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675 lines
26 KiB
675 lines
26 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 pandas as pd\n",
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"import numpy as np\n",
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"import sys\n",
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"sys.path.append('../../Scripts')\n",
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"from preprocess_daily import DailyPreprocessor\n",
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"from generate_signals import SignalGenerator"
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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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"1. 데이터를 로드합니다.\n",
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"1-1. 네트워크가 로드되었습니다.\n",
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"1-2. 테이블들이 로드되었습니다.\n",
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"1-3. 네트워크의 모든 clean state requirement들을 체크했습니다.\n",
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"1-4. 테이블들의 무결성 검사를 완료했습니다.\n",
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"1-5. 주요 객체 (리스트, 딕셔너리)들을 저장했습니다.\n"
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]
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}
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],
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"source": [
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"self = DailyPreprocessor()\n",
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"self.load_data()\n",
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"self.make_match1()\n",
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"self.make_match2()\n",
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"self.make_match3()\n",
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"self.make_match4()\n",
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"self.make_match5()\n",
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"self.make_match6()"
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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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{
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"data": {
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"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",
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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>inter_no</th>\n",
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" <th>phase_no</th>\n",
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" <th>ring_type</th>\n",
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" <th>move_no</th>\n",
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" <th>inc_dire</th>\n",
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" <th>out_dire</th>\n",
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" <th>inc_angle</th>\n",
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" <th>out_angle</th>\n",
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" <th>inc_edge_id</th>\n",
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" <th>out_edge_id</th>\n",
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" <th>node_id</th>\n",
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" <th>turn_type</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>175</td>\n",
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" <td>1</td>\n",
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" <td>A</td>\n",
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" <td>8</td>\n",
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" <td>남</td>\n",
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" <td>북</td>\n",
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" <td>179</td>\n",
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" <td>000</td>\n",
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" <td>-571542797_02</td>\n",
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" <td>571500487_01</td>\n",
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" <td>i0</td>\n",
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" <td>straight</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>175</td>\n",
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" <td>1</td>\n",
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" <td>B</td>\n",
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" <td>4</td>\n",
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" <td>북</td>\n",
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" <td>남</td>\n",
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" <td>001</td>\n",
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" <td>180</td>\n",
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" <td>-571500487_01</td>\n",
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" <td>571542797_02</td>\n",
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" <td>i0</td>\n",
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" <td>straight</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>175</td>\n",
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" <td>2</td>\n",
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" <td>A</td>\n",
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" <td>7</td>\n",
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" <td>북</td>\n",
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" <td>동</td>\n",
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" <td>001</td>\n",
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" <td>090</td>\n",
