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441 lines
18 KiB
441 lines
18 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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"41.0\n"
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]
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}
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],
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"source": [
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"time_data = \"\"\"\n",
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"3h\n",
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"0.5h\n",
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"3h\n",
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"2h\n",
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"2h\n",
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"4h\n",
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"3h\n",
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"1.5h\n",
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"4h\n",
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"3.5h\n",
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"2.5h\n",
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"2h\n",
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"3h\n",
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"3h\n",
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"2h\n",
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"1h\n",
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"0h\n",
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"0.5h\n",
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"0.5h\n",
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"\"\"\"\n",
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"time_list = time_data.strip().split('\\n')\n",
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"total_hours = sum(float(time.replace('h', '')) for time in time_list)\n",
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"print(total_hours)"
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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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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"이동류정보 불러오는 중: 100%|██████████| 17280/17280 [00:51<00:00, 334.89it/s]\n"
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]
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}
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],
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"source": [
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"import sys\n",
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"from datetime import datetime\n",
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"sys.path.append('../../Scripts')\n",
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"from preprocess_daily import DailyPreprocessor\n",
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"self = DailyPreprocessor()\n",
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"\n",
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"# 1. 데이터 준비\n",
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"self.load_data()\n",
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"\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()"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"u00 True\n",
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"u20 False\n",
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"u30 True\n",
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"u31 True\n",
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"u32 True\n",
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"u60 True\n"
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]
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},
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{
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"ename": "NameError",
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"evalue": "name 'pd' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[1;32mIn[4], line 55\u001b[0m\n\u001b[0;32m 52\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcoord \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcoord[[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minter_no\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mphase_no\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mring_type\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmove_no\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124minc_dir\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mout_dir\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124minc_angle\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mout_angle\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124minc_edge\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mout_edge\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnode_id\u001b[39m\u001b[38;5;124m'\u001b[39m]]\n\u001b[0;32m 54\u001b[0m \u001b[38;5;66;03m# display(coord)\u001b[39;00m\n\u001b[1;32m---> 55\u001b[0m cmatches \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241m.\u001b[39mconcat(cmatches)\n\u001b[0;32m 56\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmatch6 \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mconcat([\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmatch5, cmatches, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcoord])\u001b[38;5;241m.\u001b[39mdrop_duplicates()\u001b[38;5;241m.\u001b[39msort_values(by\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minter_no\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnode_id\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mphase_no\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mring_type\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m 57\u001b[0m \u001b[38;5;66;03m# self.match6.to_csv(os.path.join(self.path_intermediates, 'match6.csv'))\u001b[39;00m\n",
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"\u001b[1;31mNameError\u001b[0m: name 'pd' is not defined"
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]
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}
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],
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"source": [
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"# self.node2inter = dict(zip(self.inter_node['node_id'], self.inter_node['inter_no']))\n",
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"\n",
