indoor_first_commit

comfort.py

@@ -0,0 +1,1367 @@
+#!/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

common.py

@@ -0,0 +1,310 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+##################################################################
+#
+# Copyright (c) 2023 CICV, Inc. All Rights Reserved
+#
+##################################################################
+"""
+@Authors:           yangzihao(yangzihao@china-icv.cn)
+@Data:              2023/07/24
+@Last Modified:     2023/07/24
+@Summary:           Evaluateion functions
+"""
+
+import sys
+
+sys.path.append('../common')
+sys.path.append('../modules')
+
+import os
+import time
+import json
+import math
+import pandas as pd
+import numpy as np
+import zipfile
+import importlib
+import matplotlib.pyplot as plt
+
+
+def save_line_chart(list1, list2, save_path):
+    plt.plot(list1, label='列表1')
+    plt.plot(list2, label='列表2')
+
+    plt.xlabel('索引')
+    plt.ylabel('值')
+    plt.title('折线图')
+    plt.legend()
+
+    plt.savefig(save_path)
+    plt.close()
+
+
+def import_class(path, file):
+    sys.path.append(path)  # 将path添加到Python的搜索路径中
+    # module_name = file[:-3]  # 去掉文件名的".py"后缀
+    module = __import__(file, fromlist=['ScoreModel'])
+    class_instance = module.ScoreModel
+    return class_instance
+
+
+def import_class_lib(path, file):
+    sys.path.append(path)  # 将path添加到Python的搜索路径中
+    module = importlib.import_module(file)
+    class_name = "ScoreModel"
+    class_instance = getattr(module, class_name)
+    return class_instance
+
+
+def cal_velocity(lat_v, lon_v):
+    """
+    The function can calculate the velocity with lateral velocity and longitudinal velocity.
+
+    Args:
+        lat_v: lateral velocity, m/s
+        lon_v: longitudinal velocity, m/s
+
+    Returns:
+        v: the resultant velocity, km/h
+
+    """
+    v = (lat_v ** 2 + lon_v ** 2) ** 0.5
+    return v
+
+
+def json2dict(json_file):
+    with open(json_file, 'r', encoding='utf-8') as f:
+        json_dict = json.load(f)
+    return json_dict
+
+
+def get_subfolders_name(path):
+    return [name for name in os.listdir(path) if os.path.isdir(os.path.join(path, name))]
+
+
+def zip_dir(dir_path, zip_file_path):
+    """
+    This function can zip the files in dir_path as a zipfile.
+
+    Arguments:
+        dir_path: A str of the path of the files to be ziped.
+        zip_file_path: A str of the path of the zipfile to be stored.
+
+    Returns:
+        None
+    """
+    with zipfile.ZipFile(zip_file_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
+        for root, dirs, files in os.walk(dir_path):
+            for file in files:
+                file_path = os.path.join(root, file)
+                zipf.write(file_path)
+
+
+def score_grade(score):
+    """
+    Returns the corresponding grade based on the input score.
+
+    Arguments:
+        score: An integer or float representing the score.
+
+    Returns:
+        grade: A string representing the grade.
+    """
+    if score >= 90:
+        grade = '优秀'
+    elif score >= 80:
+        grade = '良好'
+    elif score > 60:
+        grade = '一般'
+    else:
+        grade = '较差'
+
+    return grade
+
+
+def mileage_format(mileage):
+    if mileage < 1000:
+        return f"{mileage:.2f}米"
+    else:
+        mileage = mileage / 1000
+        return f"{mileage:.2f}公里"
+
+
+def duration_format(duration):
+    if duration < 60:
+        return f"{duration:.2f}秒"
+    elif duration < 3600:
+        minute = int(duration / 60)
+        second = int(duration % 60)
+        return f"{minute}分{second}秒" if second != 0 else f"{minute}分"
+    else:
+        hour = int(duration / 3600)
+        minute = int((duration % 3600) / 60)
+        second = int(duration % 60)
+        ans = f"{hour:d}时"
+        ans = ans + f"{minute}分" if minute != 0 else ans
+        ans = ans + f"{second}秒" if second != 0 else ans
+        return ans
+
+
+def continuous_group(df):
+    time_list = df['simTime'].values.tolist()
+    frame_list = df['simFrame'].values.tolist()
+
+    group_time = []
+    group_frame = []
+    sub_group_time = []
+    sub_group_frame = []
+
+    for i in range(len(frame_list)):
+        if not sub_group_time or frame_list[i] - frame_list[i - 1] <= 1:
+            sub_group_time.append(time_list[i])
+            sub_group_frame.append(frame_list[i])
+        else:
+            group_time.append(sub_group_time)
+            group_frame.append(sub_group_frame)
+            sub_group_time = [time_list[i]]
+            sub_group_frame = [frame_list[i]]
+
+    group_time.append(sub_group_time)
+    group_frame.append(sub_group_frame)
+    group_time = [g for g in group_time if len(g) >= 2]
+    group_frame = [g for g in group_frame if len(g) >= 2]
+
+    # 输出图表值
+    time = [[g[0], g[-1]] for g in group_time]
+    frame = [[g[0], g[-1]] for g in group_frame]
+
+    time_df = pd.DataFrame(time, columns=['start_time', 'end_time'])
+    frame_df = pd.DataFrame(frame, columns=['start_frame', 'end_frame'])
+
+    result_df = pd.concat([time_df, frame_df], axis=1)
+
+    return result_df
+
+
+def continuous_group_old(df):
+    time_list = df['simTime'].values.tolist()
+    frame_list = df['simFrame'].values.tolist()
+
+    group = []
+    sub_group = []
+
+    for i in range(len(frame_list)):
+        if not sub_group or frame_list[i] - frame_list[i - 1] <= 1:
+            sub_group.append(time_list[i])
+        else:
+            group.append(sub_group)
+            sub_group = [time_list[i]]
+
+    group.append(sub_group)
+    group = [g for g in group if len(g) >= 2]
+
+    # 输出图表值
+    time = [[g[0], g[-1]] for g in group]
+    unsafe_df = pd.DataFrame(time, columns=['start_time', 'end_time'])
+
+    return unsafe_df
+
+
+def get_frame_with_time(df1, df2):
+    # 将dataframe1按照start_time与simTime进行合并
+    df1_start = df1.merge(df2[['simTime', 'simFrame']], left_on='start_time', right_on='simTime')
+    df1_start = df1_start[['start_time', 'simFrame']].copy()
+    df1_start.rename(columns={'simFrame': 'start_frame'}, inplace=True)
+
+    # 将dataframe1按照end_time与simTime进行合并
+    df1_end = df1.merge(df2[['simTime', 'simFrame']], left_on='end_time', right_on='simTime')
+    df1_end = df1_end[['end_time', 'simFrame']].copy()
+    df1_end.rename(columns={'simFrame': 'end_frame'}, inplace=True)
+
+    # 将两个合并后的数据框按照行索引进行拼接
+    result = pd.concat([df1_start, df1_end], axis=1)
+    return result
+
+
+def string_concatenate(str_list):
+    """
+    This function concatenates the input string list to generate a new string.
+    If str_list is empty, an empty string is returned.
+    If str_list has only one element, return that element.
+    If str_list has multiple elements, concatenate all the elements except the last element with ', ', and then concatenate the resulting string with the last element with 'and'.
+
+    Arguments:
+        str_list: A list of strings.
+
+    Returns:
+        ans_str: A concatenated string.
+    """
+    if not str_list:
+        return ""
+    ans_str = '、'.join(str_list[:-1])
+    ans_str = ans_str + "和" + str_list[-1] if len(str_list) > 1 else ans_str + str_list[-1]
+    return ans_str
+
+
+def zip_time_pairs(time_list, zip_list, upper_limit=9999):
+    zip_time_pairs = zip(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 replace_key_with_value(input_string, replacement_dict):
+    """
+    替换字符串中的关键字为给定的字典中的值。
+
+    :param input_string: 需要进行替换的字符串
+    :param replacement_dict: 替换规则，格式为 {key: value}
+    :return: 替换后的字符串
+    """
+    # 遍历字典中的键值对，并用值替换字符串中的键
+    for key, value in replacement_dict.items():
+        if key in input_string:
+            input_string = input_string.replace(key, value)
+
+    return input_string
+
+
+def continous_judge(frame_list):
+    if not frame_list:
+        return 0
+
+    cnt = 1
+    for i in range(1, len(frame_list)):
+        if frame_list[i] - frame_list[i - 1] <= 3:
+            continue
+        cnt += 1
+    return cnt
+
+
+def get_interpolation(x, point1, point2):
+    """
+    According to the two extreme value points, the equation of one variable is determined,
+    and the solution of the equation is obtained in the domain of definition.
+
+    Arguments:
+        x: A float number of the independent variable.
+        point1: A set of the coordinate extreme point.
+        point2: A set of the other coordinate extreme point.
+
+    Returns:
+        y: A float number of the dependent variable.
+    """
+    try:
+        k = (point1[1] - point2[1]) / (point1[0] - point2[0])
+        b = (point1[0] * point2[1] - point1[1] * point2[0]) / (point1[0] - point2[0])
+        y = x * k + b
+        return y
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+
+def statistic_analysis(data_list):
+    sorted_data_list = sorted(data_list)
+    maxx = sorted_data_list[-1]
+    minn = sorted_data_list[0]
+    meann = sum(sorted_data_list) / len(sorted_data_list)
+    percentile_99 = np.percentile(sorted_data_list, 99)
+    ans_list = [maxx, minn, meann, percentile_99]
+    return ans_list

config.json

@@ -0,0 +1,316 @@
+{
+  "comfort": {
+    "comfortLat": {
+      "zigzag": {
+        "name": "画龙",
+        "unit": "次",
+        "weight": 1.0,
+        "priority": "0",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "1",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          },
+          {
+            "kind": "-1",
+            "optimal": "10",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          },
+          {
+            "kind": "-1",
+            "optimal": "3",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      }
+    },
+    "comfortLon": {
+      "cadence": {
+        "name": "顿挫",
+        "unit": "次",
+        "weight": 0.3,
+        "priority": "0",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "1",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          },
+          {
+            "kind": "-1",
+            "optimal": "10",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          },
+          {
+            "kind": "-1",
+            "optimal": "5",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      },
+      "slamBrake": {
+        "name": "急刹车",
+        "unit": "次",
+        "weight": 0.4,
+        "priority": "1",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "1",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          },
+          {
+            "kind": "-1",
+            "optimal": "10",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          },
+          {
+            "kind": "-1",
+            "optimal": "5",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      },
+      "slamAccelerate": {
+        "name": "急加速",
+        "unit": "次",
+        "weight": 0.3,
+        "priority": "1",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "1",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          },
+          {
+            "kind": "-1",
+            "optimal": "10",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          },
+          {
+            "kind": "-1",
+            "optimal": "5",
+            "multiple": [
+              "0.2",
+              "5"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      }
+    }
+  },
+  "efficient": {
+    "efficientDrive": {
+      "averageSpeed": {
+        "name": "平均速度",
+        "unit": "km/h",
+        "weight": 1.0,
+        "priority": "1",
+        "paramList": [
+          {
+            "kind": "0",
+            "optimal": "30",
+            "multiple": [
+              "0",
+              "1"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      }
+    },
+    "efficientStop": {
+      "stopDuration": {
+        "name": "驶停时长",
+        "unit": "s",
+        "weight": 1.0,
+        "priority": "0",
+        "paramList": [
+          {
+            "kind": "-1",
+            "optimal": "5",
+            "multiple": [
+              "0.5",
+              "3"
+            ],
+            "spare": [
+              {
+                "param": null
+              },
+              {
+                "param": null
+              }
+            ]
+          }
+        ]
+      }
+    }
+  },
+  "typeWeight": {
+    "efficient": {
+      "efficientDrive": 0.5,
+      "efficientStop": 0.5
+    },
+    "comfort": {
+      "comfortLat": 0.5,
+      "comfortLon": 0.5
+    }
+  },
+  "typeName": {
+    "efficient": {
+      "efficientDrive": "行驶",
+      "efficientStop": "驶停"
+    },
+    "comfort": {
+      "comfortLat": "横向平顺性",
+      "comfortLon": "纵向平顺性"
+    }
+  },
+  "dimensionWeight": {
+    "comfort": 0.6,
+    "efficient": 0.4
+  },
+  "dimensionName": {
+    "efficient": "高效性",
+    "comfort": "平顺性"
+  },
+  "scoreModel": "builtin",
+  "scoreModel1": "linear_score"
+}

