
|
"\"\"\"\n",
|
|
"n_cols = 2\n",
|
|
"n_rows = len(inter_nos) // n_cols + 1\n",
|
|
"fig, axes = plt.subplots(n_rows, n_cols, figsize=(n_cols * 10, n_rows * 5))\n",
|
|
"for i, inter_no in enumerate(inter_nos):\n",
|
|
" row = i // n_cols\n",
|
|
" col = i % n_cols\n",
|
|
" ax = axes[row, col]\n",
|
|
" ax.scatter(infos['unique_durations'][inter_no], infos['duration_frequencies'][inter_no])\n",
|
|
" for value in infos['accepted_durations'][inter_no]:\n",
|
|
" ax.axvline(x=value, color='r', linestyle='--')\n",
|
|
" ax.set_title(f'inter_no: {inter_no}')\n",
|
|
" ax.set_xlabel('현시시간')\n",
|
|
" ax.set_ylabel('빈도')\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"k = 2 # context number\n",
|
|
"\n",
|
|
"# 이상치 dataframe, 이상치를 포함한 주변에 대한 dataframe, 정상 dataframe\n",
|
|
"infos['outlier_dfs'] = {}\n",
|
|
"infos['context_dfs'] = {}\n",
|
|
"infos['context_dfs_list'] = {}\n",
|
|
"infos['normal_dfs'] = {}\n",
|
|
"hstr_context = []\n",
|
|
"hstr_outlier = []\n",
|
|
"for inter_no in inter_nos:\n",
|
|
" df = infos['hstr'][inter_no].sort_values(by='수집날짜시각').reset_index(drop=True)\n",
|
|
" outliers = infos['unaccepted_durations'][inter_no]\n",
|
|
" ring_columns = [col for col in df.columns if '현시시간' in col]\n",
|
|
" outlier_indices = df[df[ring_columns].isin(outliers).any(axis=1)].index\n",
|
|
" context_indices = [] # 각 아웃라이어 인덱스에 대해 위아래 네 행의 인덱스를 구합니다.\n",
|
|
" context_indices_list = []\n",
|
|
" for idx in outlier_indices:\n",
|
|
" start_idx = max(idx - k, 0) # 데이터프레임의 시작을 넘어가지 않도록 합니다.\n",
|
|
" end_idx = min(idx + k + 1, len(df)) # 데이터프레임의 끝을 넘어가지 않도록 합니다.\n",
|
|
" context_indices.extend(range(start_idx, end_idx))\n",
|
|
" context_indices_list.append(list(range(start_idx, end_idx)))\n",
|
|
" context_indices = sorted(context_indices)\n",
|
|
" normal_indices = sorted(set(df.index) - set(outlier_indices))\n",
|
|
"\n",
|
|
" df['전이시간여부'] = False\n",
|
|
" for _, row in infos['transition_times'][inter_no].iterrows():\n",
|
|
" hour = row.시작시\n",
|
|
" minute = row.시작분\n",
|
|
" transition_range = ((df['수집날짜시각'].dt.hour==hour) & (df['수집날짜시각'].dt.minute >= minute) & (df['수집날짜시각'].dt.minute <= minute+20))\n",
|
|
" df.loc[transition_range, '전이시간여부'] = True\n",
|
|
"\n",
|
|
" df['이상치존재'] = False\n",
|
|
" df.loc[outlier_indices, '이상치존재'] = True\n",
|
|
"\n",
|
|
" infos['outlier_dfs'][inter_no] = df.iloc[outlier_indices].drop_duplicates()\n",
|
|
" infos['context_dfs'][inter_no] = df.iloc[context_indices].drop_duplicates()\n",
|
|
" infos['context_dfs_list'][inter_no] = []\n",
|
|
" for context_indices_ in context_indices_list:\n",
|
|
" infos['context_dfs_list'][inter_no].append(df.iloc[context_indices_])\n",
|
|
" infos['normal_dfs'][inter_no] = df.iloc[normal_indices]\n",
|
|
" hstr_context.append(infos['context_dfs'][inter_no])\n",
|
|
" hstr_outlier.append(infos['outlier_dfs'][inter_no])\n",
|
|
"hstr_context = pd.concat(hstr_context)\n",
|
|
"hstr_outlier = pd.concat(hstr_outlier)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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"text/plain": [
|
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"<Figure size 2000x1000 with 1 Axes>"
|
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]
|
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},
|
|
"metadata": {},
|
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"output_type": "display_data"
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}
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],
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"source": [
|
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"def plot_outliers(inter_no):\n",
|
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" \"\"\"\n",
|
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" 2. 이상치 시각화 (2)\n",
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"\n",
|
|
" 교차로ID 하나에 대하여 이상치 (이상 현시시간)가 나타나는 순간을 시각화했습니다.\n",
|
|
" 그림에서 파란 점들은 정상 현시시간이 나타나는 경우이고 노란 점들은 이상 현시시간이 나타나는 경우입니다.\n",
|
|
" 빨간 점선으로는 전이시각(현시조합이 바뀌는 순간)을 표시했습니다.\n",
|
|
" 날짜는 12월 12일로 한정지었습니다.\n",
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"\n",
|
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" 해석 : (교차로ID = 5037)\n",
|
|
" 전이시각으로부터 향후 20분간을 전이시간이라고 정의하면, 대부분의 이상 현시시간은 전이시간 내에서 등장합니다.\n",
|
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" 전이시간이 아니면서 이상 현시시간이 나타나는 경우가 12시 경에 한 번 있었습니다.\n",
|
|
" \"\"\"\n",
|
|
" normal_df = infos['normal_dfs'][inter_no]\n",
|
|
" normal_df = normal_df[normal_df.수집날짜시각.dt.day == 12]\n",
|
|
" outlier_df = infos['outlier_dfs'][inter_no]\n",
|
|
" outlier_df = outlier_df[outlier_df.수집날짜시각.dt.day == 12]\n",
|
|
" ring_columns = [col for col in normal_df.columns if '현시시간' in col]\n",
|
|
" plt.figure(figsize=(20,10))\n",
|
|
" for col in ring_columns:\n",
|
|
" plt.scatter(normal_df.수집날짜시각, normal_df[col], color='blue')\n",
|
|
" plt.scatter(outlier_df.수집날짜시각, outlier_df[col], color='orange')\n",
|
|
" for _, row in infos['transition_times'][inter_no].iterrows():\n",
|
|
" start_hour = int(row.시작시)\n",
|
|
" start_min = int(row.시작분)\n",
|
|
" start_time = datetime(2023, 12, 12, start_hour, start_min)\n",
|
|
" plt.axvline(x=start_time, color='r', linestyle='--')\n",
|
|
" \n",
|
|
" ax = plt.gca()\n",
|
|
" ax.xaxis.set_major_locator(mdates.HourLocator(interval=1))\n",
|
|
" ax.xaxis.set_major_formatter(mdates.DateFormatter('%H'))\n",
|
|
" ax.set_xlabel('시각 (12월 12일)')\n",
|
|
" ax.set_ylabel('현시시간')\n",
|
|
" ax.set_title(f'inter_no : {inter_no}')\n",
|
|
" plt.show()\n",
|
|
"plot_outliers(5037)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"accepted_durations \n",
|
|
" [-1 0 1 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 44 45 46 53 54 55\n",
|
|
" 62 63 64 71 72 73 79 80 81]\n",
|
|
"전이시간이 아닌데 이상치가 발생한 경우 \n",
|
|
" [1, 267, 268, 269, 270, 537, 823, 824, 1059, 1348, 1349, 1350, 1610, 1903]\n",
|
|
"6:0 22\n",
|
|
"6:30 25\n",
|
|
"7:0 28\n",
|
|
"9:30 20\n",
|
|
"16:0 20\n",
|
|
"17:0 26\n",
|
|
"19:0 28\n",
|
|
"20:30 20\n",
|
|
"22:0 22\n",
|
|
"23:30 24\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
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"<div>\n",
|
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"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
|
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" }\n",
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"\n",
|
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" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
|
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" }\n",
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"\n",
|
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" .dataframe thead th {\n",
|
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" text-align: right;\n",
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" }\n",
|
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"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>현장교차로ID</th>\n",
|
|
" <th>수집날짜시각</th>\n",
|
|
" <th>주기시간</th>\n",
|
|
" <th>옵셋시간</th>\n",
|
|
" <th>A링1현시시간</th>\n",
|
|
" <th>A링2현시시간</th>\n",
|
|
" <th>A링3현시시간</th>\n",
|
|
" <th>A링4현시시간</th>\n",
|
|
" <th>A링5현시시간</th>\n",
|
|
" <th>A링6현시시간</th>\n",
|
|
" <th>B링1현시시간</th>\n",
|
|
" <th>B링2현시시간</th>\n",
|
|
" <th>B링3현시시간</th>\n",
|
|
" <th>B링4현시시간</th>\n",
|
|
" <th>B링5현시시간</th>\n",
|
|
" <th>B링6현시시간</th>\n",
|
|
" <th>전이시간여부</th>\n",
|
|
" <th>이상치존재</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 00:01:47</td>\n",
|
|
" <td>140</td>\n",
|
|
" <td>107</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>45</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>45</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 00:04:27</td>\n",
|
|
" <td>160</td>\n",
|
|
" <td>128</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>51</td>\n",
|
|
" <td>34</td>\n",
|
|
" <td>32</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>51</td>\n",
|
|
" <td>34</td>\n",
|
|
" <td>32</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 00:06:47</td>\n",
|
|
" <td>139</td>\n",
|
|
" <td>127</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>45</td>\n",
|
|
" <td>29</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>45</td>\n",
|
|
" <td>29</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 00:09:06</td>\n",
|
|
" <td>140</td>\n",
|
|
" <td>127</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>45</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>45</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>153</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 05:59:06</td>\n",
|
|
" <td>140</td>\n",
|
|
" <td>127</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>45</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>45</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>154</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 06:01:26</td>\n",
|
|
" <td>140</td>\n",
|
|
" <td>87</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>45</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>45</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>155</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 06:04:43</td>\n",
|
|
" <td>197</td>\n",
|
|
" <td>134</td>\n",
|
|
" <td>50</td>\n",
|
|
" <td>71</td>\n",
|
|
" <td>39</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>50</td>\n",
|
|
" <td>71</td>\n",
|
|
" <td>39</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>156</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 06:07:13</td>\n",
|
|
" <td>150</td>\n",
|
|
" <td>134</td>\n",
|
|
" <td>38</td>\n",
|
|
" <td>54</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>38</td>\n",
|
|
" <td>54</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>157</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 06:09:43</td>\n",
|
|
" <td>150</td>\n",
|
|
" <td>134</td>\n",
|
|
" <td>38</td>\n",
|
|
" <td>54</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>38</td>\n",
|
|
" <td>54</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>165</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 06:29:43</td>\n",
|
|
" <td>150</td>\n",
|
|
" <td>134</td>\n",
|
|
" <td>38</td>\n",
|
|
" <td>54</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>38</td>\n",
|
|
" <td>54</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>166</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 06:32:13</td>\n",
|
|
" <td>150</td>\n",
|
|
" <td>14</td>\n",
|
|
" <td>38</td>\n",
|
|
" <td>54</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>38</td>\n",
|
|
" <td>54</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>167</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 06:34:27</td>\n",
|
|
" <td>134</td>\n",
|
|
" <td>148</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>20</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>20</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>168</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 06:37:05</td>\n",
|
|
" <td>158</td>\n",
|
|
" <td>146</td>\n",
|
|
" <td>39</td>\n",
|
|
" <td>62</td>\n",
|
|
" <td>29</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>39</td>\n",
|
|
" <td>62</td>\n",
|
|
" <td>29</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>169</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 06:39:45</td>\n",
|
|
" <td>160</td>\n",
|
|
" <td>146</td>\n",
|
|
" <td>39</td>\n",
|
|
" <td>63</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>39</td>\n",
|
|
" <td>63</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>177</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 07:01:06</td>\n",
|
|
" <td>160</td>\n",
|
|
" <td>146</td>\n",
|
|
" <td>39</td>\n",
|
|
" <td>63</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>39</td>\n",
|
|
" <td>63</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>178</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 07:03:46</td>\n",
|
|
" <td>160</td>\n",
|
|
" <td>96</td>\n",
|
|
" <td>39</td>\n",
|
|
" <td>63</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>39</td>\n",
|
|
" <td>63</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>179</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 07:07:32</td>\n",
|
|
" <td>226</td>\n",
|
|
" <td>152</td>\n",
|
|
" <td>53</td>\n",
|
|
" <td>96</td>\n",
|
|
" <td>40</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>53</td>\n",
|
|
" <td>96</td>\n",
|
|
" <td>40</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>180</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 07:10:28</td>\n",
|
|
" <td>176</td>\n",
|
|
" <td>158</td>\n",
|
|
" <td>41</td>\n",
|
|
" <td>75</td>\n",
|
|
" <td>31</td>\n",
|
|
" <td>29</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>41</td>\n",
|
|
" <td>75</td>\n",
|
|
" <td>31</td>\n",
|
|
" <td>29</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>181</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 07:13:18</td>\n",
|
|
" <td>170</td>\n",
|
|
" <td>158</td>\n",
|
|
" <td>40</td>\n",
|
|
" <td>72</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>40</td>\n",
|
|
" <td>72</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>182</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 07:16:08</td>\n",
|
|
" <td>170</td>\n",
|
|
" <td>158</td>\n",
|
|
" <td>40</td>\n",
|
|
" <td>72</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>40</td>\n",
|
|
" <td>72</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>214</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 09:32:07</td>\n",
|
|
" <td>170</td>\n",
|
|
" <td>158</td>\n",
|
|
" <td>40</td>\n",
|
|
" <td>72</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>40</td>\n",
|
|
" <td>72</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>215</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 09:34:57</td>\n",
|
|
" <td>170</td>\n",
|
|
" <td>98</td>\n",
|
|
" <td>40</td>\n",
|
|
" <td>72</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>40</td>\n",
|
|
" <td>72</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>216</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 09:38:22</td>\n",
|
|
" <td>205</td>\n",
|
|
" <td>143</td>\n",
|
|
" <td>47</td>\n",
|
|
" <td>81</td>\n",
|
|
" <td>41</td>\n",
|
|
" <td>36</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>47</td>\n",
|
|
" <td>81</td>\n",
|
|
" <td>41</td>\n",
|
|
" <td>36</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>217</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 09:41:02</td>\n",
|
|
" <td>160</td>\n",
|
|
" <td>143</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>63</td>\n",
|
|
" <td>32</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>63</td>\n",
|
|
" <td>32</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>218</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 09:43:43</td>\n",
|
|
" <td>160</td>\n",
|
|
" <td>143</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>63</td>\n",
|
|
" <td>32</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>63</td>\n",
|
|
" <td>32</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>265</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 11:49:04</td>\n",
|
|
" <td>160</td>\n",
|
|
" <td>144</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>63</td>\n",
|
|
" <td>32</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>63</td>\n",
|
|
" <td>32</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>266</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 11:51:42</td>\n",
|
|
" <td>159</td>\n",
|
|
" <td>142</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>63</td>\n",
|
|
" <td>31</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>63</td>\n",
|
|
" <td>31</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>267</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 11:53:54</td>\n",
|
|
" <td>104</td>\n",
|
|
" <td>114</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>268</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 11:55:52</td>\n",
|
|
" <td>118</td>\n",