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"# child_ids = self.inter_node[self.inter_node.inter_type=='child'].node_id.unique()\n",
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"# ch2pa = {} # child to parent\n",
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"# for child_id in child_ids:\n",
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"# parent_no = self.inter_node[self.inter_node.node_id==child_id].inter_no.iloc[0]\n",
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"# sub_inter_node = self.inter_node[self.inter_node.inter_no==parent_no]\n",
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"# ch2pa[child_id] = sub_inter_node[sub_inter_node.inter_type=='parent'].iloc[0].node_id\n",
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"# directions = ['북', '북동', '동', '남동', '남', '남서', '서', '북서'] # 정북기준 시계방향으로 8방향\n",
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"\n",
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"# # 각 uturn node에 대하여 (inc_edge_id, out_edge_id) 부여\n",
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"# cmatches = []\n",
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"# for _, row in self.uturn.iterrows():\n",
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"# child_id = row.child_id\n",
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"# parent_id = row.parent_id\n",
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"# direction = row.direction\n",
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"# condition = row.condition\n",
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"# inc_edge_id = row.inc_edge\n",
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"# out_edge_id = row.out_edge\n",
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"# # match5에서 parent_id에 해당하는 행들을 가져옴\n",
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"# cmatch = self.match5.copy()[self.match5.node_id==parent_id] # match dataframe for a child node\n",
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"# cmatch = cmatch.sort_values(by=['phase_no', 'ring_type']).reset_index(drop=True)\n",
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"# cmatch['node_id'] = child_id\n",
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"# cmatch[['inc_edge', 'out_edge']] = np.nan\n",
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"\n",
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"# # condition 별로 inc_dire, out_dire_A, out_dire_B를 정함\n",
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"# ind = directions.index(direction)\n",
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"# if condition == \"좌회전시\":\n",
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"# inc_dire = direction\n",
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"# out_dire_A = out_dire_B = directions[(ind + 2) % len(directions)]\n",
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"# elif condition == \"보행신호시\":\n",
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"# inc_dire = directions[(ind + 2) % len(directions)]\n",
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"# out_dire_A = directions[(ind - 2) % len(directions)]\n",
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"# out_dire_B = directions[(ind - 2) % len(directions)]\n",
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"# print(child_id, ((cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A)).any())\n",
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"# # (inc_dire, out_dire_A, out_dire_B) 별로 inc_edge_id, out_edge_id를 정함\n",
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"# cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]\n",
|
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"# cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_B), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]\n",
|
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"# if condition == '보행신호시':\n",
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"# # 이동류번호가 17(보행신호)이면서 유턴노드방향으로 가는 신호가 없으면 (inc_edge_id, out_edge_id)를 부여한다.\n",
|
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"# cmatch.loc[(cmatch.move_no==17) & (cmatch.out_dir!=direction), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]\n",
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"# # 유턴신호의 이동류번호를 19로 부여한다.\n",
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"# cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), 'move_no'] = 19\n",
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"# cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_B), 'move_no'] = 19\n",
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"# cmatches.append(cmatch)\n",
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"\n",
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"# # 각 coordination node에 대하여 (inc_edge_id, out_edge_id) 부여\n",
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"# self.coord['inter_no'] = self.coord['parent_id'].map(self.node2inter)\n",
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"# self.coord = self.coord.rename(columns={'child_id':'node_id'})\n",
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"# self.coord[['inc_dir', 'out_dir', 'inc_angle','out_angle']] = np.nan\n",
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"# self.coord['move_no'] = 20\n",
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"# self.coord = self.coord[['inter_no', 'phase_no', 'ring_type', 'move_no', 'inc_dir', 'out_dir', 'inc_angle','out_angle', 'inc_edge', 'out_edge', 'node_id']]\n",
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"\n",
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"# # display(coord)\n",
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"# cmatches = pd.concat(cmatches)\n",
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"# self.match6 = pd.concat([self.match5, cmatches, self.coord]).drop_duplicates().sort_values(by=['inter_no', 'node_id', 'phase_no', 'ring_type'])\n",
|
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"# # self.match6.to_csv(os.path.join(self.path_intermediates, 'match6.csv'))"
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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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{
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"ename": "AttributeError",
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"evalue": "'DailyPreprocessor' object has no attribute 'match6'",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[1;32mIn[5], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmatch6\u001b[49m\n",