config_parser.py

@@ -0,0 +1,191 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+##################################################################
+#
+# Copyright (c) 2023 CICV, Inc. All Rights Reserved
+#
+##################################################################
+"""
+@Authors:           yangzihao(yangzihao@china-icv.cn)
+@Data:              2023/11/02
+@Last Modified:     2023/11/02
+@Summary:           This module provides the function to parse the config json file.
+"""
+
+import pandas as pd
+from common import json2dict
+
+
+class ConfigParse(object):
+    """
+
+    """
+
+    def __init__(self, json_file):
+        # weight info
+        self.scoreModel = "builtin"
+        self.dimension_weight = {}
+        self.dimension_list = []
+        self.dimension_name = {}
+        self.type_weight = {}
+        self.type_list = []
+        self.type_name = {}
+        self.metric_list = []
+        self.metric_dict = {}
+        self.name_dict = {}
+        self.unit_dict = {}
+
+        self.builtinDimensionList = ["comfort", "efficient"]
+        self.builtinTypeList = []
+        self.builtinMetricList = ["zigzag", "shake", "cadence", "slamBrake", "slamAccelerate",
+                                  "averageSpeed", "stopDuration", "stopCount"]
+
+        # score info
+        self.config = {}
+
+        # initialization
+        self.config_dict = json2dict(json_file)
+        self._config_parse(self.config_dict)
+
+    def _config_parse(self, config_dict):
+
+        # dimension info
+        dimensionWeight = config_dict['dimensionWeight']
+        self.dimension_weight = dimensionWeight
+        self.dimension_name = config_dict['dimensionName']
+        self.dimension_list = list(self.dimension_weight.keys())
+
+        # type info
+        typeWeight = config_dict['typeWeight']
+        self.type_weight = typeWeight
+        self.type_name = config_dict["typeName"]
+
+        for dimension in self.dimension_list:
+            self.config[dimension] = self._dimension_config_parse(dimension, config_dict, typeWeight, dimensionWeight)
+
+            self.name_dict.update(self.config[dimension]['name'])
+            self.unit_dict.update(self.config[dimension]['unit'])
+
+            self.metric_dict[dimension] = self.config[dimension]['typeMetricDict']
+            self.metric_list.extend(self.config[dimension]['metric'])
+            self.type_list.extend(self.config[dimension]['type'])
+
+        print()
+
+    def _dimension_config_parse(self, dimension, config_dict, typeWeight, dimensionWeight):
+
+        # get weight type
+        typeWeightDimension = typeWeight[dimension]
+        typeDimension = list(typeWeightDimension.keys())  # get type list
+        typeWeightDimensionList = list(typeWeightDimension.values())
+        flagCustomDimension = not all(x is None for x in typeWeightDimensionList)
+
+        # get type name
+        typeNameDimension = self.type_name[dimension]
+
+        # Dimension
+        dimension_dict = config_dict[dimension]
+        dimension_value_dict = {}
+        typeMetricDict = {}
+
+        for type in typeDimension:
+            dimension_value_dict.update(dimension_dict[type])
+            typeMetricDict[type] = list(dimension_dict[type].keys())
+
+        df_dimension_value = pd.DataFrame(dimension_value_dict).T
+
+        # get metric list
+        metricDimension = df_dimension_value.index.tolist()
+
+        # get name list
+        nameDimension = df_dimension_value['name'].to_dict()
+        # nameDimensionList = list(nameDimension.values())
+
+        # get unit list
+        unitDimension = df_dimension_value['unit'].to_dict()
+        # unitDimensionList = list(unitDimension.values())
+
+        # get weight list
+        weightDimension = df_dimension_value['weight'].astype(float).to_dict()
+        weightDimensionList = list(weightDimension.values())
+
+        # get priority list
+        priorityDimension = df_dimension_value['priority'].astype(int).to_dict()
+        priorityDimensionList = list(priorityDimension.values())
+
+        # get paramList
+        paramDimension = df_dimension_value['paramList'].to_dict()
+
+        bulitin_first_key = next((key for key in paramDimension if key in self.builtinMetricList), None)
+        first_key = next(iter(paramDimension)) if not bulitin_first_key else bulitin_first_key
+        paramNum = len(paramDimension[first_key])
+
+        kindDimension = [{} for _ in range(paramNum)]
+        optimalDimension = [{} for _ in range(paramNum)]
+        multipleDimension = [{} for _ in range(paramNum)]
+        spareDimension = [{} for _ in range(paramNum)]
+        # spare1Dimension = [{} for _ in range(paramNum)]
+        # spare2Dimension = [{} for _ in range(paramNum)]
+
+        customMetricParam = {}  # custiom metric paramList
+
+        for key, value_list in paramDimension.items():
+            if key in self.builtinMetricList:
+                for i in range(len(value_list)):
+                    kindDimension[i][key] = int(value_list[i]['kind'])
+                    optimalDimension[i][key] = float(value_list[i]['optimal'])
+                    multipleDimension[i][key] = [float(x) for x in value_list[i]['multiple']]
+                    spareDimension[i][key] = [item["param"] for item in value_list[i]["spare"]]
+                    # spareDimension[i][key] = [float(item["param"]) for item in value_list[i]["spare"]]
+                    # spare1Dimension[i][key] = (value_list[i]['spare1'])
+                    # spare2Dimension[i][key] = (value_list[i]['spare2'])
+            else:
+                customMetricParam[key] = value_list
+
+        kindDimensionList = [value for dict_val in kindDimension for value in dict_val.values()]
+        optimalDimensionList = [value for dict_val in optimalDimension for value in dict_val.values()]
+        multipleDimensionList = [value for dict_val in multipleDimension for value in dict_val.values()]
+        spareDimensionList = [value for dict_val in spareDimension for value in dict_val.values()]
+        # spare1DimensionList = [value for dict_val in spare1Dimension for value in dict_val.values()]
+        # spare2DimensionList = [value for dict_val in spare2Dimension for value in dict_val.values()]
+
+        if paramNum == 1:
+            kindDimension = kindDimension[0]
+            optimalDimension = optimalDimension[0]
+            multipleDimension = multipleDimension[0]
+            spareDimension = spareDimension[0]
+            # spare1Dimension = spare1Dimension[0]
+            # spare2Dimension = spare2Dimension[0]
+
+        result = {
+            "weightDimension": float(dimensionWeight[dimension]),
+            "weightCustom": flagCustomDimension,
+            "type": typeDimension,
+            "typeWeight": typeWeightDimension,
+            "typeWeightList": typeWeightDimensionList,
+            "typeName": typeNameDimension,
+            "customMetricParam": customMetricParam,
+            "metric": metricDimension,
+            "typeMetricDict": typeMetricDict,
+            "name": nameDimension,
+            # "nameList": nameDimensionList,
+            "unit": unitDimension,
+            # "unitList": unitDimensionList,
+            "weight": weightDimension,
+            "weightList": weightDimensionList,
+            "priority": priorityDimension,
+            "priorityList": priorityDimensionList,
+            "kind": kindDimension,
+            "kindList": kindDimensionList,
+            "optimal": optimalDimension,
+            "optimalList": optimalDimensionList,
+            "multiple": multipleDimension,
+            "multipleList": multipleDimensionList,
+            "spare": spareDimension,
+            "spareList": spareDimensionList,
+            # "spare1": spare1Dimension,
+            # "spare1List": spare1DimensionList,
+            # "spare2": spare2Dimension,
+            # "spare2List": spare2DimensionList
+        }
+        return result

data_info.py

@@ -0,0 +1,74 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+##################################################################
+#
+# Copyright (c) 2023 CICV, Inc. All Rights Reserved
+#
+##################################################################
+"""
+@Authors:           zhanghaiwen(zhanghaiwen@china-icv.cn)
+@Data:              2023/12/01
+@Last Modified:     2023/12/01
+@Summary:           This module provides the evaluation data information.
+"""
+
+
+class CsvData(object):
+    OBJECT_INFO = [
+        'time',
+        'frame_ID',
+        'speed_x'
+    ]
+
+
+class DataInfoList(object):
+    """
+    Data info for dimensions.
+    """
+    COMFORT_INFO = [  # 'unit_scene_ID',
+        'simTime',
+        'simFrame',
+        'lon_v',
+        'lat_v',
+        'v',
+        'lat_acc',
+        'lon_acc',
+        'accel',
+        'time_diff',
+        'lon_acc_diff',
+        'lon_acc_roc',
+        'speedH',
+        'accelH']
+
+    SAFE_INFO = [  # 'unit_scene_ID',
+        'simTime',
+        'simFrame',
+        'playerId',
+        'v',
+        'posX',
+        'posY',
+        'posZ',
+        'posH',
+        'speedX',
+        'speedY',
+        'accelX',
+        'accelY',
+        'dimX',
+        'offX']
+
+    FUNCTION = [  # 'unit_scene_ID',
+        'simTime',
+        'simFrame',
+        'v']
+
+    EFFICIENT_INFO = [  # 'unit_scene_ID',
+        'simTime',
+        'simFrame',
+        'playerId',
+        'v',
+        'obstacle']
+
+    COMPLIANCE_INFO = [  # 'unit_scene_ID',
+        'simTime',
+        'simFrame',
+        'v']

