puji_indoor/comfort.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
##################################################################
#
# Copyright (c) 2023 CICV, Inc. All Rights Reserved
#
##################################################################
"""
@Authors:           zhanghaiwen(zhanghaiwen@china-icv.cn), yangzihao(yangzihao@china-icv.cn)
@Data:              2023/06/25
@Last Modified:     2023/06/25
@Summary:           Comfort metrics
"""

import os
import sys
import math
import pandas as pd
import numpy as np
import scipy.signal

sys.path.append('../common')
sys.path.append('../modules')
sys.path.append('../results')

from data_info import DataInfoList
from score_weight import cal_score_with_priority, cal_weight_from_80
from common import get_interpolation, score_grade, string_concatenate, replace_key_with_value, get_frame_with_time
import matplotlib.pyplot as plt


def peak_valley_decorator(method):
    def wrapper(self, *args, **kwargs):
        peak_valley = self._peak_valley_determination(self.df)
        pv_list = self.df.loc[peak_valley, ['simTime', 'speedH']].values.tolist()
        if len(pv_list) != 0:
            flag = True
            p_last = pv_list[0]

            for i in range(1, len(pv_list)):
                p_curr = pv_list[i]

                if self._peak_valley_judgment(p_last, p_curr):
                    # method(self, p_curr, p_last)
                    method(self, p_curr, p_last, flag, *args, **kwargs)
                else:
                    p_last = p_curr

            return method
        else:
            flag = False
            p_curr = [0, 0]
            p_last = [0, 0]
            method(self, p_curr, p_last, flag, *args, **kwargs)
            return method

    return wrapper


class Comfort(object):
    """
    Class for achieving comfort metrics for autonomous driving.

    Attributes:
        dataframe: Vehicle driving data, stored in dataframe format.
    """

    def __init__(self, data_processed, custom_data, scoreModel, casePath):
        self.eval_data = pd.DataFrame()
        self.data_processed = data_processed
        self.scoreModel = scoreModel
        self.casePath = casePath

        self.data = data_processed.obj_data[1]
        self.mileage = data_processed.report_info['mileage']
        self.ego_df = pd.DataFrame()
        self.discomfort_df = pd.DataFrame(columns=['start_time', 'end_time', 'start_frame', 'end_frame', 'type'])
        # self.df_drivectrl = data_processed.driver_ctrl_df

        self.config = data_processed.config
        comfort_config = data_processed.comfort_config
        self.comfort_config = comfort_config

        # common data
        self.bulitin_metric_list = self.config.builtinMetricList

        # dimension data
        self.weight_custom = comfort_config['weightCustom']
        self.metric_list = comfort_config['metric']
        self.type_list = comfort_config['type']
        self.type_name_dict = comfort_config['typeName']
        self.name_dict = comfort_config['name']
        self.unit_dict = comfort_config['unit']

        # custom metric data
        self.customMetricParam = comfort_config['customMetricParam']
        self.custom_metric_list = list(self.customMetricParam.keys())
        self.custom_data = custom_data
        self.custom_param_dict = {}

        # score data
        self.weight = comfort_config['weightDimension']

        self.weight_type_dict = comfort_config['typeWeight']
        self.weight_type_list = comfort_config['typeWeightList']

        self.weight_dict = comfort_config['weight']
        self.weight_list = comfort_config['weightList']

        self.priority_dict = comfort_config['priority']
        self.priority_list = comfort_config['priorityList']

        self.kind_dict = comfort_config['kind']
        self.optimal_dict = comfort_config['optimal']
        self.optimal1_dict = self.optimal_dict[0]
        self.optimal2_dict = self.optimal_dict[1]
        self.optimal3_dict = self.optimal_dict[2]
        self.multiple_dict = comfort_config['multiple']
        self.kind_list = comfort_config['kindList']
        self.optimal_list = comfort_config['optimalList']
        self.multiple_list = comfort_config['multipleList']

        # metric data
        self.metric_dict = comfort_config['typeMetricDict']
        self.lat_metric_list = self.metric_dict['comfortLat']
        self.lon_metric_list = self.metric_dict['comfortLon']
        # self.lat_metric_list = ["zigzag", "shake"]
        # self.lon_metric_list = ["cadence", "slamBrake", "slamAccelerate"]

        self.time_list = data_processed.driver_ctrl_data['time_list']
        self.frame_list = data_processed.driver_ctrl_data['frame_list']

        self.linear_accel_dict = dict()
        self.angular_accel_dict = dict()

        self.count_dict = {}
        self.duration_dict = {}
        self.strength_dict = {}

        self.discomfort_count = 0
        self.zigzag_count = 0
        self.shake_count = 0
        self.cadence_count = 0
        self.slam_brake_count = 0
        self.slam_accel_count = 0

        self.zigzag_strength = 0
        self.shake_strength = 0
        self.cadence_strength = 0
        self.slam_brake_strength = 0
        self.slam_accel_strength = 0

        self.discomfort_duration = 0
        self.zigzag_duration = 0
        self.shake_duration = 0
        self.cadence_duration = 0
        self.slam_brake_duration = 0
        self.slam_accel_duration = 0

        self.zigzag_time_list = []
        self.zigzag_frame_list = []
        self.zigzag_stre_list = []
        self.cur_ego_path_list = []
        self.curvature_list = []

        self._get_data()
        self._comf_param_cal()

    def _get_data(self):
        """

        """
        comfort_info_list = DataInfoList.COMFORT_INFO
        self.ego_df = self.data[comfort_info_list].copy()
        # self.df = self.ego_df.set_index('simFrame')  # 索引是csv原索引
        self.df = self.ego_df.reset_index(drop=True)  # 索引是csv原索引

    def _cal_cur_ego_path(self, row):
        try:
            divide = (row['speedX'] ** 2 + row['speedY'] ** 2) ** (3 / 2)
            if not divide:
                res = None
            else:
                res = (row['speedX'] * row['accelY'] - row['speedY'] * row['accelX']) / divide
        except:
            res = None
        return res

    def _cal_max_min_avg(self, num_list):
        maxx = max(num_list) if num_list else "-"
        minn = min(num_list) if num_list else "-"
        avg = sum(num_list) / len(num_list) if num_list else "-"

        result = {
            "max": maxx,
            "min": minn,
            "avg": avg
        }

        return result

    def _comf_param_cal(self):
        """

        """
        for i in range(len(self.optimal_list)):
            if i % 3 == 2:
                continue
            else:
                self.optimal_list[i] = round(self.optimal_list[i] * self.mileage / 100000, 8)

        self.optimal1_dict = {key: value * self.mileage / 100000 for key, value in self.optimal1_dict.copy().items()}
        self.optimal2_dict = {key: value * self.mileage / 100000 for key, value in self.optimal2_dict.copy().items()}

