evaluate_master/data_process_1121.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
##################################################################
#
# Copyright (c) 2023 CICV, Inc. All Rights Reserved
#
##################################################################
"""
@Authors:           yangzihao(yangzihao@china-icv.cn)
@Data:              2023/11/27
@Last Modified:     2023/11/27
@Summary:           Csv data process functions
"""

import os
import sys

import numpy as np
import pandas as pd

from status_mapping import *
from status_trigger import *
# from status_mapping import acc_status_mapping, lka_status_mapping, ldw_status_mapping
from data_quality import DataQuality, get_all_files, frame_loss_statistic
from common import cal_velocity
from data_info import CsvData
import log


class DataProcess(object):
    """
    The data process class. It is a template to get evaluation raw data and process the raw data.

    Attributes:
    """

    def __init__(self, data_path, config, case_name):

        # base info
        self.data_path = data_path
        self.case_name = case_name
        self.config = config

        # drive data
        self.ego_df = pd.DataFrame()
        self.object_df = pd.DataFrame()
        self.driver_ctrl_df = pd.DataFrame()
        self.vehicle_sys_df = pd.DataFrame()
        self.status_df = pd.DataFrame()

        # environment data
        self.lane_info_df = pd.DataFrame()
        self.road_mark_df = pd.DataFrame()
        self.road_pos_df = pd.DataFrame()
        self.traffic_light_df = pd.DataFrame()
        self.traffic_signal_df = pd.DataFrame()

        self.frame_rate = float()
        self.obj_data = {}
        self.ego_data = {}
        self.obj_id_list = {}

        self.car_info = {}
        self.report_info = {}
        self.driver_ctrl_data = {}
        self.status_trigger_dict = {}

        self._process()

    def _process(self):
        # self._signal_mapping()
        self._merge_csv()
        self._read_csv()

        self._invalid_detect()
        self._fill_missing_columns()
        # self._cal_frame_rate()
        # self._time_alignment()
        self.car_info = self._get_car_info(self.object_df)
        # self._compact_data()
        # self._abnormal_detect()
        # self._status_map(self.object_df)
        self._object_accel_get_from_egostate()
        self._object_df_process()
        self.status_trigger_dict = self._get_status_trigger_dict(self.status_df)
        self.report_info = self._get_report_info(self.obj_data[1])
        self.driver_ctrl_data = self._get_driver_ctrl_data(self.driver_ctrl_df)

    def _invalid_column_detect(self, df, csv_name):  # head and tail detect
        """
        Detect the head of the csv whether begin with 0 or not.

        Returns:
            A dataframe, which 'time' column begin with 0.

        """
        logger = log.get_logger()

        for column in df.columns:
            if df[column].nunique() == 1:
                # if 9999.00 in df[column].values or "9999.00" in df[column].values:
                logger.warning(
                    f"[case:{self.case_name}] SINGLE_CASE_EVAL: [{csv_name}] data '{column}' invalid WARNING!")

    def _csv_interpolate_by_frame(self, df):
        # df = pd.read_csv(input)  # 读取CSV文件
        df['simFrame'] = pd.to_numeric(df['simFrame'], errors='coerce')  # 转换simFrame列为数字类型
        df = df.sort_values(by='simFrame')  # 根据simFrame列进行排序
        full_simFrame_series = pd.Series(range(df['simFrame'].min(), df['simFrame'].max() + 1))  # 构建一个包含连续simFrame的完整序列
        df = df.merge(full_simFrame_series.rename('simFrame'), how='right')  # 使用merge方法将原始数据与完整序列合并，以填充缺失的simFrame行
        df = df.interpolate(method='linear')  # 对其他列进行线性插值
        df['simFrame'] = df['simFrame'].astype(int)  # 恢复simFrame列的数据类型为整数

        # df.to_csv(output, index=False)  # 保存处理后的数据到新的CSV文件
        result = df.copy()
        return result

    def _cal_frame_rate(self):
        object_df = self.object_df.copy()
        ego_df = object_df[object_df['playerId'] == 1]
        df_filtered = ego_df[(ego_df['simTime'] > 0) & (ego_df['simTime'] <= 1)]
        self.frame_rate = df_filtered.shape[0]

    def _data_time_align(self, base_time, df):
        # 特判，如果输入的dataframe无数值，那么直接返回
        if df.empty:
            return df