data_process.py

@@ -0,0 +1,339 @@
+#!/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 numpy as np
+import pandas as pd
+from common import cal_velocity
+from data_info import CsvData
+import matplotlib.pyplot as plt
+import warnings
+warnings.filterwarnings("ignore", category=FutureWarning, module='pandas')
+pd.set_option('future.no_silent_downcasting', True)
+
+
+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):
+        self.data_path = data_path
+        self.casePath = data_path
+
+        # config info
+        self.config = config
+        # self.safe_config = config.config['safe']
+        # self.function_config = config.config['function']
+        # self.compliance_config = config.config['compliance']
+        self.comfort_config = config.config['comfort']
+        self.efficient_config = config.config['efficient']
+
+        # data process
+        self.ego_df = pd.DataFrame()
+        self.object_df = pd.DataFrame()
+        # self.driver_ctrl_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.status_df = pd.DataFrame()
+
+        self.obj_data = {}
+        self.ego_data = {}
+        self.obj_id_list = {}
+
+        self.car_info = {}
+        self.report_info = {}
+        self.driver_ctrl_data = {}
+
+        self._process()
+
+    def _process(self):
+        # self._merge_csv()
+        self._read_csv()
+        self._draw_track()
+        # self._signal_mapping()
+        # self.car_info = self._get_car_info(self.object_df)
+        # self._compact_data()
+        # self._abnormal_detect()
+        # self._status_map(self.object_df)
+        self._object_df_process()
+        self.report_info = self._get_report_info(self.obj_data[1])
+        self.driver_ctrl_data = self._get_driver_ctrl_data(self.ego_df)
+
+    def _read_csv(self):
+        """
+        Read csv files to dataframe.
+
+        Args:
+            data_path: A str of the path of csv files
+
+        Returns:
+            No returns.
+
+        """
+        # self.object_df = pd.read_csv(os.path.join(self.data_path, 'merged_ObjState.csv'))
+        # self.object_df = pd.read_csv(os.path.join(self.data_path, 'ObjState.csv'))
+        self.ego_df = pd.read_csv(os.path.join(self.data_path, 'EgoState_pji.csv'))
+        self.ego_df['playerId'] = 1
+        # self.driver_ctrl_df = pd.read_csv(os.path.join(self.data_path, 'DriverCtrl.csv'))
+        # self.road_mark_df = pd.read_csv(os.path.join(self.data_path, 'RoadMark.csv'))
+        # self.road_pos_df = pd.read_csv(os.path.join(self.data_path, 'RoadPos.csv'))
+        # self.traffic_light_df = pd.read_csv(os.path.join(self.data_path, 'TrafficLight.csv'))
+        # self.traffic_signal_df = pd.read_csv(os.path.join(self.data_path, 'TrafficSign.csv'))
+
+        # self.lane_info_df = pd.read_csv(os.path.join(self.data_path, 'LaneInfo.csv')).drop_duplicates()
+        # self.status_df = pd.read_csv(os.path.join(self.data_path, 'VehicleState.csv'))
+        # self.vehicle_sys_df = pd.read_csv(
+        #     os.path.join(self.data_path, 'VehicleSystems.csv')).drop_duplicates()  # 车灯信息
+
+    def _draw_track(self):
+        """
+
+        """
+        df = self.ego_df.copy()
+        plt.scatter(df['posX'], df['posY'], c=df['simTime'], s=0.1)
+        plt.axis('equal')
+
+        # 添加坐标轴标签和标题
+        plt.xlabel('posX')
+        plt.ylabel('posY')
+        plt.title('Trajectory')
+
+        # 显示图形
+        plt.savefig(os.path.join(self.casePath, "./track.png"))
+        plt.close()
+
+    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:
+
+        """
+        first_row = df[df['playerId'] == 1].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
+        }
+
+        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 _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 _mileage_cal(self, df):
+        """
+        Calculate mileage of given df.
+
+        Args:
+            df: A dataframe of driving data.
+
+        Returns:
+            mileage: A float of the mileage(meter) of the driving data.
+
+        """
+        travelDist = df['traveledDist'].values.tolist()
+        travelDist = [x for x in travelDist if not np.isnan(x)]
+
+        # mile_start = df['travelDist'].iloc[0]
+        mile_start = travelDist[0]
+        mile_end = travelDist[-1]
+        mileage = mile_end - mile_start
+        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['ACC_status'] = df['ACC_status'].apply(lambda x: acc_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))
+
+    def _object_df_process(self):
+        """
+        Process the data of object dataframe. Save the data groupby object_ID.
+
+        Returns:
+            No returns.
+
+        """
+        data = self.ego_df.copy()
+        data.rename(
+            columns={"speedY": "lat_v", "speedX": "lon_v", "accelY": "lat_acc", "accelX": "lon_acc", "dimZ": "speedH"},
+            inplace=True)
+
+        # calculate common parameters
+        # data['lat_v'] = data['speedX'] * np.sin(data['posH']) * -1 + data['speedY'] * np.cos(data['posH'])
+        # data['lon_v'] = data['speedX'] * np.cos(data['posH']) + data['speedY'] * np.sin(data['posH'])
+        data['v'] = data.apply(lambda row: cal_velocity(row['lat_v'], row['lon_v']), axis=1)
+
+        # calculate acceleraton components
+        # data['lat_acc'] = data['accelX'] * np.sin(data['posH']) * -1 + data['accelY'] * np.cos(data['posH'])
+        # data['lon_acc'] = data['accelX'] * np.cos(data['posH']) + data['accelY'] * np.sin(data['posH'])
+        data['accel'] = data.apply(lambda row: cal_velocity(row['lat_acc'], row['lon_acc']), axis=1)
+
+        # data.rename(columns={"yawrate_roc": "accelH"}, 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]['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]['speedH_diff'] = self.obj_data[obj_id]['speedH'].diff()
+
+            self.obj_data[obj_id]['time_diff'] = self.obj_data[obj_id]['simTime'].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]['speedH_diff'] / self.obj_data[obj_id][
+                'time_diff']
+
+        # get object id list
+        self.obj_id_list = list(self.obj_data.keys())
+        self.ego_data = self.obj_data[1]
+
+    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'].values.tolist()
+        frame_list = df['simFrame'].values.tolist()
+        # df['brakePedal'] = df['brakePedal'] * 100
+        # brakePedal_list = df['brakePedal'].values.tolist()
+        # 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