|
|
" <td>72</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>33</td>\n",
|
|
" <td>20</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>33</td>\n",
|
|
" <td>20</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>269</th>\n",
|
|
" <td>5037</td>\n",
|
|
" <td>2023-12-12 11:59:25</td>\n",
|
|
" <td>213</td>\n",
|
|
" <td>125</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>84</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>84</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" 현장교차로ID 수집날짜시각 주기시간 옵셋시간 A링1현시시간 A링2현시시간 A링3현시시간 \\\n",
|
|
"0 5037 2023-12-12 00:01:47 140 107 37 45 30 \n",
|
|
"1 5037 2023-12-12 00:04:27 160 128 43 51 34 \n",
|
|
"2 5037 2023-12-12 00:06:47 139 127 37 45 29 \n",
|
|
"3 5037 2023-12-12 00:09:06 140 127 37 45 30 \n",
|
|
"153 5037 2023-12-12 05:59:06 140 127 37 45 30 \n",
|
|
"154 5037 2023-12-12 06:01:26 140 87 37 45 30 \n",
|
|
"155 5037 2023-12-12 06:04:43 197 134 50 71 39 \n",
|
|
"156 5037 2023-12-12 06:07:13 150 134 38 54 30 \n",
|
|
"157 5037 2023-12-12 06:09:43 150 134 38 54 30 \n",
|
|
"165 5037 2023-12-12 06:29:43 150 134 38 54 30 \n",
|
|
"166 5037 2023-12-12 06:32:13 150 14 38 54 30 \n",
|
|
"167 5037 2023-12-12 06:34:27 134 148 37 49 20 \n",
|
|
"168 5037 2023-12-12 06:37:05 158 146 39 62 29 \n",
|
|
"169 5037 2023-12-12 06:39:45 160 146 39 63 30 \n",
|
|
"177 5037 2023-12-12 07:01:06 160 146 39 63 30 \n",
|
|
"178 5037 2023-12-12 07:03:46 160 96 39 63 30 \n",
|
|
"179 5037 2023-12-12 07:07:32 226 152 53 96 40 \n",
|
|
"180 5037 2023-12-12 07:10:28 176 158 41 75 31 \n",
|
|
"181 5037 2023-12-12 07:13:18 170 158 40 72 30 \n",
|
|
"182 5037 2023-12-12 07:16:08 170 158 40 72 30 \n",
|
|
"214 5037 2023-12-12 09:32:07 170 158 40 72 30 \n",
|
|
"215 5037 2023-12-12 09:34:57 170 98 40 72 30 \n",
|
|
"216 5037 2023-12-12 09:38:22 205 143 47 81 41 \n",
|
|
"217 5037 2023-12-12 09:41:02 160 143 37 63 32 \n",
|
|
"218 5037 2023-12-12 09:43:43 160 143 37 63 32 \n",
|
|
"265 5037 2023-12-12 11:49:04 160 144 37 63 32 \n",
|
|
"266 5037 2023-12-12 11:51:42 159 142 37 63 31 \n",
|
|
"267 5037 2023-12-12 11:53:54 104 114 0 49 27 \n",
|
|
"268 5037 2023-12-12 11:55:52 118 72 37 33 20 \n",
|
|
"269 5037 2023-12-12 11:59:25 213 125 49 84 43 \n",
|
|
"\n",
|
|
" A링4현시시간 A링5현시시간 A링6현시시간 B링1현시시간 B링2현시시간 B링3현시시간 B링4현시시간 B링5현시시간 \\\n",
|
|
"0 28 0 0 37 45 30 28 0 \n",
|
|
"1 32 0 0 43 51 34 32 0 \n",
|
|
"2 28 0 0 37 45 29 28 0 \n",
|
|
"3 28 0 0 37 45 30 28 0 \n",
|
|
"153 28 0 0 37 45 30 28 0 \n",
|
|
"154 28 0 0 37 45 30 28 0 \n",
|
|
"155 37 0 0 50 71 39 37 0 \n",
|
|
"156 28 0 0 38 54 30 28 0 \n",
|
|
"157 28 0 0 38 54 30 28 0 \n",
|
|
"165 28 0 0 38 54 30 28 0 \n",
|
|
"166 28 0 0 38 54 30 28 0 \n",
|
|
"167 28 0 0 37 49 20 28 0 \n",
|
|
"168 28 0 0 39 62 29 28 0 \n",
|
|
"169 28 0 0 39 63 30 28 0 \n",
|
|
"177 28 0 0 39 63 30 28 0 \n",
|
|
"178 28 0 0 39 63 30 28 0 \n",
|
|
"179 37 0 0 53 96 40 37 0 \n",
|
|
"180 29 0 0 41 75 31 29 0 \n",
|
|
"181 28 0 0 40 72 30 28 0 \n",
|
|
"182 28 0 0 40 72 30 28 0 \n",
|
|
"214 28 0 0 40 72 30 28 0 \n",
|
|
"215 28 0 0 40 72 30 28 0 \n",
|
|
"216 36 0 0 47 81 41 36 0 \n",
|
|
"217 28 0 0 37 63 32 28 0 \n",
|
|
"218 28 0 0 37 63 32 28 0 \n",
|
|
"265 28 0 0 37 63 32 28 0 \n",
|
|
"266 28 0 0 37 63 31 28 0 \n",
|
|
"267 28 0 0 0 49 27 28 0 \n",
|
|
"268 28 0 0 37 33 20 28 0 \n",
|
|
"269 37 0 0 49 84 43 37 0 \n",
|
|
"\n",
|
|
" B링6현시시간 전이시간여부 이상치존재 \n",
|
|
"0 0 False False \n",
|
|
"1 0 False True \n",
|
|
"2 0 False False \n",
|
|
"3 0 False False \n",
|
|
"153 0 False False \n",
|
|
"154 0 True False \n",
|
|
"155 0 True True \n",
|
|
"156 0 True False \n",
|
|
"157 0 True False \n",
|
|
"165 0 False False \n",
|
|
"166 0 True False \n",
|
|
"167 0 True True \n",
|
|
"168 0 True False \n",
|
|
"169 0 True False \n",
|
|
"177 0 True False \n",
|
|
"178 0 True False \n",
|
|
"179 0 True True \n",
|
|
"180 0 True True \n",
|
|
"181 0 True False \n",
|
|
"182 0 True False \n",
|
|
"214 0 True False \n",
|
|
"215 0 True False \n",
|
|
"216 0 True True \n",
|
|
"217 0 True False \n",
|
|
"218 0 True False \n",
|
|
"265 0 False False \n",
|
|
"266 0 False False \n",
|
|
"267 0 False True \n",
|
|
"268 0 False True \n",
|
|
"269 0 False True "
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"\"\"\"\n",
|
|
"3. 이상치 관찰\n",
|
|
"\n",
|
|
"이상 전이시간이 발생하는 경우를 관찰하기 위한 데이터프레임입니다.\n",
|
|
"이상치가 존재하는 순간을 포함하여 기준으로 앞의 두 순간, 뒤의 두 순간을 포함하여 총 다섯 행을 만듭니다.\n",
|
|
"이 다섯 행들을 모두 합치되 중복을 제거하여, 이상치가 발생하는 순간과, 그 전후 상황이 어떻게 나타나는지 관찰할 수 있게 합니다.\n",
|
|
"\n",
|
|
"이상치가 존재하는 경우는 전이시간에 포함되는 경우, 포함되지 않는 경우로 나뉘어서 보아야 할 것이므로\n",
|
|
"\"이상치존재\" 열과 \"전이시간여부\" 행을 명시했습니다.\n",
|
|
"\n",
|
|
"해석 : (교차로ID = 5037)\n",
|
|
"첫번째로 이상치가 존재하는 경우 (index = 1)는 2현시시간인 51이 이상 현시시간입니다.\n",
|
|
"이것은 여타 현시시간과 큰 값의 차이가 나는 것은 아니지만, 정상 현시시간으로 규정한 값에 포함되지 않습니다.\n",
|
|
"두번째로 이상치가 존재하는 경우 (index = 155)에도 2현시시간인 71이 이상현시시간입니다.\n",
|
|
"\"\"\"\n",
|
|
"\n",
|
|
"inter_no = 5037\n",
|
|
"print(f\"accepted_durations \\n {infos['accepted_durations'][inter_no]}\")\n",
|
|
"print(f\"전이시간이 아닌데 이상치가 발생한 경우 \\n {sorted(infos['outlier_dfs'][inter_no][infos['outlier_dfs'][inter_no]['전이시간여부']==False].index)}\")\n",
|
|
"for (hour, minute) in infos['offsets'][inter_no]:\n",
|
|
" print(f\"{hour}:{minute} {infos['offsets'][inter_no][(hour, minute)]}\")\n",
|
|
"display(infos['context_dfs'][inter_no][:30])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2023-12-12 00:00:09\n",
|
|
"2023-12-15 16:59:54\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print([dt for dt in hstr.수집날짜시각 if dt.hour!=23][0])\n",
|
|
"print([dt for dt in hstr.수집날짜시각 if dt.hour!=23][-1])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"전체 현시시간 개수 : 32466\n",
|
|
"이상 현시시간 개수 : 940\n",
|
|
"전이시간 바깥의 이상 현시시간 개수 : 255\n",
|
|
"이상 현시시간 비율 : 2.895336659890347 %\n",
|
|
"이상 현시시간일 때, 그 이상치가 전이시간 바깥에서 존재하는 비율 : 27.127659574468083 %\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"\"\"\"\n",
|
|
"4. 이상 현시시간 비율 및 전이시간과의 관계\n",
|
|
"\"\"\"\n",
|
|
"\n",
|
|
"n_outlier = 0\n",
|
|
"n_outlier_oustide_transition_ranges = 0\n",
|
|
"for inter_no in inter_nos:\n",
|
|
" df = infos['outlier_dfs'][inter_no]\n",
|
|
" n_outlier += len(df)\n",
|
|
" n_outlier_oustide_transition_ranges += len(df[(df['전이시간여부'] == False) & (df['이상치존재'] == True)])\n",
|
|
"print(f\"전체 현시시간 개수 : {len(hstr)}\")\n",
|
|
"print(f\"이상 현시시간 개수 : {n_outlier}\")\n",
|
|
"print(f\"전이시간 바깥의 이상 현시시간 개수 : {n_outlier_oustide_transition_ranges}\")\n",
|
|
"print(f\"이상 현시시간 비율 : {n_outlier / len(hstr) * 100} %\")\n",
|
|
"print(f\"이상 현시시간일 때, 그 이상치가 전이시간 바깥에서 존재하는 비율 : {n_outlier_oustide_transition_ranges / n_outlier * 100} %\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
|
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" <th>현장교차로ID</th>\n",
|
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" <th>수집날짜시각</th>\n",
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" <th>주기시간</th>\n",
|
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" <th>옵셋시간</th>\n",
|
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" <th>A링1현시시간</th>\n",
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" <th>A링2현시시간</th>\n",
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" <th>A링3현시시간</th>\n",
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" <th>A링4현시시간</th>\n",
|
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" <th>A링5현시시간</th>\n",
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" <th>A링6현시시간</th>\n",
|
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" <th>B링1현시시간</th>\n",
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" <th>B링2현시시간</th>\n",
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" <th>B링3현시시간</th>\n",
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" <th>B링4현시시간</th>\n",
|
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" <th>B링5현시시간</th>\n",
|
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" <th>B링6현시시간</th>\n",
|
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" <th>전이시간여부</th>\n",
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" <th>이상치존재</th>\n",
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" </tr>\n",
|
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" </thead>\n",
|
|
" <tbody>\n",
|
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" <tr>\n",
|
|
" <th>776</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-13 10:31:40</td>\n",
|
|
" <td>160</td>\n",
|
|
" <td>140</td>\n",
|
|
" <td>49</td>\n",
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|
" <td>20</td>\n",
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|
" <td>27</td>\n",
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|
" <td>29</td>\n",
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|
" <td>35</td>\n",
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|
" <td>0</td>\n",
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|
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|
" <td>20</td>\n",
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|
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|
" <td>29</td>\n",
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|
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|
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" <tr>\n",
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|
" <th>777</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-13 10:34:20</td>\n",
|
|
" <td>160</td>\n",
|
|
" <td>140</td>\n",
|
|
" <td>49</td>\n",
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|
" <td>20</td>\n",
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|
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|
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|
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|
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|
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|
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|
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|
" <td>29</td>\n",
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|
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|
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|
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|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>778</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-13 10:39:07</td>\n",
|
|
" <td>31</td>\n",
|
|
" <td>107</td>\n",
|
|
" <td>49</td>\n",
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|
" <td>20</td>\n",
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|
" <td>27</td>\n",
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|
" <td>156</td>\n",
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|
" <td>35</td>\n",
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|
" <td>0</td>\n",
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|
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|
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|
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" <tr>\n",
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|
" <th>779</th>\n",
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|
" <td>5031</td>\n",
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|
" <td>2023-12-13 10:41:27</td>\n",
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|
" <td>140</td>\n",
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|
" <td>87</td>\n",
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|
" <td>37</td>\n",
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|
" <td>16</td>\n",
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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" <tr>\n",
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|
" <th>780</th>\n",
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|
" <td>5031</td>\n",
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|
" <td>2023-12-13 10:45:00</td>\n",
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|
" <td>213</td>\n",
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
" <tr>\n",
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|
" <th>781</th>\n",
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|
" <td>5031</td>\n",
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|
" <td>2023-12-13 10:47:40</td>\n",
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|
" <td>160</td>\n",
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|
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|
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|
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|
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|
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|
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|
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|
" <tr>\n",
|
|
" <th>782</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-13 10:50:20</td>\n",
|
|
" <td>160</td>\n",
|
|
" <td>140</td>\n",
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|
" <td>49</td>\n",
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
" <td>False</td>\n",
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|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>910</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-13 18:02:38</td>\n",
|
|
" <td>180</td>\n",
|
|
" <td>159</td>\n",
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
" <tr>\n",
|
|
" <th>911</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-13 18:05:38</td>\n",
|
|
" <td>180</td>\n",
|
|
" <td>159</td>\n",
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
" <td>False</td>\n",
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|
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|
|
" <tr>\n",
|
|
" <th>912</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-13 18:10:47</td>\n",
|
|
" <td>52</td>\n",
|
|
" <td>107</td>\n",
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|
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|
|
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|
|
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|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
" </tr>\n",
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|
" <tr>\n",
|
|
" <th>913</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-13 18:13:07</td>\n",
|
|
" <td>140</td>\n",
|
|
" <td>67</td>\n",
|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
" <td>25</td>\n",
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|
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|
|
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|
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|
|
" <td>False</td>\n",
|
|
" </tr>\n",
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|
" <tr>\n",
|
|
" <th>914</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-13 18:17:06</td>\n",
|
|
" <td>239</td>\n",
|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
" <tr>\n",
|
|
" <th>915</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-13 18:20:39</td>\n",
|
|
" <td>213</td>\n",
|
|
" <td>159</td>\n",
|
|
" <td>78</td>\n",
|
|
" <td>25</td>\n",
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|
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|
|
" <td>40</td>\n",
|
|
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|
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|
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|
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|
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|
" <td>40</td>\n",
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|
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|
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|
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|
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|
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|
" <tr>\n",
|
|
" <th>916</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-13 18:23:39</td>\n",
|
|
