{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import os, sys, json, argparse, pickle\n", "import sumolib, traci\n", "from tqdm import tqdm\n", "from datetime import datetime\n", "path_root = os.path.dirname(os.path.dirname(os.path.abspath('.')))\n", "path_scr = os.path.join(path_root, 'scripts')\n", "sys.path.append(path_scr)\n", "from generate_signals import SignalGenerator" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2024-07-29 09:36:32\n", "1722213392\n", "2024-07-29 09:36:32\n" ] } ], "source": [ "time_str = '2024-07-29 9:36:32'\n", "time_dt = datetime.strptime(time_str, '%Y-%m-%d %H:%M:%S')\n", "print(time_dt)\n", "time_unix = int(time_dt.timestamp())\n", "print(time_unix)\n", "print(datetime.fromtimestamp(time_unix))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "self = SignalGenerator(config_name='test_0731',\n", " file_net='new_sungnam_network_internal_target_0721.net.xml',\n", " month=7,\n", " day=31,\n", " hour=9,\n", " minute=5)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1-1. 네트워크가 로드되었습니다.\n" ] } ], "source": [ "self.load_networks()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1-2. 테이블들이 로드되었습니다.\n" ] } ], "source": [ "self.load_tables()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1722383967\n", "1722385038\n", "1722384300\n", "1722351600\n", "1722438000\n" ] } ], "source": [ "print(self.history.end_unix.min())\n", "print(self.history.end_unix.max())\n", "print(self.present_time)\n", "print(self.midnight)\n", "print(self.next_day)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# dura_As = [f'dura_A{i}' for i in range(1,9)]\n", "# dura_Bs = [f'dura_B{i}' for i in range(1,9)]\n", "# for i, row in self.plan.iterrows():\n", "# is_zero_A = (row[dura_As]==0).all()\n", "# is_zero_B = (row[dura_Bs]==0).all()\n", "# assert not (is_zero_A & is_zero_B)\n", "# if is_zero_B:\n", "# for j in range(1, 9):\n", "# self.plan.at[i, f'dura_B{j}'] = row[f'dura_A{j}']\n", "# if is_zero_A:\n", "# for j in range(1, 9):\n", "# self.plan.at[i, f'dura_A{j}'] = row[f'dura_B{j}']" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1-5. 필요한 보조 객체들이 모두 준비되었습니다.\n" ] } ], "source": [ "self.prepare_auxiliaries()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2. 신호이력 테이블을 변환합니다.\n" ] } ], "source": [ "self.process_history()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "phases = pd.read_csv(os.path.join(self.path_tables, 'TL_IF_SIGL.csv'))\n", "phases = phases.drop(columns=['FRST_REG_DT', 'RINGA_FLOW', 'RINGB_FLOW'])\n", "phases = phases.rename(columns={\n", " 'PHASE_DT':'start_unix','CRSRD_ID':'inter_no',\n", " 'RINGA_PHASE':'phas_A', 'RINGB_PHASE':'phas_B',\n", " 'MAP_MODE':'STOS_NO'\n", " })\n", "isp2move_A = self.isp2move['A']\n", "isp2move_B = self.isp2move['B']\n", "phases['move_A'] = phases.apply(\n", " lambda row: int(isp2move_A.get((row.inter_no, row.STOS_NO, row.phas_A), -1)), axis=1)\n", "phases['move_B'] = phases.apply(\n", " lambda row: int(isp2move_B.get((row.inter_no, row.STOS_NO, row.phas_B), -1)), axis=1)\n", "phases['start_unix'] = phases['start_unix'].apply(lambda x:int(datetime.strptime(x, '%Y-%m-%d %H:%M:%S').timestamp()))\n", "phases = phases[phases.inter_no.isin(self.inter_nos)].reset_index(drop=True)\n", "phases = phases.drop(columns='STOS_NO')\n", "\n", "# - 아래 절차를 5초마다 반복\n", "fsecs = list(range(self.present_time - self.sim_timespan, self.present_time + 1, 5))\n", "for fsec in range(self.present_time - self.sim_timespan, self.present_time + 1, 5): # fsec : unix time by Five SECond\n", " # 1. 