efficient.py

@@ -0,0 +1,654 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+##################################################################
+#
+# Copyright (c) 2023 CICV, Inc. All Rights Reserved
+#
+##################################################################
+"""
+@Authors:           xieguijin(xieguijin@china-icv.cn), yangzihao(yangzihao@china-icv.cn)
+@Data:              2023/08/03
+@Last Modified:     2023/08/03
+@Summary:           Functionality metrics
+"""
+
+import os
+import sys
+
+sys.path.append('../common')
+sys.path.append('../modules')
+sys.path.append('../results')
+
+import numpy as np
+import pandas as pd
+
+from data_info import DataInfoList
+from score_weight import cal_score_with_priority, cal_weight_from_80
+from common import score_grade, string_concatenate, replace_key_with_value
+import matplotlib.pyplot as plt
+
+
+class Efficient(object):
+    """
+    Class for achieving efficient metrics for autonomous driving.
+
+    Attributes:
+        df: 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.df = pd.DataFrame()
+
+        self.speed_dict = dict()
+
+        self.config = data_processed.config
+        efficient_config = data_processed.efficient_config
+        self.efficient_config = efficient_config
+
+        # common data
+        self.bulitin_metric_list = self.config.builtinMetricList
+
+        # dimension data
+        self.weight_custom = efficient_config['weightCustom']
+        self.metric_list = efficient_config['metric']
+        self.type_list = efficient_config['type']
+        self.type_name_dict = efficient_config['typeName']
+        self.name_dict = efficient_config['name']
+        self.unit_dict = efficient_config['unit']
+
+        # custom metric data
+        self.customMetricParam = efficient_config['customMetricParam']
+        self.custom_metric_list = list(self.customMetricParam.keys())
+        self.custom_data = custom_data
+        self.custom_param_dict = {}
+
+        # score data
+        self.weight = efficient_config['weightDimension']
+
+        self.weight_type_dict = efficient_config['typeWeight']
+        self.weight_type_list = efficient_config['typeWeightList']
+
+        self.weight_dict = efficient_config['weight']
+        print("self.weight_dict is", self.weight_dict)
+        self.weight_list = efficient_config['weightList']
+
+        self.priority_dict = efficient_config['priority']
+        self.priority_list = efficient_config['priorityList']
+
+        self.kind_dict = efficient_config['kind']
+        self.optimal_dict = efficient_config['optimal']
+        self.multiple_dict = efficient_config['multiple']
+        self.kind_list = efficient_config['kindList']
+        self.optimal_list = efficient_config['optimalList']
+        self.multiple_list = efficient_config['multipleList']
+
+        # metric data
+        self.metric_dict = efficient_config['typeMetricDict']
+        self.drive_metric_list = self.metric_dict['efficientDrive']
+        self.stop_metric_list = self.metric_dict['efficientStop']
+        # self.drive_metric_list = ["averageSpeed"]
+        # self.stop_metric_list = ["stopDuration", "stopCount"]
+
+        self._get_data()
+        self._effi_param_cal()
+        self.time_list = self.data['simTime'].values.tolist()
+        self.speed_list = list()
+        self.value_dict = {}
+        self.average_v = 0
+        self.stop_count = 0
+        self.stop_duration = 0
+        self.sum_stop_time = 0
+        self.pass_junction_time = 0
+
+        self.score = 0
+
+    def _get_data(self):
+        """
+        Get the data required for efficient evaluation according to efficient_INFO in DataInfoList.
+
+        Parameters:
+            df: Dataframe containing the vehicle running data.
+
+        Returns:
+            peak_valley: List of indices representing peaks and valleys.
+        """
+        efficient_info_list = DataInfoList.EFFICIENT_INFO
+        self.df = self.data[efficient_info_list].copy()
+
+    def _effi_param_cal(self):
+        """
+
+        """
+        self.df = self.df[self.df['playerId'] == 1]
+        # self.df = self.df[self.df['id'] == 1]
+        # self.df['v'] = self.df.apply(lambda row: self.velocity(row['speed_x'], row['speed_y']), axis=1)
+        # self.df['v'] = self.df.apply(lambda row: self.velocity(row['speedX'], row['speedY']), axis=1)
+
+    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 average_velocity(self):
+        """
+        1.速度的平均值
+        2.平均速度 = 里程/时间
+        速度不为0为开始时间，速度为0为结束时间
+        """
+        self.average_v = self.df['v'].mean()
+        self.speed_list = self.df['v'].values.tolist()
+        self.speed_dict = self._cal_max_min_avg(self.df['v'].dropna().values.tolist())
+
+    def stop_duration_and_count(self):
+        """
+        百公里停车次数
+        平均每次停车时长
+        驾照科目二考试判定为停车的标准：在考试项目区域内，汽车停顿2秒以上都算中途停车。
+        """
+        stop_v_threshold = 0.01
+        stop_time_threshold = 0.5  # 12.5帧
+
+        without_obstacle = self.df[self.df['obstacle'] == 0].copy()
+        stop_time_list = without_obstacle[without_obstacle['v'] <= stop_v_threshold]['simTime'].values.tolist()
+        # stop_time_list = self.df[self.df['v'] == 0]['simTime'].values.tolist()
+        stop_frame_list = without_obstacle[without_obstacle['v'] <= stop_v_threshold]['simFrame'].values.tolist()
+        # stop_frame_list = self.df[self.df['v'] == 0]['simFrame'].values.tolist()
+
+        stop_frame_group = []
+        stop_time_group = []
+        f1 = stop_frame_list[0] if stop_frame_list else 0
+        t1 = stop_time_list[0] if stop_time_list else 0
+        sum_stop_time = 0
+        for i in range(1, len(stop_frame_list)):
+            if stop_frame_list[i] - stop_frame_list[i - 1] != 1:
+                f2 = stop_frame_list[i - 1]
+                # 当帧数大于13时才确定为停车
+                # 当时长大于0.5s才确定为停车
+                if f2 - f1 >= 13:
+                    t2 = stop_time_list[i - 1]
+                    stop_frame_group.append((f1, f2))
+                    stop_time_group.append((t1, t2))
+                    sum_stop_time += (t2 - t1)
+                    self.stop_count += 1
+                # update f1, t1
+                f1 = stop_frame_list[i]
+                t1 = stop_time_list[i]
+
+        f2 = stop_frame_list[-1] if stop_frame_list else 0
+        # 如果最后一段的帧数差大于13，且停车统计列表中的最后一帧不为用例的最后一帧
+        if f2 - f1 >= 13 and f2 != self.df['simFrame'].values.tolist()[-1]:
+            t2 = stop_time_list[-1]
+            stop_frame_group.append((f1, f2))
+            stop_time_group.append((t1, t2))
+            sum_stop_time += (t2 - t1)
+            self.stop_count += 1
+
+        self.sum_stop_time = sum_stop_time
+        self.stop_duration = sum_stop_time / self.stop_count if self.stop_count != 0 else 0
+        # self.stop_count = self.stop_count / self.mileage * 1000000
+
+    def efficient_statistic(self):
+        arr_effi = []
+        self.average_velocity()
+        self.stop_duration_and_count()
+
+        if "averageSpeed" in self.metric_list:
+            average_v = self.average_v
+            arr_effi.append(average_v)
+            self.value_dict["averageSpeed"] = average_v
+
+        if "stopDuration" in self.metric_list:
+            stop_duration = self.stop_duration
+            arr_effi.append(stop_duration)
+            self.value_dict["stopDuration"] = stop_duration
+
+        if "stopCount" in self.metric_list:
+            stop_count = self.stop_count
+            arr_effi.append(stop_count)
+            self.value_dict["stopCount"] = stop_count
+
+        # arr_effi = [average_v, stop_count, stop_time]
+        return arr_effi
+
+    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 effi_score(self):
+        arr_effi = self.efficient_statistic()
+        print("\n[高效性表现及得分情况]")
+        print("高效性各指标值：", [round(num, 2) for num in arr_effi])
+        arr_effi = np.array([arr_effi])
+
+        score_model = self.scoreModel(self.kind_list, self.optimal_list, self.multiple_list, arr_effi)
+        score_sub = score_model.cal_score()
+        score_sub = list(map(lambda x: 80 if np.isnan(x) else x, score_sub))
+
+        score_metric = [round(num, 2) for num in score_sub]
+
+        metric_list = [x for x in self.metric_list if x in self.config.builtinMetricList]
+        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("efficient score_type_with_weight_dict is", score_type_with_weight_dict)
+            score_efficient = 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_efficient = 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("efficient 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_efficient = round(score_efficient, 2)
+
+        print("高效性各指标基准值：", self.optimal_list)
+        print(f"高效性得分为：{score_efficient:.2f}分。")
+        print(f"高效性各类型得分为：{score_type_dict}。")
+        print(f"高效性各指标得分为：{score_metric_dict}。")
+        return score_efficient, score_type_dict, score_metric_dict
+
+    def effi_weight_distribution(self):
+        # get weight distribution
+        weight_distribution = {}
+        weight_distribution["name"] = "高效性"
+
+        if "efficientDrive" in self.type_list:
+            drive_weight_indexes_dict = {key: f"{key}({value * 100:.2f}%)" for key, value in self.weight_dict.items() if
+                                         key in self.drive_metric_list}
+
+            weight_distribution_drive = {
+                "driveWeight": f"行驶({self.weight_type_dict['efficientDrive'] * 100:.2f}%)",
+                "indexes": drive_weight_indexes_dict
+            }
+            weight_distribution['efficientDrive'] = weight_distribution_drive
+
+        if "efficientStop" in self.type_list:
+            stop_weight_indexes_dict = {key: f"{key}({value * 100:.2f}%)" for key, value in self.weight_dict.items() if
+                                        key in self.stop_metric_list}
+
+            weight_distribution_stop = {
+                "stopWeight": f"停车({self.weight_type_dict['efficientStop'] * 100:.2f}%)",
+                "indexes": stop_weight_indexes_dict
+            }
+            weight_distribution['efficientStop'] = weight_distribution_stop
+
+        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": "高效性",
+        #     "weight": f"{self.weight * 100:.2f}%",
+        #     "weightDistribution": weight_distribution,
+        #     "score": score_efficient,
+        #     "level": grade_efficient,
+        #     'score_type': score_type,
+        #     'score_metric': score_metric,
+        #
+        #     "description1": effi_description1,
+        #     "description2": effi_description2,
+        #     "description3": effi_description3,
+        #
+        #     "efficientDrive": drive_dict,
+        #     "efficientStop": stop_dict,
+        # }
+
+        # 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": "高效性",
+            # "weight": f"{self.weight * 100:.2f}%",
+        }
+
+        # len_time = len(self.time_list)
+        # duration = self.time_list[-1]
+        # duration_minute = round(duration / 60, 2)
+        score_efficient, score_type_dict, score_metric_dict = self.effi_score()
+
+        # get weight distribution
+        # report_dict["weightDistribution"] = self._get_weight_distribution("efficient")
+
+        # score_efficient, score_metric = self.effi_score()
+        score_efficient = int(score_efficient) if int(score_efficient) == score_efficient else round(score_efficient, 2)
+        grade_efficient = score_grade(score_efficient)
+        report_dict["score"] = score_efficient
+        report_dict["level"] = grade_efficient
+
+        # for description
+        # good_type_list = []
+        # bad_type_list = []
+
+        # good_metric_list = []
+        # bad_metric_list = []
+
+        # str for description
+        # str_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": 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 = {}
+        #     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],
+        #                 "value": f'{self.value_dict[metric]:.2f}',
+        #                 # "range": f"[0, {self.optimal_dict['averageSpeed']})",
+        #                 # "deviation": f"+{avv_deviation:.2f}%" if avv_deviation > 0 else f"{avv_deviation:.2f}%"
+        #             }
+        #
+        #             if self.kind_dict[metric] == -1:
+        #                 type_dict_indexes[metric]["range"] = f"[0, {self.optimal_dict[metric]}]"
+        #                 metric_over_optimal = ((self.value_dict[metric] - self.optimal_dict[metric]) /
+        #                                        self.optimal_dict[
+        #                                            metric]) * 100
+        #                 type_dict_indexes[metric][
+        #                     "deviation"] = f"+{metric_over_optimal:.2f}%" if metric_over_optimal > 0 else f"0%"
+        #
+        #             elif self.kind_dict[metric] == 1:
+        #                 type_dict_indexes[metric]["range"] = f"[{self.optimal_dict[metric]}, inf)"
+        #                 metric_over_optimal = ((self.value_dict[metric] - self.optimal_dict[metric]) /
+        #                                        self.optimal_dict[
+        #                                            metric]) * 100
+        #                 type_dict_indexes[metric][
+        #                     "deviation"] = f"0%" if metric_over_optimal > 0 else f"{metric_over_optimal:.2f}%"
+        #
+        #             elif self.kind_dict[metric] == 0:
+        #                 value = self.value_dict[metric]
+        #                 minn = self.optimal_dict[metric] * self.multiple_dict[metric][0]
+        #                 maxx = self.optimal_dict[metric] * self.multiple_dict[metric][1]
+        #
+        #                 type_dict_indexes[metric]["range"] = f"[{minn}, {maxx}]"
+        #
+        #                 if value < minn:
+        #                     metric_over_optimal = (value - minn) / minn * 100
+        #                 elif value > maxx:
+        #                     metric_over_optimal = (value - maxx) / maxx * 100
+        #                 else:
+        #                     metric_over_optimal = 0
+        #
+        #                 type_dict_indexes[metric][
+        #                     "deviation"] = f"+{metric_over_optimal:.2f}%" if metric_over_optimal > 0 else f"{metric_over_optimal:.2f}%"
+        #
+        #             # metric_over_optimal = ((self.value_dict[metric] - self.optimal_dict[metric]) / self.optimal_dict[
+        #             #     metric]) * 100
+        #             # type_dict_indexes[metric][
+        #             #     "deviation"] = f"+{metric_over_optimal:.2f}%" if metric_over_optimal > 0 else f"{metric_over_optimal:.2f}%"
+        #
+        #             if score_metric_dict[metric] < 80:
+        #                 str_over_optimal += f'{self.name_dict[metric]}为{round(self.value_dict[metric], 2)}{self.unit_dict[metric]}，超过合理范围{metric_over_optimal}%；'
+        #
+        #         else:
+        #             # for indexes
+        #             type_dict_indexes[metric] = {
+        #                 "name": f"{self.name_dict[metric]}({self.unit_dict[metric]})",
+        #                 "score": score_metric_dict[metric],
+        #                 "value": f'{self.custom_data[metric]["value"][0]:.2f}',
+        #                 # "range": f"[0, {self.optimal_dict['averageSpeed']})",
+        #                 # "deviation": f"+{avv_deviation}%" if avv_deviation > 0 else f"{avv_deviation}%"
+        #             }
+        #
+        #             value = self.custom_data[metric]["value"][0]
+        #             optimal = self.custom_param_dict[metric]['optimal'][0]
+        #
+        #             if self.custom_param_dict[metric]['kind'][0] == -1:
+        #                 type_dict_indexes[metric][
+        #                     "range"] = f"[0, {self.custom_param_dict[metric]['optimal'][0]}]"
+        #
+        #                 metric_over_optimal = (value - optimal) / optimal * 100
+        #                 type_dict_indexes[metric][
+        #                     "deviation"] = f"+{metric_over_optimal:.2f}%" if metric_over_optimal > 0 else f"0%"
+        #
+        #             elif self.custom_param_dict[metric]['kind'][0] == 1:
+        #                 type_dict_indexes[metric][
+        #                     "range"] = f"[{self.custom_param_dict[metric]['optimal'][0]}, inf)"
+        #
+        #                 metric_over_optimal = (value - optimal) / optimal * 100
+        #                 type_dict_indexes[metric][
+        #                     "deviation"] = f"0%" if metric_over_optimal > 0 else f"{metric_over_optimal:.2f}%"
+        #
+        #             elif self.custom_param_dict[metric]['kind'][0] == 0:
+        #                 minn = value * self.custom_param_dict[metric]['multiple'][0][0]
+        #                 maxx = value * self.custom_param_dict[metric]['multiple'][0][1]
+        #
+        #                 type_dict_indexes[metric]["range"] = f"[{minn}, {maxx}]"
+        #
+        #                 if value < minn:
+        #                     metric_over_optimal = (value - minn) / minn * 100
+        #                 elif value > maxx:
+        #                     metric_over_optimal = (value - maxx) / maxx * 100
+        #                 else:
+        #                     metric_over_optimal = 0
+        #
+        #                 type_dict_indexes[metric][
+        #                     "deviation"] = f"+{metric_over_optimal:.2f}%" if metric_over_optimal > 0 else f"{metric_over_optimal:.2f}%"
+        #
+        #             # metric_over_optimal = ((self.custom_param_dict[metric]['optimal'][0] -
+        #             #                         self.custom_data[metric]["value"][0]) /
+        #             #                        self.custom_param_dict[metric]['optimal'][0]) * 100
+        #             # type_dict_indexes[metric][
+        #             #     "deviation"] = f"+{metric_over_optimal:.2f}%" if metric_over_optimal > 0 else f"{metric_over_optimal:.2f}%"
+        #
+        #             if score_metric_dict[metric] < 80:
+        #                 str_over_optimal += f'{self.name_dict[metric]}为{self.custom_data[metric]["value"][0]:.2f}{self.unit_dict[metric]}，超过合理范围{metric_over_optimal}%；'
+        #
+        #             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
+        # efficient description
+        # str_over_optimal = str_over_optimal[:-1]
+
+        # if grade_efficient == '优秀':
+        #     effi_description1 = '机器人行驶效率高；'
+        # elif grade_efficient == '良好':
+        #     effi_description1 = '机器人在本轮测试中的表现满足设计指标要求；'
+        # elif grade_efficient == '一般':
+        #     str_ineffi_type = string_concatenate(bad_metric_list)
+        #     effi_description1 = f'机器人需要在{str_ineffi_type}指标上进一步优化。其中，{str_over_optimal}；'
+        # elif grade_efficient == '较差':
+        #     str_ineffi_type = string_concatenate(bad_metric_list)
+        #     effi_description1 = f'需要提高机器人在{str_ineffi_type}指标上的表现。其中，{str_over_optimal}；'
+        #
+        # if not bad_metric_list:
+        #     effi_description2 = '高效性在各个指标上的表现俱佳'
+        #     effi_description3 = "机器人的规划控制能力良好，通行效率高"
+        # else:
+        #     str_effi_type = string_concatenate(good_metric_list)
+        #     str_ineffi_type = string_concatenate(bad_metric_list)
+        #
+        #     effi_description2 = f"{str_effi_type}指标表现良好，{str_ineffi_type}指标表现不佳。其中，{str_over_optimal}"
+        #     effi_description3 = "应该优化机器人的规划控制逻辑，提高机器人的通行效率"
+
+        # report_dict["description1"] = replace_key_with_value(effi_description1, self.name_dict)
+        # report_dict["description2"] = replace_key_with_value(effi_description2, self.name_dict)
+        # report_dict["description3"] = effi_description3
+
+        description = "· 在高效性方面，"
+
+        if self.stop_count > 0:
+            description += f"出现{self.stop_count}次无障碍物停止，需重点优化。"
+        else:
+            description += f"平均速度{self.speed_dict['avg']:.4f}m/s，未出现无障碍物停止，表现{grade_efficient}。"
+
+        report_dict["description"] = description
+
+        description1 = f"次数：{self.stop_count}次；" \
+                       f"总时长：{self.sum_stop_time:.4f}s；" \
+                       f"平均时长：{self.stop_duration:.4f}s"
+
+        description2 = f"最大值：{self.speed_dict['max']:.4f}m/s；" \
+                       f"最小值：{self.speed_dict['min']:.4f}m/s；" \
+                       f"平均值：{self.speed_dict['avg']:.4f}m/s"
+
+        report_dict["description1"] = description1
+        report_dict["description2"] = description2
+
+        plt.figure(figsize=(12, 3))
+        plt.plot(self.time_list, self.speed_list)
+
+        plt.xlabel('Time(s)')
+        plt.ylabel('Speed(m/s)')
+        # plt.legend()
+
+        # 调整布局，消除空白边界
+        plt.tight_layout()
+
+        plt.savefig(os.path.join(self.casePath, "Speed.png"))
+        plt.close()
+
+        print(report_dict)
+
+        return report_dict
+
+    def get_eval_data(self):
+        df = self.eval_data
+        return df