        # FRAME_RATE = self.frame_rate
        time_diff = 1.0 / self.frame_rate
        # 创建一个新的递增的 simTime 序列，从 0 开始，步长为 0.01 (time_diff)
        new_sim_time_values = np.arange(0, base_time.max() + time_diff, time_diff)

        # 创建一个映射字典，将原始 simTime 值映射到新的 simTime 值
        original_to_new_sim_time = {original: round(new_sim_time_values[i], 2) for i, original in enumerate(base_time)}

        # 使用isin函数来过滤df，并筛选出simFrame大于0的数据
        filtered_df1 = df[df['simTime'].isin(base_time)]
        filtered_df2 = filtered_df1[filtered_df1['simFrame'] > 0]
        filtered_df = filtered_df2.reset_index(drop=True)

        # 使用映射字典来替换 filtered_df 中的 simTime 列
        filtered_df['simTime'] = filtered_df['simTime'].map(original_to_new_sim_time)

        # 同步更新simFrame
        filtered_df['simFrame'] = (filtered_df['simTime'] * self.frame_rate + 1).round().astype(int)

        return filtered_df

    @staticmethod
    def _speed_mps2kmph(df):
        df['speedX'] = df['speedX'] * 3.6  # m/s to km/h
        df['speedY'] = df['speedY'] * 3.6  # m/s to km/h
        df['speedZ'] = df['speedZ'] * 3.6  # m/s to km/h
        return df

    def _merge_csv(self):
        # read csv files
        df_ego = pd.read_csv(os.path.join(self.data_path, 'EgoState.csv')).drop_duplicates().dropna(how='all')
        df_object = pd.read_csv(os.path.join(self.data_path, 'ObjState.csv')).drop_duplicates()  # 车辆行驶信息

        df_laneinfo = pd.read_csv(os.path.join(self.data_path, 'LaneInfo.csv')).drop_duplicates()  # 曲率信息， 曲率加速度信息
        df_roadPos = pd.read_csv(os.path.join(self.data_path, 'RoadPos.csv')).drop_duplicates()
        df_status = pd.read_csv(os.path.join(self.data_path, 'VehState.csv'), index_col=False).drop_duplicates()  # 状态机
        df_vehicleSys = pd.read_csv(os.path.join(self.data_path, 'VehicleSystems.csv')).drop_duplicates()  # 车灯信息

        self.lane_info_df = df_laneinfo
        self.vehicle_sys_df = df_vehicleSys
        self.status_df = df_status

        self.obj_roadpos_merge = pd.merge(df_object, df_roadPos, on=['simTime', 'simFrame', 'playerId'], how='inner')
        self._invalid_detect_before_merge()  # invalid detect
        # self._invalid_column_detect(df_laneinfo, 'LaneInfo.csv')
        # self._invalid_column_detect(df_object, 'ObjState.csv')
        # self._invalid_column_detect(df_vehicleSys, 'VehicleSystems.csv')
        # self._invalid_column_detect(df_status, 'VehState.csv')

        # df_ego['simTime'] = df_ego['simTime'].round(2)  # EGO: km/h
        # df_object['simTime'] = df_object['simTime'].round(2)  # OBJ: m/s, need unit conversion
        df_object = self._speed_mps2kmph(df_object)  # m/s to km/h

        # base_time = df_ego['simTime'].unique()
        # df_ego = self._data_time_align(base_time, df_ego)
        # df_object = self._data_time_align(base_time, df_object)
        # df_laneinfo = self._data_time_align(base_time, df_laneinfo)
        # df_roadPos = self._data_time_align(base_time, df_roadPos)
        # df_status = self._data_time_align(base_time, df_status)
        # df_vehicleSys = self._data_time_align(base_time, df_vehicleSys)