        # [log]
        self.ego_df['ip_acc'] = self.ego_df['v'].apply(get_interpolation, point1=[18, 4], point2=[72, 2])
        self.ego_df['ip_dec'] = self.ego_df['v'].apply(get_interpolation, point1=[18, -5], point2=[72, -3.5])
        self.ego_df['slam_brake'] = self.ego_df.apply(
            lambda row: self._slam_brake_process(row['lon_acc'], row['ip_dec']), axis=1)
        self.ego_df['slam_accel'] = self.ego_df.apply(
            lambda row: self._slam_accelerate_process(row['lon_acc'], row['ip_acc']), axis=1)

        self.ego_df['cadence'] = self.ego_df.apply(
            lambda row: self._cadence_process_new(row['lon_acc'], row['ip_acc'], row['ip_dec']), axis=1)

        self.accel_list = self.ego_df['accel'].values.tolist()
        self.accelH_list = self.ego_df['accelH'].values.tolist()

        self.linear_accel_dict = self._cal_max_min_avg(self.ego_df['accel'].dropna().values.tolist())
        self.angular_accel_dict = self._cal_max_min_avg(self.ego_df['accelH'].dropna().values.tolist())

        # for shake detector
        # self.ego_df['cur_ego_path'] = self.ego_df.apply(self._cal_cur_ego_path, axis=1)
        # self.ego_df['curvHor'] = self.ego_df['curvHor'].astype('float')
        # self.ego_df['cur_diff'] = (self.ego_df['cur_ego_path'] - self.ego_df['curvHor']).abs()
        # self.ego_df['R'] = self.ego_df['curvHor'].apply(lambda x: 10000 if x == 0 else 1 / x)
        # self.ego_df['R_ego'] = self.ego_df['cur_ego_path'].apply(lambda x: 10000 if x == 0 else 1 / x)
        # self.ego_df['R_diff'] = (self.ego_df['R_ego'] - self.ego_df['R']).abs()
        #
        # self.cur_ego_path_list = self.ego_df['cur_ego_path'].values.tolist()
        # self.curvature_list = self.ego_df['curvHor'].values.tolist()

    def _peak_valley_determination(self, df):
        """
        Determine the peak and valley of the vehicle based on its current angular velocity.

        Parameters:
            df: Dataframe containing the vehicle angular velocity.

        Returns:
            peak_valley: List of indices representing peaks and valleys.
        """

        peaks, _ = scipy.signal.find_peaks(df['speedH'], height=0.01, distance=1, prominence=0.01)
        valleys, _ = scipy.signal.find_peaks(-df['speedH'], height=0.01, distance=1, prominence=0.01)
        peak_valley = sorted(list(peaks) + list(valleys))

        return peak_valley

    def _peak_valley_judgment(self, p_last, p_curr, tw=6000, avg=0.4):
        """
        Determine if the given peaks and valleys satisfy certain conditions.

        Parameters:
            p_last: Previous peak or valley data point.
            p_curr: Current peak or valley data point.
            tw: Threshold time difference between peaks and valleys.
            avg: Angular velocity gap threshold.

        Returns:
            Boolean indicating whether the conditions are satisfied.
        """
        t_diff = p_curr[0] - p_last[0]
        v_diff = abs(p_curr[1] - p_last[1])
        s = p_curr[1] * p_last[1]

        zigzag_flag = t_diff < tw and v_diff > avg and s < 0
        if zigzag_flag and ([p_last[0], p_curr[0]] not in self.zigzag_time_list):
            self.zigzag_time_list.append([p_last[0], p_curr[0]])
        return zigzag_flag

    @peak_valley_decorator
    def zigzag_count_func(self, p_curr, p_last, flag=True):
        """
        Count the number of zigzag movements.

        Parameters:
            df: Input dataframe data.

        Returns:
            zigzag_count: Number of zigzag movements.
        """
        if flag:
            self.zigzag_count += 1
        else:
            self.zigzag_count += 0

    @peak_valley_decorator
    def cal_zigzag_strength_strength(self, p_curr, p_last, flag=True):
        """
        Calculate various strength statistics.

        Returns:
            Tuple containing maximum strength, minimum strength,
            average strength, and 99th percentile strength.
        """
        if flag:
            v_diff = abs(p_curr[1] - p_last[1])
            t_diff = p_curr[0] - p_last[0]
            self.zigzag_stre_list.append(v_diff / t_diff)  # 平均角加速度
        else:
            self.zigzag_stre_list = []

    def _shake_detector(self, Cr_diff=0.05, T_diff=0.39):
        """
        ego车横向加速度ax;
        ego车轨迹横向曲率;
        ego车轨迹曲率变化率;
        ego车所在车lane曲率;
        ego车所在车lane曲率变化率;
        转向灯（暂时存疑，可不用）Cr_diff = 0.1, T_diff = 0.04
        求解曲率公式k(t) = (x'(t) * y''(t) - y'(t) * x''(t)) / ((x'(t))^2 + (y'(t))^2)^(3/2)
        """
        time_list = []
        frame_list = []
        shake_time_list = []

        df = self.ego_df.copy()
        df = df[df['cur_diff'] > Cr_diff]
        df['frame_ID_diff'] = df['simFrame'].diff()  # 找出行车轨迹曲率与道路曲率之差大于阈值的数据段
        filtered_df = df[df.frame_ID_diff > T_diff]  # 此处是用大间隔区分多次晃动情景    。

        row_numbers = filtered_df.index.tolist()
        cut_column = pd.cut(df.index, bins=row_numbers)

        grouped = df.groupby(cut_column)
        dfs = {}
        for name, group in grouped:
            dfs[name] = group.reset_index(drop=True)

        for name, df_group in dfs.items():
            # 直道，未主动换道
            df_group['curvHor'] = df_group['curvHor'].abs()
            df_group_straight = df_group[(df_group.lightMask == 0) & (df_group.curvHor < 0.001)]
            if not df_group_straight.empty:
                tmp_list = df_group_straight['simTime'].values
                # shake_time_list.append([tmp_list[0], tmp_list[-1]])
                time_list.extend(df_group_straight['simTime'].values)
                frame_list.extend(df_group_straight['simFrame'].values)
                self.shake_count = self.shake_count + 1

            # 打转向灯，道路为直道，此时晃动判断标准车辆曲率变化率为一个更大的阈值
            df_group_change_lane = df_group[(df_group['lightMask'] != 0) & (df_group['curvHor'] < 0.001)]
            df_group_change_lane_data = df_group_change_lane[df_group_change_lane.cur_diff > Cr_diff + 0.2]
            if not df_group_change_lane_data.empty:
                tmp_list = df_group_change_lane_data['simTime'].values
                # shake_time_list.append([tmp_list[0], tmp_list[-1]])
                time_list.extend(df_group_change_lane_data['simTime'].values)
                frame_list.extend(df_group_change_lane_data['simFrame'].values)
                self.shake_count = self.shake_count + 1