상태 테이블 조회해서 전체 데이터중 필요데이터(교차로번호, A링 현시번호, A링 이동류번호, B링 현시번호, B링 이동류번호)만 수집 : A\n", " moves = [group.iloc[-1:] for _, group in phases[phases.start_unix <= fsec].groupby('inter_no')]\n", " if not moves:\n", " move = pd.DataFrame({'start_unix':[], 'inter_no':[], 'phas_A':[], 'phas_B':[], 'move_A':[], 'move_B':[]})\n", " pass\n", " else:\n", " move = pd.concat(moves)\n", " move = move.drop(columns='start_unix')\n", " # move = pd.read_csv(os.path.join(self.path_tables, 'move', f'move_{fsec}.csv'))\n", " # 2. 이력 테이블 조회해서 교차로별로 유닉스시간 최대인 데이터(교차로번호, 종료유닉스타임)만 수집 : B\n", " recent_histories = [group.iloc[-1:] for _, group in self.history[self.history['end_unix'] < fsec].groupby('inter_no')] # 교차로별로 유닉스시간이 최대인 행들\n", "\n", " if not recent_histories:\n", " rhistory = pd.DataFrame({'inter_no':[], 'end_unix':[]}) # recent history\n", " else:\n", " rhistory = pd.concat(recent_histories)\n", " recent_unix = rhistory[['inter_no', 'end_unix']]\n", " # 3. 상태 테이블 조회정보(A)와 이력 테이블 조회정보(B) 조인(키값 : 교차로번호) : C\n", " move = pd.merge(move, recent_unix, how='left', on='inter_no')\n", " # move['end_unix'] = move['end_unix'].fillna(0).astype(int)\n", " # 4. C데이터 프레임에 신규 컬럼(시작 유닉스타임) 생성 후 종료유닉스 타임 값 입력, 종료 유닉스 타임 컬럼 제거\n", " move = move.rename(columns = {'end_unix':'start_unix'})\n", " # 5. 이동류 이력정보 READ\n", " # - CSV 파일로 서버에 저장된 이동류정보를 읽어옴(파일이 없는 경우에는 데이터가 없는 프레임 D 생성)\n", " try:\n", " movement # movement가 존재할 경우 그걸 그대로 씀.\n", " except NameError: # movement가 존재하지 않는 경우 생성\n", " movement = pd.DataFrame()\n", " # 6. 이동류 이력정보 데이터테이블(D)에 C데이터 add\n", " movement = pd.concat([movement, move])\n", " # 7. D데이터 프레임에서 중복데이터 제거(교차로번호, 시작 유닉스타임, A링 현시번호, B링 현시번호 같은 행은 제거)\n", " movement = movement.drop_duplicates(['inter_no','phas_A','phas_B','start_unix'])\n", " # # 8. D데이터 보관 시간 기준시간을 시작 유닉스 타임의 최대값 - self.subtractor // 2을 값으로 산출하고, 보관 시간 기준시간보다 작은 시작 유닉스 타임을 가진 행은 모두 제거(1시간 데이터만 보관)\n", " movement = movement[movement.start_unix > fsec - self.subtractor // 2]\n", "movement['start_unix'] = movement['start_unix'].astype(int)\n", "self.movement = movement.reset_index(drop=True)\n", "display(self.movement.head())" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "self.update_movement()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# 4-1\n", "self.merge_dfs()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# 4-2\n", "self.assign_signals()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# 4-3\n", "self.attach_children()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " inter_no node_id start_unix phas_A phas_B move_A move_B duration \\\n", "0 457 106234 1722383873 1 1 6 2 45 \n", "1 457 106234 1722383873 2 2 5 1 20 \n", "2 457 106234 1722383873 3 3 17 17 40 \n", "3 457 106234 