" <td>180</td>\n",
|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
" <tr>\n",
|
|
" <th>917</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-13 18:26:38</td>\n",
|
|
" <td>179</td>\n",
|
|
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|
|
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|
|
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|
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|
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|
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|
|
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|
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|
|
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|
|
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|
|
" <td>32</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1031</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-13 23:45:40</td>\n",
|
|
" <td>140</td>\n",
|
|
" <td>0</td>\n",
|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
" <td>0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1032</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-13 23:48:00</td>\n",
|
|
" <td>140</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>16</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>25</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>16</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>25</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1033</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-13 23:52:43</td>\n",
|
|
" <td>26</td>\n",
|
|
" <td>3</td>\n",
|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
|
" <tr>\n",
|
|
" <th>1034</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-13 23:55:02</td>\n",
|
|
" <td>140</td>\n",
|
|
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|
|
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|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1035</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-13 23:57:22</td>\n",
|
|
" <td>140</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>16</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>25</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>16</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>25</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1761</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-15 07:14:41</td>\n",
|
|
" <td>180</td>\n",
|
|
" <td>161</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>34</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>34</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1762</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-15 07:17:41</td>\n",
|
|
" <td>180</td>\n",
|
|
" <td>161</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>34</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>34</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1763</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-15 07:21:58</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>58</td>\n",
|
|
" <td>126</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>34</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>126</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>34</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1764</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-15 07:24:18</td>\n",
|
|
" <td>140</td>\n",
|
|
" <td>19</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>16</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>25</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>16</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>25</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1765</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-15 07:26:49</td>\n",
|
|
" <td>151</td>\n",
|
|
" <td>169</td>\n",
|
|
" <td>39</td>\n",
|
|
" <td>25</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>25</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>39</td>\n",
|
|
" <td>25</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>25</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1766</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-15 07:29:41</td>\n",
|
|
" <td>172</td>\n",
|
|
" <td>161</td>\n",
|
|
" <td>47</td>\n",
|
|
" <td>32</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>31</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>47</td>\n",
|
|
" <td>32</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>31</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1767</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-15 07:32:41</td>\n",
|
|
" <td>180</td>\n",
|
|
" <td>161</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>34</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>34</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1768</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-15 07:35:41</td>\n",
|
|
" <td>180</td>\n",
|
|
" <td>161</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>34</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>34</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" 현장교차로ID 수집날짜시각 주기시간 옵셋시간 A링1현시시간 A링2현시시간 A링3현시시간 \\\n",
|
|
"776 5031 2023-12-13 10:31:40 160 140 49 20 27 \n",
|
|
"777 5031 2023-12-13 10:34:20 160 140 49 20 27 \n",
|
|
"778 5031 2023-12-13 10:39:07 31 107 49 20 27 \n",
|
|
"779 5031 2023-12-13 10:41:27 140 87 37 16 27 \n",
|
|
"780 5031 2023-12-13 10:45:00 213 140 69 31 31 \n",
|
|
"781 5031 2023-12-13 10:47:40 160 140 49 20 27 \n",
|
|
"782 5031 2023-12-13 10:50:20 160 140 49 20 27 \n",
|
|
"910 5031 2023-12-13 18:02:38 180 159 65 20 27 \n",
|
|
"911 5031 2023-12-13 18:05:38 180 159 65 20 27 \n",
|
|
"912 5031 2023-12-13 18:10:47 52 107 65 20 27 \n",
|
|
"913 5031 2023-12-13 18:13:07 140 67 37 16 27 \n",
|
|
"914 5031 2023-12-13 18:17:06 239 126 90 31 31 \n",
|
|
"915 5031 2023-12-13 18:20:39 213 159 78 25 31 \n",
|
|
"916 5031 2023-12-13 18:23:39 180 160 65 20 27 \n",
|
|
"917 5031 2023-12-13 18:26:38 179 159 65 20 27 \n",
|
|
"1031 5031 2023-12-13 23:45:40 140 0 37 16 27 \n",
|
|
"1032 5031 2023-12-13 23:48:00 140 0 37 16 27 \n",
|
|
"1033 5031 2023-12-13 23:52:43 26 3 179 16 27 \n",
|
|
"1034 5031 2023-12-13 23:55:02 140 2 37 16 27 \n",
|
|
"1035 5031 2023-12-13 23:57:22 140 2 37 16 27 \n",
|
|
"1761 5031 2023-12-15 07:14:41 180 161 49 35 27 \n",
|
|
"1762 5031 2023-12-15 07:17:41 180 161 49 35 27 \n",
|
|
"1763 5031 2023-12-15 07:21:58 1 58 126 35 27 \n",
|
|
"1764 5031 2023-12-15 07:24:18 140 19 37 16 27 \n",
|
|
"1765 5031 2023-12-15 07:26:49 151 169 39 25 27 \n",
|
|
"1766 5031 2023-12-15 07:29:41 172 161 47 32 27 \n",
|
|
"1767 5031 2023-12-15 07:32:41 180 161 49 35 27 \n",
|
|
"1768 5031 2023-12-15 07:35:41 180 161 49 35 27 \n",
|
|
"\n",
|
|
" A링4현시시간 A링5현시시간 A링6현시시간 B링1현시시간 B링2현시시간 B링3현시시간 B링4현시시간 B링5현시시간 \\\n",
|
|
"776 29 35 0 49 20 27 29 35 \n",
|
|
"777 29 35 0 49 20 27 29 35 \n",
|
|
"778 156 35 0 49 20 27 156 35 \n",
|
|
"779 25 35 0 37 16 27 25 35 \n",
|
|
"780 43 39 0 69 31 31 43 39 \n",
|
|
"781 29 35 0 49 20 27 29 35 \n",
|
|
"782 29 35 0 49 20 27 29 35 \n",
|
|
"910 33 35 0 65 20 27 33 35 \n",
|
|
"911 33 35 0 65 20 27 33 35 \n",
|
|
"912 161 35 0 65 20 27 161 35 \n",
|
|
"913 25 35 0 37 16 27 25 35 \n",
|
|
"914 48 39 0 90 31 31 48 39 \n",
|
|
"915 40 39 0 78 25 31 40 39 \n",
|
|
"916 33 35 0 65 20 27 33 35 \n",
|
|
"917 32 35 0 65 20 27 32 35 \n",
|
|
"1031 25 35 0 37 16 27 25 35 \n",
|
|
"1032 25 35 0 37 16 27 25 35 \n",
|
|
"1033 25 35 0 179 16 27 25 35 \n",
|
|
"1034 25 35 0 37 16 27 25 35 \n",
|
|
"1035 25 35 0 37 16 27 25 35 \n",
|
|
"1761 34 35 0 49 35 27 34 35 \n",
|
|
"1762 34 35 0 49 35 27 34 35 \n",
|
|
"1763 34 35 0 126 35 27 34 35 \n",
|
|
"1764 25 35 0 37 16 27 25 35 \n",
|
|
"1765 25 35 0 39 25 27 25 35 \n",
|
|
"1766 31 35 0 47 32 27 31 35 \n",
|
|
"1767 34 35 0 49 35 27 34 35 \n",
|
|
"1768 34 35 0 49 35 27 34 35 \n",
|
|
"\n",
|
|
" B링6현시시간 전이시간여부 이상치존재 \n",
|
|
"776 0 False False \n",
|
|
"777 0 False False \n",
|
|
"778 0 False True \n",
|
|
"779 0 False False \n",
|
|
"780 0 False True \n",
|
|
"781 0 False False \n",
|
|
"782 0 False False \n",
|
|
"910 0 False False \n",
|
|
"911 0 False False \n",
|
|
"912 0 False True \n",
|
|
"913 0 False False \n",
|
|
"914 0 False True \n",
|
|
"915 0 False True \n",
|
|
"916 0 False False \n",
|
|
"917 0 False False \n",
|
|
"1031 0 True False \n",
|
|
"1032 0 True False \n",
|
|
"1033 0 False True \n",
|
|
"1034 0 False False \n",
|
|
"1035 0 False False \n",
|
|
"1761 0 True False \n",
|
|
"1762 0 True False \n",
|
|
"1763 0 False True \n",
|
|
"1764 0 False False \n",
|
|
"1765 0 False False \n",
|
|
"1766 0 False True \n",
|
|
"1767 0 False False \n",
|
|
"1768 0 False False "
|
|
]
|
|
},
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"\"\"\"\n",
|
|
"5. 전이시간 바깥에서 발생하는 이상현시시간 관찰\n",
|
|
"\n",
|
|
"이상치존재가 True이고 전이시간여부가 False인 경우에 대하여 앞의 두 번 뒤의 두 번을 포함해 관찰합니다.\n",
|
|
"\"\"\"\n",
|
|
"\n",
|
|
"infos['context_dfs_outside_the_transition_ranges'] = {}\n",
|
|
"infos['outlier_lists'] = {}\n",
|
|
"infos['outliers'] = []\n",
|
|
"for inter_no in inter_nos:\n",
|
|
" dfs = infos['context_dfs_list'][inter_no]\n",
|
|
" dfs = [dfs[i] for i in range(len(dfs)) if ~dfs[i].iloc[2]['전이시간여부']]\n",
|
|
" infos['context_dfs_outside_the_transition_ranges'][inter_no] = pd.concat(dfs).drop_duplicates()\n",
|
|
"\n",
|
|
" df = infos['outlier_dfs'][inter_no].iloc[:,4:-2]\n",
|
|
" is_outlier = df.isin(infos['unaccepted_durations'][inter_no])\n",
|
|
" temp = df[is_outlier].values.flatten()\n",
|
|
" temp = np.unique(temp[pd.notna(temp)])\n",
|
|
" infos['outlier_lists'][inter_no] = np.unique(temp[pd.notna(temp)])\n",
|
|
" infos['outliers'].extend(infos['outlier_lists'][inter_no])\n",
|
|
"infos['context_dfs_outside_the_transition_ranges'][5031]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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6dOiQ+vbtq6VLl+ovf/mLWrdu7XHfmjVr6vjx43r33Xc1ffp0xcTEKCcnR++++67efPNNvfzyy1q5cqUk6dixY6X279y5s9vXzz77rDp37qzbbrvNbbmvXrXK2rx5swYPHqw2bdro5MmTmjRpkq6//nrt2LHDbdj38ePH6x//+IdeeOEFdejQQTk5OWXW9P3332vcuHG67LLLdPDgQY0ePVq9evXSDz/84DbOyUMPPaRvvvlGzz//vFq3bq0TJ04oPz/fks+Ki0zgXuMC+NdLL71kevXq5fp66NChZvjw4W7bzJs3zyQlJVX6GI8//rgZPHhwpff3pKioyFx99dVm/PjxXrfZsmWLkWS+/fZb17KcnBxjt9vNBx984HGfH3/8sdSyAQMGmO7du5dZz+OPP266dOlSvuK9+OGHH4wkk5+f77Z84sSJpmfPnl73W7VqlbnrrrtMUlKSufLKK82cOXPcXmyWlZVlxo0bZ+rUqWPatm1rBg0aZHbv3l2qnQMHDhhJ5vjx48aY4pfO2Wy2UtslJSWZefPmlflZ6tWrZ+bOnVtqua+X7/nLwYMHjSSzbNky17LNmzebqKgos2PHjkq3u3btWiPJ7Nq1y7Vs4cKFJj4+3mRkZJxTzYAnXFLBBSMmJsbt/6jz8vIUGxurffv2uW4yLOuSSnnk5OSU+3XuOTk5Sk5O1rRp08rcbtWqVdq1a5dGjhzpdZuS0TpjY2Ndy+x2uyIiIry+u+Pyyy8vtezSSy/1eimjRGFhoerVq1fmNr6U1Jmdne22PDs7u8zXubdp00Zdu3bVF198oe+//14PPPCAwsJO/5qqWrWqnn/+ef3222968cUXdemll6px48al2il5N0nJz4PD4XAte/PNN/XII4/okUce8djDcaZ9+/bp8OHD2rBhg9dt1q9fr9WrV2vHjh1ltjVy5MhKvcyv5PyeeU6mT5+ue++9V5deemmF2zuz3fDwcNWqVcu1bNq0aXryyScVHx9f6XYBbwgcuGB4ChwxMTGKj4/Xzz//rKysLNfTKmeaMGFCuZ+KmDt3rqZOner6+sybG88WHh6uRo0aqXbt2mXWvWTJEv3xj39UWlqaWrdurbi4OP3pT39ydftLxW9pvf7665WSkqJDhw4pKytLTz31lOrVq6c+ffqU+3u0adMmr29rPXHihP7+978rNTVV48aNK3ebnlQ2cFStWlVPPPGE18slJaKionTDDTfomWeecQskJTwFjpKX0NWvX1/NmjVTs2bNfN6PM3PmTDVr1kxz5851XZY72+DBg3X33Xdr+vTpZbYVHx/vMRx5U1BQoO+//14DBw7UoEGD3N7Uu2TJEl177bUaPny46tevr7p162rgwIE6fvy4z3YdDofWrl2rYcOGafz48a4AnZeXp1WrVqlt27a6//77VadOHSUmJiolJaXMn3Og3ALdxQKcq6lTp5rY2FgTExNjoqKiTGxsrImNjTXR0dEmOjra1KxZ01x++eXGGM+XVE6cOGF2797tNo0dO9Z06tTJ9fXzzz9vWrduXWq7oqKic67/jjvuMFdeeaVp3769Wbx4sVm9erW55557jN1uN7/++qtruyNHjpikpCQjydhsNhMbG2u2bNlS7uMsWrTIREZGmrVr17otf/PNN01ERISRZBo3bmy+/vrrSn+Wu+66y0gq97R582bXvjNnzqzQvmdOP/30kzGm+DLTzJkzXW1NmTLFzJw504wZM8bUrFnTzJgxw0yZMsV8//33xpiyL6kcOHDAxMXFmVWrVpmePXuaRx55xG29lZdU6tWrZ2w2m5FkHn30UZOTk+Nal5mZaSSZP/zhD2bEiBFm3bp1ZsGCBSY5Odlcf/31Xtv8/fffjd1uN5JMeHi4GTdunHE6na71JZft2rVrZyZNmmTWr19vZs2aZeLj40t9dqAyCBy4qKxbt86MHDnS53Yvv/yyue6661xfv/3226Zt27aW1NSjRw9Tu3Zt1/0GxhhTWFhorrjiCjNkyBBjTPEfmTZt2phbbrnFLFu2zKxevdoMHjzY1K9f3+0avDf/+7//a2JjY83MmTNLrTt+/LjZtm2bWb58uZkwYYKJi4szr732WqU+S2ZmpsnIyDDbtm1z3XOSkZHhmgYMGGDuvvtu19dn/sHLyckx6enpHqdu3bqZJ5980uv6goICY4wxGRkZ5sYbb/Q43XTTTaZPnz5m4MCBZsOGDcYY74HD4XCYbt26ub7/Bw8eNPXr1zcTJ050bWNl4Pj555/Nxo0bzQcffGC6du1qWrRoYQ4fPmyMMWbfvn1GknnooYfc9lm3bp2RZNavX++xzcLCQvPTTz+ZdevWmdmzZ5s2bdqYjh07mlOnThljjFm9erWR5PYZjTFm/vz5JiwszBw6dMjvnxMXFwIHLjiff/656dOnj2nYsKGJjIw0ERERpkGDBqZXr17mww8/9Lpffn6++eWXX0xhYeF5DRw9e/Y0vXv3LrV82LBhrps3J06caFq3bu32B9oYY/r162fuvPNOr21nZ2ebu+++2yQmJpqVK1eWq57333/fREREnNONgyU3Op59Q+fjjz9uBgwYUOH2brzxRjNu3LhK1+ONp8Bx/Phxc+ONN5quXbuavLw81/Jt27aZevXqmdtvv9389ttv5+2m0fz8fHPFFVeYlJQUY4wxhw8fNpLMRx99VGrbatWqmffee69c7f7++++mTp065o033jDGGPPNN98YSa4wVuLo0aOlbloFKoN7OHBBee6553TXXXepTZs2+uSTT7Rr1y7t2bNHX3zxhTp37qwhQ4Zo+PDhHvfdu3evmjdvrszMzPNac5MmTTwOJnbmo4pr165V+/btS73qvUuXLlqzZo3HdrOzs9W9e3edOHFCW7ZsUZcuXcpVT+fOnVVQUKBff/21/B/Cz9q0aaPNmzefUxtz587Vgw8+WOH9+vfvL6fTqc8++0x2u921vGXLllq/fr3y8/PP689IRESE2rdvr+3bt0uS6tSpo6pVq/r8mfElLi5OrVu3drXbpEkTSSrVbkXaBMrCOBy4YOTl5Wnq1Kn629/+poEDB7qta9Cgga6++mp16NBB1113nZ599tkyn8Ro166d25gHbdq00QMPPGBJ3TfccIPeeecd7dq1S5dccomk4vE+0tLS1LdvX1f9mzZtkjHG7Q/AunXrVL9+fY/tjh07VlFRUVq0aJHXmyOLiopK3XS5fv16ValSxVVLeZ05amjJkxWFhYVuy01xr6rbsrCwsFI1bN26VTk5ORU6/tmOHj2qXbt2lbnNnj17Si376KOPVK1atVLhTpIaN26sRYsWed33XJ19fqXi7+GmTZvUtWtXScUBoEePHpo3b54eeeQR13arVq1SVlaWOnXqVKpdT+c5JydHP/74o26++WZJxUGmTZs2mjdvnnr06OHaLjU1VdHR0Wrbtq3fPicuUoHtYAH8Jzc310RERJhZs2Z53Wb58uXGZrN5vB79yy+/GEnmxIkTfqnH4XCY6667zuc4D4WFhaZdu3bm8ssvN2lpaWb9+vWmb9++JiEhwXXdfsuWLSY6Otrce++9Zt26debbb781o0aNMjabzdW1vmLFCtOxY0dz5MgRY4wxCQkJ5oUXXih1o+vu3btd42M8+eSTZtKkSeY///mP2bJli5kxY4apXbu2+ctf/uKqb+HChSYxMdHnDbKq5A2fZx6rRJUqVcyqVatcX+fm5hqHw1GO7/ppr7/+uunUqVOF9qmIil5SeeWVV0z//v19ttm9e3fz8ccfu+6r6dOnj6lTp47Zu3eva7uNGzeayMhI89hjj5lNmzaZhQsXmoYNG7rd19G/f3/z6quvGmOKfzbuuOMOk5qaan788UezePFic+2115rmzZubzMxM1z6fffaZCQsLM88++6z57rvvzJw5c0yNGjVK3dcBVAY9HLhgREVF6ZlnntGQIUOUnp6unj17qn79+rLZbDp8+LCWLl2ql19+WU888USZvRsHDhwo9TjnmWw2mxITE33WU1BQoB07dvgc9yIsLEypqakaMWKE+vfvr4KCAnXv3l3//ve/VbduXUlS69attX79eo0bN0633nqrHA6HrrrqKi1ZssT1f6MlQ4rn5uZKKh5pddy4cR4fcf3hhx/UqlUr3XDDDZo0aZJefvll2Ww2tWzZUjNmzFD//v1d265cuVJ9+/b12bV+5iivFeHtMdmMjAzt27evzH0bNGjg8bHYEk6nU0ePHi2zDbvdbulQ5SV+++037dy5s8xt6tSpo6ZNmyolJUVHjhxRYmKievTooe+++04NGjRwbde2bVulpqZqzJgx6tixo2rXrq2HHnpIEydOdG2zY8cO18/ppZdeqoiICD3yyCM6ceKEGjdurD59+mjs2LFun/3WW2/Vv/71L02aNElTp05VYmKinnvuOY0YMcLP3w1cjGzGVPKlCUCQ+uyzz/Tuu+/q22+/1ZEjR2SMUXx8vP7whz9owIABuuuuuzzut3PnTjVv3txn+95eOnahatu2raZPn+6xq94q4eHhXgc0O9PBgweVkJDgcd0bb7xRrj+Uffr08fj6eV/27NmjJk2alFkDgNMIHAC8OnnypFq3bq29e/dy86AHTqfTspf5ARcaAgcAALAcj8UCAADLETgAAIDlCBwAAMByBA4AAGC5oLm9uqioSAcOHFC1atW4Gx4AgBBhjFFWVpbPcXGCJnAcOHBAjRo1CnQZAACgEtLT09WwYUOv64MmcJSMdpeenu72DgugTDk5UskIjAcOSLGxga0HAC4ymZmZatSokc8Re4MmcJRcRqlevTqBA+V35gu2qlcncABAgPi6HYKbRgEAgOWCpocDqJTwcGnAgNPzAICgxG9ohDa7XZo1K9BVAAB84JIKAACwHD0cCG3GSKdOFc/HxEiM4QIAQYkeDoS2U6ekqlWLp5LgAQAIOgQOAABgOQIHAACwHIEDAABYjsABAAAsV+HAYYzRnDlz1LFjR7flmzdvVocOHZSUlKSWLVtq6dKlfisSAACEtgo9FvvVV19p1KhRys3NVfgZozpmZWXplltu0axZs9SjRw+tXLlSffr00fbt25WQkOD3ogEAQGipUA9HTk6Opk6dqn/84x9uy+fNm6d27dqpR48ekqSuXbuqS5cu+uCDD/xXKeBJlSpS377F05kvcgMABJUK9XDceeedkqQVK1a4LV+7dq06derktqx9+/b67rvvvLblcDjkcDhcX2dmZlakFKBYVJT00UeBrgIA4INfbho9ePCg6tWr57asbt26OnbsmNd9Jk+erLi4ONfUqFEjf5RyQUkenark0amBLgMAgHPml8DhdDpljHFbVlhYKFsZw0yPGTNGJ0+edE3p6en+KAUAAAQhv7xLpVatWjp69KjbsoyMjDJvGLXb7bLb7f44PC5mOTnFw5pLUna2FBsb2HoAAB75pYejbdu2WrNmjduyNWvWlHp0FgAAXJz8Ejjuu+8+ff3111q2bJkk6csvv9RPP/2kfv36+aN5AAAQ4vxySaVhw4aaP3++hgwZouPHj6tZs2b64osvFEv3NgAAUCUDR7du3bR9+3a3ZTfeeGOpZQAAABLvUgEAAOcBgQMAAFjOL/dwAAFTpYrUq9fpeQBAUCJwILRFRUmpjMYKAMGOSyoAAMByBA4AAGA5AgdCW05O8XDmsbHF8wCAoMQ9HAh9p04FugIAgA/0cAAAAMsROAAAgOUIHAAAwHIEDgAAYDkCBwAAsBxPqSC0hYVJXbuengcABCUCB0JbdLS0YkWgqwAA+MD/EgIAAMsROAAAgOUIHAhtOTlSnTrFE0ObA0DQ4h4OhL6jRwNdAQDAB3o4AACA5QgcAADAcgQOAABgOQIHAACwHIEDAABYjqdUENrCwqRrrjk9DwAISgQOhLboaGnDhkBXAQDwgf8lBAAAliNwAAAAy3FJJQglj04N+LH3TOkdsBoq5NQpqWXL4vkff5RiYgJbDwDAIwIHQpsx0t69p+cBAEGJSyoAAMByBA4AAGA5AgcAALAcgQMAAFiOwAEAACzHUyoIbTbb6cdibbbA1gIA8IrAgdAWEyNt2xboKgAAPnBJBQAAWI7AAQAALEfgQGg7dUq64ori