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" <td>-571500487_01</td>\n",
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" <td>571545870_01</td>\n",
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" <td>i0</td>\n",
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" <td>left</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>175</td>\n",
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" <td>2</td>\n",
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" <td>B</td>\n",
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" <td>3</td>\n",
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" <td>남</td>\n",
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" <td>서</td>\n",
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" <td>179</td>\n",
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" <td>270</td>\n",
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" <td>-571542797_02</td>\n",
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" <td>571510153_01</td>\n",
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" <td>i0</td>\n",
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" <td>left</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>175</td>\n",
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" <td>3</td>\n",
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" <td>A</td>\n",
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" <td>6</td>\n",
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" <td>동</td>\n",
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" <td>서</td>\n",
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" <td>090</td>\n",
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" <td>270</td>\n",
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" <td>571545870_02</td>\n",
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" <td>571510153_01</td>\n",
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" <td>i0</td>\n",
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" <td>straight</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>103</th>\n",
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" <td>210</td>\n",
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" <td>2</td>\n",
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" <td>B</td>\n",
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" <td>2</td>\n",
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" <td>서</td>\n",
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" <td>동</td>\n",
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" <td>270</td>\n",
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" <td>090</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>u60</td>\n",
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" <td>straight</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>104</th>\n",
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" <td>210</td>\n",
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" <td>3</td>\n",
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" <td>A</td>\n",
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" <td>7</td>\n",
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" <td>북</td>\n",
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" <td>동</td>\n",
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" <td>000</td>\n",
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" <td>090</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>u60</td>\n",
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" <td>left</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>105</th>\n",
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" <td>210</td>\n",
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" <td>3</td>\n",
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" <td>B</td>\n",
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" <td>4</td>\n",
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" <td>북</td>\n",
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" <td>남</td>\n",
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" <td>000</td>\n",
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" <td>180</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>u60</td>\n",
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" <td>straight</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>106</th>\n",
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" <td>210</td>\n",
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" <td>4</td>\n",
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" <td>A</td>\n",
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" <td>8</td>\n",
|
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" <td>남</td>\n",
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" <td>북</td>\n",
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" <td>180</td>\n",
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" <td>000</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>u60</td>\n",
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" <td>straight</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>107</th>\n",
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" <td>210</td>\n",
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" <td>4</td>\n",
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" <td>B</td>\n",
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" <td>3</td>\n",
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" <td>남</td>\n",
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" <td>서</td>\n",
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" <td>180</td>\n",