log.py

@@ -0,0 +1,39 @@
+# -*- coding: utf-8 -*-
+
+import logging
+import os
+
+# 全局日志记录器变量
+logger = None
+
+
+def setup_logger(log_file_path):
+    """
+    设置全局日志记录器。
+    """
+    global logger
+
+    # 确保日志文件路径存在
+    os.makedirs(os.path.dirname(log_file_path), exist_ok=True)
+
+    # 配置日志记录器
+    logger = logging.getLogger(__name__)
+    logger.setLevel(logging.DEBUG)
+
+    # 创建一个文件处理器，将日志输出到文件
+    file_handler = logging.FileHandler(filename=log_file_path, mode='a', encoding='utf-8')
+    file_handler.setLevel(logging.DEBUG)
+
+    # 创建一个格式器，定义日志输出的格式
+    formatter = logging.Formatter("[%(asctime)s] - [%(levelname)s] [%(filename)s] [line:%(lineno)d] - [%(message)s]")
+    file_handler.setFormatter(formatter)
+
+    # 将处理器添加到记录器
+    logger.addHandler(file_handler)
+
+
+def get_logger():
+    """
+    返回已配置的全局日志记录器。
+    """
+    return logger

pji_run.py

@@ -0,0 +1,92 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+##################################################################
+#
+# Copyright (c) 2023 CICV, Inc. All Rights Reserved
+#
+##################################################################
+"""
+@Authors:           yangzihao(yangzihao@china-icv.cn)
+@Data:              2024/04/21
+@Last Modified:     2024/04/21
+@Summary:           Evaluation entrance
+"""
+
+import os
+import sys
+import time
+from single_case_eval import single_case_eval
+from common import get_subfolders_name
+from pjirebot_evaluate_csv import parsebag
+import log
+
+if __name__ == "__main__":
+
+    if len(sys.argv) >= 3:
+        bagPath = sys.argv[1]
+        resultPath = sys.argv[2]
+        # graphPath = sys.argv[3]
+
+        logFileName = f'{resultPath}/log.txt'
+        log.setup_logger(logFileName)
+        logger = log.get_logger()
+
+        case_name = bagPath.split('/')[-1].split('.')[0]
+        # case_name = os.path.basename(os.path.dirname(dataPath))
+
+        if not os.path.exists(bagPath):
+            print('Invalid bagPath!')
+            logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Invalid bagPath!")
+            sys.exit(-1)
+        else:
+            try:
+                logger.info(f"[case:{case_name}] SINGLE_CASE_EVAL: Start evaluating:")
+                print("SINGLE_CASE_EVAL程序开始运行：")
+                configPath = r"./config.json"
+                print(f"   configPath: {configPath},\n   resultPath: {resultPath}")
+                t1 = time.time()
+                parsebag(bagPath, resultPath)
+                single_case_eval(configPath, resultPath, case_name)
+                t2 = time.time()
+                print(f"程序结束，执行时间：{int(t2 - t1)} s")
+                logger.info(f"[case:{case_name}] SINGLE_CASE_EVAL: End.")
+                sys.exit(0)
+            except Exception as e:
+                print("异常，退出...")
+                print(repr(e))
+                sys.exit(-1)
+    else:
+        print('No enough arguments!')
+        sys.exit(-1)
+
+    # bagPath = './bag/2024-04-22-08-48-30_obstacledetection_30.bag'
+    # resultPath = './result/integration'
+    #
+    # logFileName = f'{resultPath}/log.txt'
+    # log.setup_logger(logFileName)
+    # logger = log.get_logger()
+    #
+    # case_name = bagPath.split('/')[-1].split('.')[0]
+    # # case_name = os.path.basename(os.path.dirname(dataPath))
+    #
+    # if not os.path.exists(bagPath):
+    #     print('Invalid bagPath!')
+    #     logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Invalid bagPath!")
+    #     sys.exit(-1)
+    # else:
+    #     try:
+    #         logger.info(f"[case:{case_name}] SINGLE_CASE_EVAL: Start evaluating:")
+    #         print("SINGLE_CASE_EVAL程序开始运行：")
+    #         configPath = r"./config.json"
+    #         print(f"   configPath: {configPath},\n   resultPath: {resultPath}")
+    #         t1 = time.time()
+    #         parsebag(bagPath, resultPath)
+    #         single_case_eval(configPath, resultPath, case_name)
+    #         t2 = time.time()
+    #         print(f"程序结束，执行时间：{int(t2 - t1)} s")
+    #         logger.info(f"[case:{case_name}] SINGLE_CASE_EVAL: End.")
+    #         sys.exit(0)
+    #     except Exception as e:
+    #         print("异常，退出...")
+    #         print(repr(e))
+    #         sys.exit(-1)

pji_run_test.py

@@ -0,0 +1,94 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+##################################################################
+#
+# Copyright (c) 2023 CICV, Inc. All Rights Reserved
+#
+##################################################################
+"""
+@Authors:           yangzihao(yangzihao@china-icv.cn)
+@Data:              2024/04/21
+@Last Modified:     2024/04/21
+@Summary:           Evaluation entrance
+"""
+
+import os
+import sys
+import time
+from single_case_eval import single_case_eval
+import pandas as pd
+from common import get_subfolders_name
+# from pjirebot_evaluate_csv import parsebag
+import log
+import warnings
+warnings.filterwarnings("ignore", category=FutureWarning, module='pandas')
+pd.set_option('future.no_silent_downcasting', True)
+
+
+if __name__ == "__main__":
+
+    dataPath = './task/pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9'
+    resultPath = dataPath
+    # graphPath = sys.argv[3]
+
+    logFileName = f'{resultPath}/log.txt'
+    log.setup_logger(logFileName)
+    logger = log.get_logger()
+
+    case_name = dataPath.split('/')[-1].split('.')[0]
+    # case_name = os.path.basename(os.path.dirname(dataPath))
+
+    if not os.path.exists(dataPath):
+        print('Invalid bagPath!')
+        logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Invalid bagPath!")
+        sys.exit(-1)
+    else:
+        try:
+            logger.info(f"[case:{case_name}] SINGLE_CASE_EVAL: Start evaluating:")
+            print("SINGLE_CASE_EVAL程序开始运行：")
+            configPath = r"./config.json"
+            print(f"   configPath: {configPath},\n   resultPath: {resultPath}")
+            t1 = time.time()
+            # parsebag(bagPath, resultPath)
+            single_case_eval(configPath, resultPath, case_name)
+            t2 = time.time()
+            print(f"程序结束，执行时间：{int(t2 - t1)} s")
+            logger.info(f"[case:{case_name}] SINGLE_CASE_EVAL: End.")
+            sys.exit(0)
+        except Exception as e:
+            print("异常，退出...")
+            print(repr(e))
+            sys.exit(-1)
+
+
+    # bagPath = './bag/2024-04-22-08-48-30_obstacledetection_30.bag'
+    # resultPath = './result/integration'
+    #
+    # logFileName = f'{resultPath}/log.txt'
+    # log.setup_logger(logFileName)
+    # logger = log.get_logger()
+    #
+    # case_name = bagPath.split('/')[-1].split('.')[0]
+    # # case_name = os.path.basename(os.path.dirname(dataPath))
+    #
+    # if not os.path.exists(bagPath):
+    #     print('Invalid bagPath!')
+    #     logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Invalid bagPath!")
+    #     sys.exit(-1)
+    # else:
+    #     try:
+    #         logger.info(f"[case:{case_name}] SINGLE_CASE_EVAL: Start evaluating:")
+    #         print("SINGLE_CASE_EVAL程序开始运行：")
+    #         configPath = r"./config.json"
+    #         print(f"   configPath: {configPath},\n   resultPath: {resultPath}")
+    #         t1 = time.time()
+    #         parsebag(bagPath, resultPath)
+    #         single_case_eval(configPath, resultPath, case_name)
+    #         t2 = time.time()
+    #         print(f"程序结束，执行时间：{int(t2 - t1)} s")
+    #         logger.info(f"[case:{case_name}] SINGLE_CASE_EVAL: End.")
+    #         sys.exit(0)
+    #     except Exception as e:
+    #         print("异常，退出...")
+    #         print(repr(e))
+    #         sys.exit(-1)