6dSpQFcDAPCCezgQ2owpfodKyTwAICjRwwEAACxH4AAAAJYjcAAAAMsROAAAgOUIHAAAwHI8pYLQZrNJSUmn5wEAQYnAgdAWEyPt2RPoKgAAPnBJBQAAWI7AAQAALEfgQGjLzZXatSuecnMDXQ0AwAvu4UBoKyqSNm48PQ8ACEr0cAAAAMsROAAAgOUIHAAAwHIEDgAAYDkCBwAAsBxPqSD0xccHugIAgA8EDoS22FgpIyPQVQAAfPDrJZX9+/frlltuUWJioi655BJNmjTJn80DAIAQ5dfA8eCDD6ply5bat2+fNm7cqI8//lizZs3y5yEAAEAI8mvg2Lx5sx544AHZbDbVqlVLN998szaWjAIJWCE3V+rWrXhiaHMACFp+DRx9+/bVX//6V+Xn52vv3r367LPP1LdvX38eAnBXVCStXFk8MbQ5AAQtv940+sILL6hdu3aqWbOmcnNzNWzYMHXr1s3jtg6HQw6Hw/V1ZmamP0sBAABBxG89HIWFherVq5dSUlJ08uRJ7d+/X99//72mTZvmcfvJkycrLi7ONTVq1MhfpcAPkkenKnl0aqDLAABcIPwWOJYtW6b8/HylpKQoPDxc9evX12uvvaaXXnrJ4/ZjxozRyZMnXVN6erq/SgEAAEHGb5dU8vPzFR7u3lxERITy8/M9bm+322W32/11eAAAEMT81sPRuXNnHTp0SPPmzZMkZWdna9y4cdw0CgAA/Bc44uLilJaWppkzZyo5OVlXXnmlmjVrpldffdVfhwA8i4kpngAAQcuvT6m0atVKS5Ys8WeTQNliY6WcnEBXAQDwgbfFAgAAyxE4AACA5QgcCG15eVLv3sVTXl6gqwEAeMHr6RHaCgulL788PQ8ACEr0cAAAAMsROAAAgOUIHAAAwHIEDgAAYDkCBwAAsByBAwAAWI7HYhHaYmMlYwJdBQDAB3o4AACA5QgcAADAcgQOhLa8PKlfv+KJoc0BIGgROBDaCgulBQuKJ4Y2B4CgReAAAACWI3AAAADLETgAAIDlCBwAAMByBA4AAGA5AgcAALAcQ5sjtMXESNnZp+cBAEGJwIHQZrMVv08FABDUuKQCAAAsR+BAaHM4pIEDiyeHI9DVAAC8IHAgtDmd0uzZxZPTGehqAABeEDgAAIDlCBwAAMByBA4AAGA5HosNoOTRqZKkPVN6B0Ud57pPoD/HuQqW8wEAFyJ6OAAAgOUIHAAAwHJcUkFoi4mRjhw5PQ8ACEoEDoQ2m02qUyfQVQAAfOCSCgAAsByBA6HN4ZCGDi2eGNocAIIWgQOhzemUZswonhjaHACCFoEDAABYjsABAAAsR+AAAACWI3AAAADLETgAAIDlCBwAAMByjDSK0BYdLe3efXoeABCUCBwIbWFhUnJyoKsAAPjAJRUAAGA5AgdCW36+NGpU8ZSfH+hqAABeEDgQ2goKpFdeKZ4KCgJdDQDACwIHAACwHIEDAABYjsABAAAsR+AAAACWI3AAAADLETgAAIDlGGkUoS06Wtq69fQ8ACAoETgQ2sLCpCuuCHQVAAAfuKQCAAAsRw8HQlt+vvTii8XzY8dKkZGBrQcA4BGBA6GtoECaOLF4ftQoAgcABCm/X1JZv369unTpoqSkJDVo0ECffPKJvw8BAABCjF97OLZv367bbrtNc+bMUY8ePZSfn6/ff//dn4cAAAAhyK89HOPGjdPw4cPVo0cPSVJkZKTq1q3rz0MAAIAQ5LfAkZeXp0WLFumhhx7yV5MAAOAC4bfA8fPPPys6OlrLly/XlVdeqUsuuUSPP/64MjMzPW7vcDiUmZnpNgEAgAuT3+7hyMrKktPp1MaNG7V+/XoVFBRowIABeuqppzRz5sxS20+ePFkTS54uwHmRPDpVkrRnSu8K71OiPPtWZh9/q8xnBQBYx289HPHx8SooKNCUKVMUFRWlatWqacKECfr88889bj9mzBidPHnSNaWnp/urFFxMoqKk9euLp6ioQFcDAPDCbz0cSUlJioyMVF5eniIiIiRJYWFhivLyR8But8tut/vr8LhYVakitWsX6CoAAD74rYcjKipKDz74oEaOHCmn0ymHw6Hx48fr/vvv99chAABAiPLrY7FTp05Vbm6uEhMTdcUVV6hZs2aaNGmSPw8BuMvPl15+uXjKzw90NQAAL/w68FfVqlX1z3/+059NAmUrKJCeeaZ4fsgQhjYHgCDF22IBAIDlCBwAAMByBA4AAGA5AgcAALAcgQMAAFiOwAEAACzn18digfMuKkpavvz0PAAgKBE4ENqqVJG6dQt0FQAAH7ikAgAALEcPB0JbQYH0zjvF8489Jv3/FwcCAIILgQOhLT9fGjaseH7gQAIHAAQpLqkAAADLETgAAIDlCBwAAMByBA4AAGA5AgcAALAcgQMAAFiOx2IR2ux2adGi0/MAgKBE4EBoCw+XevcOdBUAAB+4pAIAACxHDwdCW0GB9K9/Fc/fdx8jjQJAkCJwILTl50sPPVQ8368fgQMAghSXVAAAgOUIHAAAwHIEDgAAYDkCBwAAsByBAwAAWI7AAQAALMdjsQhtdrv04Yen5wEAQemiCxzJo1MlSXum+G847JI2/d2uv5xZXyge5+x23b7H4eHF42+Us42KnJ+z9/H0+c5u71x+voL95wgAzgWXVAAAgOUuuh4OXGCcTunTT4vnb7+9uMcDABB0+O2M0OZwSP37F89nZxM4ACBIcUkFAABYjsABAAAsR+AAAACWI3AAAADLETgAAIDlCBwAAMByPEOI0BYZKc2ceXoeABCUCBwIbRER0sCBga4CAOADl1QAAIDl6OFAaHM6pbS04vkbb2SkUQAIUvx2RmhzOKSbby6eZ2hzAAhaXFIBAACWI3AAAADLETgAAIDlCBwAAMByBA4AAGA5AgcAALAczxAitEVGSn/96+l5AEBQInAgtEVESEOHBroKAIAPXFIBAACWo4cDoa2wUFq1qnj+2mulKlUCWw8AwCMCB0JbXp7UvXvxfHa2FBsb2HoAAB5xSQUAAFiOwAEAACxH4AAAAJYjcAAAAMsROAAAgOUsCxxPPPGELrvsMquaBwAAIcSSx2LT09M1Z84cNWrUyIrmgdMiIqSXXjo9DwAISpYEjhEjRuihhx7S//3f/1nRPHBaZKQ0alSgqwAA+OD3Syqpqak6duyY+vbtW+Z2DodDmZmZbhMAALgw+bWH49ixY3ryySeVmpqqQ4cOlbnt5MmTNXHiRH8e/rxJHp0qSdozpXeF1pW33bKWn91ueWqp6PGsdPYxvX2fylNb8uhUhRUVqtXhXyVJW+s11a6Xbi2zHU/fL398n8raxtdnDsR5uFCcy783AOeX33o4jDEaNGiQUlJSynWz6JgxY3Ty5EnXlJ6e7q9ScBGxOwv0+Zyn9fmcp2V3FgS6HACAF37r4ZgyZYoKCgo0bNiwcm1vt9tlt9v9dXgAABDE/BY4pk+frpycHNWsWVOS5HQ6lZubqxo1amjDhg1q3ry5vw4FAABCjN8Cx8GDB92+XrFihQYPHqzt27f76xAAACBEMdIoAACwnGWBo1u3bvRuAAAASfRwAACA88CSkUaB88VZpYre6HSPax4AEJwIHAhpBVUi9Ebn+wJdBgDABy6pAAAAy9HDgZBmM0VqdrR4lNqd8bydGACCFYEDIS2qIF9L3xsqSbp8xIIAVwMA8IZLKgAAwHIEDgAAYDkCBwAAsByBAwAAWI7AAQAALEfgAAAAluOxWIQ0Z5Uq+tsf73DNAwCCE4EDIa2gSoQmd3840GUAAHzgkgoAALAcPRwIaTZTpMTMDEnS/up1AlwNAMAbAgdCWlRBvlb/7yBJDG0OAMGMSyoAAMByBA4AAGA5AgcAALAcgQMAAFiOwAEAACxH4AAAAJbjsViEtMKwKprzh96ueQBAcCJwIKTlh0fouRueCHQZAAAfuKQCAAAsRw8HQpsxqpWbKUk6Hl09wMUAALwhcCCkRRc49O2b90liaHMACGZcUgEAAJajh+McJI9OtWTbyrQXyFr8ebzzVVtZxzlf9Vakhj1Telf4uGfvU55ayrOPt2NX5HgALj70cAAAAMsROAAAgOUIHAAAwHIEDgAAYDluGkVIKwyrogWtrnPNAwCCE4EDIS0/PEL/1XtEoMsAAPjAJRUAAGA5ejgQ2oxRdIFDkpQbYQ9wMQAAbwgcCGnRBQ799HpfSQxtDgDBjEsqAADAcgQOAABgOQIHAACwHIEDAABYjsABAAAsR+AAAACW47FYhLSisDCltujkmgcABCcCB0KaIzxSQ28bE+gyAAA+8L+EAADAcgQOAABgOS6pIKRF5+cxtDkAhAB6OAAAgOUIHAAAwHIEDgAAYDkCBwAAsByBAwAAWI7AAQAALMdjsQhpRWFhWnbJNa55AEBwInAgpDnCI/VwvwmBLgMA4AP/SwgAACzn18CxbNkyderUSc2aNVPTpk315ptv+rN5AAAQovx6SeWzzz7Te++9pxYtWmjXrl3q0qWLmjdvrptuusmfhwFcovPztOmv90mS2g77V4CrAQB449cejmnTpqlFixaSpEsuuUT9+/fXsmXL/HkIoJSYAodiChyBLgMAUAZL7+HIyMhQXFyclYcAAAAhwLLAsX79ei1atEj33nuvx/UOh0OZmZluEwAAuDBZ8ljs/PnzlZKSotmzZ6tJkyYet5k8ebImTpxoxeH9Jnl0akD2DUS7gTp+SXt7pvT2Sw2B/v6UsKrGiux79vf2zH29fb8rcz4qwur2L0Z8TxEq/Bo4CgsLNXz4cC1fvlxpaWm66qqrvG47ZswYPf30066vMzMz1ahRI3+WAwAAgoRfA0dKSop27dqljRs3KjY2tsxt7Xa77Ha7Pw8PAACClN8CR15ent5++22lp6f7DBuAvxTZbFrXqJVrHgAQnPwWOHbt2qWioiJ17NjRbXmLFi2Ulpbmr8MAbhwRdt1975RAlwEA8MFvgaNly5YqKiryV3MAAOACwrtUAACA5QgcCGnR+XnaNP1ebZp+r6Lz8wJdDgDAC15Pj5BXO5dB4wAg2NHDAQAALEfgAAAAliNwAAAAyxE4AACA5QgcAADAcjylgpBWZLPp+4TmrnkAQHAicCCkOSLs6jPg9UCXAQDwgUsqAADAcgQOAABgOQIHQlpUQZ5Wv/2wVr/9sKIKGNocAIIV93AgpNmM1DDziGseABCc6OEAAACWI3AAAADLETgAAIDlCBwAAMByBA4AAGA5nlJBSDM26efajV3zAIDgROBASMuLiNINj8wIdBkAAB+4pAIAACxH4AAAAJYjcCCkRRXkack/hmjJP4YwtDkABDHu4UBIsxnp0mO/ueYBAMGJHg4AAGA5AgcAALAcgQMAAFiOwAEAACxnM8YExa12mZmZiouL08mTJ1W9enW/t588OtXt6z1TentcjtASnZ+nn17vK0m6fMQC5UZGBbgiawTq57XkuBU9dmXq9cdnPLNeb0raP5dtPS33ta2nGitSi7f2PLXra9+zv9cVOX5lnFmzt2N5q62sfSrSjr9/Nsrblr/aO5f2rT7P5f37zVMqCGnGJu2rXtc1DwAITgQOhLS8iCh1fuK9QJcBAPCBezgAAIDlCBwAAMByBA6ENHuBQ5/NHqHPZo+QvcAR6HIAAF5wDwdCWpgxuurQL655AEBwoocDAABYjsABAAAsR+AAAACWI3AAAADLETgAAIDleEoFIe9YtP/fvQMA8C8CB0JabmSU2j75fqDLAAD4wCUVAABgOQIHAACwHIEDIc1e4ND890dr/vujGdocAIIY93AgpIUZow7pW13zAIDgRA8HAACwHIEDAABYjsABAAAsR+AAAACWI3AAAADL8ZQKQt6pCHugSwAA+EDgQEjLjYxSy6c/DnQZAAAfuKQCAAAsR+AAAACW45IKQprdma+3P31RkvTE7WPlCI8McEUAAE8IHAhpYUVF+vOuja55AEBw4pIKAACwHIEDAABYzq+BIzc3V4899piSkpLUsGFDPfPMMzK8wRMAgIueXwPHyJEjVVRUpF9//VXbtm3T8uXL9de//tWfhwAAACHIb4EjOztbs2fP1ksvvaTw8HDFxcVpzJgxeu+99/x1CAAAEKL89pTKpk2b1KRJE9WqVcu1rH379tq6dasKCwtVpUoVt+0dDoccDofr65MnT0qSMjMz/VWSmyLHKbevS45z9nKElsL8PJX8xBQ6TqnIXJhPqgTq5/XMf48VOXZl6vXHZyzP74+S9s9lW0/LfW3rqcaK1OKtPU/t+tr37O+1Vb93zz5uWcfyVltZ+1SkHX//bJS3LX+1dy7tW32eS9r1eQuF8ZN58+aZP//5z27L8vPzjSRz7NixUtuPHz/eSGJiYmJiYmK6AKb09PQyc4LfejicTmepdFNYWChJstlspbYfM2aMnn76adfXRUVFOn78uGrXru1xewRWZmamGjVqpPT0dFWvXj3Q5aAcOGehh3MWmi7282aMUVZWlho0aFDmdn4LHLVq1dLRo0fdlmVkZCgqKkpxcXGltrfb7bLb3d/yWaNGDX+VA4tUr179ovwHFco4Z6GHcxaaLubz5unv/Nn8dtPo1VdfrR07dujEiROuZWvWrFH79u0VFsZwHwAAXMz8lgQSEhJ00003aezYsXI6nTp69KheeOEFpaSk+OsQAAAgRPm16+Hdd9/VgQMHVL9+fV1zzTV67LHHdNttt/nzEAgQu92u8ePHl7oMhuDFOQs9nLPQxHkrH5thKFAAAGAxbq4AAACWI3AAAADLETgAAIDlCBxwM2zYMMXFxSk5Odk17d27V5K0efNmdejQQUlJSWrZsqWWLl0a4GovbsYYzZkzRx07dnRb7us8vfHGG2rWrJkSExN1++2369ixY+ez7Iuat3NWtWpVJSYmuv7N9evXz2095yxwli1bpk6dOqlZs2Zq2rSp3nzzTde6PXv26Prrr1dSUpKaNWumuXPnuu07b948XX755WrYsKG6d++u3bt3n+/yg4ufRjbHBWLo0KHmueeeK7U8MzPTJCYmmqVLlxpjjFmxYoWJi4szBw8ePN8lwhizePFi06pVK9O0aVPTokUL13Jf5+mDDz4wf/jDH8yxY8eM0+k0gwcPNnfccUdAPsPFxts5M8aY2NhYs2vXLo/7cc4C68knnzTbt283xhjz66+/msTERLN48WLjdDpNq1atzMyZM40xxmzbts3UrFnTbN682RhjzJo1a0xycrLZu3evMcaYF154wbRt2zYQHyFoEDjgZujQoea1114rtfxvf/ubue2229yW3XLLLeaNN944X6XhDAsWLDCpqalm+fLlbn+8fJ2njh07moULF7rWZWRkmPDwcI/vO4J/eTtnxhQHjuPHj3vcj3MWXEaMGGFGjRpl0tLSTJs2bdzWDR8+3KSkpBhjjLnnnnvcfj8WFBSYWrVqme++++681htMuKSCUjwNMb927Vp16tTJbVn79u313XffnZ+i4ObOO+9Ur169Si0v6zw5nU5t3LjRbX18fLySk5P1ww8/WF7zxc7bOZOksLAwj0NDc86CT0ZGhuLi4nz+Tjx7fXh4uK6++uqL+ncmgQOljBkzRo0bN1b37t21ZMkSSdLBgwdVr149t+3q1q3LteQgU9Z5Onr0qAoLCxUfH+9xPQLHZrOpadOmuvTSSzVo0CAdOHBAkjhnQWb9+vVatGiR7r33Xp+/E/mdWRqBA26mT5+uQ4cOaffu3Ro1apT69++vTZs2eX0bMG/2DS5lnSen0ylJnMcgdOLECe3evVsbNmxQTEyMbrnlFhljOGdBZP78+br11ls1e/ZsNWnSxOfvRH5nlua3t8XiwlDyor0qVaqoV69euueee7Rw4UKvbwNOSEgIRJnwoqzzVLNmTRljdOLECdWqVavUegROyb+7uLg4TZs2TdWrV9euXbuUkJDAOQuwwsJCDR8+XMuXL1daWpquuuoqSWX/WztzfePGjT2uvxjRw4EyOZ1ORUZGqm3btlqzZo3bujVr1pR6vA+BVdZ5io2NVYsWLdzWHzx4UIcPH3b9EkXgFRUVqaioSJGRkZyzIJCSkqJdu3Zp48aNbt9zX78Tz16fn5+vTZs2qUOHDuen8GAUsNtVEZS++uorU1hYaIwxJi0tzdSsWdNs27bNpKenmxo1apivv/7aGGNMamqqSUpKMtnZ2YEs96J39hMPvs7Ta6+9Zq655hpz4sQJ43A4zIABA1x31eP8OPuc7dy50+zYscMYY0xeXp4ZMmSI6dKli2s95yxwcnNzTZUqVcyBAwdKrcvJyTH169c3//znP40xxmzYsMHUr1/fpKenG2OM+eSTT0xycrJJT083TqfTPPvss6WeILvYcEkFbl5//XU98MADiomJUePGjfXpp5+qZcuWkoqvYQ4ZMkTHjx9Xs2bN9MUXXyg2NjbAFeNMDRs2LPM8PfXUU9q/f78uvfRShYeHq0+fPpoyZUqAq764HT9+XPfcc49yc3Nlt9t13XXXacGCBa71nLPA2bVrl4qKikr15LZo0UJpaWn64osv9Oijj+rpp59WQkKC3n//fTVs2FCSdPvtt2vnzp364x//qKKiInXr1k3vvfdeID5G0OBtsQAAwHLcwwEAACxH4AAAAJYjcAAAAMsROAAAgOUIHAAAwHIEDgAAYDkCBwAAsByBAwAAWI7AAQAALEfgAAAAliNwAAAAyxE4AACA5f4fZQdW0cElupQAAAAASUVORK5CYII=",
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
|
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]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
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},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
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"text": [
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"임계값 = 75초 -> 소실비율 = 36.93 %\n",
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"임계값 = 90초 -> 소실비율 = 21.86 %\n",
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"임계값 = 120초 -> 소실비율 = 8.04 %\n",
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"임계값 = 180초 -> 소실비율 = 1.76 %\n",
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"임계값 = 240초 -> 소실비율 = 0.00 %\n"
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]
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}
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],
|
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"source": [
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"\"\"\"\n",
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"6. 이상 현시시간 분포\n",
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"\n",
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"이상 현시시간은 그림에서 나오는 것과 같은 분포를 가집니다.\n",
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"정규분포는 아니고 카이제곱분포처럼 생겼습니다.\n",
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"\n",
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"신호이력을 수집하는 시간간격(threshold, 임계값)을 바꿔가면서 얼마만큼의 비율의 이상 현시시간이 소실되는지를 관찰했습니다.\n",
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"임계값이 4분이면 아무런 소실이 없습니다.\n",
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"그밖에 임계값을 2분 혹은 3분으로 하면서 8.04 %, 1.76 % 의 소실비율을 용인하는 것도 가능합니다.\n",
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"\"\"\"\n",
|
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"outliers = np.array(infos['outliers'])\n",
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"Unique_values, Frequency = np.unique(outliers, return_counts=True)\n",
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"mean = \"{:.2f}\".format(outliers.mean())\n",
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"std = \"{:.2f}\".format(outliers.std())\n",
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"# plt.figure(figsize=(20,10))\n",
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"plt.hist(outliers, bins=int(outliers.max() - outliers.min() + 1))\n",
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"plt.axvline(x=float(mean), color='r', linestyle='--')\n",
|
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"plt.title(f'이상 현시시간 분포\\n 평균 : {mean}, 표준편차 : {std}')\n",
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"plt.show()\n",
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"plt.scatter(Unique_values, Frequency)\n",
|
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"plt.axvline(x=float(mean), color='r', linestyle='--')\n",
|
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"plt.title(f'이상 현시시간 분포\\n 평균 : {mean}, 표준편차 : {std}')\n",
|
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"plt.close()\n",
|
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"thresholds = [75, 90, 120, 180, 240]\n",
|
|
"for threshold in thresholds:\n",
|
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" n_greater = [outlier for outlier in outliers if outlier > threshold]\n",
|
|
" print(f\"임계값 = {threshold}초 -> 소실비율 = \" + \"{:.2f}\".format(len(n_greater)/len(outliers)*100) + \" %\")\n",
|
|
" mean = \"{:.2f}\".format(outliers.mean())"
|
|
]
|
|
},
|
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{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
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"metadata": {},
|
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"outputs": [],
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"source": [
|
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"# k = 2 # context number\n",
|
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"\n",
|
|
"# # 이상치 dataframe, 이상치를 포함한 주변에 대한 dataframe, 정상 dataframe\n",
|
|
"# infos['outlier_dfs'] = {}\n",
|
|
"# infos['context_dfs'] = {}\n",
|
|
"# infos['context_dfs_list'] = {}\n",
|
|
"# infos['normal_dfs'] = {}\n",
|
|
"# hstr_context = []\n",
|
|
"# hstr_outlier = []\n",
|
|
"# for inter_no in inter_nos:\n",
|
|
"# df = infos['hstr'][inter_no].sort_values(by='수집날짜시각').reset_index(drop=True)\n",
|
|
"# outliers = infos['unaccepted_durations'][inter_no]\n",
|
|
"# ring_columns = [col for col in df.columns if '현시시간' in col]\n",
|
|
"# outlier_indices = df[df[ring_columns].isin(outliers).any(axis=1)].index\n",
|
|
"# context_indices = [] # 각 아웃라이어 인덱스에 대해 위아래 네 행의 인덱스를 구합니다.\n",
|
|
"# context_indices_list = []\n",
|
|
"# for idx in outlier_indices:\n",
|
|
"# start_idx = max(idx - k, 0) # 데이터프레임의 시작을 넘어가지 않도록 합니다.\n",
|
|
"# end_idx = min(idx + k + 1, len(df)) # 데이터프레임의 끝을 넘어가지 않도록 합니다.\n",
|
|
"# context_indices.extend(range(start_idx, end_idx))\n",
|
|
"# context_indices_list.append(list(range(start_idx, end_idx)))\n",
|
|
"# context_indices = sorted(context_indices)\n",
|
|
"# normal_indices = sorted(set(df.index) - set(outlier_indices))\n",
|
|
"\n",
|
|
"# df['전이시간여부'] = False\n",
|
|
"# for _, row in infos['transition_times'][inter_no].iterrows():\n",
|
|
"# hour = row.시작시\n",
|
|
"# minute = row.시작분\n",
|
|
"# transition_range = ((df['수집날짜시각'].dt.hour==hour) & (df['수집날짜시각'].dt.minute >= minute) & (df['수집날짜시각'].dt.minute <= minute+20))\n",
|
|
"# df.loc[transition_range, '전이시간여부'] = True\n",
|
|
"\n",
|
|
"# df['이상치존재'] = False\n",
|
|
"# df.loc[outlier_indices, '이상치존재'] = True\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"k = 3 # context number\n",
|
|
"max_cycle = timeplans['주기시간'].max()\n",
|
|
"infos['missing_df'] = {}\n",
|
|
"\n",
|
|
"for inter_no in inter_nos:\n",
|
|
" df = infos['hstr'][inter_no].sort_values(by='수집날짜시각').reset_index(drop=True)\n",
|
|
" df['시간차이'] = df['수집날짜시각'].diff().dt.total_seconds()\n",
|
|
" df.dropna(inplace=True)\n",
|
|
" df = df.reset_index(drop=True)\n",
|
|
" missed_indices = df[df['시간차이'] > max_cycle + 10].index\n",
|
|
" df['결측여부'] = False\n",
|
|
" df.loc[missed_indices, '결측여부'] = True\n",
|
|
"\n",
|
|
" df['전이시간여부'] = False\n",
|
|
" for _, row in infos['transition_times'][inter_no].iterrows():\n",
|
|
" hour = row.시작시\n",
|
|
" minute = row.시작분\n",
|
|
" transition_range = ((df['수집날짜시각'].dt.hour==hour) & (df['수집날짜시각'].dt.minute >= minute) & (df['수집날짜시각'].dt.minute <= minute+20))\n",
|
|
" df.loc[transition_range, '전이시간여부'] = True\n",
|
|
"\n",
|
|
" ring_columns = [col for col in df.columns if '현시시간' in col]\n",
|
|
" df['주기일치'] = (df['주기시간'].astype(float) == df[ring_columns].sum(axis=1)/2)\n",
|
|
"\n",
|
|
" outliers = infos['unaccepted_durations'][inter_no]\n",
|
|
" df['이상치존재'] = False\n",
|
|
" df.loc[outlier_indices, '이상치존재'] = True\n",
|
|
"\n",
|
|
" infos['missing_df'][inter_no] = missed_indices\n",
|
|
" context_indices = []\n",
|
|
" for idx in missed_indices:\n",
|
|
" start_idx = max(idx - k, 0) # 데이터프레임의 시작을 넘어가지 않도록 합니다.\n",
|
|
" end_idx = min(idx + k + 1, len(df)) # 데이터프레임의 끝을 넘어가지 않도록 합니다.\n",
|
|