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" <td>270</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>u60</td>\n",
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" <td>left</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>108 rows × 12 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" inter_no phase_no ring_type move_no inc_dire out_dire inc_angle \\\n",
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"0 175 1 A 8 남 북 179 \n",
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"1 175 1 B 4 북 남 001 \n",
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"2 175 2 A 7 북 동 001 \n",
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"3 175 2 B 3 남 서 179 \n",
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"4 175 3 A 6 동 서 090 \n",
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".. ... ... ... ... ... ... ... \n",
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"103 210 2 B 2 서 동 270 \n",
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"104 210 3 A 7 북 동 000 \n",
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"105 210 3 B 4 북 남 000 \n",
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"106 210 4 A 8 남 북 180 \n",
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"107 210 4 B 3 남 서 180 \n",
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"\n",
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" out_angle inc_edge_id out_edge_id node_id turn_type \n",
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"0 000 -571542797_02 571500487_01 i0 straight \n",
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"1 180 -571500487_01 571542797_02 i0 straight \n",
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"2 090 -571500487_01 571545870_01 i0 left \n",
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"3 270 -571542797_02 571510153_01 i0 left \n",
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"4 270 571545870_02 571510153_01 i0 straight \n",
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".. ... ... ... ... ... \n",
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"103 090 NaN NaN u60 straight \n",
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"104 090 NaN NaN u60 left \n",
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"105 180 NaN NaN u60 straight \n",
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"106 000 NaN NaN u60 straight \n",
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"107 270 NaN NaN u60 left \n",
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"\n",
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"[108 rows x 12 columns]"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"self.match6"
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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": 10,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
|
|
"<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",
|
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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>inter_no</th>\n",
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" <th>phase_no</th>\n",
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" <th>ring_type</th>\n",
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" <th>move_no</th>\n",
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" <th>inc_dire</th>\n",
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" <th>out_dire</th>\n",
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" <th>inc_angle</th>\n",
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" <th>out_angle</th>\n",
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" <th>inc_edge_id</th>\n",
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" <th>out_edge_id</th>\n",
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" <th>node_id</th>\n",
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" <th>turn_type</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>80</th>\n",
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" <td>202</td>\n",
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" <td>1</td>\n",
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" <td>A</td>\n",
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" <td>6</td>\n",
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" <td>동</td>\n",
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" <td>서</td>\n",
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" <td>090</td>\n",
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" <td>270</td>\n",
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" <td>571510152_02</td>\n",
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" <td>-571510152_01</td>\n",
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" <td>i9</td>\n",
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" <td>straight</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>81</th>\n",
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" <td>202</td>\n",
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" <td>1</td>\n",
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" <td>B</td>\n",
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" <td>2</td>\n",
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" <td>서</td>\n",
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" <td>동</td>\n",
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" <td>270</td>\n",
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" <td>090</td>\n",
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" <td>571510152_01</td>\n",
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" <td>571510152_01.65</td>\n",