pjirebot_evaluate_csv.py

@@ -0,0 +1,124 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Dec 25 17:35:56 2023
+
+@author: dell
+"""
+
+# coding: utf-8
+# !/usr/bin/env python2
+import os
+import rosbag
+import csv
+import math
+
+import sys
+import time
+from datetime import datetime
+import rospy
+
+
+def parsebag(input_dir, output_dir):
+    dic_EgoState = ['Time', 'simTime', 'simFrame', 'posX', 'posY', 'posZ', 'speedX', 'speedY', 'speedZ', 'accelX',
+                    'accelY', 'accelZ', 'dimX', 'dimY', 'dimZ', 'obstacle', 'travelDist']
+    # dic_DriverCtrl=['Time','simTime','simFrame','tarspeedX','tarspeedY','tarspeedZ','tardimX','tardimY','tardimZ']
+
+    EgoState_file = open(output_dir + "/" + "EgoState_pji.csv", 'w')
+    # DriverCtrl_file = open(output_dir + "/"+"DriverCtrl_pji.csv", 'w')
+    writer_EgoState = csv.writer(EgoState_file)
+    writer_EgoState.writerow(dic_EgoState)
+    # writer_DriverCtrl = csv.writer(DriverCtrl_file)
+    # writer_DriverCtrl.writerow(dic_DriverCtrl)
+
+    frame_max = sys.maxsize
+    count = 1
+    with rosbag.Bag(input_dir, 'r') as bag:
+        odom_flag = False
+        first_message_time = None
+        Frame_imu = 1
+        Frame_cmd_vel = 1
+        obstacle_state = 0
+        cur_mileage = ''
+
+        for topic, msg, t in bag.read_messages():  # t代表时间
+
+            if first_message_time is None:
+                first_message_time = t
+                first_message_time = rospy.Time.to_sec(first_message_time)
+                first_message_time = datetime.fromtimestamp(first_message_time)
+
+            if topic == "/obstacle_detection":
+                obstacle_state = msg.data
+                # print(msg.data)
+
+            if topic == "/sys_info":
+                cur_mileage = msg.cur_mileage
+
+            if topic == "/odom":
+                odom_flag = True
+                posX = msg.pose.pose.position.x
+                posY = msg.pose.pose.position.y
+                posZ = msg.pose.pose.position.z
+                speedX = msg.twist.twist.linear.x * 3.6
+                speedY = msg.twist.twist.linear.y * 3.6
+                speedZ = msg.twist.twist.linear.z * 3.6
+                dimX = msg.twist.twist.angular.x
+                dimY = msg.twist.twist.angular.y
+                dimZ = msg.twist.twist.angular.z
+
+            if topic == "/imu":
+                if odom_flag:
+                    accelX = msg.linear_acceleration.x
+                    accelY = msg.linear_acceleration.y
+                    accelZ = msg.linear_acceleration.z
+                    timestamp = rospy.Time.to_sec(t)
+                    date_time_imu = datetime.fromtimestamp(timestamp)
+                    simtime_imu = (date_time_imu - first_message_time).total_seconds()
+
+                    message_EgoState = [date_time_imu, simtime_imu, Frame_imu, posX, posY, posZ, speedX, speedY, speedZ,
+                                        accelX, accelY, accelZ, dimX, dimY, dimZ, obstacle_state, cur_mileage]
+
+                    writer_EgoState.writerow(message_EgoState)
+                    Frame_imu += 1
+                else:
+                    print('6666')
+
+            '''        
+            if topic =='/cmd_vel':
+                timestamp = rospy.Time.to_sec(t)
+                date_time_cmd_vel = datetime.fromtimestamp(timestamp)
+                simtime_cmd_vel=(date_time_cmd_vel-first_message_time).total_seconds()
+                message_DriverCtrl =[date_time_cmd_vel,simtime_cmd_vel,Frame_cmd_vel,msg.linear.x*3.6,msg.linear.y*3.6,msg.linear.z*3.6,
+                                     msg.angular.x,msg.angular.y,msg.angular.z]
+                
+                                     
+                writer_DriverCtrl.writerow(message_DriverCtrl)
+                Frame_cmd_vel+=1
+            '''
+
+        EgoState_file.close()
+        # DriverCtrl_file.close()
+
+
+if __name__ == "__main__":
+    #    print("Input the path of file...."+'\n')
+
+    # input_dir = sys.argv[1]
+    input_dir = './bag/example_4.bag'
+    bagname = input_dir.split('/')[-1].split('.')[0]
+    # output_dir = sys.argv[2]
+    output_dir = './'
+    output_dir = os.path.join(output_dir, bagname)
+
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    parsebag(input_dir, output_dir)
+
+    '''
+    try:
+        parsebag(input_dir, output_dir)
+        print('successfully analysis '+input_dir)
+    except Exception as e:
+        print(e)
+    '''

report.json

@@ -0,0 +1,111 @@
+{
+  "name": "case_name",
+  "algorithmComprehensiveScore": 75.93,
+  "algorithmLevel": "一般",
+  "testDuration": "xx",
+  "testMileage": "xx",
+  "algorithmResultDescription": "算法总体表现，高效性表现xx，平顺性表现xx",
+  "tableData": [
+    [
+      "评价维度",
+      "评价指标",
+      "指标描述"
+    ],
+    [
+      "高效性",
+      "有障碍物停止",
+      "xxx"
+    ],
+    [
+      "高效性",
+      "无障碍物停止",
+      "xxx"
+    ],
+    [
+      "高效性",
+      "速度",
+      "xxx"
+    ],
+    [
+      "平顺性",
+      "线加速度",
+      "xxx"
+    ],
+    [
+      "平顺性",
+      "角加速度",
+      "xxx"
+    ]
+  ],
+  "details": {
+    "efficient": {
+      "score": "100",
+      "level": "优秀",
+      "description": "在高效性方面，出现x次无障碍物停止，需重点优化",
+      "tableData": [
+        [
+          "评价维度",
+          "评价指标",
+          "指标描述"
+        ],
+        [
+          "高效性",
+          "有障碍物停止",
+          "xxx"
+        ],
+        [
+          "高效性",
+          "无障碍物停止",
+          "xxx"
+        ],
+        [
+          "高效性",
+          "速度",
+          "xxx"
+        ]
+      ],
+      "graphData": [
+        {
+          "name": "speed",
+          "path": "./xxx/xxx.png"
+        },
+        {
+          "name": "speed",
+          "path": "./xxx/xxx.png"
+        }
+      ]
+    },
+    "comfort": {
+      "score": "100",
+      "level": "优秀",
+      "description": "在平顺性方面，线加速度变化剧烈，有急刹、急加速情况，需重点优化",
+      "tableData": [
+        [
+          "评价维度",
+          "评价指标",
+          "指标描述"
+        ],
+        [
+          "平顺性",
+          "线加速度",
+          "xxx"
+        ],
+        [
+          "平顺性",
+          "角加速度",
+          "xxx"
+        ]
+      ],
+      "graphData": [
+        {
+          "name": "speed",
+          "path": "./xxx/xxx.png"
+        },
+        {
+          "name": "speed",
+          "path": "./xxx/xxx.png"
+        }
+      ]
+    }
+  }
+}