1722383873 4 4 8 3 35 \n", "4 457 106234 1722383873 5 5 7 4 40 \n", "5 456 106231 1722383877 1 1 5 2 15 \n", "6 456 106231 1722383877 2 2 6 2 28 \n", "7 456 106231 1722383877 3 3 7 7 12 \n", "8 456 106231 1722383877 4 4 17 17 35 \n", "9 455 109901 1722383886 1 1 6 2 63 \n", "10 442 106332 1722383898 1 1 6 1 31 \n", "11 442 106332 1722383898 2 2 6 2 27 \n", "12 442 106332 1722383898 3 3 5 2 32 \n", "13 442 106332 1722383898 4 4 7 4 49 \n", "14 442 106332 1722383898 5 5 8 3 45 \n", "15 442 106332 1722383898 6 6 8 3 16 \n", "16 443 108769 1722383902 1 1 6 2 125 \n", "17 443 108769 1722383902 2 2 5 2 25 \n", "18 443 108769 1722383902 3 3 7 18 50 \n", "19 456 106231 1722383967 1 1 5 2 15 \n", "20 456 106231 1722383967 2 2 6 2 28 \n", "21 456 106231 1722383967 3 3 7 -1 12 \n", "22 456 106231 1722383967 4 4 17 -1 35 \n", "23 456 106231 1722383968 1 1 5 2 15 \n", "24 456 106231 1722383968 2 2 6 2 28 \n", "25 456 106231 1722383968 3 3 7 7 12 \n", "26 456 106231 1722383968 4 4 17 17 35 \n", "27 455 109901 1722383975 1 1 6 2 63 \n", "28 455 109901 1722383976 1 1 6 2 63 \n", "29 455 109901 1722383976 2 2 -1 -1 27 \n", "30 437 109986 1722384007 1 1 6 2 77 \n", "31 437 109986 1722384007 2 2 5 1 28 \n", "32 437 109986 1722384007 3 3 8 3 46 \n", "33 437 109986 1722384007 4 4 7 4 49 \n", "34 436 109836 1722384036 1 1 5 2 50 \n", "35 436 109836 1722384036 2 2 8 3 85 \n", "36 436 109836 1722384036 3 3 7 4 35 \n", "37 436 109836 1722384036 4 4 6 1 30 \n", "38 457 106234 1722384052 1 1 6 2 45 \n", "39 457 106234 1722384052 2 2 5 1 20 \n", "40 457 106234 1722384052 3 3 17 17 40 \n", "41 457 106234 1722384052 4 4 8 3 40 \n", "42 457 106234 1722384052 5 5 7 4 35 \n", "43 456 106231 1722384057 1 1 5 2 15 \n", "44 456 106231 1722384057 2 2 6 2 28 \n", "45 456 106231 1722384057 3 3 7 7 12 \n", "46 456 106231 1722384057 4 4 17 17 35 \n", "47 456 106231 1722384058 1 1 5 2 15 \n", "48 456 106231 1722384058 2 2 6 2 28 \n", "49 456 106231 1722384058 3 3 7 7 12 \n", "50 456 106231 1722384058 4 4 17 17 35 \n", "51 455 109901 1722384066 1 1 6 2 63 \n", "52 455 109901 1722384067 1 1 6 2 63 \n", "53 442 106332 1722384097 1 1 6 1 35 \n", "54 442 106332 1722384097 2 2 6 2 27 \n", "55 442 106332 1722384097 3 3 5 2 30 \n", "56 442 106332 1722384097 4 4 7 4 47 \n", "57 442 106332 1722384097 5 5 8 3 45 \n", "58 442 106332 1722384097 6 6 8 3 16 \n", "59 443 108769 1722384102 1 1 6 2 125 \n", "60 443 108769 1722384102 2 2 5 2 25 \n", "61 443 108769 1722384102 3 3 7 18 50 \n", "62 456 106231 1722384147 1 1 5 2 15 \n", "63 456 106231 1722384147 1 1 5 2 15 \n", "64 456 106231 1722384147 2 2 6 2 28 \n", "65 456 106231 1722384147 2 2 6 2 28 \n", "66 456 106231 1722384147 3 3 7 7 12 \n", "67 456 106231 1722384147 3 3 7 7 12 \n", "68 456 106231 1722384147 4 4 17 17 35 \n", "69 456 106231 1722384147 4 4 17 17 35 \n", "70 455 109901 1722384155 1 1 6 2 63 \n", "71 455 109901 1722384156 1 1 6 2 63 \n", "72 457 106234 1722384232 1 1 6 2 45 \n", "73 457 106234 1722384232 2 2 5 1 20 \n", "74 457 106234 1722384232 3 3 17 17 40 \n", "75 436 109836 1722384236 1 1 5 2 50 \n", "76 436 109836 1722384236 2 2 8 3 85 \n", "77 