            # 转弯，打转向灯
            df_group_turn = df_group[(df_group['lightMask'] != 0) & (df_group['curvHor'].abs() > 0.001)]
            df_group_turn_data = df_group_turn[df_group_turn.cur_diff.abs() > Cr_diff + 0.1]
            if not df_group_turn_data.empty:
                tmp_list = df_group_turn_data['simTime'].values
                # shake_time_list.append([tmp_list[0], tmp_list[-1]])
                time_list.extend(df_group_turn_data['simTime'].values)
                frame_list.extend(df_group_turn_data['simFrame'].values)
                self.shake_count = self.shake_count + 1

        t_list = time_list
        f_list = frame_list
        group_time = []
        group_frame = []
        sub_group_time = []
        sub_group_frame = []
        for i in range(len(f_list)):
            if not sub_group_time or t_list[i] - t_list[i - 1] <= 0.2:
                sub_group_time.append(t_list[i])
                sub_group_frame.append(f_list[i])
            else:
                group_time.append(sub_group_time)
                group_frame.append(sub_group_frame)
                sub_group_time = [t_list[i]]
                sub_group_frame = [f_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) >= 3]
        # group_frame = [g for g in group_frame if len(g) >= 3]
        #
        # group_time = []
        # sub_group = []
        # for i in range(len(t_list)):
        #     if not sub_group or t_list[i] - t_list[i - 1] <= 0.2:
        #         sub_group.append(t_list[i])
        #     else:
        #         group_time.append(sub_group)
        #         sub_group = [t_list[i]]
        #
        # group_time.append(sub_group)
        # group_time = [g for g in group_time if len(g) >= 3]

        # 输出图表值
        shake_time = [[g[0], g[-1]] for g in group_time]
        shake_frame = [[g[0], g[-1]] for g in group_frame]
        self.shake_count = len(shake_time)

        if shake_time:
            time_df = pd.DataFrame(shake_time, columns=['start_time', 'end_time'])
            frame_df = pd.DataFrame(shake_frame, columns=['start_frame', 'end_frame'])
            discomfort_df = pd.concat([time_df, frame_df], axis=1)
            discomfort_df['type'] = 'shake'
            self.discomfort_df = pd.concat([self.discomfort_df, discomfort_df], ignore_index=True)

        return time_list

    def _cadence_process(self, lon_acc_roc, ip_dec_roc):
        if abs(lon_acc_roc) >= abs(ip_dec_roc) or abs(lon_acc_roc) < 1:
            return np.nan
        # elif abs(lon_acc_roc) == 0:
        elif abs(lon_acc_roc) == 0:
            return 0
        elif lon_acc_roc > 0 and lon_acc_roc < -ip_dec_roc:
            return 1
        elif lon_acc_roc < 0 and lon_acc_roc > ip_dec_roc:
            return -1

    def _slam_brake_process(self, lon_acc, ip_dec):
        if lon_acc - ip_dec < 0:
            return 1
        else:
            return 0

    def _slam_accelerate_process(self, lon_acc, ip_acc):
        if lon_acc - ip_acc > 0:
            return 1
        else:
            return 0

    def _cadence_process_new(self, lon_acc, ip_acc, ip_dec):
        if abs(lon_acc) < 1 or lon_acc > ip_acc or lon_acc < ip_dec:
            return np.nan
        # elif abs(lon_acc_roc) == 0:
        elif abs(lon_acc) == 0:
            return 0
        elif lon_acc > 0 and lon_acc < ip_acc:
            return 1
        elif lon_acc < 0 and lon_acc > ip_dec:
            return -1

    def _cadence_detector(self):
        """
        # 加速度突变：先加后减，先减后加，先加然后停，先减然后停
        # 顿挫：2s内多次加速度变化率突变
        # 求出每一个特征点，然后提取，然后将每一个特征点后面的2s做一个窗口，统计频率，避免无效运算

        # 将特征点筛选出来
        # 将特征点时间作为聚类标准，大于1s的pass，小于等于1s的聚类到一个分组
        # 去掉小于3个特征点的分组
        """
        # data = self.ego_df[['simTime', 'simFrame', 'lon_acc_roc', 'cadence']].copy()
        data = self.ego_df[['simTime', 'simFrame', 'lon_acc', 'lon_acc_roc', 'cadence']].copy()
        time_list = data['simTime'].values.tolist()

        data = data[data['cadence'] != np.nan]
        data['cadence_diff'] = data['cadence'].diff()
        data.dropna(subset='cadence_diff', inplace=True)
        data = data[data['cadence_diff'] != 0]

        t_list = data['simTime'].values.tolist()
        f_list = data['simFrame'].values.tolist()

        group_time = []
        group_frame = []
        sub_group_time = []
        sub_group_frame = []
        for i in range(len(f_list)):
            if not sub_group_time or t_list[i] - t_list[i - 1] <= 1:  # 特征点相邻一秒内的，算作同一组顿挫
                sub_group_time.append(t_list[i])
                sub_group_frame.append(f_list[i])
            else:
                group_time.append(sub_group_time)
                group_frame.append(sub_group_frame)
                sub_group_time = [t_list[i]]
                sub_group_frame = [f_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) >= 1]  # 有一次特征点则算作一次顿挫
        group_frame = [g for g in group_frame if len(g) >= 1]

        # group_time = []
        # sub_group = []
        #
        # for i in range(len(f_list)):
        #     if not sub_group or t_list[i] - t_list[i - 1] <= 1:  # 特征点相邻一秒内的，算作同一组顿挫
        #         sub_group.append(t_list[i])
        #     else:
        #         group_time.append(sub_group)
        #         sub_group = [t_list[i]]
        #
        # group_time.append(sub_group)
        # group_time = [g for g in group_time if len(g) >= 1]  # 有一次特征点则算作一次顿挫

        # 输出图表值
        cadence_time = [[g[0], g[-1]] for g in group_time]
        cadence_frame = [[g[0], g[-1]] for g in group_frame]

        if cadence_time:
            time_df = pd.DataFrame(cadence_time, columns=['start_time', 'end_time'])
            frame_df = pd.DataFrame(cadence_frame, columns=['start_frame', 'end_frame'])
            discomfort_df = pd.concat([time_df, frame_df], axis=1)
            discomfort_df['type'] = 'cadence'
            self.discomfort_df = pd.concat([self.discomfort_df, discomfort_df], ignore_index=True)

        # 将顿挫组的起始时间为组重新统计时间
        cadence_time_list = [time for pair in cadence_time for time in time_list if pair[0] <= time <= pair[1]]

        # time_list = [element for sublist in group_time for element in sublist]
        # merged_list = [element for sublist in res_group for element in sublist]
        # res_df = data[data['simTime'].isin(merged_list)]

        stre_list = []
        freq_list = []
        for g in group_time:
            # calculate strength
            g_df = data[data['simTime'].isin(g)]
            strength = g_df['lon_acc'].abs().mean()
            stre_list.append(strength)

            # calculate frequency
            cnt = len(g)
            t_start = g_df['simTime'].iloc[0]
            t_end = g_df['simTime'].iloc[-1]
            t_delta = t_end - t_start
            frequency = cnt / t_delta
            freq_list.append(frequency)

        self.cadence_count = len(freq_list)
        cadence_stre = sum(stre_list) / len(stre_list) if stre_list else 0

        return cadence_time_list

    def _slam_brake_detector(self):
        # 统计急刹全为1的分段的个数，记录分段开头的frame_ID
        # data = self.ego_df[['simTime', 'simFrame', 'lon_acc_roc', 'ip_dec_roc', 'slam_brake']].copy()
        data = self.ego_df[['simTime', 'simFrame', 'lon_acc', 'lon_acc_roc', 'ip_dec', 'slam_brake']].copy()
        # data['slam_diff'] = data['slam_brake'].diff()
        # res_df = data[data['slam_diff'] == 1]

        res_df = data[data['slam_brake'] == 1]
        t_list = res_df['simTime'].values
        f_list = res_df['simFrame'].values.tolist()

        group_time = []
        group_frame = []
        sub_group_time = []
        sub_group_frame = []
        for i in range(len(f_list)):
            if not sub_group_time or f_list[i] - f_list[i - 1] <= 1:  # 连续帧的算作同一组急刹
                sub_group_time.append(t_list[i])
                sub_group_frame.append(f_list[i])
            else:
                group_time.append(sub_group_time)
                group_frame.append(sub_group_frame)
                sub_group_time = [t_list[i]]
                sub_group_frame = [f_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]  # 达到两帧算作一次急刹
        group_frame = [g for g in group_frame if len(g) >= 2]