" context_indices.extend(range(start_idx, end_idx))\n",
|
|
" infos['missing_df'][inter_no] = df.iloc[context_indices].drop_duplicates()\n",
|
|
"# for inter_no in inter_nos:\n",
|
|
"# print(inter_no)\n",
|
|
"# with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
|
|
"# display(infos['missing_df'][inter_no])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"infos['missing_dfs'] = {}\n",
|
|
"for inter_no in inter_nos:\n",
|
|
" DF = infos['missing_df'][inter_no]\n",
|
|
" dfs = []\n",
|
|
" temp_indices = []\n",
|
|
"\n",
|
|
" for i in DF.index:\n",
|
|
" if not temp_indices or i == temp_indices[-1] + 1: # 첫 인덱스이거나 이전 인덱스와 연속적인 경우\n",
|
|
" temp_indices.append(i)\n",
|
|
" else: # 연속이 끊긴 경우\n",
|
|
" dfs.append(DF.loc[temp_indices]) # 현재까지의 임시 데이터프레임을 결과 리스트에 추가\n",
|
|
" temp_indices = [i] # 새로운 임시 데이터프레임 시작\n",
|
|
"\n",
|
|
" if temp_indices:\n",
|
|
" dfs.append(DF.loc[temp_indices])\n",
|
|
" infos['missing_dfs'][inter_no] = dfs"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
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|
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|
|
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|
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"text/plain": [
|
|
" 현장교차로ID 수집날짜시각 주기시간 옵셋시간 A링1현시시간 A링2현시시간 A링3현시시간 \\\n",
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"197 5031 2023-12-12 08:05:40 180 161 49 35 27 \n",
|
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"198 5031 2023-12-12 08:08:41 180 161 49 35 27 \n",
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"199 5031 2023-12-12 08:11:41 180 161 49 35 27 \n",
|
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"200 5031 2023-12-12 09:02:41 180 161 49 35 27 \n",
|
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"201 5031 2023-12-12 09:05:41 180 161 49 35 27 \n",
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"202 5031 2023-12-12 09:08:41 180 161 49 35 27 \n",
|
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"203 5031 2023-12-12 09:11:41 180 161 49 35 27 \n",
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"\n",
|
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" A링4현시시간 A링5현시시간 A링6현시시간 B링1현시시간 B링2현시시간 B링3현시시간 B링4현시시간 B링5현시시간 \\\n",
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"199 34 35 0 49 35 27 34 35 \n",
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"200 34 35 0 49 35 27 34 35 \n",
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"201 34 35 0 49 35 27 34 35 \n",
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"202 34 35 0 49 35 27 34 35 \n",
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"203 34 35 0 49 35 27 34 35 \n",
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"\n",
|
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" B링6현시시간 시간차이 결측여부 전이시간여부 주기일치 이상치존재 \n",
|
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"197 0 180.0 False False True False \n",
|
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"198 0 181.0 False False True False \n",
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"1.0\n",
|
|
"{(6, 0): 150, (6, 30): 160, (7, 0): 180, (9, 30): 160, (16, 0): 170, (17, 0): 180, (20, 30): 170, (21, 30): 160, (22, 30): 150, (23, 30): 140}\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"inter_no = 5031\n",
|
|
"j = 1\n",
|
|
"cycle = 180\n",
|
|
"with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
|
|
" display(infos['missing_dfs'][inter_no][j])\n",
|
|
"sum_of_durations = infos['missing_dfs'][inter_no][j].시간차이.sum()\n",
|
|
"print(sum_of_durations)\n",
|
|
"print(sum_of_durations % cycle)\n",
|
|
"print(infos['cycles'][inter_no])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
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|
|
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|
|
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|
|
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|
|
" <th>779</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-13 10:45:00</td>\n",
|
|
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|
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|
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|
|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
" <tr>\n",
|
|
" <th>780</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-13 10:47:40</td>\n",
|
|
" <td>160</td>\n",
|
|
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|
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|
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
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"</div>"
|
|
],
|
|
"text/plain": [
|
|
" 현장교차로ID 수집날짜시각 주기시간 옵셋시간 A링1현시시간 A링2현시시간 A링3현시시간 \\\n",
|
|
"774 5031 2023-12-13 10:29:00 160 140 49 20 27 \n",
|
|
"775 5031 2023-12-13 10:31:40 160 140 49 20 27 \n",
|
|
"776 5031 2023-12-13 10:34:20 160 140 49 20 27 \n",
|
|
"777 5031 2023-12-13 10:39:07 31 107 49 20 27 \n",
|
|
"778 5031 2023-12-13 10:41:27 140 87 37 16 27 \n",
|
|
"779 5031 2023-12-13 10:45:00 213 140 69 31 31 \n",
|
|
"780 5031 2023-12-13 10:47:40 160 140 49 20 27 \n",
|
|
"\n",
|
|
" A링4현시시간 A링5현시시간 A링6현시시간 B링1현시시간 B링2현시시간 B링3현시시간 B링4현시시간 B링5현시시간 \\\n",
|
|
"774 29 35 0 49 20 27 29 35 \n",
|
|
"775 29 35 0 49 20 27 29 35 \n",
|
|
"776 29 35 0 49 20 27 29 35 \n",
|
|
"777 156 35 0 49 20 27 156 35 \n",
|
|
"778 25 35 0 37 16 27 25 35 \n",
|
|
"779 43 39 0 69 31 31 43 39 \n",
|
|
"780 29 35 0 49 20 27 29 35 \n",
|
|
"\n",
|
|
" B링6현시시간 시간차이 결측여부 전이시간여부 주기일치 이상치존재 \n",
|
|
"774 0 160.0 False False True False \n",
|
|
"775 0 160.0 False False True False \n",
|
|
"776 0 160.0 False False True False \n",
|
|
"777 0 287.0 True False False False \n",
|
|
"778 0 140.0 False False True False \n",
|
|
"779 0 213.0 False False True False \n",
|
|
"780 0 160.0 False False True False "
|
|
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|
},
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|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"1280.0\n",
|
|
"0.0\n",
|
|
"{(6, 0): 150, (6, 30): 160, (7, 0): 180, (9, 30): 160, (16, 0): 170, (17, 0): 180, (20, 30): 170, (21, 30): 160, (22, 30): 150, (23, 30): 140}\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"inter_no = 5031\n",
|
|
"j = 3\n",
|
|
"cycle = 160\n",
|
|
"with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
|
|
" display(infos['missing_dfs'][inter_no][j])\n",
|
|
"sum_of_durations = infos['missing_dfs'][inter_no][j].시간차이.sum()\n",
|
|
"print(sum_of_durations)\n",
|
|
"print(sum_of_durations % cycle)\n",
|
|
"print(infos['cycles'][inter_no])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
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|
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|
|
" <th>908</th>\n",
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|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>911</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-13 18:10:47</td>\n",
|
|
" <td>52</td>\n",
|
|
" <td>107</td>\n",
|
|
" <td>65</td>\n",
|
|
" <td>20</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>161</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>65</td>\n",
|
|
" <td>20</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>161</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>309.0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>912</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-13 18:13:07</td>\n",
|
|
" <td>140</td>\n",
|
|
" <td>67</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>16</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>25</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>16</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>25</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>140.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>True</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>913</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-13 18:17:06</td>\n",
|
|
" <td>239</td>\n",
|
|
" <td>126</td>\n",
|
|
" <td>90</td>\n",
|
|
" <td>31</td>\n",
|
|
" <td>31</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>39</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>90</td>\n",
|
|
" <td>31</td>\n",
|
|
" <td>31</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>39</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>239.0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>914</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-13 18:20:39</td>\n",
|
|
" <td>213</td>\n",
|
|
" <td>159</td>\n",
|
|
" <td>78</td>\n",
|
|
" <td>25</td>\n",
|
|
" <td>31</td>\n",
|
|
" <td>40</td>\n",
|
|
" <td>39</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>78</td>\n",
|
|
" <td>25</td>\n",
|
|
" <td>31</td>\n",
|
|
" <td>40</td>\n",
|
|
" <td>39</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>213.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>915</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-13 18:23:39</td>\n",
|
|
" <td>180</td>\n",
|
|
" <td>160</td>\n",
|
|
" <td>65</td>\n",
|
|
" <td>20</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>33</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>65</td>\n",
|
|
" <td>20</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>33</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>180.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>916</th>\n",
|
|
" <td>5031</td>\n",
|
|
" <td>2023-12-13 18:26:38</td>\n",
|
|
" <td>179</td>\n",
|
|
" <td>159</td>\n",
|
|
" <td>65</td>\n",
|
|
" <td>20</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>32</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>65</td>\n",
|
|
" <td>20</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>32</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>179.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" 현장교차로ID 수집날짜시각 주기시간 옵셋시간 A링1현시시간 A링2현시시간 A링3현시시간 \\\n",
|
|
"908 5031 2023-12-13 17:59:38 180 159 65 20 27 \n",
|
|
"909 5031 2023-12-13 18:02:38 180 159 65 20 27 \n",
|
|
"910 5031 2023-12-13 18:05:38 180 159 65 20 27 \n",
|
|
"911 5031 2023-12-13 18:10:47 52 107 65 20 27 \n",
|
|
"912 5031 2023-12-13 18:13:07 140 67 37 16 27 \n",
|
|
"913 5031 2023-12-13 18:17:06 239 126 90 31 31 \n",
|
|
"914 5031 2023-12-13 18:20:39 213 159 78 25 31 \n",
|
|
"915 5031 2023-12-13 18:23:39 180 160 65 20 27 \n",
|
|
"916 5031 2023-12-13 18:26:38 179 159 65 20 27 \n",
|
|
"\n",
|
|
" A링4현시시간 A링5현시시간 A링6현시시간 B링1현시시간 B링2현시시간 B링3현시시간 B링4현시시간 B링5현시시간 \\\n",
|
|
"908 33 35 0 65 20 27 33 35 \n",
|
|
"909 33 35 0 65 20 27 33 35 \n",
|
|
"910 33 35 0 65 20 27 33 35 \n",
|
|
"911 161 35 0 65 20 27 161 35 \n",
|
|
"912 25 35 0 37 16 27 25 35 \n",
|
|
"913 48 39 0 90 31 31 48 39 \n",
|
|
"914 40 39 0 78 25 31 40 39 \n",
|
|
"915 33 35 0 65 20 27 33 35 \n",
|
|
"916 32 35 0 65 20 27 32 35 \n",
|
|
"\n",
|
|
" B링6현시시간 시간차이 결측여부 전이시간여부 주기일치 이상치존재 \n",
|
|
"908 0 180.0 False False True False \n",
|
|
"909 0 180.0 False False True False \n",
|
|
"910 0 180.0 False False True True \n",
|
|
"911 0 309.0 True False False True \n",
|
|
"912 0 140.0 False False True True \n",
|
|
"913 0 239.0 True False True False \n",
|
|
"914 0 213.0 False False True False \n",
|
|
"915 0 180.0 False False True False \n",
|
|
"916 0 179.0 False False True False "
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"1800.0\n",
|
|
"0.0\n",
|
|
"{(6, 0): 150, (6, 30): 160, (7, 0): 180, (9, 30): 160, (16, 0): 170, (17, 0): 180, (20, 30): 170, (21, 30): 160, (22, 30): 150, (23, 30): 140}\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"inter_no = 5031\n",
|
|
"j = 5\n",
|
|
"cycle = 180\n",
|
|
"with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
|
|
" display(infos['missing_dfs'][inter_no][j])\n",
|
|
"sum_of_durations = infos['missing_dfs'][inter_no][j].시간차이.sum()\n",
|
|
"print(sum_of_durations)\n",
|
|
"print(sum_of_durations % cycle)\n",
|
|
"print(infos['cycles'][inter_no])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 36,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>현장교차로ID</th>\n",
|
|
" <th>수집날짜시각</th>\n",
|
|
" <th>주기시간</th>\n",
|
|
" <th>옵셋시간</th>\n",
|
|
" <th>A링1현시시간</th>\n",
|
|
" <th>A링2현시시간</th>\n",
|
|
" <th>A링3현시시간</th>\n",
|
|
" <th>A링4현시시간</th>\n",
|
|
" <th>A링5현시시간</th>\n",
|
|
" <th>A링6현시시간</th>\n",
|
|
" <th>B링1현시시간</th>\n",
|
|
" <th>B링2현시시간</th>\n",
|
|
" <th>B링3현시시간</th>\n",
|
|
" <th>B링4현시시간</th>\n",
|
|
" <th>B링5현시시간</th>\n",
|
|
" <th>B링6현시시간</th>\n",
|
|
" <th>시간차이</th>\n",
|
|
" <th>결측여부</th>\n",
|
|
" <th>전이시간여부</th>\n",
|
|
" <th>주기일치</th>\n",
|
|
" <th>이상치존재</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>200</th>\n",
|
|
" <td>5032</td>\n",
|
|
" <td>2023-12-12 08:08:15</td>\n",
|
|
" <td>170</td>\n",
|
|
" <td>55</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>61</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>61</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>170.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>201</th>\n",
|
|
" <td>5032</td>\n",
|
|
" <td>2023-12-12 08:11:05</td>\n",
|
|
" <td>170</td>\n",
|
|
" <td>55</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>61</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>61</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>170.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>202</th>\n",
|
|
" <td>5032</td>\n",
|
|
" <td>2023-12-12 08:13:55</td>\n",
|
|
" <td>170</td>\n",
|
|
" <td>55</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>61</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>61</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>170.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>203</th>\n",
|
|
" <td>5032</td>\n",
|
|
" <td>2023-12-12 09:02:04</td>\n",
|
|
" <td>169</td>\n",
|
|
" <td>55</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>61</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>23</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>61</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>23</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>2889.0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>204</th>\n",
|
|
" <td>5032</td>\n",
|
|
" <td>2023-12-12 09:04:54</td>\n",
|
|
" <td>170</td>\n",
|
|
" <td>55</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>61</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>61</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>170.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>205</th>\n",
|
|
" <td>5032</td>\n",
|
|
" <td>2023-12-12 09:07:44</td>\n",
|
|
" <td>170</td>\n",
|
|
" <td>55</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>61</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>61</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>170.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>206</th>\n",
|
|
" <td>5032</td>\n",
|
|
" <td>2023-12-12 09:10:34</td>\n",
|
|
" <td>170</td>\n",
|
|
" <td>55</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>61</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>61</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>0</td>\n",
|
|
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" 현장교차로ID 수집날짜시각 주기시간 옵셋시간 A링1현시시간 A링2현시시간 A링3현시시간 \\\n",
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|
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|
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"\n",
|
|
" A링4현시시간 A링5현시시간 A링6현시시간 B링1현시시간 B링2현시시간 B링3현시시간 B링4현시시간 B링5현시시간 \\\n",
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|
|
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|
|
"\n",
|
|
" B링6현시시간 시간차이 결측여부 전이시간여부 주기일치 이상치존재 \n",
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"3909.0\n",
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|
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]
|
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}
|
|
],
|
|
"source": [
|
|
"inter_no = 5032\n",
|
|
"j = 0\n",
|
|
"cycle = 170\n",
|
|
"with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
|
|
" display(infos['missing_dfs'][inter_no][j])\n",