456 106231 1722384237 1 1 5 2 15 \n", "78 456 106231 1722384237 2 2 6 2 28 \n", "79 456 106231 1722384237 3 3 7 7 12 \n", "80 456 106231 1722384237 4 4 17 17 35 \n", "81 455 109901 1722384246 1 1 6 2 63 \n", "82 442 106332 1722384297 1 1 6 1 35 \n", "83 443 108769 1722384302 1 1 6 2 125 \n", "84 443 108769 1722384302 2 2 5 2 25 \n", "85 443 108769 1722384302 3 3 7 18 50 \n", "86 456 106231 1722384327 1 1 5 2 15 \n", "87 456 106231 1722384327 2 2 6 2 28 \n", "88 456 106231 1722384327 3 3 7 7 12 \n", "89 456 106231 1722384327 4 4 17 17 35 \n", "90 455 109901 1722384336 1 1 6 2 63 \n", "91 457 106234 1722384412 1 1 6 2 45 \n", "92 457 106234 1722384412 2 2 5 1 20 \n", "93 457 106234 1722384412 3 3 17 17 40 \n", "94 457 106234 1722384412 4 4 8 3 40 \n", "95 457 106234 1722384412 5 5 7 4 35 \n", "96 456 106231 1722384417 1 1 5 2 15 \n", "97 456 106231 1722384417 2 2 6 2 28 \n", "98 456 106231 1722384417 3 3 7 7 12 \n", "99 456 106231 1722384417 4 4 17 17 35 \n", "100 455 109901 1722384426 1 1 6 2 63 \n", "101 436 109836 1722384436 1 1 5 2 50 \n", "102 436 109836 1722384436 2 2 8 3 85 \n", "103 436 109836 1722384436 3 3 7 4 35 \n", "104 436 109836 1722384436 4 4 6 1 30 \n", "105 442 106332 1722384497 1 1 6 1 35 \n", "106 442 106332 1722384497 2 2 6 2 27 \n", "107 442 106332 1722384497 3 3 5 2 30 \n", "108 442 106332 1722384497 4 4 7 4 47 \n", "109 442 106332 1722384497 5 5 8 3 45 \n", "110 442 106332 1722384497 6 6 8 3 16 \n", "111 443 108769 1722384502 1 1 6 2 125 \n", "112 443 108769 1722384502 2 2 5 2 25 \n", "113 443 108769 1722384502 3 3 7 18 50 \n", "114 456 106231 1722384507 1 1 5 2 15 \n", "115 456 106231 1722384507 2 2 6 2 28 \n", "116 456 106231 1722384507 3 3 7 7 12 \n", "117 456 106231 1722384507 4 4 17 17 35 \n", "118 455 109901 1722384516 1 1 6 2 63 \n", "119 457 106234 1722384592 1 1 6 2 45 \n", "120 457 106234 1722384592 2 2 5 1 20 \n", "121 457 106234 1722384592 3 3 17 17 40 \n", "122 457 106234 1722384592 4 4 8 3 40 \n", "123 457 106234 1722384592 5 5 7 4 35 \n", "124 456 106231 1722384597 1 1 5 2 15 \n", "125 456 106231 1722384597 2 2 6 2 28 \n", "126 456 106231 1722384597 3 3 7 7 12 \n", "127 456 106231 1722384597 4 4 17 17 35 \n", "128 455 109901 1722384606 1 1 6 2 63 \n", "129 436 109836 1722384636 1 1 5 2 50 \n", "130 436 109836 1722384636 2 2 8 3 85 \n", "131 436 109836 1722384636 3 3 7 4 35 \n", "132 436 109836 1722384636 4 4 6 1 30 \n", "133 456 106231 1722384687 1 1 5 2 15 \n", "134 456 106231 1722384687 2 2 6 2 28 \n", "135 456 106231 1722384687 3 3 7 7 12 \n", "136 456 106231 1722384687 4 4 17 17 35 \n", "137 455 109901 1722384696 1 1 6 2 63 \n", "138 442 106332 1722384697 1 1 6 1 35 \n", "139 442 106332 1722384697 2 2 6 2 27 \n", "140 442 106332 1722384697 3 3 5 2 30 \n", "141 442 106332 1722384697 4 4 7 4 47 \n", "142 442 106332 1722384697 5 5 8 3 45 \n", "143 442 106332 1722384697 6 6 8 3 16 \n", "144 457 106234 1722384700 1 1 6 2 27 \n", "145 457 106234 1722384700 2 2 5 1 12 \n", "146 457 106234 1722384700 3 3 17 17 24 \n", "147 457 106234 1722384700 4 4 8 3 24 \n", "148 457 106234 1722384700 5 5 7 4 21 \n", "149 443 108769 