        EGO_PLAYER_ID = 1
        # 合并 ego_df 和 obj_df
        df_ego['playerId'] = EGO_PLAYER_ID
        combined_df = pd.concat([df_object, df_ego]).drop_duplicates(subset=['simTime', 'simFrame', 'playerId'])
        df_object = combined_df.sort_values(
            by=['simTime', 'simFrame', 'playerId']).copy()  # 按simTime/simFrame/playerId排序

        df_laneinfo['curvHor'] = df_laneinfo['curvHor'].round(3)
        df_laneinfo.rename(columns={"id": 'laneId'}, inplace=True)
        result = pd.merge(df_roadPos, df_laneinfo, how='inner', on=["simTime", "simFrame", "playerId", "laneId"])
        df_laneinfo_new = result[["simTime", "simFrame", "playerId", "curvHor", "curvHorDot"]].copy().drop_duplicates()

        # status mapping
        df_status = self._status_mapping(df_status)
        df_status = df_status[['simTime', 'ACC_status', 'Aeb_status', 'LKA_status', 'ICA_status', 'LDW_status',
                               'set_cruise_speed', 'set_headway_time']].copy()

        df_roadPos = df_roadPos[["simTime", "simFrame", "playerId", "laneOffset", "rollRel", "pitchRel", "roadId", "laneId"]].copy()

        # df merge
        df_vehicleSys = df_vehicleSys[['simTime', 'simFrame', 'lightMask', 'steering']].copy()
        merged_df = pd.merge(df_object, df_vehicleSys, on=["simTime", "simFrame"], how="left")
        merged_df1 = pd.merge(merged_df, df_laneinfo_new, on=["simTime", "simFrame", "playerId"], how="left")
        merged_df1 = pd.merge(merged_df1, df_roadPos, on=["simTime", "simFrame", "playerId"], how="left")
        merged_df2 = pd.merge_asof(merged_df1, df_status, on="simTime", direction='nearest')
        mg_df1 = merged_df2.drop_duplicates()  # 去重
        mg_df1 = mg_df1[mg_df1.simFrame > 0].copy()

        mask_player1 = mg_df1['playerId'] == 1
        player1_road_lane = mg_df1.loc[mask_player1, ['simTime', 'roadId', 'laneId']]
        mask_player1 = (mg_df1['simTime'].isin(player1_road_lane['simTime'])) & \
                       (mg_df1['roadId'].isin(player1_road_lane['roadId'])) & \
                       (mg_df1['laneId'].isin(player1_road_lane['laneId']))

        mg_df = mg_df1.loc[mask_player1]

        mg_df.to_csv(os.path.join(self.data_path, 'merged_ObjState.csv'), index=False)



        print('The files are merged.')

    def _read_csv(self):
        """
        Read csv files to dataframe.

        Args:
            data_path: A str of the path of csv files

        Returns:
            No returns.

        """
        self.driver_ctrl_df = pd.read_csv(os.path.join(self.data_path, 'DriverCtrl.csv')).drop_duplicates()
        self.ego_df = pd.read_csv(os.path.join(self.data_path, 'EgoState.csv')).drop_duplicates()
        self.object_df = pd.read_csv(os.path.join(self.data_path, 'merged_ObjState.csv')).drop_duplicates(
            subset=['simTime', 'simFrame', 'playerId'])
        self.road_mark_df = pd.read_csv(os.path.join(self.data_path, 'RoadMark.csv')).drop_duplicates()
        self.road_pos_df = pd.read_csv(os.path.join(self.data_path, 'RoadPos.csv')).drop_duplicates()
        self.traffic_light_df = pd.read_csv(os.path.join(self.data_path, 'TrafficLight.csv')).drop_duplicates()
        self.traffic_signal_df = pd.read_csv(os.path.join(self.data_path, 'TrafficSign.csv')).drop_duplicates()

    def _invalid_detect_before_merge(self):
        # invalid detect
        self._invalid_column_detect(self.lane_info_df, 'LaneInfo.csv')
        # self._invalid_column_detect(self.object_df, 'ObjState.csv')
        self._invalid_column_detect(self.vehicle_sys_df, 'VehicleSystems.csv')
        self._invalid_column_detect(self.status_df, 'VehState.csv')