|
|
"sum_of_durations = infos['missing_dfs'][inter_no][j].시간차이.sum()\n",
|
|
"print(sum_of_durations)\n",
|
|
"print(sum_of_durations % cycle)\n",
|
|
"print(infos['cycles'][inter_no])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
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" <th>현장교차로ID</th>\n",
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" <th>주기시간</th>\n",
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" <th>시간차이</th>\n",
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" <td>5032</td>\n",
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" <td>2023-12-13 05:09:01</td>\n",
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" <td>140</td>\n",
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|
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|
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|
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|
|
" <th>654</th>\n",
|
|
" <td>5032</td>\n",
|
|
" <td>2023-12-13 05:11:20</td>\n",
|
|
" <td>139</td>\n",
|
|
" <td>60</td>\n",
|
|
" <td>20</td>\n",
|
|
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|
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|
|
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|
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|
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|
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|
|
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|
|
" <th>655</th>\n",
|
|
" <td>5032</td>\n",
|
|
" <td>2023-12-13 05:13:40</td>\n",
|
|
" <td>141</td>\n",
|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
|
" <tr>\n",
|
|
" <th>656</th>\n",
|
|
" <td>5032</td>\n",
|
|
" <td>2023-12-13 05:17:53</td>\n",
|
|
" <td>252</td>\n",
|
|
" <td>32</td>\n",
|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
|
" <tr>\n",
|
|
" <th>657</th>\n",
|
|
" <td>5032</td>\n",
|
|
" <td>2023-12-13 05:19:50</td>\n",
|
|
" <td>117</td>\n",
|
|
" <td>9</td>\n",
|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
" <td>False</td>\n",
|
|
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|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>658</th>\n",
|
|
" <td>5032</td>\n",
|
|
" <td>2023-12-13 05:22:55</td>\n",
|
|
" <td>186</td>\n",
|
|
" <td>55</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>60</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>29</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>60</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>37</td>\n",
|
|
" <td>29</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>185.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>659</th>\n",
|
|
" <td>5032</td>\n",
|
|
" <td>2023-12-13 05:25:20</td>\n",
|
|
" <td>145</td>\n",
|
|
" <td>60</td>\n",
|
|
" <td>21</td>\n",
|
|
" <td>45</td>\n",
|
|
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|
|
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|
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|
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|
|
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|
|
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|
|
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|
|
" <td>37</td>\n",
|
|
" <td>21</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>145.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" 현장교차로ID 수집날짜시각 주기시간 옵셋시간 A링1현시시간 A링2현시시간 A링3현시시간 \\\n",
|
|
"653 5032 2023-12-13 05:09:01 140 61 20 43 20 \n",
|
|
"654 5032 2023-12-13 05:11:20 139 60 20 43 20 \n",
|
|
"655 5032 2023-12-13 05:13:40 141 60 20 43 20 \n",
|
|
"656 5032 2023-12-13 05:17:53 252 32 132 43 20 \n",
|
|
"657 5032 2023-12-13 05:19:50 117 9 18 24 19 \n",
|
|
"658 5032 2023-12-13 05:22:55 186 55 30 60 30 \n",
|
|
"659 5032 2023-12-13 05:25:20 145 60 21 45 21 \n",
|
|
"\n",
|
|
" A링4현시시간 A링5현시시간 A링6현시시간 B링1현시시간 B링2현시시간 B링3현시시간 B링4현시시간 B링5현시시간 \\\n",
|
|
"653 37 20 0 20 43 20 37 20 \n",
|
|
"654 37 19 0 20 43 20 37 19 \n",
|
|
"655 38 20 0 20 43 20 38 20 \n",
|
|
"656 37 20 0 132 43 20 37 20 \n",
|
|
"657 37 19 0 18 24 19 37 19 \n",
|
|
"658 37 29 0 30 60 30 37 29 \n",
|
|
"659 37 21 0 21 45 21 37 21 \n",
|
|
"\n",
|
|
" B링6현시시간 시간차이 결측여부 전이시간여부 주기일치 이상치존재 \n",
|
|
"653 0 140.0 False False True False \n",
|
|
"654 0 139.0 False False True False \n",
|
|
"655 0 140.0 False False True False \n",
|
|
"656 0 253.0 True False True False \n",
|
|
"657 0 117.0 False False True False \n",
|
|
"658 0 185.0 False False True False \n",
|
|
"659 0 145.0 False False True False "
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"1119.0\n",
|
|
"139.0\n",
|
|
"{(6, 0): 150, (6, 30): 160, (7, 0): 170, (9, 30): 170, (16, 0): 170, (17, 0): 180, (20, 30): 170, (21, 30): 160, (22, 30): 150, (23, 30): 140}\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"inter_no = 5032\n",
|
|
"j = 1\n",
|
|
"cycle = 140\n",
|
|
"with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
|
|
" display(infos['missing_dfs'][inter_no][j])\n",
|
|
"sum_of_durations = infos['missing_dfs'][inter_no][j].시간차이.sum()\n",
|
|
"print(sum_of_durations)\n",
|
|
"print(sum_of_durations % cycle)\n",
|
|
"print(infos['cycles'][inter_no])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 46,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
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|
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|
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|
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|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>현장교차로ID</th>\n",
|
|
" <th>수집날짜시각</th>\n",
|
|
" <th>주기시간</th>\n",
|
|
" <th>옵셋시간</th>\n",
|
|
" <th>A링1현시시간</th>\n",
|
|
" <th>A링2현시시간</th>\n",
|
|
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|
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|
|
" <th>A링5현시시간</th>\n",
|
|
" <th>A링6현시시간</th>\n",
|
|
" <th>B링1현시시간</th>\n",
|
|
" <th>B링2현시시간</th>\n",
|
|
" <th>B링3현시시간</th>\n",
|
|
" <th>B링4현시시간</th>\n",
|
|
" <th>B링5현시시간</th>\n",
|
|
" <th>B링6현시시간</th>\n",
|
|
" <th>시간차이</th>\n",
|
|
" <th>결측여부</th>\n",
|
|
" <th>전이시간여부</th>\n",
|
|
" <th>주기일치</th>\n",
|
|
" <th>이상치존재</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>200</th>\n",
|
|
" <td>5033</td>\n",
|
|
" <td>2023-12-12 08:07:51</td>\n",
|
|
" <td>170</td>\n",
|
|
" <td>32</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>86</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>86</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>170.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>201</th>\n",
|
|
" <td>5033</td>\n",
|
|
" <td>2023-12-12 08:10:41</td>\n",
|
|
" <td>170</td>\n",
|
|
" <td>32</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>86</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>86</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>170.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>202</th>\n",
|
|
" <td>5033</td>\n",
|
|
" <td>2023-12-12 08:13:32</td>\n",
|
|
" <td>170</td>\n",
|
|
" <td>32</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>86</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>86</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>171.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>203</th>\n",
|
|
" <td>5033</td>\n",
|
|
" <td>2023-12-12 09:01:42</td>\n",
|
|
" <td>170</td>\n",
|
|
" <td>32</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>86</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>86</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>2890.0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>204</th>\n",
|
|
" <td>5033</td>\n",
|
|
" <td>2023-12-12 09:04:32</td>\n",
|
|
" <td>170</td>\n",
|
|
" <td>32</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>86</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>86</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>170.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>205</th>\n",
|
|
" <td>5033</td>\n",
|
|
" <td>2023-12-12 09:07:22</td>\n",
|
|
" <td>170</td>\n",
|
|
" <td>32</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>86</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>86</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>170.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>206</th>\n",
|
|
" <td>5033</td>\n",
|
|
" <td>2023-12-12 09:10:12</td>\n",
|
|
" <td>170</td>\n",
|
|
" <td>32</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>86</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>86</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>170.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" 현장교차로ID 수집날짜시각 주기시간 옵셋시간 A링1현시시간 A링2현시시간 A링3현시시간 \\\n",
|
|
"200 5033 2023-12-12 08:07:51 170 32 42 86 42 \n",
|
|
"201 5033 2023-12-12 08:10:41 170 32 42 86 42 \n",
|
|
"202 5033 2023-12-12 08:13:32 170 32 42 86 42 \n",
|
|
"203 5033 2023-12-12 09:01:42 170 32 42 86 42 \n",
|
|
"204 5033 2023-12-12 09:04:32 170 32 42 86 42 \n",
|
|
"205 5033 2023-12-12 09:07:22 170 32 42 86 42 \n",
|
|
"206 5033 2023-12-12 09:10:12 170 32 42 86 42 \n",
|
|
"\n",
|
|
" A링4현시시간 A링5현시시간 A링6현시시간 B링1현시시간 B링2현시시간 B링3현시시간 B링4현시시간 B링5현시시간 \\\n",
|
|
"200 0 0 0 42 86 42 0 0 \n",
|
|
"201 0 0 0 42 86 42 0 0 \n",
|
|
"202 0 0 0 42 86 42 0 0 \n",
|
|
"203 0 0 0 42 86 42 0 0 \n",
|
|
"204 0 0 0 42 86 42 0 0 \n",
|
|
"205 0 0 0 42 86 42 0 0 \n",
|
|
"206 0 0 0 42 86 42 0 0 \n",
|
|
"\n",
|
|
" B링6현시시간 시간차이 결측여부 전이시간여부 주기일치 이상치존재 \n",
|
|
"200 0 170.0 False False True False \n",
|
|
"201 0 170.0 False False True False \n",
|
|
"202 0 171.0 False False True False \n",
|
|
"203 0 2890.0 True False True False \n",
|
|
"204 0 170.0 False False True False \n",
|
|
"205 0 170.0 False False True False \n",
|
|
"206 0 170.0 False False True False "
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"3911.0\n",
|
|
"1.0\n",
|
|
"{(6, 0): 150, (6, 30): 160, (7, 0): 170, (9, 30): 170, (16, 0): 170, (17, 0): 180, (20, 30): 170, (21, 30): 160, (22, 30): 150, (23, 30): 140}\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"inter_no = 5033\n",
|
|
"j = 0\n",
|
|
"cycle = 170\n",
|
|
"with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
|
|
" display(infos['missing_dfs'][inter_no][j])\n",
|
|
"sum_of_durations = infos['missing_dfs'][inter_no][j].시간차이.sum()\n",
|
|
"print(sum_of_durations)\n",
|
|
"print(sum_of_durations % cycle)\n",
|
|
"print(infos['cycles'][inter_no])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 49,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
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|
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|
|
" .dataframe thead th {\n",
|
|
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|
|
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|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>현장교차로ID</th>\n",
|
|
" <th>수집날짜시각</th>\n",
|
|
" <th>주기시간</th>\n",
|
|
" <th>옵셋시간</th>\n",
|
|
" <th>A링1현시시간</th>\n",
|
|
" <th>A링2현시시간</th>\n",
|
|
" <th>A링3현시시간</th>\n",
|
|
" <th>A링4현시시간</th>\n",
|
|
" <th>A링5현시시간</th>\n",
|
|
" <th>A링6현시시간</th>\n",
|
|
" <th>B링1현시시간</th>\n",
|
|
" <th>B링2현시시간</th>\n",
|
|
" <th>B링3현시시간</th>\n",
|
|
" <th>B링4현시시간</th>\n",
|
|
" <th>B링5현시시간</th>\n",
|
|
" <th>B링6현시시간</th>\n",
|
|
" <th>시간차이</th>\n",
|
|
" <th>결측여부</th>\n",
|
|
" <th>전이시간여부</th>\n",
|
|
" <th>주기일치</th>\n",
|
|
" <th>이상치존재</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>891</th>\n",
|
|
" <td>5033</td>\n",
|
|
" <td>2023-12-13 17:30:43</td>\n",
|
|
" <td>180</td>\n",
|
|
" <td>44</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>94</td>\n",
|
|
" <td>44</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>94</td>\n",
|
|
" <td>44</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>180.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>892</th>\n",
|
|
" <td>5033</td>\n",
|
|
" <td>2023-12-13 17:33:43</td>\n",
|
|
" <td>180</td>\n",
|
|
" <td>44</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>94</td>\n",
|
|
" <td>44</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>94</td>\n",
|
|
" <td>44</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>180.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>893</th>\n",
|
|
" <td>5033</td>\n",
|
|
" <td>2023-12-13 17:36:43</td>\n",
|
|
" <td>180</td>\n",
|
|
" <td>44</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>94</td>\n",
|
|
" <td>44</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>94</td>\n",
|
|
" <td>44</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>180.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>894</th>\n",
|
|
" <td>5033</td>\n",
|
|
" <td>2023-12-13 17:42:34</td>\n",
|
|
" <td>94</td>\n",
|
|
" <td>34</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>94</td>\n",
|
|
" <td>214</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>94</td>\n",
|
|
" <td>214</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>351.0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>895</th>\n",
|
|
" <td>5033</td>\n",
|
|
" <td>2023-12-13 17:44:33</td>\n",
|
|
" <td>119</td>\n",
|
|
" <td>153</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>53</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>42</td>\n",
|
|
" <td>53</td>\n",
|
|
" <td>24</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>119.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>896</th>\n",
|
|
" <td>5033</td>\n",
|
|
" <td>2023-12-13 17:48:32</td>\n",
|
|
" <td>239</td>\n",
|
|
" <td>32</td>\n",
|
|
" <td>54</td>\n",
|
|
" <td>126</td>\n",
|
|
" <td>59</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>54</td>\n",
|
|
" <td>126</td>\n",
|
|
" <td>59</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>239.0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>897</th>\n",
|
|
" <td>5033</td>\n",
|
|
" <td>2023-12-13 17:51:44</td>\n",
|
|
" <td>192</td>\n",
|
|
" <td>44</td>\n",
|
|
" <td>45</td>\n",
|
|
" <td>100</td>\n",
|
|
" <td>47</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>45</td>\n",
|
|
" <td>100</td>\n",
|
|
" <td>47</td>\n",
|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
|
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|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>898</th>\n",
|
|
" <td>5033</td>\n",
|
|
" <td>2023-12-13 17:54:44</td>\n",
|
|
" <td>180</td>\n",
|
|
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|
|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
|
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|
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|
|
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|
|
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|
|
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|
|
" <tr>\n",
|
|
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|
|
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|
|
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|
|
" <td>180</td>\n",
|
|
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|
|
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|
|
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|
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|
|
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" 현장교차로ID 수집날짜시각 주기시간 옵셋시간 A링1현시시간 A링2현시시간 A링3현시시간 \\\n",
|
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|
|
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|
|
"\n",
|
|
" A링4현시시간 A링5현시시간 A링6현시시간 B링1현시시간 B링2현시시간 B링3현시시간 B링4현시시간 B링5현시시간 \\\n",
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|
|
" B링6현시시간 시간차이 결측여부 전이시간여부 주기일치 이상치존재 \n",
|
|
"891 0 180.0 False False True False \n",
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|
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|
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|
|
],
|
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"source": [
|
|
"inter_no = 5033\n",
|
|
"j = 2\n",
|
|
"cycle = 180\n",
|
|
"with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
|
|
" display(infos['missing_dfs'][inter_no][j])\n",
|
|