1722384700 1 1 6 2 124 \n", "150 443 108769 1722384700 2 2 5 2 25 \n", "151 443 108769 1722384700 3 3 7 18 49 \n", "152 437 109986 1722384700 1 1 6 2 71 \n", "153 437 109986 1722384700 2 2 5 1 28 \n", "154 437 109986 1722384700 3 3 8 3 58 \n", "155 437 109986 1722384700 4 4 7 4 43 \n", "\n", " state_A state_B \n", "0 gGGGrgrrrrgrrrrgrrr grrrrgrrrrgGGGrgrrr \n", "1 grrrrgrrrrgrrrGgrrr grrrGgrrrrgrrrrgrrr \n", "2 grrrrgrrrrgrrrrgrrr grrrrgrrrrgrrrrgrrr \n", "3 grrrrgGGrrgrrrrgrrr grrrrgrrGGgrrrrgrrr \n", "4 grrrrgrrrrgrrrrgrrG grrrrgrrrrgrrrrgGGr \n", "5 grgrrrrG grgrrGGr \n", "6 grgGGrrr grgrrGGr \n", "7 gGgrrrrr gGgrrrrr \n", "8 grgrrrrr grgrrrrr \n", "9 GGrr rrGG \n", "10 gGGGrrrrrrgrrgrrrrrrrrgrrrr grrrrGGrrrgrrgrrrrrrrrgrrrr \n", "11 gGGGrrrrrrgrrgrrrrrrrrgrrrr grrrrrrrrrgrrgrGGGGGrrgrrrr \n", "12 grrrrrrrrrgrrgrrrrrrGGgrrrr grrrrrrrrrgrrgrGGGGGrrgrrrr \n", "13 grrrrrrrrrgrrgrrrrrrrrgrrrG grrrrrrrrrgrrgrrrrrrrrgGGGr \n", "14 grrrrrrGGrgrrgrrrrrrrrgrrrr grrrrrrrrGgrrgrrrrrrrrgrrrr \n", "15 grrrrrrGGrgrrgrrrrrrrrgrrrr grrrrrrrrGgrrgrrrrrrrrgrrrr \n", "16 gGGGGGrrrrrrgrr grrrrrGGGGGrgrr \n", "17 grrrrrrrrrrGgrr grrrrrGGGGGrgrr \n", "18 grrrrrrrrrrrgGG grrrrrrrrrrrgrr \n", "19 grgrrrrG grgrrGGr \n", "20 grgGGrrr grgrrGGr \n", "21 gGgrrrrr grgrrrrr \n", "22 grgrrrrr grgrrrrr \n", "23 grgrrrrG grgrrGGr \n", "24 grgGGrrr grgrrGGr \n", "25 gGgrrrrr gGgrrrrr \n", "26 grgrrrrr grgrrrrr \n", "27 GGrr rrGG \n", "28 GGrr rrGG \n", "29 rrrr rrrr \n", "30 gGGGGrgrrgrrrrrgrr grrrrrgrrgGGGGrgrr \n", "31 grrrrrgrrgrrrrGgrr grrrrGgrrgrrrrrgrr \n", "32 grrrrrgGrgrrrrrgrr grrrrrgrGgrrrrrgrr \n", "33 grrrrrgrrgrrrrrgrG grrrrrgrrgrrrrrgGr \n", "34 grrrrgrrgrGgrr grrrrgrrgGrgrr \n", "35 grrrrgGrgrrgrr grrrrgrGgrrgrr \n", "36 grrrrgrrgrrgrG grrrrgrrgrrgGr \n", "37 gGGGrgrrgrrgrr grrrGgrrgrrgrr \n", "38 gGGGrgrrrrgrrrrgrrr grrrrgrrrrgGGGrgrrr \n", "39 grrrrgrrrrgrrrGgrrr grrrGgrrrrgrrrrgrrr \n", "40 grrrrgrrrrgrrrrgrrr grrrrgrrrrgrrrrgrrr \n", "41 grrrrgGGrrgrrrrgrrr grrrrgrrGGgrrrrgrrr \n", "42 grrrrgrrrrgrrrrgrrG grrrrgrrrrgrrrrgGGr \n", "43 grgrrrrG grgrrGGr \n", "44 grgGGrrr grgrrGGr \n", "45 gGgrrrrr gGgrrrrr \n", "46 grgrrrrr grgrrrrr \n", "47 grgrrrrG grgrrGGr \n", "48 grgGGrrr grgrrGGr \n", "49 gGgrrrrr gGgrrrrr \n", "50 grgrrrrr grgrrrrr \n", "51 GGrr rrGG \n", "52 GGrr rrGG \n", "53 gGGGrrrrrrgrrgrrrrrrrrgrrrr grrrrGGrrrgrrgrrrrrrrrgrrrr \n", "54 gGGGrrrrrrgrrgrrrrrrrrgrrrr grrrrrrrrrgrrgrGGGGGrrgrrrr \n", "55 grrrrrrrrrgrrgrrrrrrGGgrrrr grrrrrrrrrgrrgrGGGGGrrgrrrr \n", "56 grrrrrrrrrgrrgrrrrrrrrgrrrG grrrrrrrrrgrrgrrrrrrrrgGGGr \n", "57 grrrrrrGGrgrrgrrrrrrrrgrrrr grrrrrrrrGgrrgrrrrrrrrgrrrr \n", "58 grrrrrrGGrgrrgrrrrrrrrgrrrr grrrrrrrrGgrrgrrrrrrrrgrrrr \n", "59 gGGGGGrrrrrrgrr grrrrrGGGGGrgrr \n", "60 grrrrrrrrrrGgrr grrrrrGGGGGrgrr \n", "61 grrrrrrrrrrrgGG grrrrrrrrrrrgrr \n", "62 grgrrrrG grgrrGGr \n", "63 grgrrrrG grgrrGGr \n", "64 grgGGrrr grgrrGGr \n", "65 grgGGrrr grgrrGGr \n", "66 gGgrrrrr gGgrrrrr \n", "67 gGgrrrrr gGgrrrrr \n", "68 grgrrrrr grgrrrrr \n", "69 grgrrrrr grgrrrrr \n", "70 GGrr rrGG \n", "71 GGrr rrGG \n", "72 gGGGrgrrrrgrrrrgrrr grrrrgrrrrgGGGrgrrr \n", "73 grrrrgrrrrgrrrGgrrr grrrGgrrrrgrrrrgrrr \n", "74 grrrrgrrrrgrrrrgrrr grrrrgrrrrgrrrrgrrr \n", "75 grrrrgrrgrGgrr grrrrgrrgGrgrr \n", "76 grrrrgGrgrrgrr grrrrgrGgrrgrr \n", "77 grgrrrrG grgrrGGr \n", "78 grgGGrrr grgrrGGr \n", "79 gGgrrrrr gGgrrrrr \n", "80 grgrrrrr grgrrrrr \n", "81 GGrr rrGG \n", "82 gGGGrrrrrrgrrgrrrrrrrrgrrrr grrrrGGrrrgrrgrrrrrrrrgrrrr \n", "83 gGGGGGrrrrrrgrr grrrrrGGGGGrgrr \n", "84 grrrrrrrrrrGgrr grrrrrGGGGGrgrr \n", "85 grrrrrrrrrrrgGG grrrrrrrrrrrgrr \n", "86 grgrrrrG grgrrGGr \n", "87 grgGGrrr grgrrGGr \n", "88 gGgrrrrr gGgrrrrr \n", "89 grgrrrrr grgrrrrr \n", "90 GGrr rrGG \n", "91 gGGGrgrrrrgrrrrgrrr grrrrgrrrrgGGGrgrrr \n", "92 grrrrgrrrrgrrrGgrrr grrrGgrrrrgrrrrgrrr \n", "93 grrrrgrrrrgrrrrgrrr grrrrgrrrrgrrrrgrrr \n", "94 grrrrgGGrrgrrrrgrrr grrrrgrrGGgrrrrgrrr \n", "95 grrrrgrrrrgrrrrgrrG grrrrgrrrrgrrrrgGGr \n", "96 grgrrrrG grgrrGGr \n", "97 grgGGrrr grgrrGGr \n", "98 gGgrrrrr gGgrrrrr \n", "99 grgrrrrr grgrrrrr \n", "100 GGrr rrGG \n", "101 grrrrgrrgrGgrr grrrrgrrgGrgrr \n", "102 grrrrgGrgrrgrr grrrrgrGgrrgrr \n", "103 grrrrgrrgrrgrG grrrrgrrgrrgGr \n", "104 gGGGrgrrgrrgrr grrrGgrrgrrgrr \n", "105 gGGGrrrrrrgrrgrrrrrrrrgrrrr grrrrGGrrrgrrgrrrrrrrrgrrrr \n", "106 gGGGrrrrrrgrrgrrrrrrrrgrrrr grrrrrrrrrgrrgrGGGGGrrgrrrr \n", "107 grrrrrrrrrgrrgrrrrrrGGgrrrr grrrrrrrrrgrrgrGGGGGrrgrrrr \n", "108 grrrrrrrrrgrrgrrrrrrrrgrrrG grrrrrrrrrgrrgrrrrrrrrgGGGr \n", "109 grrrrrrGGrgrrgrrrrrrrrgrrrr grrrrrrrrGgrrgrrrrrrrrgrrrr \n", "110 grrrrrrGGrgrrgrrrrrrrrgrrrr grrrrrrrrGgrrgrrrrrrrrgrrrr \n", "111 gGGGGGrrrrrrgrr grrrrrGGGGGrgrr \n", "112 grrrrrrrrrrGgrr grrrrrGGGGGrgrr \n", "113 grrrrrrrrrrrgGG grrrrrrrrrrrgrr \n", "114 grgrrrrG grgrrGGr \n", "115 grgGGrrr grgrrGGr \n", "116 gGgrrrrr gGgrrrrr \n", "117 grgrrrrr grgrrrrr \n", "118 GGrr rrGG \n", "119 gGGGrgrrrrgrrrrgrrr grrrrgrrrrgGGGrgrrr \n", "120 grrrrgrrrrgrrrGgrrr grrrGgrrrrgrrrrgrrr \n", "121 grrrrgrrrrgrrrrgrrr grrrrgrrrrgrrrrgrrr \n", "122 grrrrgGGrrgrrrrgrrr grrrrgrrGGgrrrrgrrr \n", "123 grrrrgrrrrgrrrrgrrG grrrrgrrrrgrrrrgGGr \n", "124 grgrrrrG grgrrGGr \n", "125 grgGGrrr grgrrGGr \n", "126 gGgrrrrr gGgrrrrr \n", "127 grgrrrrr grgrrrrr \n", "128 GGrr rrGG \n", "129 grrrrgrrgrGgrr grrrrgrrgGrgrr \n", "130 grrrrgGrgrrgrr grrrrgrGgrrgrr \n", "131 grrrrgrrgrrgrG grrrrgrrgrrgGr \n", "132 gGGGrgrrgrrgrr grrrGgrrgrrgrr \n", "133 grgrrrrG grgrrGGr \n", "134 grgGGrrr grgrrGGr \n", "135 gGgrrrrr gGgrrrrr \n", "136 grgrrrrr grgrrrrr \n", "137 GGrr rrGG \n", "138 gGGGrrrrrrgrrgrrrrrrrrgrrrr grrrrGGrrrgrrgrrrrrrrrgrrrr \n", "139 gGGGrrrrrrgrrgrrrrrrrrgrrrr grrrrrrrrrgrrgrGGGGGrrgrrrr \n", "140 grrrrrrrrrgrrgrrrrrrGGgrrrr grrrrrrrrrgrrgrGGGGGrrgrrrr \n", "141 grrrrrrrrrgrrgrrrrrrrrgrrrG grrrrrrrrrgrrgrrrrrrrrgGGGr \n", "142 grrrrrrGGrgrrgrrrrrrrrgrrrr grrrrrrrrGgrrgrrrrrrrrgrrrr \n", "143 grrrrrrGGrgrrgrrrrrrrrgrrrr grrrrrrrrGgrrgrrrrrrrrgrrrr \n", "144 gGGGrgrrrrgrrrrgrrr grrrrgrrrrgGGGrgrrr \n", "145 grrrrgrrrrgrrrGgrrr grrrGgrrrrgrrrrgrrr \n", "146 grrrrgrrrrgrrrrgrrr grrrrgrrrrgrrrrgrrr \n", "147 grrrrgGGrrgrrrrgrrr grrrrgrrGGgrrrrgrrr \n", "148 grrrrgrrrrgrrrrgrrG