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"\u001b[1;31mAttributeError\u001b[0m: 'DailyPreprocessor' object has no attribute 'match6'"
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]
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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": "markdown",
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"metadata": {},
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"source": [
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"splits 딕셔너리 다시 만들기"
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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": 21,
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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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"[37 39 55 29 0 0 0 0]\n",
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"[37 39 25 59 0 0 0 0]\n",
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"[ 37 76 131 160 160 160 160 160]\n",
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"[ 37 76 101 160 160 160 160 160]\n",
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"[ 37 76 101 131 160]\n",
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"[37 39 25 30 29]\n",
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"{(1, 1): 37, (2, 2): 39, (3, 3): 25, (3, 4): 55, (4, 4): 29, (4, 5): 59, (5, 5): 0, (6, 6): 0, (7, 7): 0, (8, 8): 0}\n",
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"\n",
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"[37 39 55 29 0 0 0 0]\n",
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"[37 39 25 59 0 0 0 0]\n",
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"[ 0 39 55 29 0 0 0 0]\n",
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"[ 0 0 55 29 0 0 0 0]\n",
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"[ 0 0 30 29 0 0 0 0]\n",
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"[ 0 0 30 -1 0 0 0 0]\n",
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"[ 0 0 30 -1 -29 0 0 0]\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"row = self.plan.iloc[0]\n",
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"# print(row)\n",
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"inter_no = row.inter_no\n",
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"start_hour = row.start_hour\n",
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"start_minute = row.start_minute\n",
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"cycle = row.cycle\n",
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"\n",
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"dura_A = np.array(row[[f'dura_A{j}' for j in range(1, 9)]])\n",
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"dura_B = np.array(row[[f'dura_B{j}' for j in range(1, 9)]])\n",
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"\n",
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"print(np.array(dura_A))\n",
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"print(np.array(dura_B))\n",
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"\n",
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"cums_A = row[[f'dura_A{j}' for j in range(1,9)]].cumsum()\n",
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"cums_B = row[[f'dura_B{j}' for j in range(1,9)]].cumsum()\n",
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"\n",
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"print(np.array(cums_A))\n",
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"print(np.array(cums_B))\n",
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"\n",
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"detailed_cums = []\n",
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"combined_row = np.unique(np.concatenate((cums_A,cums_B)))\n",
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"print(combined_row)\n",
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"detailed_durations = np.concatenate(([combined_row[0]], np.diff(combined_row)))\n",
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"\n",
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"print(detailed_durations)\n",
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"\n",
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"desired_dict = {(1, 1): 37, (2, 2): 39, (3, 3): 25, (3, 4): 55, (4, 4): 29, (4, 5): 59, (5, 5): 0, (6, 6): 0, (7, 7): 0, (8, 8): 0}\n",
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"print(desired_dict)\n",
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"\n",
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"print()\n",
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"print(dura_A)\n",
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"print(dura_B)\n",
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"j = 0\n",
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"for i in range(len(detailed_durations)):\n",
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" dura_A[j] -= detailed_durations[i]\n",
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" print(dura_A)\n",
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" j += 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": 10,
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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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"[37 39 55 29 0 0 0 0]\n",
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"[37 39 25 59 0 0 0 0]\n",
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"[ 37 76 131 160 160 160 160 160]\n",
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"[ 37 76 101 160 160 160 160 160]\n",
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"[ 37 76 101 131 160]\n",
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"[37 39 25 30 29]\n",
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"{(1, 1): 37, (2, 2): 39, (3, 3): 25, (3, 4): 55, (4, 4): 29, (4, 5): 59, (5, 5): 0, (6, 6): 0, (7, 7): 0, (8, 8): 0}\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"row = self.plan.iloc[0]\n",
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"inter_no = row.inter_no\n",
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"start_hour = row.start_hour\n",
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"start_minute = row.start_minute\n",
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"cycle = row.cycle\n",
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"\n",