    def _invalid_detect(self):
        # invalid detect
        self._invalid_column_detect(self.ego_df, 'EgoState.csv')
        self._invalid_column_detect(self.object_df, 'ObjState.csv')
        self._invalid_column_detect(self.driver_ctrl_df, 'DriverCtrl.csv')
        self._invalid_column_detect(self.road_mark_df, 'RoadMark.csv')
        self._invalid_column_detect(self.road_pos_df, 'RoadPos.csv')
        self._invalid_column_detect(self.traffic_light_df, 'TrafficLight.csv')
        self._invalid_column_detect(self.traffic_signal_df, 'TrafficSign.csv')

    def _fill_missing_columns(self):
        pass

    def _time_alignment(self):
        base_time = self.ego_df['simTime'].unique()
        self.driver_ctrl_df = self._data_time_align(base_time, self.driver_ctrl_df)
        self.ego_df = self._data_time_align(base_time, self.ego_df)
        self.object_df = self._data_time_align(base_time, self.object_df)
        self.road_mark_df = self._data_time_align(base_time, self.road_mark_df)
        self.road_pos_df = self._data_time_align(base_time, self.road_pos_df)
        self.traffic_light_df = self._data_time_align(base_time, self.traffic_light_df)
        self.traffic_signal_df = self._data_time_align(base_time, self.traffic_signal_df)
        print("The data is aligned.")

        # interpolate data
        # self.driver_ctrl_df = self._csv_interpolate_by_frame(self.driver_ctrl_df)
        # self.ego_df = self._csv_interpolate_by_frame(self.ego_df)
        # self.object_df = self._csv_interpolate_by_frame(self.object_df)

        # self.road_mark_df = self._csv_interpolate_by_frame(self.road_mark_df)
        # self.road_pos_df = self._csv_interpolate_by_frame(self.road_pos_df)
        # self.traffic_light_df = self._csv_interpolate_by_frame(self.traffic_light_df)
        # self.traffic_signal_df = self._csv_interpolate_by_frame(self.traffic_signal_df)

    def _signal_mapping(self):
        pass
        # singal mapping
        # signal_json = r'./signal.json'
        # signal_dict = json2dict(signal_json)
        # df_objectstate = signal_name_map(df_objectstate, signal_dict, 'objectState')
        # df_roadmark = signal_name_map(df_roadmark, signal_dict, 'roadMark')
        # df_roadpos = signal_name_map(df_roadpos, signal_dict, 'roadPos')
        # df_trafficlight = signal_name_map(df_trafficlight, signal_dict, 'trafficLight')
        # df_trafficsignal = signal_name_map(df_trafficsignal, signal_dict, 'trafficSignal')
        # df_drivectrl = signal_name_map(df_drivectrl, signal_dict, 'driverCtrl')
        # df_laneinfo = signal_name_map(df_laneinfo, signal_dict, 'laneInfo')
        # df_status = signal_name_map(df_status, signal_dict, 'statusMachine')
        # df_vehiclesys = signal_name_map(df_vehiclesys, signal_dict, 'vehicleSys')

    def _get_car_info(self, df):
        """

        Args:
            df:

        Returns:

        """

        EGO_PLAYER_ID = 1
        first_row = df[df['playerId'] == EGO_PLAYER_ID].iloc[0].to_dict()

        # length = first_row['dimX']
        # width = first_row['dimY']
        # height = first_row['dimZ']
        # offset = first_row['offX']
        #
        # car_info = {
        #     "length": length,
        #     "width": width,
        #     "height": height,
        #     "offset": offset
        # }

        car_info = {
            "length": 4,
            "width": 2,
            "height": 2,
            "offset": 1
        }

        return car_info

    def _compact_data(self):
        """
        Extra necessary data from dataframes.

        Returns:

        """
        self.object_df = self.object_df[CsvData.OBJECT_INFO].copy()

    def _abnormal_detect(self):  # head and tail detect
        """
        Detect the head of the csv whether begin with 0 or not.