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" <td>i9</td>\n",
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" <td>straight</td>\n",
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" </tr>\n",
|
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" inter_no phase_no ring_type move_no inc_dire out_dire inc_angle \\\n",
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"80 202 1 A 6 동 서 090 \n",
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"81 202 1 B 2 서 동 270 \n",
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"\n",
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" out_angle inc_edge_id out_edge_id node_id turn_type \n",
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"80 270 571510152_02 -571510152_01 i9 straight \n",
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"81 090 571510152_01 571510152_01.65 i9 straight "
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"self.match6[self.match6.node_id==parent_id].dropna(subset=['inc_edge_id', 'out_edge_id'])"
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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": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"self.match7 = self.match6.copy()\n",
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"self.match7 = self.match7[['inter_no', 'node_id', 'move_no', 'inc_angle', 'out_angle', 'inc_dire', 'out_dire', 'inc_edge_id', 'out_edge_id']]\n",
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"# parent_id = self.parent_ids[1]\n",
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"\n",
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"for parent_id in self.parent_ids:\n",
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" m7 = self.match7[self.match7.node_id==parent_id].dropna(subset=['inc_edge_id', 'out_edge_id'])\n",
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" m6 = self.match6[self.match6.node_id==parent_id].dropna(subset=['inc_edge_id', 'out_edge_id'])\n",
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" inc_angles = [int(angle) for angle in np.unique(m7.inc_angle.values)]\n",
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" out_angles = [int(angle) for angle in np.unique(m7.out_angle.values)]\n",
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" # print(parent_id)\n",
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" # display(m6)\n",
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" # display(m7)\n",
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" # print(inc_angles)\n",
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" # print(out_angles)"
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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": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"# (1) 가능한 (진입방향, 진출방향) 목록 \n",
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"flows = self.nema.dropna().apply(lambda row: (row['inc_dire'], row['out_dire']), axis=1).tolist()\n",
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"# (2) 각 교차로별 방향 목록 : pdires (possible directions)\n",
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"pdires = {}\n",
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"for node_id in self.parent_ids:\n",
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" dires = self.match7[self.match7.node_id == node_id][['inc_dire','out_dire']].values.flatten()\n",
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" dires = {dire for dire in dires if type(dire)==str}\n",
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" pdires[node_id] = dires\n",
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"# (3) 각 (교차로, 진입방향) 별 진입id 목록 : inc2id (incoming direction to incoming edge_id)\n",
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"inc2id = {}\n",
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"for node_id in self.parent_ids:\n",
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" for inc_dire in pdires[node_id]:\n",
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" df = self.match7[(self.match7.node_id==node_id) & (self.match7.inc_dire==inc_dire)]\n",
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" inc2id[(node_id, inc_dire)] = df.inc_edge_id.iloc[0]\n",
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"# (4) 각 (교차로, 진출방향) 별 진출id 목록 : out2id (outgoing direction to outgoing edge_id)\n",
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"out2id = {}\n",
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"for node_id in self.parent_ids:\n",
|
|
" for out_dire in pdires[node_id]:\n",
|
|
" df = self.match7[(self.match7.node_id==node_id) & (self.match7.out_dire==out_dire)]\n",
|
|
" out2id[(node_id, out_dire)] = df.out_edge_id.iloc[0]\n",
|
|
"# (5) 각 교차로별 가능한 (진입방향, 진출방향) 목록 : pflow (possible flows)\n",
|
|
"pflow = {}\n",
|
|
"for node_id in self.parent_ids:\n",
|
|
" pflow[node_id] = [flow for flow in flows if set(flow).issubset(pdires[node_id])]"
|
|
]
|
|
},
|
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{
|
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"cell_type": "code",
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"execution_count": 51,
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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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"i0\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"{1: '571545870_02',\n",
|
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" 2: '571510153_02',\n",
|
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" 3: '-571542797_02',\n",
|
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" 4: '-571500487_01',\n",
|
