#!/usr/bin/env python # -*- coding: utf-8 -*- ################################################################## # # Copyright (c) 2023 CICV, Inc. All Rights Reserved # ################################################################## """ @Authors: zhanghaiwen, yangzihao @Data: 2024/02/21 @Last Modified: 2024/02/21 @Summary: The template of custom indicator. """ """ 设计思路： """ import math import pandas as pd import numpy as np from common import zip_time_pairs, continuous_group, get_status_active_data, _cal_THW, _cal_v_ego_projection #from common import zip_time_pairs, continuous_group, get_status_active_data, _cal_THW, _cal_v_ego_projection from log import logger import pandas as pd import matplotlib.pyplot as plt import seaborn as sns """import functions""" Max_error = 100 # custom metric codes class CustomMetric(object): def __init__(self, all_data, case_name): self.data = all_data self.optimal_dict = self.data.config self.status_trigger_dict = self.data.status_trigger_dict self.case_name = case_name self.markline_df = pd.DataFrame(columns=['start_time', 'end_time', 'start_frame', 'end_frame', 'type']) self.graph_list = [] self.df = pd.DataFrame() self.ego_df = pd.DataFrame() self.df_acc = pd.DataFrame() self.stable_average_THW = None self.steady_error_THW = Max_error self.result = { "name": "跟车速度稳态误差", "value": [], # "weight": [], "tableData": { "avg": "", # 平均值，或指标值 "max": "", "min": "" }, "reportData": { "name": "跟车速度稳态误差（%）", # "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": [], }, "statusFlag": {} } self.run() print(f"跟车速度稳态误差: {self.result['value']}") def data_extract(self): self.df = self.data.object_df self.ego_df = self.data.ego_data active_time_ranges = self.status_trigger_dict['ACC']['ACC_active_time'] self.df_acc = get_status_active_data(active_time_ranges, self.df) # self.df_ica = self.df[self.df['ACC_status'] == "Shut_off"].copy() # 数字3对应ICA的Active # self.df_ica = self.df[self.df['ACC_status'] == "Active"].copy() # 数字3对应ICA的Active if self.df_acc.empty: self.result['statusFlag']['function_ACC'] = False else: self.result['statusFlag']['function_ACC'] = True def _get_first_change_index_THW(self): """ 获取DataFrame中'set_headway_time'列首次发生变化的索引值。 Args: 无参数。 Returns: Union[int, None]: 如果存在变化，则返回首次发生变化的索引值（int类型），否则返回None。 """ change_indices = self.df_acc[self.df_acc['set_headway_time'] != self.df_acc['set_headway_time'].shift()].index if not change_indices.empty: first_change_index = change_indices.min() else: first_change_index = None return first_change_index def _find_stable_THW(self, window_size, percent_deviation, set_value): """ 在给定的数据窗口中查找稳定跟车时距离THW，并计算该段内THW的平均值。 Args: window_size (int): 窗口大小，表示在数据中寻找稳定段时考虑的连续数据点数量。 percent_deviation (float): THW值相对于设定值的允许偏差百分比。 set_value (float): THW的设定值。 Returns: None """ ego_x = self.df_acc[self.df_acc['playerId'] == 1]['posX'].reset_index(drop=True) ego_y = self.df_acc[self.df_acc['playerId'] == 1]['posY'].reset_index(drop=True) obj_x = self.df_acc[self.df_acc['playerId'] == 2]['posX'].reset_index(drop=True) obj_y = self.df_acc[self.df_acc['playerId'] == 2]['posY'].reset_index(drop=True) ego_speedx = self.df_acc[self.df_acc['playerId'] == 1]['speedX'].reset_index(drop=True) ego_speedy = self.df_acc[self.df_acc['playerId'] == 1]['speedY'].reset_index(drop=True) obj_speedx = self.df_acc[self.df_acc['playerId'] == 2]['speedX'].reset_index(drop=True) obj_speedy = self.df_acc[self.df_acc['playerId'] == 2]['speedY'].reset_index(drop=True) dx = obj_x - ego_x dy = obj_y - ego_y # vx = obj_speedx - ego_speedx # vy = obj_speedy - ego_speedy dist = np.sqrt(dx ** 2 + dy ** 2) ego_v_projection_in_dist = _cal_v_ego_projection(dx, dy, ego_speedx, ego_speedy) thw1 = _cal_THW(dist, ego_v_projection_in_dist) thw = thw1.tolist() THW = [] for item in thw: THW.append(item) THW.append(item) self.df_acc['THW'] = THW THW = self.df_acc['THW'].values deviation = set_value * (percent_deviation / 100) stable_start = None stable_average_THW = None for i in range(len(THW) - window_size + 1): window_data = THW[i:i + window_size] if all(set_value - deviation <= s <= set_value + deviation for s in window_data): if stable_start is None: stable_start = i stable_end = i + window_size - 1 stable_average_THW = np.mean(window_data) j = i + window_size while j < len(THW) - window_size + 1: next_window_data = THW[j:j + window_size] if all(set_value - deviation <= s <= set_value + deviation for s in next_window_data): stable_end = j + window_size - 1 stable_average_THW = (stable_average_THW * (j - stable_start) + sum(next_window_data)) / ( j - stable_start + window_size) j += window_size else: stable_start = j + window_size - 1 stable_end = i + window_size - 1 stable_average_THW = np.mean(window_data) break # self.stable_start_time_THW = self.df_ica['simTime'].iloc[stable_start] self.stable_average_THW = stable_average_THW def data_analyze(self): first_change_index = self._get_first_change_index_THW() if not first_change_index: self.steady_error_THW = 0 self.result['value'] = [abs(round(self.steady_error_THW, 3))] print(f"steady_error_THW: {abs(self.steady_error_THW)}") else: set_headway_time = self.df_acc.loc[first_change_index, 'set_headway_time'] self._find_stable_THW(window_size=25, percent_deviation=10, set_value=set_headway_time) if self.stable_average_THW: self.steady_error_THW = (self.stable_average_THW - set_headway_time) * 100 / self.stable_average_THW else: self.steady_error_THW = Max_error self.result['value'] = [abs(round(self.steady_error_THW, 3))] print(f"steady_error_THW: {abs(self.steady_error_THW)}") def markline_statistic(self): pass def report_data_statistic(self): # time_list = self.ego_df['simTime'].values.tolist() # graph_list = [x for x in self.graph_list if not np.isnan(x)] self.result['tableData']['avg'] = self.result['value'][0] if not self.df_acc.empty else '-' self.result['tableData']['max'] = '-' self.result['tableData']['min'] = '-' # zip_vs_time = zip_time_pairs(time_list, self.graph_list) self.result['reportData']['data'] = [] # self.markline_statistic() # markline_slices = self.markline_df.to_dict('records') self.result['reportData']['markLine'] = [] self.result['reportData']['range'] = [0, 1.2] def run(self): # logger.info(f"Custom metric run:[{self.result['name']}].") logger.info(f"[case:{self.case_name}] Custom metric:[steady_error_THW:{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