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"dura_A = row[[f'dura_A{j}' for j in range(1, 9)]]\n",
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"dura_B = row[[f'dura_B{j}' for j in range(1, 9)]]\n",
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"\n",
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"print(np.array(dura_A))\n",
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"print(np.array(dura_B))\n",
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"\n",
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"cums_A = row[[f'dura_A{j}' for j in range(1,9)]].cumsum()\n",
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"cums_B = row[[f'dura_B{j}' for j in range(1,9)]].cumsum()\n",
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"\n",
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"print(np.array(cums_A))\n",
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"print(np.array(cums_B))\n",
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"\n",
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"detailed_cums = []\n",
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"combined_row = np.unique(np.concatenate((cums_A,cums_B)))\n",
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"print(combined_row)\n",
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"detailed_durations = np.concatenate(([combined_row[0]], np.diff(combined_row)))\n",
|
|
"\n",
|
|
"print(detailed_durations)\n",
|
|
"\n",
|
|
"duration_dict = {}\n",
|
|
"# 두 시리즈의 길이가 같다고 가정합니다.\n",
|
|
"for i in range(len(dura_A)):\n",
|
|
" # A와 B의 현시시간이 같은 경우\n",
|
|
" if dura_A[i] == dura_B[i]:\n",
|
|
" duration_dict[(i+1, i+1)] = dura_A[i]\n",
|
|
" # A와 B의 현시시간이 다른 경우\n",
|
|
" else:\n",
|
|
" duration_dict[(i+1, i+1)] = min(dura_A[i], dura_B[i])\n",
|
|
" duration_dict[(i+1, i+2)] = max(dura_A[i], dura_B[i])\n",
|
|
"\n",
|
|
"print(duration_dict)\n",
|
|
"# cums_A = row[[f'dura_A{j}' for j in range(1,9)]].cumsum()\n",
|
|
"# cums_B = row[[f'dura_B{j}' for j in range(1,9)]].cumsum()\n",
|
|
"# print(cums_A)\n",
|
|
"# print(cums_B)\n",
|
|
"# detailed_cums = []\n",
|
|
"# combined_row = np.unique(np.concatenate((cums_A,cums_B)))\n",
|
|
"# print(combined_row)\n",
|
|
"# detailed_durations = np.concatenate(([combined_row[0]], np.diff(combined_row)))\n",
|
|
"# print(detailed_durations)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 38,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# split, isplit : A,B 분리 혹은 통합시 사용될 수 있는 딕셔너리 \n",
|
|
"self.splits = {} # splits maps (inter_no, start_hour, start_minute) to split \n",
|
|
"for i, row in self.plan.iterrows():\n",
|
|
" inter_no = row.inter_no\n",
|
|
" start_hour = row.start_hour\n",
|
|
" start_minute = row.start_minute\n",
|
|
" cycle = row.cycle\n",
|
|
" cums_A = row[[f'dura_A{j}' for j in range(1,9)]].cumsum()\n",
|
|
" cums_B = row[[f'dura_B{j}' for j in range(1,9)]].cumsum()\n",
|
|
" self.splits[(inter_no, start_hour, start_minute)] = {} # split maps (phas_A, phas_B) to k\n",
|
|
" k = 0\n",
|
|
" for t in range(cycle):\n",
|
|
" new_phas_A = len(cums_A[cums_A < t]) + 1\n",
|
|
" new_phas_B = len(cums_B[cums_B < t]) + 1\n",
|
|
" if k == 0 or ((new_phas_A, new_phas_B) != (phas_A, phas_B)):\n",
|
|
" k += 1\n",
|
|
" phas_A = new_phas_A\n",
|
|
" phas_B = new_phas_B\n",
|
|
" self.splits[(inter_no, start_hour, start_minute)][(phas_A, phas_B)] = k\n",
|
|
"self.isplits = {} # the inverse of splits\n",
|
|
"for i in self.splits:\n",
|
|
" self.isplits[i] = {self.splits[i][k]:k for k in self.splits[i]} # isplit maps k to (phas_A, phas_B)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 49,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{(175, 0, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (3, 4): 4, (4, 4): 5},\n",
|
|
" (175, 7, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (3, 4): 4, (4, 4): 5},\n",
|
|
" (175, 9, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (3, 4): 4, (4, 4): 5},\n",
|
|
" (175, 18, 30): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (3, 4): 4, (4, 4): 5},\n",
|
|
" (176, 0, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3},\n",
|
|
" (176, 7, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3},\n",
|
|
" (176, 9, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3},\n",
|
|
" (176, 18, 30): {(1, 1): 1, (2, 2): 2, (3, 3): 3},\n",
|
|
" (177, 0, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 4): 4},\n",
|
|
" (177, 7, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 4): 4},\n",
|
|
" (177, 9, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 4): 4},\n",
|
|
" (177, 18, 30): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 4): 4},\n",
|
|
" (178, 0, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 4): 4},\n",
|
|
" (178, 7, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 3): 4, (4, 4): 5},\n",
|
|
" (178, 9, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 3): 4, (4, 4): 5},\n",
|
|
" (178, 18, 30): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 3): 4, (4, 4): 5},\n",
|
|
" (201, 0, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 4): 4, (5, 5): 5},\n",
|
|
" (201, 7, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 4): 4, (5, 5): 5},\n",
|
|
" (201, 9, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 4): 4, (5, 5): 5},\n",
|
|
" (201, 18, 30): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 4): 4, (5, 5): 5},\n",
|
|
" (202, 0, 0): {(1, 1): 1, (2, 2): 2},\n",
|
|
" (202, 7, 0): {(1, 1): 1, (2, 2): 2},\n",
|
|
" (202, 9, 0): {(1, 1): 1, (2, 2): 2},\n",
|
|
" (202, 18, 30): {(1, 1): 1, (2, 2): 2},\n",
|
|
" (206, 0, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 4): 4},\n",
|
|
" (206, 7, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 4): 4},\n",
|
|
" (206, 9, 0): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 4): 4},\n",
|
|
" (206, 18, 30): {(1, 1): 1, (2, 2): 2, (3, 3): 3, (4, 4): 4},\n",
|
|
" (210, 0, 0): {(1, 1): 1, (1, 2): 2, (2, 2): 3, (3, 3): 4, (4, 4): 5},\n",
|
|
" (210, 7, 0): {(1, 1): 1, (1, 2): 2, (2, 2): 3, (3, 3): 4, (4, 4): 5},\n",
|
|
" (210, 9, 0): {(1, 1): 1, (1, 2): 2, (2, 2): 3, (3, 3): 4, (4, 4): 5},\n",
|
|
" (210, 18, 30): {(1, 1): 1, (1, 2): 2, (2, 2): 3, (3, 3): 4, (4, 4): 5}}"
|
|
]
|
|
},
|
|
"execution_count": 49,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"self.splits"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "rts",
|
|
"language": "python",
|
|
"name": "rts"
|
|
},
|
|
"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
|
|
}
|