        Returns:
            A dataframe, which 'time' column begin with 0.

        """
        pass

    def _unit_unified(self):
        pass

    def _object_accel_get_from_egostate(self):
        # 使用merge函数来合并两个DataFrame，基于simTime和playerId列
        # 我们使用how='left'来确保df_object中的所有行都被保留
        # 并且当在df_ego中找到匹配时，使用df_ego中的accel值
        merged = pd.merge(self.object_df, self.ego_df[['simTime', 'playerId', 'accelX']],
                          on=['simTime', 'playerId'], how='left', suffixes=('', '_y'))

        merged = pd.merge(merged, self.ego_df[['simTime', 'playerId', 'accelY']],
                          on=['simTime', 'playerId'], how='left', suffixes=('', '_y'))

        # 因为我们使用了suffixes参数来避免列名冲突（尽管在这个特定情况下可能不是必需的），
        # 但现在我们有一个名为'accel_y'的列，它包含了我们要更新的值
        # 如果不担心列名冲突，可以省略suffixes参数，并直接使用'accel'作为列名
        # 在这种情况下，你只需要选择正确的列来更新df_object

        # 更新df_object的accel列
        # 如果使用了suffixes参数，则使用'accel_y'
        # 如果没有使用suffixes参数，并且确信没有列名冲突，则直接使用'accel'
        self.object_df['accelX'] = merged['accelX_y']  # 如果使用了suffixes
        self.object_df['accelY'] = merged['accelY_y']  # 如果使用了suffixes

    def _object_df_process(self):
        """
        Process the data of object dataframe. Save the data groupby object_ID.

        Returns:
            No returns.

        """

        EGO_PLAYER_ID = 1
        data = self.object_df.copy()

        # calculate common parameters
        data['lat_v'] = data['speedY'] * 1
        data['lon_v'] = data['speedX'] * 1
        data['v'] = data.apply(lambda row: cal_velocity(row['lat_v'], row['lon_v']), axis=1)
        data['v'] = data['v']  # km/h

        # calculate acceleraton components
        data['lat_acc'] = data['accelY'] * 1
        data['lon_acc'] = data['accelX'] * 1
        data['accel'] = data.apply(lambda row: cal_velocity(row['lat_acc'], row['lon_acc']), axis=1)

        data = data.dropna(subset=['type'])
        data.reset_index(drop=True, inplace=True)
        self.object_df = data.copy()

        # calculate respective parameters
        for obj_id, obj_data in data.groupby("playerId"):
            self.obj_data[obj_id] = obj_data
            self.obj_data[obj_id]['time_diff'] = self.obj_data[obj_id]['simTime'].diff()

            self.obj_data[obj_id]['lat_acc_diff'] = self.obj_data[obj_id]['lat_acc'].diff()
            self.obj_data[obj_id]['lon_acc_diff'] = self.obj_data[obj_id]['lon_acc'].diff()
            self.obj_data[obj_id]['yawrate_diff'] = self.obj_data[obj_id]['speedH'].diff()

            self.obj_data[obj_id]['lat_acc_roc'] = self.obj_data[obj_id]['lat_acc_diff'] / self.obj_data[obj_id][
                'time_diff']
            self.obj_data[obj_id]['lon_acc_roc'] = self.obj_data[obj_id]['lon_acc_diff'] / self.obj_data[obj_id][
                'time_diff']
            self.obj_data[obj_id]['accelH'] = self.obj_data[obj_id]['yawrate_diff'] / self.obj_data[obj_id][
                'time_diff']

            self.obj_data[obj_id]['lat_acc_roc'] = self.obj_data[obj_id]['lat_acc_roc'].replace([np.inf, -np.inf],
                                                                                                [9999, -9999])
            self.obj_data[obj_id]['lon_acc_roc'] = self.obj_data[obj_id]['lon_acc_roc'].replace([np.inf, -np.inf],
                                                                                                [9999, -9999])
            self.obj_data[obj_id]['accelH'] = self.obj_data[obj_id]['accelH'].replace([np.inf, -np.inf], [9999, -9999])