grrrrgrrrrgrrrrgGGr \n", "149 gGGGGGrrrrrrgrr grrrrrGGGGGrgrr \n", "150 grrrrrrrrrrGgrr grrrrrGGGGGrgrr \n", "151 grrrrrrrrrrrgGG grrrrrrrrrrrgrr \n", "152 gGGGGrgrrgrrrrrgrr grrrrrgrrgGGGGrgrr \n", "153 grrrrrgrrgrrrrGgrr grrrrGgrrgrrrrrgrr \n", "154 grrrrrgGrgrrrrrgrr grrrrrgrGgrrrrrgrr \n", "155 grrrrrgrrgrrrrrgrG grrrrrgrrgrrrrrgGr " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n", " display(self.histids)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# 5-1\n", "self.set_timepoints()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['106231',\n", " '106234',\n", " '106238',\n", " '106332',\n", " '106350',\n", " '108769',\n", " '109836',\n", " '109901',\n", " '109986']" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "self.node_ids" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['107587', '109295', '109296', '109297', '109313', '109333']" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "self.uturn_ids" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(self.parent_ids)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "c:\\github\\siggen\\scripts\\generate_signals.py:801: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", " SIG[['phas_A', 'state_A']] = SIG[['phas_A', 'state_A']].fillna(method='ffill')\n", "c:\\github\\siggen\\scripts\\generate_signals.py:802: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", " SIG[['phas_B', 'state_B']] = SIG[['phas_B', 'state_B']].fillna(method='ffill')\n", "c:\\github\\siggen\\scripts\\generate_signals.py:801: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", " SIG[['phas_A', 'state_A']] = SIG[['phas_A', 'state_A']].fillna(method='ffill')\n", "c:\\github\\siggen\\scripts\\generate_signals.py:802: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", " SIG[['phas_B', 'state_B']] = SIG[['phas_B', 'state_B']].fillna(method='ffill')\n" ] }, { "ename": "KeyError", "evalue": "\"Only a column name can be used for the key in a dtype mappings argument. 'red_A' not found in columns.\"", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[19], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# 5-2. 적색 및 황색신호 부여\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43massign_red_yellow\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[1;32mc:\\github\\siggen\\scripts\\generate_signals.py:789\u001b[0m, in \u001b[0;36mSignalGenerator.assign_red_yellow\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 787\u001b[0m yellow_B \u001b[38;5;241m=\u001b[39m prow[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124myellow_B\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mphas_B\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m 788\u001b[0m sig\u001b[38;5;241m.