#!/usr/bin/env python # -*- coding: utf-8 -*- ################################################################## # # Copyright (c) 2023 CICV, Inc. All Rights Reserved # ################################################################## """ @Authors: yangzihao(yangzihao@china-icv.cn) @Data: 2024/02/21 @Last Modified: 2024/02/21 @Summary: The template of custom indicator. """ import pandas as pd import numpy as np from common import zip_time_pairs, continuous_group from log import logger """import functions""" # def zip_time_pairs(time_list, zip_list): # zip_time_pairs = zip(time_list, zip_list) # zip_vs_time = [[x, y] for x, y in zip_time_pairs if not math.isnan(y)] # return zip_vs_time # def continuous_group(df): # time_list = df['simTime'].values.tolist() # frame_list = df['simFrame'].values.tolist() # # group_time = [] # group_frame = [] # sub_group_time = [] # sub_group_frame = [] # # for i in range(len(frame_list)): # if not sub_group_time or frame_list[i] - frame_list[i - 1] <= 1: # sub_group_time.append(time_list[i]) # sub_group_frame.append(frame_list[i]) # else: # group_time.append(sub_group_time) # group_frame.append(sub_group_frame) # sub_group_time = [time_list[i]] # sub_group_frame = [frame_list[i]] # # group_time.append(sub_group_time) # group_frame.append(sub_group_frame) # group_time = [g for g in group_time if len(g) >= 2] # group_frame = [g for g in group_frame if len(g) >= 2] # # # 输出图表值 # time = [[g[0], g[-1]] for g in group_time] # frame = [[g[0], g[-1]] for g in group_frame] # # time_df = pd.DataFrame(time, columns=['start_time', 'end_time']) # frame_df = pd.DataFrame(frame, columns=['start_frame', 'end_frame']) # # result_df = pd.concat([time_df, frame_df], axis=1) # # return result_df # def continous_judge(frame_list): # if not frame_list: # return 0 # # cnt = 1 # for i in range(1, len(frame_list)): # if frame_list[i] - frame_list[i - 1] <= 3: # continue # cnt += 1 # return cnt # custom metric codes class CustomMetric(object): def __init__(self, all_data, case_name): self.data = all_data self.optimal_dict = self.data.config self.case_name = case_name self.markline_df = pd.DataFrame(columns=['start_time', 'end_time', 'start_frame', 'end_frame', 'type']) self.df = pd.DataFrame() self.df_follow = pd.DataFrame() self.time_list_follow = list() self.frame_list_follow = list() self.v_relative_list = list() self.v_deviation_list = list() self.v_relative_list_full_time = list() self.result = { "name": "跟车速度偏差", "value": [], # "weight": [], "tableData": { "avg": "", # 平均值，或指标值 "max": "", "min": "" }, "reportData": { "name": "跟车速度偏差（km/h）", # "legend": [], # 如果有多个data，则需要增加data对应的说明，如：["横向加速度", "纵向加速度"] "data": [], "markLine": [], "range": [], } } self.run() def data_extract(self): self.df = self.data.object_df self.df_follow = self.df[self.df['ACC_status'] == "Shut_off"].copy() # 数字3对应ICA的Active # self.df_follow = self.df[self.df['ACC_status'] == "Active"].copy() # 数字3对应ICA的Active def data_analyze(self): df = self.df_follow.copy() col_list = ['simTime', 'simFrame', 'playerId', 'v', 'posX', 'posY'] # target_id df = df[col_list].copy() ego_df = df[df['playerId'] == 1][['simTime', 'simFrame', 'v', 'posX', 'posY']] # 筛选目标车（同一车道内，距离最近的前车） # obj_df = df[df['playerId'] == df['target_id']] target_id = 2 obj_df = df[df['playerId'] == target_id][['simTime', 'simFrame', 'v', 'posX', 'posY']] # 目标车 obj_df = obj_df.rename(columns={'v': 'v_obj', 'posX': 'posX_obj', 'posY': 'posY_obj'}) df_merge = pd.merge(ego_df, obj_df, on=['simTime', 'simFrame'], how='left') df_merge['v_relative'] = df_merge['v'] - df_merge['v_obj'] df_merge.replace([np.inf, -np.inf], np.nan, inplace=True) # 异常值处理 self.time_list_follow = df_merge['simTime'].values.tolist() self.frame_list_follow = df_merge['simFrame'].values.tolist() self.v_relative_list = df_merge['v_relative'].values.tolist() self.v_deviation_list = abs(df_merge['v_relative']).values.tolist() tmp_df = ego_df[['simTime', 'simFrame']].copy() v_rel_df = df_merge[['simTime', 'v_relative']].copy() df_merged1 = pd.merge(tmp_df, v_rel_df, on='simTime', how='left') self.v_relative_list_full_time = df_merged1['v_relative'].values.tolist() max_velocity_deviation = abs(df_merge['v_relative']).max() self.result['value'] = [round(max_velocity_deviation, 2)] if not np.isnan(max_velocity_deviation) else [0] def markline_statistic(self): unfunc_df = pd.DataFrame({'simTime': self.time_list_follow, 'simFrame': self.frame_list_follow, 'v_deviation': self.v_deviation_list}) unfunc_df = unfunc_df[unfunc_df['simFrame'] > 1] v_df = unfunc_df[unfunc_df['v_deviation'] > 5] v_df = v_df[['simTime', 'simFrame', 'v_deviation']] v_follow_df = continuous_group(v_df) v_follow_df['type'] = "ICA" self.markline_df = pd.concat([self.markline_df, v_follow_df], ignore_index=True) def report_data_statistic(self): time_list = self.df['simTime'].values.tolist() graph_list = [x for x in self.v_deviation_list if not np.isnan(x)] self.result['tableData']['avg'] = f'{np.mean(graph_list):.2f}' if graph_list else 0 self.result['tableData']['max'] = f'{max(graph_list):.2f}' if graph_list else 0 self.result['tableData']['min'] = f'{min(graph_list):.2f}' if graph_list else 0 zip_vs_time = zip_time_pairs(time_list, self.v_deviation_list) self.result['reportData']['data'] = zip_vs_time self.markline_statistic() markline_slices = self.markline_df.to_dict('records') self.result['reportData']['markLine'] = markline_slices self.result['reportData']['range'] = f"[-5.0, 5.0]" def run(self): # logger.info(f"Custom metric run:[{self.result['name']}].") logger.info(f"[case:{self.case_name}] Custom metric:[ica_speed_deviation:{self.result['name']}] evaluate.") try: self.data_extract() except Exception as e: logger.error(f"[case:{self.case_name}] Custom metric:{self.result['name']} data extract ERROR!", e) try: self.data_analyze() except Exception as e: logger.error(f"[case:{self.case_name}] Custom metric:{self.result['name']} data analyze ERROR!", e) try: self.report_data_statistic() except Exception as e: logger.error(f"[case:{self.case_name}] Custom metric:{self.result['name']} report data statistic ERROR!", e) # if __name__ == "__main__": # pass