        # get object id list
        self.obj_id_list = list(self.obj_data.keys())
        self.ego_data = self.obj_data[EGO_PLAYER_ID]

    def _get_status_trigger_dict(self, status_df):

        # calculate ACC trigger time
        acc_trigger = ACCTrigger(status_df)
        acc_status_trigger = acc_trigger.ACC_active_time_statistics()

        # calculate LKA trigger time
        lka_trigger = LKATrigger(status_df)
        lka_status_trigger = lka_trigger.LKA_active_time_statistics()

        # calculate ICA trigger time
        ica_trigger = ICATrigger(status_df)
        ica_status_trigger = ica_trigger.ICA_active_time_statistics()
        # ica_status_trigger = {'ICA_active_time': []}

        return {
            "ACC": acc_status_trigger,
            "LKA": lka_status_trigger,
            "ICA": ica_status_trigger
        }

    def _mileage_cal(self, df1):
        """
        Calculate mileage of given df.

        Args:
            df1: A dataframe of driving data.

        Returns:
            mileage: A float of the mileage(meter) of the driving data.

        """
        df = df1.copy()

        # if 9999.00 in df['travelDist'].values or "9999.00" in df['travelDist'].values:
        if df['travelDist'].nunique() == 1:
            df['time_diff'] = df['simTime'].diff()  # 计算时间间隔
            df['avg_speed'] = (df['v'] + df['v'].shift()) / 2  # 计算每个时间间隔的平均速度
            df['distance_increment'] = df['avg_speed'] * df['time_diff'] / 3.6  # 计算每个时间间隔的距离增量

            # 计算当前里程
            df['travelDist'] = df['distance_increment'].cumsum()
            df['travelDist'] = df['travelDist'].fillna(0)

        mile_start = df['travelDist'].iloc[0]
        mile_end = df['travelDist'].iloc[-1]
        mileage = round(mile_end - mile_start, 2)
        return mileage

    def _duration_cal(self, df):
        """
        Calculate duration of given df.
        Args:
            df: A dataframe of driving data.

        Returns:
            duration: A float of the duration(second) of the driving data.

        """
        time_start = df['simTime'].iloc[0]
        time_end = df['simTime'].iloc[-1]
        duration = time_end - time_start
        return duration

    def _get_report_info(self, df):
        """
        Get report infomation from dataframe.

        Args:
            df: A dataframe of driving data.

        Returns:
            report_info: A dict of report infomation.

        """
        mileage = self._mileage_cal(df)
        duration = self._duration_cal(df)
        report_info = {
            "mileage": mileage,
            "duration": duration
        }
        return report_info