"sum_of_durations = infos['missing_dfs'][inter_no][j].시간차이.sum()\n",
|
|
"print(sum_of_durations)\n",
|
|
"print(sum_of_durations % cycle)\n",
|
|
"print(infos['cycles'][inter_no])"
|
|
]
|
|
},
|
|
{
|
|
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|
|
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"text/plain": [
|
|
" 현장교차로ID 수집날짜시각 주기시간 옵셋시간 A링1현시시간 A링2현시시간 A링3현시시간 \\\n",
|
|
"186 5034 2023-12-12 08:05:37 180 158 53 33 51 \n",
|
|
"187 5034 2023-12-12 08:08:37 180 157 53 33 51 \n",
|
|
"188 5034 2023-12-12 08:11:38 181 159 53 33 52 \n",
|
|
"189 5034 2023-12-12 09:02:37 180 158 53 33 51 \n",
|
|
"190 5034 2023-12-12 09:05:37 180 158 53 33 51 \n",
|
|
"191 5034 2023-12-12 09:08:38 180 158 53 33 51 \n",
|
|
"192 5034 2023-12-12 09:11:38 180 158 53 33 51 \n",
|
|
"\n",
|
|
" A링4현시시간 A링5현시시간 A링6현시시간 B링1현시시간 B링2현시시간 B링3현시시간 B링4현시시간 B링5현시시간 \\\n",
|
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"186 43 0 0 26 60 51 43 0 \n",
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|
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|
|
"191 43 0 0 26 60 51 43 0 \n",
|
|
"192 43 0 0 26 60 51 43 0 \n",
|
|
"\n",
|
|
" B링6현시시간 시간차이 결측여부 전이시간여부 주기일치 이상치존재 \n",
|
|
"186 0 180.0 False False True False \n",
|
|
"187 0 180.0 False False True False \n",
|
|
"188 0 181.0 False False True False \n",
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|
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},
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"metadata": {},
|
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"output_type": "display_data"
|
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},
|
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{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
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"4141.0\n",
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"1.0\n",
|
|
"{(5, 30): 160, (6, 30): 170, (7, 0): 180, (9, 30): 170, (17, 0): 180, (21, 0): 170, (22, 0): 160, (23, 30): 150}\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"inter_no = 5034\n",
|
|
"j = 0\n",
|
|
"cycle = 180\n",
|
|
"with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
|
|
" display(infos['missing_dfs'][inter_no][j])\n",
|
|
"sum_of_durations = infos['missing_dfs'][inter_no][j].시간차이.sum()\n",
|
|
"print(sum_of_durations)\n",
|
|
"print(sum_of_durations % cycle)\n",
|
|
"print(infos['cycles'][inter_no])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 56,
|
|
"metadata": {},
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"outputs": [
|
|
{
|
|
"data": {
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|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>현장교차로ID</th>\n",
|
|
" <th>수집날짜시각</th>\n",
|
|
" <th>주기시간</th>\n",
|
|
" <th>옵셋시간</th>\n",
|
|
" <th>A링1현시시간</th>\n",
|
|
" <th>A링2현시시간</th>\n",
|
|
" <th>A링3현시시간</th>\n",
|
|
" <th>A링4현시시간</th>\n",
|
|
" <th>A링5현시시간</th>\n",
|
|
" <th>A링6현시시간</th>\n",
|
|
" <th>B링1현시시간</th>\n",
|
|
" <th>B링2현시시간</th>\n",
|
|
" <th>B링3현시시간</th>\n",
|
|
" <th>B링4현시시간</th>\n",
|
|
" <th>B링5현시시간</th>\n",
|
|
" <th>B링6현시시간</th>\n",
|
|
" <th>시간차이</th>\n",
|
|
" <th>결측여부</th>\n",
|
|
" <th>전이시간여부</th>\n",
|
|
" <th>주기일치</th>\n",
|
|
" <th>이상치존재</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>384</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-12 18:19:05</td>\n",
|
|
" <td>57</td>\n",
|
|
" <td>65</td>\n",
|
|
" <td>57</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>31</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>88.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>385</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-12 18:22:05</td>\n",
|
|
" <td>180</td>\n",
|
|
" <td>65</td>\n",
|
|
" <td>57</td>\n",
|
|
" <td>33</td>\n",
|
|
" <td>45</td>\n",
|
|
" <td>45</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>31</td>\n",
|
|
" <td>59</td>\n",
|
|
" <td>45</td>\n",
|
|
" <td>45</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>180.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>386</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-12 18:24:35</td>\n",
|
|
" <td>150</td>\n",
|
|
" <td>35</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>21</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>21</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>150.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>387</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-12 18:28:35</td>\n",
|
|
" <td>240</td>\n",
|
|
" <td>95</td>\n",
|
|
" <td>76</td>\n",
|
|
" <td>44</td>\n",
|
|
" <td>60</td>\n",
|
|
" <td>60</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>41</td>\n",
|
|
" <td>79</td>\n",
|
|
" <td>60</td>\n",
|
|
" <td>60</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>240.0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>388</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-12 18:32:35</td>\n",
|
|
" <td>240</td>\n",
|
|
" <td>155</td>\n",
|
|
" <td>76</td>\n",
|
|
" <td>44</td>\n",
|
|
" <td>60</td>\n",
|
|
" <td>60</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>41</td>\n",
|
|
" <td>79</td>\n",
|
|
" <td>60</td>\n",
|
|
" <td>60</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>240.0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>389</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-12 18:35:39</td>\n",
|
|
" <td>184</td>\n",
|
|
" <td>159</td>\n",
|
|
" <td>58</td>\n",
|
|
" <td>34</td>\n",
|
|
" <td>46</td>\n",
|
|
" <td>46</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>32</td>\n",
|
|
" <td>60</td>\n",
|
|
" <td>46</td>\n",
|
|
" <td>46</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>184.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>390</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-12 18:38:39</td>\n",
|
|
" <td>180</td>\n",
|
|
" <td>159</td>\n",
|
|
" <td>57</td>\n",
|
|
" <td>33</td>\n",
|
|
" <td>45</td>\n",
|
|
" <td>45</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>31</td>\n",
|
|
" <td>59</td>\n",
|
|
" <td>45</td>\n",
|
|
" <td>45</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>180.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>391</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-12 18:41:39</td>\n",
|
|
" <td>180</td>\n",
|
|
" <td>159</td>\n",
|
|
" <td>57</td>\n",
|
|
" <td>33</td>\n",
|
|
" <td>45</td>\n",
|
|
" <td>45</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>31</td>\n",
|
|
" <td>59</td>\n",
|
|
" <td>45</td>\n",
|
|
" <td>45</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>180.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
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|
|
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|
|
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|
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|
|
],
|
|
"text/plain": [
|
|
" 현장교차로ID 수집날짜시각 주기시간 옵셋시간 A링1현시시간 A링2현시시간 A링3현시시간 \\\n",
|
|
"384 5034 2023-12-12 18:19:05 57 65 57 0 0 \n",
|
|
"385 5034 2023-12-12 18:22:05 180 65 57 33 45 \n",
|
|
"386 5034 2023-12-12 18:24:35 150 35 43 21 43 \n",
|
|
"387 5034 2023-12-12 18:28:35 240 95 76 44 60 \n",
|
|
"388 5034 2023-12-12 18:32:35 240 155 76 44 60 \n",
|
|
"389 5034 2023-12-12 18:35:39 184 159 58 34 46 \n",
|
|
"390 5034 2023-12-12 18:38:39 180 159 57 33 45 \n",
|
|
"391 5034 2023-12-12 18:41:39 180 159 57 33 45 \n",
|
|
"\n",
|
|
" A링4현시시간 A링5현시시간 A링6현시시간 B링1현시시간 B링2현시시간 B링3현시시간 B링4현시시간 B링5현시시간 \\\n",
|
|
"384 0 0 0 31 0 0 0 0 \n",
|
|
"385 45 0 0 31 59 45 45 0 \n",
|
|
"386 43 0 0 21 43 43 43 0 \n",
|
|
"387 60 0 0 41 79 60 60 0 \n",
|
|
"388 60 0 0 41 79 60 60 0 \n",
|
|
"389 46 0 0 32 60 46 46 0 \n",
|
|
"390 45 0 0 31 59 45 45 0 \n",
|
|
"391 45 0 0 31 59 45 45 0 \n",
|
|
"\n",
|
|
" B링6현시시간 시간차이 결측여부 전이시간여부 주기일치 이상치존재 \n",
|
|
"384 0 88.0 False False False False \n",
|
|
"385 0 180.0 False False True False \n",
|
|
"386 0 150.0 False False True False \n",
|
|
"387 0 240.0 True False True False \n",
|
|
"388 0 240.0 True False True False \n",
|
|
"389 0 184.0 False False True False \n",
|
|
"390 0 180.0 False False True False \n",
|
|
"391 0 180.0 False False True False "
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"1442.0\n",
|
|
"2.0\n",
|
|
"{(5, 30): 160, (6, 30): 170, (7, 0): 180, (9, 30): 170, (17, 0): 180, (21, 0): 170, (22, 0): 160, (23, 30): 150}\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"inter_no = 5034\n",
|
|
"j = 1\n",
|
|
"cycle = 180\n",
|
|
"with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
|
|
" display(infos['missing_dfs'][inter_no][j])\n",
|
|
"sum_of_durations = infos['missing_dfs'][inter_no][j].시간차이.sum()\n",
|
|
"print(sum_of_durations)\n",
|
|
"print(sum_of_durations % cycle)\n",
|
|
"print(infos['cycles'][inter_no])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 57,
|
|
"metadata": {},
|
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"outputs": [
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" <th>705</th>\n",
|
|
" <td>5034</td>\n",
|
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" <td>2023-12-13 08:47:40</td>\n",
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" <td>180</td>\n",
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" <th>706</th>\n",
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" <td>5034</td>\n",
|
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" <td>2023-12-13 08:50:40</td>\n",
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" <td>180</td>\n",
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" <th>707</th>\n",
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|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>708</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-13 08:59:12</td>\n",
|
|
" <td>46</td>\n",
|
|
" <td>132</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>46</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>46</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>332.0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>709</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-13 09:02:40</td>\n",
|
|
" <td>208</td>\n",
|
|
" <td>160</td>\n",
|
|
" <td>61</td>\n",
|
|
" <td>39</td>\n",
|
|
" <td>59</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>70</td>\n",
|
|
" <td>59</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>208.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>710</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-13 09:05:40</td>\n",
|
|
" <td>180</td>\n",
|
|
" <td>160</td>\n",
|
|
" <td>53</td>\n",
|
|
" <td>33</td>\n",
|
|
" <td>51</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>26</td>\n",
|
|
" <td>60</td>\n",
|
|
" <td>51</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>180.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>711</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-13 09:08:40</td>\n",
|
|
" <td>180</td>\n",
|
|
" <td>160</td>\n",
|
|
" <td>53</td>\n",
|
|
" <td>33</td>\n",
|
|
" <td>51</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>26</td>\n",
|
|
" <td>60</td>\n",
|
|
" <td>51</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>180.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" 현장교차로ID 수집날짜시각 주기시간 옵셋시간 A링1현시시간 A링2현시시간 A링3현시시간 \\\n",
|
|
"705 5034 2023-12-13 08:47:40 180 160 53 33 51 \n",
|
|
"706 5034 2023-12-13 08:50:40 180 160 53 33 51 \n",
|
|
"707 5034 2023-12-13 08:53:40 180 160 53 33 51 \n",
|
|
"708 5034 2023-12-13 08:59:12 46 132 0 0 0 \n",
|
|
"709 5034 2023-12-13 09:02:40 208 160 61 39 59 \n",
|
|
"710 5034 2023-12-13 09:05:40 180 160 53 33 51 \n",
|
|
"711 5034 2023-12-13 09:08:40 180 160 53 33 51 \n",
|
|
"\n",
|
|
" A링4현시시간 A링5현시시간 A링6현시시간 B링1현시시간 B링2현시시간 B링3현시시간 B링4현시시간 B링5현시시간 \\\n",
|
|
"705 43 0 0 26 60 51 43 0 \n",
|
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"706 43 0 0 26 60 51 43 0 \n",
|
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"707 43 0 0 26 60 51 43 0 \n",
|
|
"708 46 0 0 0 0 0 46 0 \n",
|
|
"709 49 0 0 30 70 59 49 0 \n",
|
|
"710 43 0 0 26 60 51 43 0 \n",
|
|
"711 43 0 0 26 60 51 43 0 \n",
|
|
"\n",
|
|
" B링6현시시간 시간차이 결측여부 전이시간여부 주기일치 이상치존재 \n",
|
|
"705 0 180.0 False False True False \n",
|
|
"706 0 180.0 False False True False \n",
|
|
"707 0 180.0 False False True False \n",
|
|
"708 0 332.0 True False True False \n",
|
|
"709 0 208.0 False False True False \n",
|
|
"710 0 180.0 False False True False \n",
|
|
"711 0 180.0 False False True False "
|
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]
|
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},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"1440.0\n",
|
|
"0.0\n",
|
|
"{(5, 30): 160, (6, 30): 170, (7, 0): 180, (9, 30): 170, (17, 0): 180, (21, 0): 170, (22, 0): 160, (23, 30): 150}\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"inter_no = 5034\n",
|
|
"j = 2\n",
|
|
"cycle = 180\n",
|
|
"with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
|
|
" display(infos['missing_dfs'][inter_no][j])\n",
|
|
"sum_of_durations = infos['missing_dfs'][inter_no][j].시간차이.sum()\n",
|
|
"print(sum_of_durations)\n",
|
|
"print(sum_of_durations % cycle)\n",
|
|
"print(infos['cycles'][inter_no])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 59,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
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|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>현장교차로ID</th>\n",
|
|
" <th>수집날짜시각</th>\n",
|
|
" <th>주기시간</th>\n",
|
|
" <th>옵셋시간</th>\n",
|
|
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|
|
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|
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|
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|
|
" <th>B링5현시시간</th>\n",
|
|
" <th>B링6현시시간</th>\n",
|
|
" <th>시간차이</th>\n",
|
|
" <th>결측여부</th>\n",
|
|
" <th>전이시간여부</th>\n",
|
|
" <th>주기일치</th>\n",
|
|
" <th>이상치존재</th>\n",
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|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>825</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-13 14:32:27</td>\n",
|
|
" <td>170</td>\n",
|
|
" <td>157</td>\n",
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|
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|
|
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|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>826</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-13 14:35:17</td>\n",
|
|
" <td>170</td>\n",
|
|
" <td>157</td>\n",
|
|
" <td>51</td>\n",
|
|
" <td>33</td>\n",
|
|
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|
|
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|
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|
|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
|
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|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>827</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-13 14:38:07</td>\n",
|
|
" <td>170</td>\n",
|
|
" <td>157</td>\n",
|
|
" <td>51</td>\n",
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|
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|
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|
|
" <td>0</td>\n",
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|
" <td>0</td>\n",
|
|
" <td>170.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>828</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-13 16:00:18</td>\n",
|
|
" <td>170</td>\n",
|
|
" <td>157</td>\n",
|
|
" <td>51</td>\n",
|
|
" <td>33</td>\n",
|
|
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|
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|
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|
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|
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|
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|
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|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>829</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-13 16:03:08</td>\n",
|
|
" <td>170</td>\n",