        # group_time = []
        # sub_group = []
        #
        # for i in range(len(f_list)):
        #     if not sub_group or f_list[i] - f_list[i - 1] <= 1:  # 连续帧的算作同一组急刹
        #         sub_group.append(t_list[i])
        #     else:
        #         group_time.append(sub_group)
        #         sub_group = [t_list[i]]
        #
        # group_time.append(sub_group)
        # group_time = [g for g in group_time if len(g) >= 2]  # 达到两帧算作一次急刹

        # 输出图表值
        slam_brake_time = [[g[0], g[-1]] for g in group_time]
        slam_brake_frame = [[g[0], g[-1]] for g in group_frame]

        if slam_brake_time:
            time_df = pd.DataFrame(slam_brake_time, columns=['start_time', 'end_time'])
            frame_df = pd.DataFrame(slam_brake_frame, columns=['start_frame', 'end_frame'])
            discomfort_df = pd.concat([time_df, frame_df], axis=1)
            discomfort_df['type'] = 'slam_brake'
            self.discomfort_df = pd.concat([self.discomfort_df, discomfort_df], ignore_index=True)

        time_list = [element for sublist in group_time for element in sublist]
        self.slam_brake_count = len(group_time)  # / self.mileage  # * 1000000
        return time_list

    def _slam_accel_detector(self):
        # 统计急刹全为1的分段的个数，记录分段开头的frame_ID
        # data = self.ego_df[['simTime', 'simFrame', 'lon_acc_roc', 'ip_acc_roc', 'slam_accel']].copy()
        data = self.ego_df[['simTime', 'simFrame', 'lon_acc', 'ip_acc', 'slam_accel']].copy()
        # data['slam_diff'] = data['slam_accel'].diff()
        # res_df = data.loc[data['slam_diff'] == 1]

        res_df = data.loc[data['slam_accel'] == 1]
        t_list = res_df['simTime'].values
        f_list = res_df['simFrame'].values.tolist()

        group_time = []
        group_frame = []
        sub_group_time = []
        sub_group_frame = []
        for i in range(len(f_list)):
            if not group_time or f_list[i] - f_list[i - 1] <= 1:  # 连续帧的算作同一组急加速
                sub_group_time.append(t_list[i])
                sub_group_frame.append(f_list[i])
            else:
                group_time.append(sub_group_time)
                group_frame.append(sub_group_frame)
                sub_group_time = [t_list[i]]
                sub_group_frame = [f_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]

        # group_time = []
        # sub_group = []
        #
        # for i in range(len(f_list)):
        #     if not sub_group or f_list[i] - f_list[i - 1] <= 1:  # 连续帧的算作同一组急加速
        #         sub_group.append(t_list[i])
        #     else:
        #         group_time.append(sub_group)
        #         sub_group = [t_list[i]]
        #
        # group_time.append(sub_group)
        # group_time = [g for g in group_time if len(g) >= 2]  # 达到两帧算作一次急加速

        # 输出图表值
        slam_accel_time = [[g[0], g[-1]] for g in group_time]
        slam_accel_frame = [[g[0], g[-1]] for g in group_frame]

        if slam_accel_time:
            time_df = pd.DataFrame(slam_accel_time, columns=['start_time', 'end_time'])
            frame_df = pd.DataFrame(slam_accel_frame, columns=['start_frame', 'end_frame'])
            discomfort_df = pd.concat([time_df, frame_df], axis=1)
            discomfort_df['type'] = 'slam_accel'
            self.discomfort_df = pd.concat([self.discomfort_df, discomfort_df], ignore_index=True)

        time_list = [element for sublist in group_time for element in sublist]
        self.slam_accel_count = len(group_time)  # / self.mileage  # * 1000000
        return time_list

    def comf_statistic(self):
        """

        """
        # df = self.ego_df[['simTime', 'cur_diff', 'lon_acc', 'lon_acc_roc', 'accelH']].copy()
        df = self.ego_df[['simTime', 'lon_acc', 'lon_acc_roc', 'accelH']].copy()

        self.zigzag_count_func()
        self.cal_zigzag_strength_strength()
        if self.zigzag_time_list:
            zigzag_df = pd.DataFrame(self.zigzag_time_list, columns=['start_time', 'end_time'])
            zigzag_df = get_frame_with_time(zigzag_df, self.ego_df)
            zigzag_df['type'] = 'zigzag'
            self.discomfort_df = pd.concat([self.discomfort_df, zigzag_df], ignore_index=True)
            # discomfort_df = pd.concat([time_df, frame_df], axis=1)
            # self.discomfort_df = pd.concat([self.discomfort_df, discomfort_df], ignore_index=True)

        zigzag_t_list = []
        # 只有[t_start, t_end]数对，要提取为完整time list
        t_list = df['simTime'].values.tolist()
        for t_start, t_end in self.zigzag_time_list:
            index_1 = t_list.index(t_start)
            index_2 = t_list.index(t_end)
            zigzag_t_list.extend(t_list[index_1:index_2 + 1])
        zigzag_t_list = list(set(zigzag_t_list))
        # shake_t_list = self._shake_detector()
        cadence_t_list = self._cadence_detector()
        slam_brake_t_list = self._slam_brake_detector()
        slam_accel_t_list = self._slam_accel_detector()

        # comfort_time_dict = {
        #     'zigzag_time_list': zigzag_t_list,
        #     'shake_time_list': shake_t_list,
        #     'cadence_time_list': cadence_t_list,
        #     'slam_brake_time_list': slam_brake_t_list,
        #     'slam_accelerate_time_list': slam_accel_t_list
        # }

        discomfort_time_list = zigzag_t_list + cadence_t_list + slam_brake_t_list + slam_accel_t_list
        discomfort_time_list = sorted(discomfort_time_list)  # 排序
        discomfort_time_list = list(set(discomfort_time_list))  # 去重
        time_diff = self.time_list[3] - self.time_list[2]
        # time_diff = 0.4

        self.discomfort_duration = len(discomfort_time_list) * time_diff

        df['flag_zigzag'] = df['simTime'].apply(lambda x: 1 if x in zigzag_t_list else 0)
        # df['flag_shake'] = df['simTime'].apply(lambda x: 1 if x in shake_t_list else 0)
        df['flag_cadence'] = df['simTime'].apply(lambda x: 1 if x in cadence_t_list else 0)
        df['flag_slam_brake'] = df['simTime'].apply(lambda x: 1 if x in slam_brake_t_list else 0)
        df['flag_slam_accel'] = df['simTime'].apply(lambda x: 1 if x in slam_accel_t_list else 0)