report_generate.py

@@ -0,0 +1,239 @@
+# -*- coding: utf-8 -*-
+# ! /usr/bin/env python3
+# @Author           : yangzihao
+# @CreateTime       : 2024/04/17
+
+"""
+This file is for PDF report generate.
+Refer to the CSDN: https://blog.csdn.net/cainiao_python/article/details/128928298
+"""
+
+from reportlab.pdfbase import pdfmetrics  # 注册字体
+from reportlab.pdfbase.ttfonts import TTFont  # 字体类
+from reportlab.platypus import Table, SimpleDocTemplate, Paragraph, Image  # 报告内容相关类
+from reportlab.lib.pagesizes import A4  # 页面的标志尺寸(8.5*inch, 11*inch)
+from reportlab.lib.styles import getSampleStyleSheet  # 文本样式
+from reportlab.lib import colors  # 颜色模块
+from reportlab.graphics.charts.barcharts import VerticalBarChart  # 图表类
+from reportlab.graphics.charts.legends import Legend  # 图例类
+from reportlab.graphics.shapes import Drawing  # 绘图工具
+from reportlab.lib.units import cm  # 单位：cm
+from reportlab.lib import utils
+
+import json
+import pandas as pd
+
+# 注册字体(提前准备好字体文件, 如果同一个文件需要多种字体可以注册多个)
+pdfmetrics.registerFont(TTFont('SimSun', 'SimSun.ttf'))
+
+
+class ReportPDF:
+    """
+    This class is for PDF report generate.
+    """
+
+    # 绘制标题
+    @staticmethod
+    def draw_title(title: str):
+        # 获取所有样式表
+        style = getSampleStyleSheet()
+        # 拿到标题样式
+        ct = style['Heading4']
+        # 单独设置样式相关属性
+        ct.fontName = 'SimSun'  # 字体名
+        ct.fontSize = 18  # 字体大小
+        ct.leading = 30  # 行间距
+        ct.textColor = colors.black  # 字体颜色
+        ct.alignment = 1  # 居中
+        ct.bold = True
+        # 创建标题对应的段落，并且返回
+        return Paragraph(title, ct)
+
+    # 绘制小标题
+    @staticmethod
+    def draw_little_title(title: str):
+        # 获取所有样式表
+        style = getSampleStyleSheet()
+        # 拿到标题样式
+        ct = style['Normal']
+        # 单独设置样式相关属性
+        ct.fontName = 'SimSun'  # 字体名
+        ct.fontSize = 15  # 字体大小
+        # ct.leading = 15  # 行间距
+        ct.textColor = colors.darkblue  # 字体颜色
+        # 创建标题对应的段落，并且返回
+        return Paragraph(title, ct)
+
+    # 绘制普通段落内容
+    @staticmethod
+    def draw_text(text: str):
+        # 获取所有样式表
+        style = getSampleStyleSheet()
+        # 获取普通样式
+        ct = style['Normal']
+        ct.fontName = 'SimSun'
+        ct.fontSize = 10
+        # ct.wordWrap = 'CJK'  # 设置自动换行
+        ct.alignment = 0  # 左对齐
+        # ct.firstLineIndent = 32  # 第一行开头空格
+        ct.leading = 15
+        return Paragraph(text, ct)
+
+    # 绘制普通段落内容
+    @staticmethod
+    def draw_line_feed(text: str):
+        # 获取所有样式表
+        style = getSampleStyleSheet()
+        # 获取普通样式
+        ct = style['Normal']
+        ct.fontName = 'SimSun'
+        ct.fontSize = 10
+        ct.textColor = colors.white
+        # ct.wordWrap = 'CJK'  # 设置自动换行
+        ct.alignment = 0  # 左对齐
+        # ct.firstLineIndent = 32  # 第一行开头空格
+        ct.leading = 15
+        return Paragraph(text, ct)
+
+    # 绘制表格
+    @staticmethod
+    def draw_table(*args):
+        # 列宽度
+        # col_width = 120
+        style = [
+            ('FONTNAME', (0, 0), (-1, -1), 'SimSun'),  # 字体
+            ('FONTSIZE', (0, 0), (-1, 0), 9),  # 第一行的字体大小
+            ('FONTSIZE', (0, 1), (-1, -1), 9),  # 第二行到最后一行的字体大小
+            ('BACKGROUND', (0, 0), (-1, 0), '#d5dae6'),  # 设置第一行背景颜色
+            ('ALIGN', (0, 0), (-1, -1), 'CENTER'),  # 第一行水平居中
+            ('ALIGN', (0, 1), (-1, -1), 'CENTER'),  # 第二行到最后一行左右左对齐
+            ('VALIGN', (0, 0), (-1, -1), 'MIDDLE'),  # 所有表格上下居中对齐
+            ('TEXTCOLOR', (0, 0), (-1, -1), colors.darkslategray),  # 设置表格内文字颜色
+            ('GRID', (0, 0), (-1, -1), 0.5, colors.grey),  # 设置表格框线为grey色，线宽为0.5
+            # ('SPAN', (0, 1), (0, 2)),  # 合并第一列二三行
+            # ('SPAN', (0, 3), (0, 4)),  # 合并第一列三四行
+            # ('SPAN', (0, 5), (0, 6)),  # 合并第一列五六行
+            # ('SPAN', (0, 7), (0, 8)),  # 合并第一列五六行
+        ]
+        # table = Table(args, colWidths=col_width, style=style)
+        table = Table(args, style=style)
+        return table
+
+    # 创建图表
+    @staticmethod
+    def draw_bar(bar_data: list, ax: list, items: list):
+        drawing = Drawing(500, 250)
+        bc = VerticalBarChart()
+        bc.x = 45  # 整个图表的x坐标
+        bc.y = 45  # 整个图表的y坐标
+        bc.height = 200  # 图表的高度
+        bc.width = 350  # 图表的宽度
+        bc.data = bar_data
+        bc.strokeColor = colors.black  # 顶部和右边轴线的颜色
+        bc.valueAxis.valueMin = 5000  # 设置y坐标的最小值
+        bc.valueAxis.valueMax = 26000  # 设置y坐标的最大值
+        bc.valueAxis.valueStep = 2000  # 设置y坐标的步长
+        bc.categoryAxis.labels.dx = 2
+        bc.categoryAxis.labels.dy = -8
+        bc.categoryAxis.labels.angle = 20
+        bc.categoryAxis.categoryNames = ax
+
+        # 图示
+        leg = Legend()
+        leg.fontName = 'SimSun'
+        leg.alignment = 'right'
+        leg.boxAnchor = 'ne'
+        leg.x = 475  # 图例的x坐标
+        leg.y = 240
+        leg.dxTextSpace = 10
+        leg.columnMaximum = 3
+        leg.colorNamePairs = items
+        drawing.add(leg)
+        drawing.add(bc)
+        return drawing
+
+    # 绘制图片
+    @staticmethod
+    def draw_img(path):
+        img = Image(path)  # 读取指定路径下的图片
+        img.drawWidth = 0.8 * cm  # 设置图片的宽度
+        img.drawHeight = 0.8 * cm  # 设置图片的高度
+        img.hAlign = 0
+        return img
+
+
+def report_generate(reportDict, reportPdf):
+    # with open(f'{reportJson}', 'r', encoding='utf-8') as f:
+    #     data = json.load(f)
+
+    data = reportDict  # dict
+    # 创建PDF文档
+    styles = getSampleStyleSheet()
+
+    # 提取数据
+    name = data["name"]
+    # algorithm_score = data["algorithmComprehensiveScore"]
+    # algorithm_level = data["algorithmLevel"]
+    test_mileage = data["testMileage"]
+    test_duration = data["testDuration"]
+    algorithm_result_description = data["algorithmResultDescription"]
+    efficient_description = data["details"]["efficient"]["description"]
+    comfort_description = data["details"]["comfort"]["description"]
+
+    efficient_description1 = data["details"]["efficient"]["description1"]
+    efficient_description2 = data["details"]["efficient"]["description2"]
+    comfort_description1 = data["details"]["comfort"]["description1"]
+    comfort_description2 = data["details"]["comfort"]["description2"]
+
+    # 创建表格数据
+    # table_data = [
+    #     ["评价维度", "评价指标", "指标描述"],
+    #     ["高效性", "无障碍物停止", efficient_description1],
+    #     ["高效性", "速度", efficient_description2],
+    #     ["平顺性", "线加速度", comfort_description1],
+    #     ["平顺性", "角加速度", comfort_description2]
+    # ]
+    table_data = [
+        ("评价维度", "评价指标", "指标描述"),
+        ("高效性", "无障碍物停止", efficient_description1),
+        ("高效性", "速度", efficient_description2),
+        ("平顺性", "线加速度", comfort_description1),
+        ("平顺性", "角加速度", comfort_description2)
+    ]
+
+    # 创建内容对应的空列表
+    content = list()
+    content.append(ReportPDF.draw_img('Pji.png'))
+    content.append(ReportPDF.draw_title('朴津室内AMR算法评价报告'))
+
+    infos = f"数据包: {name}, 行驶时长: {test_duration}, 行驶里程: {test_mileage}"
+    content.append(ReportPDF.draw_text(infos))
+    content.append(ReportPDF.draw_text(algorithm_result_description))
+    content.append(ReportPDF.draw_text(efficient_description))
+    content.append(ReportPDF.draw_text(comfort_description))
+    content.append(ReportPDF.draw_line_feed("换行"))
+
+    content.append(ReportPDF.draw_table(*table_data))
+
+    content.append(ReportPDF.draw_line_feed("换行"))
+
+    # 添加图片到PDF文档
+    for key, value in data["graphPath"].items():
+        # content.append(Paragraph(key, styles['Heading5']))
+        content.append(ReportPDF.draw_text(key))
+        content.append(Image(value, width=480, height=120))
+
+    # 创建PDF文档
+    # pdf_file = "./report123.pdf"
+    pdf_file = reportPdf
+    doc = SimpleDocTemplate(pdf_file, pagesize=A4)
+
+    doc.build(content)
+    print("PDF报告已生成！")
+
+
+if __name__ == '__main__':
+    reportJson = './report.json'
+    reportPdf = "./report123.pdf"
+    report_generate(reportJson, reportPdf)
+    print('over')

run.spec

@@ -0,0 +1,44 @@
+# -*- mode: python ; coding: utf-8 -*-
+
+
+a = Analysis(
+    ['pji_run.py', 'comfort.py', 'common.py', 'config_parser.py', 'data_info.py', 'data_process.py', 'efficient.py',
+    'log.py', 'pjirebot_evaluate_csv.py', 'score_weight.py', 'single_case_eval.py', 'single_case_evaluate.py'],
+    pathex=[],
+    binaries=[],
+    datas=[],
+    hiddenimports=['scipy'],
+    hookspath=[],
+    hooksconfig={},
+    runtime_hooks=[],
+    excludes=[],
+    noarchive=False,
+)
+pyz = PYZ(a.pure)
+
+exe = EXE(
+    pyz,
+    a.scripts,
+    [],
+    exclude_binaries=True,
+    name='single_case_eval',
+    debug=False,
+    bootloader_ignore_signals=False,
+    strip=False,
+    upx=True,
+    console=True,
+    disable_windowed_traceback=False,
+    argv_emulation=False,
+    target_arch=None,
+    codesign_identity=None,
+    entitlements_file=None,
+)
+coll = COLLECT(
+    exe,
+    a.binaries,
+    a.datas,
+    strip=False,
+    upx=True,
+    upx_exclude=[],
+    name='run',
+)