|
|
" <td>157</td>\n",
|
|
" <td>51</td>\n",
|
|
" <td>33</td>\n",
|
|
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|
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|
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|
|
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|
|
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|
|
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|
|
" <td>43</td>\n",
|
|
" <td>43</td>\n",
|
|
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|
|
" <td>0</td>\n",
|
|
" <td>170.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>830</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-13 16:05:58</td>\n",
|
|
" <td>170</td>\n",
|
|
" <td>157</td>\n",
|
|
" <td>51</td>\n",
|
|
" <td>33</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>57</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>170.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>831</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-13 16:08:48</td>\n",
|
|
" <td>170</td>\n",
|
|
" <td>157</td>\n",
|
|
" <td>51</td>\n",
|
|
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|
|
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|
|
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|
|
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|
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|
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|
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|
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|
|
" <td>43</td>\n",
|
|
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|
|
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|
|
" <td>170.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" 현장교차로ID 수집날짜시각 주기시간 옵셋시간 A링1현시시간 A링2현시시간 A링3현시시간 \\\n",
|
|
"825 5034 2023-12-13 14:32:27 170 157 51 33 43 \n",
|
|
"826 5034 2023-12-13 14:35:17 170 157 51 33 43 \n",
|
|
"827 5034 2023-12-13 14:38:07 170 157 51 33 43 \n",
|
|
"828 5034 2023-12-13 16:00:18 170 157 51 33 43 \n",
|
|
"829 5034 2023-12-13 16:03:08 170 157 51 33 43 \n",
|
|
"830 5034 2023-12-13 16:05:58 170 157 51 33 43 \n",
|
|
"831 5034 2023-12-13 16:08:48 170 157 51 33 43 \n",
|
|
"\n",
|
|
" A링4현시시간 A링5현시시간 A링6현시시간 B링1현시시간 B링2현시시간 B링3현시시간 B링4현시시간 B링5현시시간 \\\n",
|
|
"825 43 0 0 27 57 43 43 0 \n",
|
|
"826 43 0 0 27 57 43 43 0 \n",
|
|
"827 43 0 0 27 57 43 43 0 \n",
|
|
"828 43 0 0 27 57 43 43 0 \n",
|
|
"829 43 0 0 27 57 43 43 0 \n",
|
|
"830 43 0 0 27 57 43 43 0 \n",
|
|
"831 43 0 0 27 57 43 43 0 \n",
|
|
"\n",
|
|
" B링6현시시간 시간차이 결측여부 전이시간여부 주기일치 이상치존재 \n",
|
|
"825 0 170.0 False False True False \n",
|
|
"826 0 170.0 False False True False \n",
|
|
"827 0 170.0 False False True False \n",
|
|
"828 0 4931.0 True False True False \n",
|
|
"829 0 170.0 False False True False \n",
|
|
"830 0 170.0 False False True False \n",
|
|
"831 0 170.0 False False True False "
|
|
]
|
|
},
|
|
"metadata": {},
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"text": [
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"5951.0\n",
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"{(5, 30): 160, (6, 30): 170, (7, 0): 180, (9, 30): 170, (17, 0): 180, (21, 0): 170, (22, 0): 160, (23, 30): 150}\n"
|
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]
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}
|
|
],
|
|
"source": [
|
|
"inter_no = 5034\n",
|
|
"j = 3\n",
|
|
"cycle = 170\n",
|
|
"with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
|
|
" display(infos['missing_dfs'][inter_no][j])\n",
|
|
"sum_of_durations = infos['missing_dfs'][inter_no][j].시간차이.sum()\n",
|
|
"print(sum_of_durations)\n",
|
|
"print(sum_of_durations % cycle)\n",
|
|
"print(infos['cycles'][inter_no])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 61,
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|
" <th>주기시간</th>\n",
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" <th>시간차이</th>\n",
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|
" <td>2023-12-13 17:42:38</td>\n",
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|
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|
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|
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|
" <th>865</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-13 17:45:38</td>\n",
|
|
" <td>180</td>\n",
|
|
" <td>39</td>\n",
|
|
" <td>57</td>\n",
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|
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|
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|
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|
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|
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|
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|
|
" <th>866</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-13 17:48:08</td>\n",
|
|
" <td>150</td>\n",
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|
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|
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|
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|
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|
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|
" <tr>\n",
|
|
" <th>867</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-13 17:52:08</td>\n",
|
|
" <td>240</td>\n",
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|
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|
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|
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|
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|
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|
|
" <th>868</th>\n",
|
|
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|
|
" <td>2023-12-13 17:56:08</td>\n",
|
|
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|
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|
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|
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|
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|
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|
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|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>869</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-13 17:59:38</td>\n",
|
|
" <td>210</td>\n",
|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
" <tr>\n",
|
|
" <th>870</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-13 18:02:38</td>\n",
|
|
" <td>180</td>\n",
|
|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
|
" <tr>\n",
|
|
" <th>871</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-13 18:05:38</td>\n",
|
|
" <td>180</td>\n",
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|
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|
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|
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|
|
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|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
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|
|
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|
|
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|
|
],
|
|
"text/plain": [
|
|
" 현장교차로ID 수집날짜시각 주기시간 옵셋시간 A링1현시시간 A링2현시시간 A링3현시시간 \\\n",
|
|
"864 5034 2023-12-13 17:42:38 57 38 57 0 0 \n",
|
|
"865 5034 2023-12-13 17:45:38 180 39 57 33 45 \n",
|
|
"866 5034 2023-12-13 17:48:08 150 8 43 21 43 \n",
|
|
"867 5034 2023-12-13 17:52:08 240 68 76 44 60 \n",
|
|
"868 5034 2023-12-13 17:56:08 240 128 76 44 60 \n",
|
|
"869 5034 2023-12-13 17:59:38 210 159 67 38 53 \n",
|
|
"870 5034 2023-12-13 18:02:38 180 159 57 33 45 \n",
|
|
"871 5034 2023-12-13 18:05:38 180 159 57 33 45 \n",
|
|
"\n",
|
|
" A링4현시시간 A링5현시시간 A링6현시시간 B링1현시시간 B링2현시시간 B링3현시시간 B링4현시시간 B링5현시시간 \\\n",
|
|
"864 0 0 0 31 0 0 0 0 \n",
|
|
"865 45 0 0 31 59 45 45 0 \n",
|
|
"866 43 0 0 21 43 43 43 0 \n",
|
|
"867 60 0 0 41 79 60 60 0 \n",
|
|
"868 60 0 0 41 79 60 60 0 \n",
|
|
"869 52 0 0 36 69 53 52 0 \n",
|
|
"870 45 0 0 31 59 45 45 0 \n",
|
|
"871 45 0 0 31 59 45 45 0 \n",
|
|
"\n",
|
|
" B링6현시시간 시간차이 결측여부 전이시간여부 주기일치 이상치존재 \n",
|
|
"864 0 59.0 False False False False \n",
|
|
"865 0 180.0 False False True False \n",
|
|
"866 0 150.0 False False True False \n",
|
|
"867 0 240.0 True False True False \n",
|
|
"868 0 240.0 True False True False \n",
|
|
"869 0 210.0 False False True False \n",
|
|
"870 0 180.0 False False True False \n",
|
|
"871 0 180.0 False False True False "
|
|
]
|
|
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|
|
"metadata": {},
|
|
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|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"1439.0\n",
|
|
"179.0\n",
|
|
"{(5, 30): 160, (6, 30): 170, (7, 0): 180, (9, 30): 170, (17, 0): 180, (21, 0): 170, (22, 0): 160, (23, 30): 150}\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"inter_no = 5034\n",
|
|
"j = 4\n",
|
|
"cycle = 180\n",
|
|
"with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
|
|
" display(infos['missing_dfs'][inter_no][j])\n",
|
|
"sum_of_durations = infos['missing_dfs'][inter_no][j].시간차이.sum()\n",
|
|
"print(sum_of_durations)\n",
|
|
"print(sum_of_durations % cycle)\n",
|
|
"print(infos['cycles'][inter_no])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 62,
|
|
"metadata": {},
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"outputs": [
|
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|
|
"data": {
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|
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|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>현장교차로ID</th>\n",
|
|
" <th>수집날짜시각</th>\n",
|
|
" <th>주기시간</th>\n",
|
|
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|
|
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" <th>B링6현시시간</th>\n",
|
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" <th>시간차이</th>\n",
|
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" <th>결측여부</th>\n",
|
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" <th>전이시간여부</th>\n",
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" <th>주기일치</th>\n",
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" <th>이상치존재</th>\n",
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|
" </tr>\n",
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|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>1201</th>\n",
|
|
" <td>5034</td>\n",
|
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" <td>2023-12-14 08:59:38</td>\n",
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" <td>180</td>\n",
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" <td>158</td>\n",
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" <tr>\n",
|
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" <th>1202</th>\n",
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" <td>5034</td>\n",
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|
|
" <td>51</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>180.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1203</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-14 09:05:38</td>\n",
|
|
" <td>180</td>\n",
|
|
" <td>158</td>\n",
|
|
" <td>53</td>\n",
|
|
" <td>33</td>\n",
|
|
" <td>51</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>26</td>\n",
|
|
" <td>60</td>\n",
|
|
" <td>51</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>180.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1204</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-14 09:10:40</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>100</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>302.0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1205</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-14 09:13:09</td>\n",
|
|
" <td>150</td>\n",
|
|
" <td>69</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>21</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>21</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>149.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1206</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-14 09:17:08</td>\n",
|
|
" <td>239</td>\n",
|
|
" <td>128</td>\n",
|
|
" <td>70</td>\n",
|
|
" <td>44</td>\n",
|
|
" <td>68</td>\n",
|
|
" <td>57</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>34</td>\n",
|
|
" <td>80</td>\n",
|
|
" <td>68</td>\n",
|
|
" <td>57</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>239.0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1207</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-14 09:20:38</td>\n",
|
|
" <td>210</td>\n",
|
|
" <td>158</td>\n",
|
|
" <td>62</td>\n",
|
|
" <td>38</td>\n",
|
|
" <td>60</td>\n",
|
|
" <td>50</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>70</td>\n",
|
|
" <td>60</td>\n",
|
|
" <td>50</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>210.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1208</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-14 09:23:38</td>\n",
|
|
" <td>180</td>\n",
|
|
" <td>159</td>\n",
|
|
" <td>53</td>\n",
|
|
" <td>33</td>\n",
|
|
" <td>51</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>26</td>\n",
|
|
" <td>60</td>\n",
|
|
" <td>51</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>180.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1209</th>\n",
|
|
" <td>5034</td>\n",
|
|
" <td>2023-12-14 09:26:37</td>\n",
|
|
" <td>179</td>\n",
|
|
" <td>157</td>\n",
|
|
" <td>53</td>\n",
|
|
" <td>33</td>\n",
|
|
" <td>50</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>26</td>\n",
|
|
" <td>60</td>\n",
|
|
" <td>50</td>\n",
|
|
" <td>43</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>179.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" 현장교차로ID 수집날짜시각 주기시간 옵셋시간 A링1현시시간 A링2현시시간 A링3현시시간 \\\n",
|
|
"1201 5034 2023-12-14 08:59:38 180 158 53 33 51 \n",
|
|
"1202 5034 2023-12-14 09:02:38 180 158 53 33 51 \n",
|
|
"1203 5034 2023-12-14 09:05:38 180 158 53 33 51 \n",
|
|
"1204 5034 2023-12-14 09:10:40 43 100 0 0 0 \n",
|
|
"1205 5034 2023-12-14 09:13:09 150 69 43 21 43 \n",
|
|
"1206 5034 2023-12-14 09:17:08 239 128 70 44 68 \n",
|
|
"1207 5034 2023-12-14 09:20:38 210 158 62 38 60 \n",
|
|
"1208 5034 2023-12-14 09:23:38 180 159 53 33 51 \n",
|
|
"1209 5034 2023-12-14 09:26:37 179 157 53 33 50 \n",
|
|
"\n",
|
|
" A링4현시시간 A링5현시시간 A링6현시시간 B링1현시시간 B링2현시시간 B링3현시시간 B링4현시시간 B링5현시시간 \\\n",
|
|
"1201 43 0 0 26 60 51 43 0 \n",
|
|
"1202 43 0 0 26 60 51 43 0 \n",
|
|
"1203 43 0 0 26 60 51 43 0 \n",
|
|
"1204 43 0 0 0 0 0 43 0 \n",
|
|
"1205 43 0 0 21 43 43 43 0 \n",
|
|
"1206 57 0 0 34 80 68 57 0 \n",
|
|
"1207 50 0 0 30 70 60 50 0 \n",
|
|
"1208 43 0 0 26 60 51 43 0 \n",
|
|
"1209 43 0 0 26 60 50 43 0 \n",
|
|
"\n",
|
|
" B링6현시시간 시간차이 결측여부 전이시간여부 주기일치 이상치존재 \n",
|
|
"1201 0 180.0 False False True False \n",
|
|
"1202 0 180.0 False False True False \n",
|
|
"1203 0 180.0 False False True False \n",
|
|
"1204 0 302.0 True False True False \n",
|
|
"1205 0 149.0 False False True False \n",
|
|
"1206 0 239.0 True False True False \n",
|
|
"1207 0 210.0 False False True False \n",
|
|
"1208 0 180.0 False False True False \n",
|
|
"1209 0 179.0 False False True False "
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"1799.0\n",
|
|
"179.0\n",
|
|
"{(5, 30): 160, (6, 30): 170, (7, 0): 180, (9, 30): 170, (17, 0): 180, (21, 0): 170, (22, 0): 160, (23, 30): 150}\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"inter_no = 5034\n",
|
|
"j = 5\n",
|
|
"cycle = 180\n",
|
|
"with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
|
|
" display(infos['missing_dfs'][inter_no][j])\n",
|
|
"sum_of_durations = infos['missing_dfs'][inter_no][j].시간차이.sum()\n",
|
|
"print(sum_of_durations)\n",
|
|
"print(sum_of_durations % cycle)\n",
|
|
"print(infos['cycles'][inter_no])"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "rts",
|
|
"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": "3.8.10"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|