        # hectokilometer = 100000  # 百公里
        self.zigzag_duration = df['flag_zigzag'].sum() * time_diff  # / self.mileage * hectokilometer
        # self.shake_duration = df['flag_shake'].sum() * time_diff  # / self.mileage * hectokilometer
        self.cadence_duration = df['flag_cadence'].sum() * time_diff  # / self.mileage * hectokilometer
        self.slam_brake_duration = df['flag_slam_brake'].sum() * time_diff  # / self.mileage * hectokilometer
        self.slam_accel_duration = df['flag_slam_accel'].sum() * time_diff  # / self.mileage * hectokilometer

        # 强度取值可考虑最大值，暂定平均值，具体视数据情况而定
        # self.zigzag_strength = np.mean(self.zigzag_stre_list) if self.zigzag_stre_list else 0
        self.zigzag_strength = (df['flag_zigzag'] * abs(df['accelH'])).mean()
        # self.shake_strength = (df['flag_shake'] * abs(df['cur_diff'])).mean()
        self.cadence_strength = (df['flag_cadence'] * abs(df['lon_acc'])).mean()
        self.slam_brake_strength = (df['flag_slam_brake'] * abs(df['lon_acc'])).mean()
        self.slam_accel_strength = (df['flag_slam_accel'] * abs(df['lon_acc'])).mean()

        self.zigzag_strength = self._nan_detect(self.zigzag_strength)
        # self.shake_strength = self._nan_detect(self.shake_strength)
        self.cadence_strength = self._nan_detect(self.cadence_strength)
        self.slam_brake_strength = self._nan_detect(self.slam_brake_strength)
        self.slam_accel_strength = self._nan_detect(self.slam_accel_strength)

        self.count_dict = {
            "zigzag": self.zigzag_count,
            # "shake": self.shake_count,
            "cadence": self.cadence_count,
            "slamBrake": self.slam_brake_count,
            "slamAccelerate": self.slam_accel_count
        }

        self.duration_dict = {
            "zigzag": self.zigzag_duration,
            # "shake": self.shake_duration,
            "cadence": self.cadence_duration,
            "slamBrake": self.slam_brake_duration,
            "slamAccelerate": self.slam_accel_duration
        }

        self.strength_dict = {
            "zigzag": self.zigzag_strength,
            # "shake": self.shake_strength,
            "cadence": self.cadence_strength,
            "slamBrake": self.slam_brake_strength,
            "slamAccelerate": self.slam_accel_strength
        }

        zigzag_list = [self.zigzag_count, self.zigzag_duration, self.zigzag_strength]
        # shake_list = [self.shake_count, self.shake_duration, self.shake_strength]
        cadence_list = [self.cadence_count, self.cadence_duration, self.cadence_strength]
        slam_brake_list = [self.slam_brake_count, self.slam_brake_duration, self.slam_brake_strength]
        slam_accel_list = [self.slam_accel_count, self.slam_accel_duration, self.slam_accel_strength]

        tmp_comf_arr = []
        if "zigzag" in self.metric_list:
            tmp_comf_arr += zigzag_list
            self.discomfort_count += self.zigzag_count

        # if "shake" in self.metric_list:
        #     tmp_comf_arr += shake_list
        #     self.discomfort_count += self.shake_count

        if "cadence" in self.metric_list:
            tmp_comf_arr += cadence_list
            self.discomfort_count += self.cadence_count

        if "slamBrake" in self.metric_list:
            tmp_comf_arr += slam_brake_list
            self.discomfort_count += self.slam_brake_count

        if "slamAccelerate" in self.metric_list:
            tmp_comf_arr += slam_accel_list
            self.discomfort_count += self.slam_accel_count

        comf_arr = [tmp_comf_arr]
        return comf_arr

    def _nan_detect(self, num):
        if math.isnan(num):
            return 0
        return num

    def custom_metric_param_parser(self, param_list):
        """
        param_dict = {
            "paramA" [
                {
                    "kind": "-1",
                    "optimal": "1",
                    "multiple": ["0.5","5"],
                    "spare1": null,
                    "spare2": null
                }
            ]
        }
        """
        kind_list = []
        optimal_list = []
        multiple_list = []
        spare_list = []
        # spare1_list = []
        # spare2_list = []

        for i in range(len(param_list)):
            kind_list.append(int(param_list[i]['kind']))
            optimal_list.append(float(param_list[i]['optimal']))
            multiple_list.append([float(x) for x in param_list[i]['multiple']])
            spare_list.append([item["param"] for item in param_list[i]["spare"]])
            # spare1_list.append(param_list[i]['spare1'])
            # spare2_list.append(param_list[i]['spare2'])

        result = {
            "kind": kind_list,
            "optimal": optimal_list,
            "multiple": multiple_list,
            "spare": spare_list,
            # "spare1": spare1_list,
            # "spare2": spare2_list
        }
        return result

    def custom_metric_score(self, metric, value, param_list):
        """

        """
        param = self.custom_metric_param_parser(param_list)
        self.custom_param_dict[metric] = param

        score_model = self.scoreModel(param['kind'], param['optimal'], param['multiple'], np.array([value]))
        score_sub = score_model.cal_score()
        score = sum(score_sub) / len(score_sub)
        return score

    def comf_score_new(self):
        arr_comf = self.comf_statistic()
        print("\n[平顺性表现及得分情况]")
        print("平顺性各指标值：", [[round(num, 2) for num in row] for row in arr_comf])
        arr_comf = np.array(arr_comf)

        score_model = self.scoreModel(self.kind_list, self.optimal_list, self.multiple_list, arr_comf)
        score_sub = score_model.cal_score()
        score_sub = list(map(lambda x: 80 if np.isnan(x) else x, score_sub))

        metric_list = [x for x in self.metric_list if x in self.config.builtinMetricList]
        score_metric = []
        for i in range(len(metric_list)):
            score_tmp = (score_sub[i * 3 + 0] + score_sub[i * 3 + 1] + score_sub[i * 3 + 2]) / 3
            score_metric.append(round(score_tmp, 2))

        score_metric_dict = {key: value for key, value in zip(metric_list, score_metric)}

        custom_metric_list = list(self.customMetricParam.keys())
        for metric in custom_metric_list:
            value = self.custom_data[metric]['value']
            param_list = self.customMetricParam[metric]
            score = self.custom_metric_score(metric, value, param_list)
            score_metric_dict[metric] = round(score, 2)

        score_metric_dict = {key: score_metric_dict[key] for key in self.metric_list}
        score_metric = list(score_metric_dict.values())

        score_type_dict = {}
        if self.weight_custom:  # 自定义权重
            score_metric_with_weight_dict = {key: score_metric_dict[key] * self.weight_dict[key] for key in
                                             self.weight_dict}

            for type in self.type_list:
                type_score = sum(
                    value for key, value in score_metric_with_weight_dict.items() if key in self.metric_dict[type])
                score_type_dict[type] = round(type_score, 2)