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" 5: '571510153_02',\n",
|
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" 6: '571545870_02',\n",
|
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" 7: '-571500487_01',\n",
|
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" 8: '-571542797_02',\n",
|
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" 9: '-571500487_01',\n",
|
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" 10: '571510153_02',\n",
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" 11: '571545870_02',\n",
|
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" 12: '-571500487_01',\n",
|
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" 13: '571510153_02',\n",
|
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" 14: '-571500487_01',\n",
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" 15: '-571500487_01',\n",
|
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" 16: '571545870_02'}"
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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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{
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"data": {
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"text/plain": [
|
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"{1: '571542797_02',\n",
|
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" 2: '571545870_01',\n",
|
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" 3: '571510153_01',\n",
|
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" 4: '571542797_02',\n",
|
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" 5: '571500487_01',\n",
|
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" 6: '571510153_01',\n",
|
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" 7: '571545870_01',\n",
|
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" 8: '571500487_01',\n",
|
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" 9: '571545870_01',\n",
|
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" 10: '571500487_01',\n",
|
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" 11: '571510153_01',\n",
|
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" 12: '571545870_01',\n",
|
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" 13: '571500487_01',\n",
|
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" 14: '571510153_01',\n",
|
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" 15: '571500487_01',\n",
|
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" 16: '571500487_01'}"
|
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]
|
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},
|
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"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
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],
|
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"source": [
|
|
"p2am = dict() # parent id to a list of aligned movement numbers\n",
|
|
"for parent_id in self.parent_ids:\n",
|
|
" pnema = self.nema[self.nema.inc_dire.isin(pdires[parent_id]) & self.nema.out_dire.isin(pdires[parent_id])]\n",
|
|
" p2am[parent_id] = list(pnema.move_no)\n",
|
|
"\n",
|
|
"dire2vec = dict() # direction to unit vector\n",
|
|
"theta = np.pi/2\n",
|
|
"for dire in self.dires:\n",
|
|
" dire2vec[dire] = np.array([np.cos(theta), np.sin(theta)])\n",
|
|
" theta -= np.pi/4\n",
|
|
"\n",
|
|
"plain_move_nos = list(range(1,17)) # plain movement numbers\n",
|
|
"\n",
|
|
"p2pm2inc_edge_id = dict() # parent id to pm2inc_edge_id\n",
|
|
"p2pm2out_edge_id = dict() # parent id to pm2out_edge_id\n",
|
|
"for parent_id in self.parent_ids:\n",
|
|
" pm2inc_edge_id = dict() # plain movement to incoming edge id\n",
|
|
" pm2out_edge_id = dict() # plain movement to outgoing edge id\n",
|
|
" for pm in plain_move_nos:\n",
|
|
" row = self.nema[self.nema.move_no==pm].iloc[0]\n",
|
|
" inc_dire = row.inc_dire\n",
|
|
" out_dire = row.out_dire\n",
|
|
" inc_vec_true = dire2vec[inc_dire]\n",
|
|
" out_vec_true = dire2vec[out_dire]\n",
|
|
"\n",
|
|
" node = self.net.getNode(parent_id)\n",
|
|
" # 교차로의 모든 (from / to) edges\n",
|
|
" inc_edges = [edge for edge in node.getIncoming() if edge.getFunction() == ''] # incoming edges\n",
|
|
" out_edges = [edge for edge in node.getOutgoing() if edge.getFunction() == ''] # outgoing edges\n",
|
|
" # 교차로의 모든 (from / to) unit vector\n",
|
|
" inc_vecs = []\n",
|
|
" for inc_edge in inc_edges:\n",
|
|
" start = inc_edge.getShape()[-1]\n",
|
|
" end = inc_edge.getShape()[-2]\n",
|
|
" inc_vec = np.array(end) - np.array(start)\n",
|
|
" inc_vec = inc_vec / (inc_vec ** 2).sum() ** 0.5\n",
|
|
" inc_vecs.append(inc_vec)\n",
|
|
" out_vecs = []\n",
|
|
" for out_edge in out_edges:\n",
|
|
" start = out_edge.getShape()[0]\n",
|
|
" end = out_edge.getShape()[1]\n",
|
|
" out_vec = np.array(end) - np.array(start)\n",
|
|
" out_vec = out_vec / (out_vec ** 2).sum() ** 0.5\n",
|
|
" out_vecs.append(out_vec)\n",
|
|
" # 매칭 엣지 반환\n",
|
|
" inc_index = np.array([np.dot(inc_vec, inc_vec_true) for inc_vec in inc_vecs]).argmax()\n",
|
|
" out_index = np.array([np.dot(out_vec, out_vec_true) for out_vec in out_vecs]).argmax()\n",
|
|
" inc_edge_id = inc_edges[inc_index].getID()\n",
|
|
" out_edge_id = out_edges[out_index].getID()\n",
|
|
" pm2inc_edge_id[pm] = inc_edge_id\n",
|
|
" pm2out_edge_id[pm] = out_edge_id\n",
|
|
" p2pm2inc_edge_id[parent_id] = pm2inc_edge_id\n",
|
|
" p2pm2out_edge_id[parent_id] = pm2out_edge_id\n",
|
|
"\n",
|
|
"parent_id = self.parent_ids[0]\n",
|
|
"print(parent_id)\n",
|
|
"display(p2pm2inc_edge_id[parent_id])\n",
|
|