\u001b[39mloc[i, [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mred_A\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mred_B\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124myellow_A\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124myellow_B\u001b[39m\u001b[38;5;124m'\u001b[39m]] \u001b[38;5;241m=\u001b[39m red_A, red_B, yellow_A, yellow_B\n\u001b[1;32m--> 789\u001b[0m sig \u001b[38;5;241m=\u001b[39m \u001b[43msig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mred_A\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mred_B\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43myellow_A\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43myellow_B\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 790\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mphas_A\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mphas_B\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 791\u001b[0m sig \u001b[38;5;241m=\u001b[39m sig\u001b[38;5;241m.\u001b[39mdrop([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmove_A\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmove_B\u001b[39m\u001b[38;5;124m'\u001b[39m], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m 793\u001b[0m sig_A \u001b[38;5;241m=\u001b[39m sig[[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstart_unix\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mphas_A\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mduration\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstate_A\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mred_A\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124myellow_A\u001b[39m\u001b[38;5;124m'\u001b[39m]]\u001b[38;5;241m.\u001b[39mreset_index(drop\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", "File \u001b[1;32mc:\\github\\siggen\\siggen_env\\Lib\\site-packages\\pandas\\core\\generic.py:6605\u001b[0m, in \u001b[0;36mNDFrame.astype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m 6603\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m col_name \u001b[38;5;129;01min\u001b[39;00m dtype_ser\u001b[38;5;241m.\u001b[39mindex:\n\u001b[0;32m 6604\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m col_name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[1;32m-> 6605\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\n\u001b[0;32m 6606\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOnly a column name can be used for the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 6607\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkey in a dtype mappings argument. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 6608\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcol_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m not found in columns.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 6609\u001b[0m )\n\u001b[0;32m 6611\u001b[0m dtype_ser \u001b[38;5;241m=\u001b[39m dtype_ser\u001b[38;5;241m.\u001b[39mreindex(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns, fill_value\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m 6613\u001b[0m results \u001b[38;5;241m=\u001b[39m []\n", "\u001b[1;31mKeyError\u001b[0m: \"Only a column name can be used for the key in a dtype mappings argument. 'red_A' not found in columns.\"" ] } ], "source": [ "# 5-2. 적색 및 황색신호 부여\n", "self.assign_red_yellow()" ] } ], "metadata": { "kernelspec": { "display_name": "siggen_env", "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": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 2 }