    def _status_mapping(self, df):
        # df['Abpb_status'] = df['Abpb_status'].apply(lambda x: abpb_status_mapping(x))
        df['ACC_status'] = df['ACC_status'].apply(lambda x: acc_status_mapping(x))
        df['Aeb_status'] = df['Aeb_status'].apply(lambda x: aeb_status_mapping(x))
        # df['Awb_status'] = df['Awb_status'].apply(lambda x: ldw_status_mapping(x))
        # df['DOW_status'] = df['DOW_status'].apply(lambda x: ldw_status_mapping(x))
        # df['Eba_status'] = df['Eba_status'].apply(lambda x: ldw_status_mapping(x))
        # df['ELK_status'] = df['ELK_status'].apply(lambda x: ldw_status_mapping(x))
        # df['ESA_status'] = df['ESA_status'].apply(lambda x: ldw_status_mapping(x))
        # df['Fcw_status'] = df['Fcw_status'].apply(lambda x: ldw_status_mapping(x))
        df['ICA_status'] = df['ICA_status'].apply(lambda x: ica_status_mapping(x))
        # df['ISLC_status'] = df['ISLC_status'].apply(lambda x: ldw_status_mapping(x))
        # df['JA_status'] = df['JA_status'].apply(lambda x: ldw_status_mapping(x))
        df['LKA_status'] = df['LKA_status'].apply(lambda x: lka_status_mapping(x))
        df['LDW_status'] = df['LDW_status'].apply(lambda x: ldw_status_mapping(x))
        # df['NOA_status'] = df['NOA_status'].apply(lambda x: ldw_status_mapping(x))
        # df['RCW_status'] = df['RCW_status'].apply(lambda x: ldw_status_mapping(x))
        # df['TLC_status'] = df['TLC_status'].apply(lambda x: ldw_status_mapping(x))
        # df['FVSR_status'] = df['FVSR_status'].apply(lambda x: ldw_status_mapping(x))
        # df['BSD_status'] = df['BSD_status'].apply(lambda x: ldw_status_mapping(x))
        # df['RCTA_status'] = df['RCTA_status'].apply(lambda x: ldw_status_mapping(x))
        # df['FCTA_status'] = df['FCTA_status'].apply(lambda x: ldw_status_mapping(x))
        # df['ISA_status'] = df['ISA_status'].apply(lambda x: ldw_status_mapping(x))
        # df['TSR_status'] = df['TSR_status'].apply(lambda x: ldw_status_mapping(x))
        # df['AVM_status'] = df['AVM_status'].apply(lambda x: ldw_status_mapping(x))
        # df['PDC_status'] = df['PDC_status'].apply(lambda x: ldw_status_mapping(x))
        # df['APA_status'] = df['APA_status'].apply(lambda x: ldw_status_mapping(x))
        # df['MEB_status'] = df['MEB_status'].apply(lambda x: ldw_status_mapping(x))
        # df['RDA_status'] = df['RDA_status'].apply(lambda x: ldw_status_mapping(x))
        return df

    def _get_driver_ctrl_data(self, df):
        """
        Process and get drive ctrl information. Such as brake pedal, throttle pedal and steering wheel.

        Args:
            df: A dataframe of driver ctrl data.

        Returns:
            driver_ctrl_data: A dict of driver ctrl info.

        """
        time_list = df['simTime'].round(2).values.tolist()
        frame_list = df['simFrame'].values.tolist()
        max_brakePedal = df['brakePedal'].max()
        if max_brakePedal < 1:
            df['brakePedal'] = df['brakePedal'] * 100
        brakePedal_list = df['brakePedal'].values.tolist()
        max_throttlePedal = df['throttlePedal'].max()
        if max_throttlePedal < 1:
            df['throttlePedal'] = df['throttlePedal'] * 100
        throttlePedal_list = df['throttlePedal'].values.tolist()
        steeringWheel_list = df['steeringWheel'].values.tolist()

        driver_ctrl_data = {
            "time_list": time_list,
            "frame_list": frame_list,
            "brakePedal_list": brakePedal_list,
            "throttlePedal_list": throttlePedal_list,
            "steeringWheel_list": steeringWheel_list
        }
        return driver_ctrl_data


class StatusTime(object):
    """
    # 调用方式：使用isin()方法来筛选DataFrame中simTime列与time_list中值一致的行
    filtered_df = df[df['simTime'].isin(self.status_time_dict['ACC_status'])]
    """

    def __init__(self, status_df):
        self.status_df = status_df
        self.status_list = []
        self.no_status_time_list = []
        self.status_time_dict = {}

        self._run()

    def _get_status_list(self):
        status_columns_list = self.status_df.columns.tolist()
        self.status_list = [x for x in status_columns_list if x not in ['simTime', 'simFrame']]

    def _get_no_status_time_list(self):
        # 筛选出所有状态机列值都为0的行
        filtered_rows = self.status_df[self.status_df[self.status_list].eq(0).all(axis=1)]

        # 将筛选后的simTime列的值作为列表输出
        self.no_status_time_list = filtered_rows['simTime'].tolist()

    def _get_status_time_dict(self):
        for status in self.status_list:
            status_time = self.status_df[self.status_df[status] != 0]['simTime'].values.tolist()
            self.status_time_dict[status] = status_time

        self.status_time_dict['no_status'] = self.no_status_time_list

    def _run(self):
        self._get_status_list()
        self._get_no_status_time_list()
        self._get_status_time_dict()