"display(p2pm2out_edge_id[parent_id])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 40,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"plain_move_nos = list(range(1,17)) # plain movement numbers\n",
|
|
"p2pm2inc_edge_id = dict() # parent id to pm2inc_edge_id\n",
|
|
"p2pm2out_edge_id = dict() # parent id to pm2out_edge_id\n",
|
|
"for parent_id in self.parent_ids:\n",
|
|
" pm2inc_angle = dict()\n",
|
|
" for pm in plain_move_nos:\n",
|
|
" row = self.nema[self.nema.move_no==pm].iloc[0]\n",
|
|
" # inc_dire = row.\n",
|
|
"# plain movement number(1-16) to (inc_angle, out_angle)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[('동', '남'), ('서', '동'), ('남', '서'), ('북', '남'), ('서', '북'), ('동', '서'), ('북', '동'), ('남', '북'), ('북동', '남동'), ('남서', '북동'), ('남동', '남서'), ('북서', '남동'), ('남서', '북서'), ('북동', '남서'), ('북서', '북동'), ('남동', '북서')]\n",
|
|
"{'i0': {'북', '남', '동', '서'}, 'i1': {'북', '남', '서'}, 'i2': {'북', '남'}, 'i3': {'북', '남', '동', '서'}, 'i6': {'북', '남', '동', '서'}, 'i7': {'북', '남'}, 'i8': {'북', '남', '서', '동'}, 'i9': {'동', '서'}}\n",
|
|
"{('i0', '북'): '-571500487_01', ('i0', '남'): '-571542797_02', ('i0', '동'): '571545870_02', ('i0', '서'): '571510153_02', ('i1', '북'): '571542797_02.99', ('i1', '남'): '-571542810_01', ('i1', '서'): '571543469_02', ('i2', '북'): '571542811_02', ('i2', '남'): '-571542809_01', ('i3', '북'): '571556450_02', ('i3', '남'): '571540304_02', ('i3', '동'): '-571500475_01', ('i3', '서'): '571540303_02.21', ('i6', '북'): '571511538_02.121', ('i6', '남'): '571500585_02', ('i6', '동'): '-571542115_01', ('i6', '서'): '571500535_02.18', ('i7', '북'): '571542073_01', ('i7', '남'): '-571511538_02', ('i8', '북'): '571500583_01', ('i8', '남'): '-571500569_01', ('i8', '서'): '571500618_02', ('i8', '동'): '571500617_02', ('i9', '동'): '571510152_02', ('i9', '서'): '571510152_01'}\n",
|
|
"{('i0', '북'): '571500487_01', ('i0', '남'): '571542797_02', ('i0', '동'): '571545870_01', ('i0', '서'): '571510153_01', ('i1', '북'): '-571542797_02.99', ('i1', '남'): '571542810_01', ('i1', '서'): '571543469_01', ('i2', '북'): '571542811_01', ('i2', '남'): '571542809_01', ('i3', '북'): '571556450_01', ('i3', '남'): '571540304_01', ('i3', '동'): '571500475_01', ('i3', '서'): '571540303_01', ('i6', '북'): '571511538_01', ('i6', '남'): '571500585_01', ('i6', '동'): '571542115_01', ('i6', '서'): '571500535_01', ('i7', '북'): '571542073_02', ('i7', '남'): '571511538_02', ('i8', '북'): '571500583_02', ('i8', '남'): '571500569_01', ('i8', '서'): '571500618_01', ('i8', '동'): '571500617_01', ('i9', '동'): '571510152_01.65', ('i9', '서'): '-571510152_01'}\n",
|
|
"{'i0': [('동', '남'), ('서', '동'), ('남', '서'), ('북', '남'), ('서', '북'), ('동', '서'), ('북', '동'), ('남', '북')], 'i1': [('남', '서'), ('북', '남'), ('서', '북'), ('남', '북')], 'i2': [('북', '남'), ('남', '북')], 'i3': [('동', '남'), ('서', '동'), ('남', '서'), ('북', '남'), ('서', '북'), ('동', '서'), ('북', '동'), ('남', '북')], 'i6': [('동', '남'), ('서', '동'), ('남', '서'), ('북', '남'), ('서', '북'), ('동', '서'), ('북', '동'), ('남', '북')], 'i7': [('북', '남'), ('남', '북')], 'i8': [('동', '남'), ('서', '동'), ('남', '서'), ('북', '남'), ('서', '북'), ('동', '서'), ('북', '동'), ('남', '북')], 'i9': [('서', '동'), ('동', '서')]}\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(flows)\n",
|
|
"print(pdires)\n",
|
|
"print(inc2id)\n",
|
|
"print(out2id)\n",
|
|
"print(pflow)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"\n",
|
|
"# (6) 가능한 이동류에 대하여 진입id, 진출id 배정 : matching\n",
|
|
"dires_right = ['북', '북서', '서', '남서', '남', '남동', '동', '북동'] # ex (북, 서), (서, 남) 등은 우회전 flow\n",
|
|
"self.matching = []\n",
|
|
"for node_id in self.parent_ids:\n",
|
|
" inter_no = self.node2inter[node_id]\n",
|
|
" # 좌회전과 직진(1 ~ 16)\n",
|
|
" for (inc_dire, out_dire) in pflow[node_id]:\n",
|
|
" move_no = self.nema[(self.nema.inc_dire==inc_dire) & (self.nema.out_dire==out_dire)].move_no.iloc[0]\n",
|
|
" inc_edge_id = inc2id[(node_id, inc_dire)]\n",
|
|
" out_edge_id = out2id[(node_id, out_dire)]\n",
|
|
" new_row = pd.DataFrame({'inter_no':[inter_no], 'move_no':[move_no],\n",
|
|
" 'inc_dire':[inc_dire], 'out_dire':[out_dire],\n",
|
|
" 'inc_edge_id':[inc_edge_id], 'out_edge_id':[out_edge_id], 'node_id':[node_id]})\n",
|
|
" self.matching.append(new_row)\n",
|
|
" # 보행신호(17), 전적색(18)\n",
|
|
" new_row = pd.DataFrame({'inter_no':[inter_no] * 2, 'move_no':[17, 18],\n",
|
|
" 'inc_dire':[None]*2, 'out_dire':[None]*2,\n",
|
|
" 'inc_edge_id':[None]*2, 'out_edge_id':[None]*2, 'node_id':[node_id]*2})\n",
|
|
" self.matching.append(new_row)\n",
|
|
" # 신호우회전(21)\n",
|
|
" for d in range(len(dires_right)):\n",
|
|
" inc_dire = dires_right[d]\n",
|
|
" out_dire = dires_right[(d+2)%8]\n",
|
|
" if {inc_dire, out_dire}.issubset(pdires[node_id]):\n",
|
|
" inc_edge_id = inc2id[(node_id, inc_dire)]\n",
|
|
" out_edge_id = out2id[(node_id, out_dire)]\n",
|
|
" new_row = pd.DataFrame({'inter_no':[inter_no], 'move_no':[21],\n",
|
|
" 'inc_dire':[inc_dire], 'out_dire':[out_dire],\n",
|
|
" 'inc_edge_id':[inc_edge_id], 'out_edge_id':[out_edge_id], 'node_id':[node_id]})\n",
|
|
" self.matching.append(new_row)\n",
|
|
"self.matching.append(self.match7[self.match7.node_id.isin(self.child_ids)])\n",
|
|
"self.matching = pd.concat(self.matching)\n",
|
|
"self.matching = self.matching.dropna(subset=['inc_edge_id', 'out_edge_id'])\\\n",
|
|
" .sort_values(by=['inter_no', 'node_id', 'move_no']).reset_index(drop=True)\n",
|
|
"self.matching['move_no'] = self.matching['move_no'].astype(int)\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
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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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"nbformat_minor": 2
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