            score_type_with_weight_dict = {key: score_type_dict[key] * self.weight_type_dict[key] for key in
                                           score_type_dict}
            print("comfort score_type_with_weight_dict is", score_type_with_weight_dict)
            score_comfort = sum(score_type_with_weight_dict.values())
        else:  # 客观赋权
            self.weight_list = cal_weight_from_80(score_metric)
            self.weight_dict = {key: value for key, value in zip(self.metric_list, self.weight_list)}
            score_comfort = cal_score_with_priority(score_metric, self.weight_list, self.priority_list)

            for type in self.type_list:
                type_weight = sum(value for key, value in self.weight_dict.items() if key in self.metric_dict[type])
                self.weight_dict = {key: round(value / type_weight, 4) for key, value in self.weight_dict.items() if
                                    key in self.metric_dict[type]}
                type_score_metric = [value for key, value in score_metric_dict.items() if key in self.metric_dict[type]]
                type_weight_list = [value for key, value in self.weight_dict.items() if key in self.metric_dict[type]]
                print("comfort type_weight_list is", type_weight_list)
                type_priority_list = [value for key, value in self.priority_dict.items() if
                                      key in self.metric_dict[type]]

                type_score = cal_score_with_priority(type_score_metric, type_weight_list, type_priority_list)
                score_type_dict[type] = round(type_score, 2)

        score_comfort = round(score_comfort, 2)

        print("平顺性各指标基准值：", self.optimal_list)
        print(f"平顺性得分为：{score_comfort:.2f}分。")
        print(f"平顺性各类型得分为：{score_type_dict}。")
        print(f"平顺性各指标得分为：{score_metric_dict}。")
        return score_comfort, score_type_dict, score_metric_dict

    def zip_time_pairs(self, zip_list, upper_limit=9999):
        zip_time_pairs = zip(self.time_list, zip_list)
        zip_vs_time = [[x, upper_limit if y > upper_limit else y] for x, y in zip_time_pairs if not math.isnan(y)]
        return zip_vs_time

    def comf_weight_distribution(self):
        # get weight distribution
        weight_distribution = {}
        weight_distribution["name"] = "平顺性"

        if "comfortLat" in self.type_list:
            lat_weight_indexes_dict = {key: f"{key}({value * 100:.2f}%)" for key, value in self.weight_dict.items() if
                                       key in self.lat_metric_list}

            weight_distribution_lat = {
                "latWeight": f"横向舒适度({self.weight_type_dict['comfortLat'] * 100:.2f}%)",
                "indexes": lat_weight_indexes_dict
            }
            weight_distribution['comfortLat'] = weight_distribution_lat

        if "comfortLon" in self.type_list:
            lon_weight_indexes_dict = {key: f"{key}({value * 100:.2f}%)" for key, value in self.weight_dict.items() if
                                       key in self.lon_metric_list}

            weight_distribution_lon = {
                "lonWeight": f"纵向舒适度({self.weight_type_dict['comfortLon'] * 100:.2f}%)",
                "indexes": lon_weight_indexes_dict
            }
            weight_distribution['comfortLon'] = weight_distribution_lon

        return weight_distribution

    def _get_weight_distribution(self, dimension):
        # get weight distribution
        weight_distribution = {}
        weight_distribution["name"] = self.config.dimension_name[dimension]

        for type in self.type_list:
            type_weight_indexes_dict = {key: f"{self.name_dict[key]}({value * 100:.2f}%)" for key, value in
                                        self.weight_dict.items() if
                                        key in self.metric_dict[type]}

            weight_distribution_type = {
                "weight": f"{self.type_name_dict[type]}({self.weight_type_dict[type] * 100:.2f}%)",
                "indexes": type_weight_indexes_dict
            }
            weight_distribution[type] = weight_distribution_type

        return weight_distribution

    def report_statistic(self):
        """

        Returns:

        """
        # report_dict = {
        #     "name": "平顺性",
        #     "weight": f"{self.weight * 100:.2f}%",
        #     "weightDistribution": weight_distribution,
        #     "score": score_comfort,
        #     "level": grade_comfort,

        #     'discomfortCount': self.discomfort_count,
        #     "description1": comf_description1,
        #     "description2": comf_description2,
        #     "description3": comf_description3,
        #     "description4": comf_description4,
        #
        #     "comfortLat": lat_dict,
        #     "comfortLon": lon_dict,
        #
        #     "speData": ego_speed_vs_time,
        #     "speMarkLine": discomfort_slices,
        #
        #     "accData": lon_acc_vs_time,
        #     "accMarkLine": discomfort_acce_slices,
        #
        #     "anvData": yawrate_vs_time,
        #     "anvMarkLine": discomfort_zigzag_slices,
        #
        #     "anaData": yawrate_roc_vs_time,
        #     "anaMarkLine": discomfort_zigzag_slices,
        #
        #     "curData": [cur_ego_path_vs_time, curvature_vs_time],
        #     "curMarkLine": discomfort_shake_slices,
        # }
        # brakePedal_list = self.data_processed.driver_ctrl_data['brakePedal_list']
        # throttlePedal_list = self.data_processed.driver_ctrl_data['throttlePedal_list']
        # steeringWheel_list = self.data_processed.driver_ctrl_data['steeringWheel_list']
        #
        # # common parameter calculate
        # brake_vs_time = self.zip_time_pairs(brakePedal_list, 100)
        # throttle_vs_time = self.zip_time_pairs(throttlePedal_list, 100)
        # steering_vs_time = self.zip_time_pairs(steeringWheel_list)

        report_dict = {
            "name": "平顺性",
            # "weight": f"{self.weight * 100:.2f}%",

            # 'discomfortCount': self.discomfort_count,
        }

        # upper_limit = 40
        # times_upper = 2
        # len_time = len(self.time_list)
        duration = self.time_list[-1]

        # comfort score and grade
        score_comfort, score_type_dict, score_metric_dict = self.comf_score_new()

        # get weight distribution
        # report_dict["weightDistribution"] = self._get_weight_distribution("comfort")

        score_comfort = int(score_comfort) if int(score_comfort) == score_comfort else round(score_comfort, 2)
        grade_comfort = score_grade(score_comfort)
        report_dict["score"] = score_comfort
        report_dict["level"] = grade_comfort

        # comfort data for graph
        ego_speed_list = self.ego_df['v'].values.tolist()
        ego_speed_vs_time = self.zip_time_pairs(ego_speed_list)

        lon_acc_list = self.ego_df['lon_acc'].values.tolist()
        lon_acc_vs_time = self.zip_time_pairs(lon_acc_list)

        yawrate_list = self.ego_df['speedH'].values.tolist()
        yawrate_vs_time = self.zip_time_pairs(yawrate_list)

        yawrate_roc_list = self.ego_df['accelH'].values.tolist()
        yawrate_roc_vs_time = self.zip_time_pairs(yawrate_roc_list)

        cur_ego_path_vs_time = self.zip_time_pairs(self.cur_ego_path_list)
        curvature_vs_time = self.zip_time_pairs(self.curvature_list)

