#!/usr/bin/env python
# -*- coding: utf-8 -*-
##################################################################
#
# Copyright (c) 2024 CICV, Inc. All Rights Reserved
#
##################################################################
"""
@Authors:           xieguijin(xieguijin@china-icv.cn), yangzihao(yangzihao@china-icv.cn)
@Data:              2024/09/20
@Last Modified:     2024/09/20
@Summary:           Evaluation functions
"""


class ACCTrigger(object):
    def __init__(self, df_vehState):
        self.df_vehState = df_vehState

    def find_start_end_time(self, change_speed_time_list):
        start_end_time_list = []
        # 初始化一个变量来跟踪当前序列的开始值
        # 和一个变量来跟踪当前序列的长度
        start_of_sequence = None
        sequence_length = 0

        # 遍历sim_times列表（从第二个元素开始比较）
        for i in range(1, len(change_speed_time_list)):
            # 检查当前元素与上一个元素的差是否接近0.04（考虑浮点数精度）
            if abs(change_speed_time_list[i] - change_speed_time_list[i - 1] - 0.01) < 0.04:
                # 如果这是序列的开始，则记录开始值
                if start_of_sequence is None:
                    start_of_sequence = change_speed_time_list[i - 1]
                    # 增加序列长度
                sequence_length += 1
            else:
                # 如果序列中断且长度足够（例如，至少为2），则添加序列到结果列表中
                if start_of_sequence is not None and sequence_length >= 0:
                    start_end_time_list.append([start_of_sequence, change_speed_time_list[i - 1]])
                    # 重置开始值和序列长度
                start_of_sequence = None
                sequence_length = 0

            # 处理最后一个序列（如果存在且长度足够）
        if start_of_sequence is not None and sequence_length >= 1:
            start_end_time_list.append([start_of_sequence, change_speed_time_list[-1]])
        return start_end_time_list

    def ACC_active_time_statistics(self):
        start_end_time_dict = {}
        ACC_STATUS = "Active"
        ACC_time = self.df_vehState[self.df_vehState['ACC_status'] == ACC_STATUS]['simTime'].tolist()
        ACC_start_end_time_list = self.find_start_end_time(ACC_time)
        start_end_time_dict['ACC_active_time'] = ACC_start_end_time_list
        return start_end_time_dict


class LKATrigger(object):
    def __init__(self, df_vehState):
        self.df_vehState = df_vehState

    def lane_keep_warning_status(self, left_or_right):
        '''

        :return:
        start_vehstate: 状态机开始的时间，以列表形式存储
        close_vehstate：状态机结束的时间，以列表形式存储
        '''
        groups = (self.df_vehState['LKA_status'] != self.df_vehState['LKA_status'].shift()).cumsum()
        LKA_left_groups = self.df_vehState[self.df_vehState['LKA_status'] == left_or_right].groupby(groups)
        # 初始化结果列表
        result_times = []

        # 使用enumerate来跟踪分组的顺序（模拟全局索引的奇偶性）
        for idx, (name, group) in enumerate(LKA_left_groups, start=1):
            result_time = []
            # 获取片段的simTime值
            sim_times = group['simTime'].tolist()
            # 根据分组的奇偶性（即这里的idx）选择simTime
            # if idx % 2 == 0:
            # 偶数组，选择第一个
            if sim_times:  # 确保列表不为空
                result_time.append(sim_times[-1])
                result_time.append(sim_times[-1])
            result_times.append(result_time)
        return result_times

    def LKA_active_time_statistics(self):
        warning_status_time_dict = {}
        lka_warning = "Active"
        # left_warning = 1
        # right_warning = 2
        LKA_vehstate = self.lane_keep_warning_status(lka_warning)
        # LKA_left_vehstate = self.lane_keep_warning_status(left_warning)
        # LKA_right_vehstate = self.lane_keep_warning_status(right_warning)
        warning_status_time_dict['LKA_active_time'] = LKA_vehstate
        # warning_status_time_dict['LKA_left_active_time'] = LKA_left_vehstate
        # warning_status_time_dict['LKA_right_active_time'] = LKA_right_vehstate
        return warning_status_time_dict


class ICATrigger():
    def __init__(self, df_vehState):
        self.df_vehState = df_vehState

    def ICA_active_time_statistics(self):
        ICA_start_end_time_dict = {}
        # 寻找ICA相应状态机下的simTime和set_cruise_speed数据
        ICA_CONSTANT_SPEED = "LLC_Follow_Line"
        ICA_FOLLOW_CAR = "LLC_Follow_Vehicle"
        ICA_LONGITUDINAL = "Only_Longitudinal_Control"
        ICA_LATERAL = ["LLC_Follow_Line", "LLC_Follow_Vehicle"]
        ICA_ACTIVE = ["LLC_Follow_Line", "LLC_Follow_Vehicle", "Only_Longitudinal_Control"]

        constant_speed_time = self.df_vehState[self.df_vehState['ICA_status'] == ICA_CONSTANT_SPEED]['simTime'].tolist()
        follow_car_time = self.df_vehState[self.df_vehState['ICA_status'] == ICA_FOLLOW_CAR]['simTime'].tolist()
        longitudinal_time_list = self.df_vehState[self.df_vehState['ICA_status'] == ICA_LONGITUDINAL][
            'simTime'].tolist()
        lateral_time_list = self.df_vehState[self.df_vehState['ICA_status'].isin(ICA_LATERAL)]['simTime'].tolist()
        active_time_list = self.df_vehState[self.df_vehState['ICA_status'].isin(ICA_ACTIVE)]['simTime'].tolist()

        ACC = ACCTrigger(self.df_vehState)
        ICA_speed_start_end_time_list = ACC.find_start_end_time(constant_speed_time)
        ICA_dist_start_end_time_list = ACC.find_start_end_time(follow_car_time)
        ICA_longitudinal_start_end_time_list = ACC.find_start_end_time(longitudinal_time_list)
        ICA_lateral_start_end_time_list = ACC.find_start_end_time(lateral_time_list)
        ICA_active_start_end_time_list = ACC.find_start_end_time(active_time_list)

        ICA_start_end_time_dict['ICA_cruise_time'] = ICA_speed_start_end_time_list
        ICA_start_end_time_dict['ICA_follow_time'] = ICA_dist_start_end_time_list
        ICA_start_end_time_dict['ICA_Longitudinal_time'] = ICA_longitudinal_start_end_time_list
        ICA_start_end_time_dict['ICA_Lateral_time'] = ICA_lateral_start_end_time_list
        ICA_start_end_time_dict['ICA_active_time'] = ICA_active_start_end_time_list

        return ICA_start_end_time_dict