        # markline
        # discomfort_df = self.discomfort_df.copy()
        # discomfort_df['type'] = "origin"
        # discomfort_slices = discomfort_df.to_dict('records')
        #
        # discomfort_zigzag_df = self.discomfort_df.copy()
        # discomfort_zigzag_df.loc[discomfort_zigzag_df['type'] != 'zigzag', 'type'] = "origin"
        # discomfort_zigzag_slices = discomfort_zigzag_df.to_dict('records')
        #
        # discomfort_shake_df = self.discomfort_df.copy()
        # discomfort_shake_df.loc[discomfort_shake_df['type'] != 'shake', 'type'] = "origin"
        # discomfort_shake_slices = discomfort_shake_df.to_dict('records')
        #
        # discomfort_acce_df = self.discomfort_df.copy()
        # discomfort_acce_df.loc[discomfort_acce_df['type'] == 'zigzag', 'type'] = "origin"
        # discomfort_acce_df.loc[discomfort_acce_df['type'] == 'shake', 'type'] = "origin"
        # discomfort_acce_slices = discomfort_acce_df.to_dict('records')

        # for description
        good_type_list = []
        bad_type_list = []

        good_metric_list = []
        bad_metric_list = []

        # str for comf description 1&2
        str_uncomf_count = ''
        str_uncomf_over_optimal = ''

        # type_details_dict = {}

        # for type in self.type_list:
        #     bad_type_list.append(type) if score_type_dict[type] < 80 else good_type_list.append(type)
        #
        #     type_dict = {
        #         "name": f"{self.type_name_dict[type]}",
        #     }
        #
        #     builtin_graph_dict = {}
        #     custom_graph_dict = {}
        #
        #     score_type = score_type_dict[type]
        #     grade_type = score_grade(score_type)
        #     type_dict["score"] = score_type
        #     type_dict["level"] = grade_type
        #
        #     type_dict_indexes = {}
        #
        #     flag_acc = False
        #     for metric in self.metric_dict[type]:
        #         bad_metric_list.append(metric) if score_metric_dict[metric] < 80 else good_metric_list.append(metric)
        #
        #         if metric in self.bulitin_metric_list:
        #             # for indexes
        #             type_dict_indexes[metric] = {
        #                 "name": f"{self.name_dict[metric]}({self.unit_dict[metric]})",
        #                 "score": score_metric_dict[metric],
        #                 "numberReal": f"{self.count_dict[metric]}",
        #                 "numberRef": f"{self.optimal1_dict[metric]:.4f}",
        #                 "durationReal": f"{self.duration_dict[metric]:.2f}",
        #                 "durationRef": f"{self.optimal2_dict[metric]:.4f}",
        #                 "strengthReal": f"{self.strength_dict[metric]:.2f}",
        #                 "strengthRef": f"{self.optimal3_dict[metric]}"
        #             }
        #
        #             # for description
        #             str_uncomf_count += f'{self.count_dict[metric]}次{self.name_dict[metric]}行为、'
        #             if self.count_dict[metric] > self.optimal1_dict[metric]:
        #                 over_optimal = ((self.count_dict[metric] - self.optimal1_dict[metric]) / self.optimal1_dict[
        #                     metric]) * 100
        #                 str_uncomf_over_optimal += f'{self.name_dict[metric]}次数比基准值高{over_optimal:.2f}%，'
        #
        #             if self.duration_dict[metric] > self.optimal2_dict[metric]:
        #                 over_optimal = ((self.duration_dict[metric] - self.optimal2_dict[metric]) / self.optimal2_dict[
        #                     metric]) * 100
        #                 str_uncomf_over_optimal += f'{self.name_dict[metric]}时长比基准值高{over_optimal:.2f}%，'
        #
        #             if self.strength_dict[metric] > self.optimal3_dict[metric]:
        #                 over_optimal = ((self.strength_dict[metric] - self.optimal3_dict[metric]) / self.optimal3_dict[
        #                     metric]) * 100
        #                 str_uncomf_over_optimal += f'{self.name_dict[metric]}强度比基准值高{over_optimal:.2f}%；'
        #
        #             # report_dict["speData"] = ego_speed_vs_time
        #             # report_dict["accData"] = lon_acc_vs_time
        #             # report_dict["anvData"] = yawrate_vs_time
        #             # report_dict["anaData"] = yawrate_roc_vs_time
        #             # report_dict["curData"] = [cur_ego_path_vs_time, curvature_vs_time]
        #
        #             # report_dict["speMarkLine"] = discomfort_slices
        #             # report_dict["accMarkLine"] = discomfort_acce_slices
        #             # report_dict["anvMarkLine"] = discomfort_zigzag_slices
        #             # report_dict["anaMarkLine"] = discomfort_zigzag_slices
        #             # report_dict["curMarkLine"] = discomfort_shake_slices
        #
        #             if metric == "zigzag":
        #                 metric_data = {
        #                     "name": "横摆角加速度(rad/s²)",
        #                     "data": yawrate_roc_vs_time,
        #                     "range": f"[0, {self.optimal3_dict[metric]}]",
        #                     # "markLine": discomfort_zigzag_slices
        #                 }
        #                 builtin_graph_dict[metric] = metric_data
        #
        #             elif metric == "shake":
        #                 metric_data = {
        #                     "name": "曲率(1/m)",
        #                     "legend": ["自车轨迹曲率", "车道中心线曲率"],
        #                     "data": [cur_ego_path_vs_time, curvature_vs_time],
        #                     "range": f"[0, {self.optimal3_dict[metric]}]",
        #                     # "markLine": discomfort_shake_slices
        #                 }
        #                 builtin_graph_dict[metric] = metric_data
        #
        #             elif metric in ["cadence", "slamBrake", "slamAccelerate"] and not flag_acc:
        #                 metric_data = {
        #                     "name": "自车纵向加速度(m/s²)",
        #                     "data": lon_acc_vs_time,
        #                     "range": f"[0, {self.optimal3_dict[metric]}]",
        #                     # "markLine": discomfort_acce_slices
        #                 }
        #                 flag_acc = True
        #
        #                 builtin_graph_dict[metric] = metric_data
        #
        #         else:
        #             # for indexes
        #             type_dict_indexes[metric] = {
        #                 "name": f"{self.name_dict[metric]}({self.unit_dict[metric]})",
        #                 "score": score_metric_dict[metric],
        #                 "numberReal": f"{self.custom_data[metric]['tableData']['avg']}",
        #                 "numberRef": f"-",
        #                 "durationReal": f"{self.custom_data[metric]['tableData']['max']}",
        #                 "durationRef": f"-",
        #                 "strengthReal": f"{self.custom_data[metric]['tableData']['min']}",
        #                 "strengthRef": f"-"
        #             }
        #             custom_graph_dict[metric] = self.custom_data[metric]['reportData']
        #
        #         str_uncomf_over_optimal = str_uncomf_over_optimal[:-1] + "；"
        #         type_dict["indexes"] = type_dict_indexes
        #         type_dict["builtin"] = builtin_graph_dict
        #         type_dict["custom"] = custom_graph_dict
        #
        #     type_details_dict[type] = type_dict

        # report_dict["details"] = type_details_dict

        # str for comf description2
        # if grade_comfort == '优秀':
        #     comf_description1 = '机器人在本轮测试中行驶平顺；'
        # elif grade_comfort == '良好':
        #     comf_description1 = '机器人在本轮测试中的表现满⾜设计指标要求；'
        # elif grade_comfort == '一般':
        #     str_bad_metric = string_concatenate(bad_metric_list)
        #     comf_description1 = f'未满足设计指标要求。需要在{str_bad_metric}上进一步优化。在{(self.mileage / 1000):.2f}公里内，共发生{str_uncomf_count[:-1]}；'
        # elif grade_comfort == '较差':
        #     str_bad_metric = string_concatenate(bad_metric_list)
        #     comf_description1 = f'机器人行驶极不平顺，未满足设计指标要求。需要在{str_bad_metric}上进一步优化。在{(self.mileage / 1000):.2f}公里内，共发生{str_uncomf_count[:-1]}；'
        #
        # if not bad_metric_list:
        #     str_comf_type = string_concatenate(good_metric_list)
        #     comf_description2 = f"{str_comf_type}均表现良好"
        # else:
        #     str_bad_metric = string_concatenate(bad_metric_list)
        #
        #     if not good_metric_list:
        #         comf_description2 = f"{str_bad_metric}表现不佳。其中{str_uncomf_over_optimal}"
        #     else:
        #         str_comf_type = string_concatenate(good_metric_list)
        #         comf_description2 = f"{str_comf_type}表现良好；{str_bad_metric}表现不佳。其中{str_uncomf_over_optimal}"
        #
        # # str for comf description3
        # control_type = []
        # if 'zigzag' in bad_metric_list or 'shake' in bad_metric_list:
        #     control_type.append('横向')
        # if 'cadence' in bad_metric_list or 'slamBrake' in bad_metric_list or 'slamAccelerate' in bad_metric_list in bad_metric_list:
        #     control_type.append('纵向')
        # str_control_type = '和'.join(control_type)
        #
        # if not control_type:
        #     comf_description3 = f"机器人的横向和纵向控制表现俱佳，行驶平顺"
        # else:
        #     comf_description3 = f"应该优化对机器人的{str_control_type}控制，优化行驶平顺性"
        #
        # uncomf_time = self.discomfort_duration
        # if uncomf_time == 0:
        #     comf_description4 = ""
        # else:
        #     percent4 = uncomf_time / duration * 100
        #     # comf_description4 = f"在{duration}s时间内，机器人有{percent4:.2f}%的时间存在行驶不平顺的情况。"
        #     comf_description4 = f"在{duration}s时间内，机器人有{uncomf_time:.2f}s的时间存在行驶不平顺的情况。"

        # report_dict["description1"] = replace_key_with_value(comf_description1, self.name_dict)
        # report_dict["description2"] = replace_key_with_value(comf_description2, self.name_dict)
        # report_dict["description3"] = comf_description3
        # report_dict["description4"] = comf_description4
        description = "· 在平顺性方面，"

        if any(score_metric_dict[metric] < 80 for metric in self.lon_metric_list):
            description += "线加速度变化剧烈，"
            tmp = [metric for metric in self.lon_metric_list if score_metric_dict[metric] < 80]
            str_tmp = "、".join(tmp)
            description += f"有{str_tmp}情况，需重点优化。"

        if any(score_metric_dict[metric] < 80 for metric in self.lat_metric_list):
            description += "角加速度变化剧烈，"
            tmp = [metric for metric in self.lat_metric_list if score_metric_dict[metric] < 80]
            str_tmp = "、".join(tmp)
            description += f"有{str_tmp}情况，需重点优化。"

        if description == "在平顺性方面，":
            description += f"线加速度和角加速度变化平顺，表现{grade_comfort}。"

        report_dict["description"] = replace_key_with_value(description, self.name_dict)

        description1 = f"最大值：{self.linear_accel_dict['max']:.4f}m/s²；" \
                       f"最小值：{self.linear_accel_dict['min']:.4f}m/s²；" \
                       f"平均值：{self.linear_accel_dict['avg']:.4f}m/s²"

        description2 = f"最大值：{self.angular_accel_dict['max']:.4f}rad/s²；" \
                       f"最小值：{self.angular_accel_dict['min']:.4f}rad/s²；" \
                       f"平均值：{self.angular_accel_dict['avg']:.4f}rad/s²"
        report_dict["description1"] = description1
        report_dict["description2"] = description2

        plt.figure(figsize=(12, 3))
        plt.plot(self.time_list, self.accel_list)

        plt.xlabel('Time(s)')
        plt.ylabel('Linear Accelerate(m/s^2)')
        # plt.legend()

        # 调整布局，消除空白边界
        plt.tight_layout()

        plt.savefig(os.path.join(self.casePath, "LinearAccelerate.png"))
        plt.close()

        plt.figure(figsize=(12, 3))
        plt.plot(self.time_list, self.accelH_list)

        plt.xlabel('Time(s)')
        plt.ylabel('Angular Accelerate(rad/s^2)')
        # plt.legend()

        # 调整布局，消除空白边界
        plt.tight_layout()

        plt.savefig(os.path.join(self.casePath, "AngularAccelerate.png"))
        plt.close()

        print(report_dict)

        # report_dict['commonData'] = {
        #     # "per": {
        #     #     "name": "刹车/油门踏板开度（百分比）",
        #     #     "legend": ["刹车踏板开度", "油门踏板开度"],
        #     #     "data": [brake_vs_time, throttle_vs_time]
        #     # },
        #     # "ang": {
        #     #     "name": "方向盘转角（角度°）",
        #     #     "data": steering_vs_time
        #     # },
        #     "spe": {
        #         "name": "速度（km/h）",
        #         # "legend": ["自车速度", "目标车速度", "自车与目标车相对速度"],
        #         "data": ego_speed_vs_time
        #
        #     },
        #     # "acc": {
        #     #     "name": "自车纵向加速度（m/s²)",
        #     #     "data": lon_acc_vs_time
        #     #
        #     # },
        #     # "dis": {
        #     #     "name": "前车距离（m）",
        #     #     "data": distance_vs_time
        #     # }
        # }
        # report_dict["commonMarkLine"] = discomfort_slices

        # report_dict = {
        #     "name": "平顺性",
        #     "weight": f"{self.weight * 100:.2f}%",
        #     "weightDistribution": weight_distribution,
        #     "score": score_comfort,
        #     "level": grade_comfort,
        #     'discomfortCount': self.discomfort_count,
        #     "description1": comf_description1,
        #     "description2": comf_description2,
        #     "description3": comf_description3,
        #     "description4": comf_description4,
        #
        #     "comfortLat": lat_dict,
        #     "comfortLon": lon_dict,
        #
        #     "speData": ego_speed_vs_time,
        #     "speMarkLine": discomfort_slices,
        #
        #     "accData": lon_acc_vs_time,
        #     "accMarkLine": discomfort_acce_slices,
        #
        #     "anvData": yawrate_vs_time,
        #     "anvMarkLine": discomfort_zigzag_slices,
        #
        #     "anaData": yawrate_roc_vs_time,
        #     "anaMarkLine": discomfort_zigzag_slices,
        #
        #     "curData": [cur_ego_path_vs_time, curvature_vs_time],
        #     "curMarkLine": discomfort_shake_slices,
        # }
        # self.eval_data = self.ego_df.copy()
        # self.eval_data['playerId'] = 1

        return report_dict

    def get_eval_data(self):
        df = self.eval_data[
            ['simTime', 'simFrame', 'playerId', 'ip_acc', 'ip_dec', 'slam_brake', 'slam_accel', 'cadence']].copy()
        return df