score_weight.py

@@ -0,0 +1,266 @@
+#!/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:              2024/01/12
+@Last Modified:     2024/01/12
+@Summary:           Weight cal
+"""
+
+import sys
+
+sys.path.append('../common')
+sys.path.append('modules')
+sys.path.append('score')
+
+import numpy as np
+from common import get_interpolation
+from functools import reduce
+
+
+def cal_weight_from_80(score_list):
+    # weight process
+    s_list = [80.1 if x == 80 else x for x in score_list]
+    weight_list = abs((np.array(s_list) - 80) / 100)
+
+    # normalization
+    weight_list = weight_list / sum(weight_list)
+    return weight_list
+
+
+def cal_score_with_priority(score_list, weight_list, priority_list):
+    """
+
+    """
+    rho = 1
+    flag = any(i < 80 for i in score_list)
+    if flag:
+        rho = 0.9
+        for i in range(len(score_list)):
+            if score_list[i] < 80 and priority_list[i] == 0:
+                rho = 0.8
+
+    # calculate score
+    score_all = np.dot(weight_list, score_list) * rho
+    return score_all
+
+
+def cal_score_from_80(score_list):
+    """
+
+    """
+    # weight process
+    weight_list = cal_weight_from_80(score_list)
+    # calculate score
+    score_all = np.dot(weight_list, score_list)
+    # score_all = score_all * 0.8 if flag else score_all
+    return round(score_all, 2)
+
+
+class ScoreModel(object):
+    """
+    信息量越大，权重越大
+    对比强度和冲突性越大，信息量越大
+    标准差越大，对比强度越大
+    相关性越小，冲突性越大
+    ————————————————————————
+    单列标准差大，列之间相关性小 -> 则权重大
+    """
+
+    def __init__(self, kind_list, optimal_value, multiple_list, arr):
+        # n for cases
+        # m for indicators
+        self.n, self.m = arr.shape
+        self.kind = kind_list
+        self.optimal = optimal_value
+        self.X = arr
+        self.rho = 1 / 3  # 一般选0.5，最低分计算公式: rho/(1+rho)
+        self.multiple = np.array(multiple_list)
+
+    def calculate_score(self):
+        # m个指标，n个场景
+        val_mean = []
+        optimal_value = self.optimal
+
+        for i in range(self.m):
+            opt_val = optimal_value[i]
+            val_mean_i = (sum(self.X[:, i]) + opt_val) / (self.n + 1)  # Eq(15)
+            val_mean.append(val_mean_i)
+
+        self.X = self.X / np.array(val_mean)  # 无量纲化
+        optimal_value = np.array(optimal_value) / np.array(val_mean)  # 最优值无量纲化
+        abs_X = abs(optimal_value - self.X)
+        minn = 0
+        maxx = 2 * (self.multiple[0][1] - 1) / (self.multiple[0][1] + 1)  # 五倍时参数为1.333333，三倍时参数为1
+        eta = (minn + self.rho * maxx) / (abs_X + self.rho * maxx)  # Eq(16)
+        Eta = [x * 80 for x in list(np.mean(eta, axis=0))]
+        return Eta
+
+    def cal_score(self):
+        """
+        数据处理前进行特判，先将无需打分的数据直接给分
+        例如，大于基准值五倍的值，直接给出100分、0分
+        单列均为同一个值时，符合预期值则100分，否则0分
+
+        先完成单用例特判，再考虑多用例特判
+        """
+        # 单用例版本
+        # for j in range(self.n):
+        # multiple = 5
+
+        inteval_20_coefficient = 3
+
+        flag_list = [-1] * self.m
+        column_list = []
+        for i in range(self.m):
+            optimal = self.optimal[i]
+            multiple = self.multiple[i]
+            if self.kind[i] == 1:  # 极大型
+                if np.all(self.X[:, i] >= optimal * multiple[1]):  # 补充线性插值
+                    flag_list[i] = 100
+                elif np.all(self.X[:, i] >= optimal):
+                    flag_list[i] = float(get_interpolation(self.X[:, i], [optimal, 80], [optimal * multiple[1], 100]))
+                elif self.X[:, i] <= optimal * multiple[0]:
+                    flag_list[i] = 0
+                else:
+                    column_list.append(i)
+            elif self.kind[i] == -1:  # 极小型
+                if np.all(self.X[:, i] <= optimal * multiple[0]):
+                    flag_list[i] = 100
+                elif np.all(self.X[:, i] <= optimal):
+                    flag_list[i] = float(get_interpolation(self.X[:, i], [optimal, 80], [optimal * multiple[0], 100]))
+                elif self.X[:, i] >= optimal * multiple[1]:
+                    flag_list[i] = 0
+                else:
+                    column_list.append(i)
+            elif self.kind[i] == 0:  # 区间型
+                if np.all(optimal * multiple[0] <= self.X[:, i] <= optimal):
+                    flag_list[i] = float(
+                        get_interpolation(optimal - self.X[:, i], [abs(optimal - optimal * multiple[0]), 80], [0, 100]))
+                elif np.all(optimal <= self.X[:, i] <= optimal * multiple[1]):
+                    flag_list[i] = float(
+                        get_interpolation(self.X[:, i] - optimal, [abs(optimal * multiple[1] - optimal), 80], [0, 100]))
+                elif np.all(self.X[:, i] < optimal * multiple[0]):
+                    dist = optimal * multiple[0] - self.X[:, i]
+                    interval_dist = (optimal - optimal * multiple[0]) / inteval_20_coefficient
+                    if dist < interval_dist:
+                        flag_list[i] = float(get_interpolation(dist, [interval_dist, 20], [0, 80]))
+                    else:
+                        flag_list[i] = 0
+                elif np.all(optimal * multiple[1] < self.X[:, i]):
+                    dist = self.X[:, i] - optimal * multiple[1]
+                    interval_dist = (optimal * multiple[1] - optimal) / inteval_20_coefficient
+                    if dist < interval_dist:
+                        flag_list[i] = float(get_interpolation(dist, [interval_dist, 20], [0, 80]))
+                    else:
+                        flag_list[i] = 0
+                else:
+                    column_list.append(i)
+
+        arr_temp = self.X[:, column_list]
+        kind_temp = [self.kind[i] for i in range(len(flag_list)) if flag_list[i] == -1]
+        optimal_temp = [self.optimal[i] for i in range(len(flag_list)) if flag_list[i] == -1]
+        multiple_temp = [self.multiple[i] for i in range(len(flag_list)) if flag_list[i] == -1]
+
+        # n_temp = len(arr_temp)
+        m_temp = len(arr_temp[0])
+
+        critic_m = ScoreModel(kind_temp, optimal_temp, multiple_temp, arr_temp)
+
+        if -1 not in flag_list:  # 全为特殊值
+            score = sum(flag_list) / len(flag_list)
+        elif all(x == -1 for x in flag_list):  # 无特殊值
+            score_temp = critic_m.calculate_score()
+            # score = sum(score_temp) / len(score_temp)
+            flag_list = score_temp
+        else:  # 部分为特殊值
+            score_temp = critic_m.calculate_score()
+            # score_temp_mean = sum(score_temp) / len(score_temp)
+            # w_temp = m_temp / self.m
+            # score = 100 * (1 - w_temp) + score_temp_mean * w_temp
+            index = 0
+            for i, flag in enumerate(flag_list):
+                if flag == -1:
+                    flag_list[i] = score_temp[index]
+                    index += 1
+
+        score_temp = flag_list
+
+        return score_temp
+
+
+class AHP:
+    def __init__(self, matrix):
+        self.A = np.array(matrix)
+        self.n = len(matrix)
+
+    def _get_consistency_ratio(self, w_max):
+        RI = [0, 0, 0.0001, 0.52, 0.89, 1.12, 1.26, 1.36,
+              1.41, 1.46, 1.49, 1.52, 1.54, 1.56, 1.58, 1.59,
+              1.5943, 1.6064, 1.6133, 1.6207, 1.6292]
+        CI = (w_max - self.n) / (self.n - 1)
+        CR = CI / RI[self.n]
+        return CR
+
+    def get_weights(self, method='eigenvalue'):
+        # Check consistency of pairwise comparison matrix
+        w, v = np.linalg.eig(self.A)
+        w_index = np.argmax(w)
+        w_max = np.real(w[w_index])
+        cr = self._get_consistency_ratio(w_max)
+
+        if cr > 0.1:
+            raise ValueError('The pairwise comparison matrix is inconsistent.')
+
+        # Normalize matrix
+        line_sum = [sum(m) for m in zip(*self.A)]
+        D = np.zeros((self.n, self.n))
+        for i in range(self.n):
+            for j in range(self.n):
+                D[i][j] = self.A[i][j] / line_sum[j]
+
+        # Calculate weights with selected method
+        if method == 'arithmetic':
+            weights = np.zeros(self.n)
+            for i in range(self.n):
+                weights[i] = np.average(D[i])
+        elif method == 'geometric':
+            weights = np.zeros(self.n)
+            for i in range(self.n):
+                weights[i] = reduce(lambda x, y: x * y, self.A[i])
+                weights[i] = pow(weights[i], 1 / self.n)
+            weights = [e / np.sum(weights) for e in weights]
+        elif method == 'eigenvalue':
+            weights = np.zeros(self.n)
+            v_index = np.argmax(v)
+            v_max = np.real(v[:, v_index])
+            weights = [e / np.sum(v_max) for e in v_max]
+
+        print("weights are", weights)
+        return weights
+
+
+if __name__ == "__main__":
+    kind_list = [-1]
+    optimal_value = [6]
+    multiple_list = [[0.5, 2]]  # [3, 12]
+
+    arr = [[1.999]]
+    # arr = [[2.1]]
+    # arr = [[2.999]]
+    # arr = [[3]]
+    # arr = [[4]]
+    # arr = [[6]]
+    # arr = [[11]]
+    # arr = [[11.999]]
+    # arr = [[12]]
+    # arr = [[12.1]]
+
+    cc = ScoreModel(kind_list, optimal_value, multiple_list, np.array(arr))
+    res = cc.cal_score()
+    print(res)

single_case_eval.py

@@ -0,0 +1,106 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+##################################################################
+#
+# Copyright (c) 2023 CICV, Inc. All Rights Reserved
+#
+##################################################################
+"""
+@Authors:           yangzihao(yangzihao@china-icv.cn)
+@Data:              2024/01/30
+@Last Modified:     2024/01/30
+@Summary:           Evaluateion functions
+"""
+
+import os
+import sys
+
+sys.path.append('../config')
+sys.path.append('../common')
+sys.path.append('../modules')
+sys.path.append('../results')
+
+import json
+import traceback
+
+import log
+from config_parser import ConfigParse
+from single_case_evaluate import single_case_evaluate, single_case_statistic
+# from data_quality import frame_loss_statistic
+# from pdf_report_generate import report_generate
+from report_generate import report_generate
+
+
+def single_case_eval(configPath, resultPath, case_name):
+    logger = log.get_logger()
+    # case_name = os.path.basename(os.path.dirname(dataPath))
+    # case_name = dataPath.split('/')[-1]
+
+    # 判断文件夹是否为空
+    if len(os.listdir(resultPath)) == 0:
+        print("No files in data_path!")  # 路径异常
+        logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: No files in data_path!")
+        sys.exit(-1)
+
+    # 加载配置文件
+    try:
+        # json_file = os.path.join(configPath, 'config.json')
+        config = ConfigParse(configPath)
+    except Exception as e:
+        print('Config file parsing ERROR!', e)
+        traceback.print_exc()
+        logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Config file parsing ERROR: {repr(e)}!", exc_info=True)
+        sys.exit(-1)
+
+    # 单用例评价，并生成报告
+    try:
+        case_dict = single_case_evaluate(resultPath, config, case_name)  # 评估单用例
+        single_case_dict = single_case_statistic(case_dict)  # 对单用例结果增加内容，并生成报告
+
+        # 将单用例测试结果保存为json文件
+        # single_case_dict.pop('playbackData')
+        # single_case_dict.pop('evalData')
+
+        # with open(f'{resultPath}/report.json', 'w', encoding='utf-8') as f:
+        #     f.write(json.dumps(single_case_dict, ensure_ascii=False))
+
+        reportDict = single_case_dict
+        reportPdf = f'{resultPath}/report.pdf'
+        report_generate(reportDict, reportPdf)
+
+    except Exception as e:
+        traceback.print_exc()
+        logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Evaluate single case ERROR: {repr(e)}!", exc_info=True)
+        sys.exit(-1)
+
+# def data_quality_detect(dataPath, case_name):
+#     logger = log.get_logger()
+#
+#     # first_order_loss = 0.01  # first : little loss 100
+#     second_order_loss = 0.05  # second : could use  95
+#     third_order_loss = 0.10  # third : could not use    90
+#
+#     frame_loss_dict = frame_loss_statistic(dataPath)
+#
+#     flag_frame_loss = False
+#     for key, value in frame_loss_dict.items():
+#         # sensor data
+#         sensor_data = ["RoadMark", "RoadPos", "TrafficLight", "TrafficSign"]
+#         if any(file in key for file in sensor_data):
+#         # if "RoadMark" in key or "RoadPos" in key or "TrafficLight" in key or "TrafficSign" in key:
+#         # if key[:-4] in ["RoadMark", "RoadPos", "TrafficLight", "TrafficSign"]:
+#             logger.info(f"[case:{case_name}] SINGLE_CASE_EVAL: [{key}] : {value['result']}")
+#             continue
+#
+#         if value['frame_loss_rate'] > third_order_loss:
+#             logger.error(
+#                 f"[case:{case_name}] SINGLE_CASE_EVAL: [{key}] frame loss rate > {third_order_loss * 100}%: {value['result']}")
+#             flag_frame_loss = True
+#         elif value['frame_loss_rate'] > second_order_loss:
+#             logger.info(
+#                 f"[case:{case_name}] SINGLE_CASE_EVAL: [{key}] frame loss rate > {second_order_loss * 100}%: {value['result']}")
+#         else:
+#             logger.info(
+#                 f"[case:{case_name}] SINGLE_CASE_EVAL: [{key}] frame loss rate < {second_order_loss * 100}%: {value['result']}")
+#
+#     return flag_frame_loss

single_case_evaluate.py

@@ -0,0 +1,323 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+##################################################################
+#
+# Copyright (c) 2023 CICV, Inc. All Rights Reserved
+#
+##################################################################
+"""
+@Authors:           yangzihao(yangzihao@china-icv.cn)
+@Data:              2023/11/28
+@Last Modified:     2023/11/28
+@Summary:           Evaluate single case.
+"""
+
+import os
+import sys
+import pandas as pd
+import numpy as np
+import requests
+import json
+import pathlib
+import time
+import traceback
+
+import log
+from common import score_grade, mileage_format, duration_format, string_concatenate, replace_key_with_value, \
+    import_class
+from data_process import DataProcess
+# from customDimension import CustomDimension
+# from custom_run import custom_run
+# from safe import Safe
+# from function import Function
+# from compliance import Compliance
+from comfort import Comfort
+from efficient import Efficient
+
+
+def single_case_evaluate(path, config, case_name):
+    """
+    This function takes a str of single case' path and a dict of config infos.
+    The function uses the scripts of 5 dimensions to analyze and evaluate the single case.
+
+    Arguments:
+        path: A str of a single case's csv files path.
+        config: A dict of config infos, which contains the algorithm info, index info and so on.
+        case_name: A str of case name.
+
+    Returns:
+        case_dict: A dict containing evaluation results. Basic infos, scores and descriptions of 5 dimensions.
+    """
+    logger = log.get_logger()
+
+    try:
+        data_processed = DataProcess(path, config)
+    except Exception as e:
+        traceback.print_exc()
+        logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Data processed ERROR: {repr(e)}!", exc_info=True)
+
+    # custom metrics evaluate
+    # customMetricPath = '../custom'
+    # custom_metric_list = [metric for metric in config.metric_list if metric not in config.builtinMetricList]
+
+    custom_data = dict()
+
+    # if customMetricPath if empty
+    # try:
+    #     if custom_metric_list:
+    #         custom_data = custom_run(customMetricPath, data_processed, custom_metric_list, case_name)
+    #     else:
+    #         custom_data = dict()
+    #
+    # except Exception as e:
+    #     traceback.print_exc()
+    #     logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Custom metric evaluate ERROR: {repr(e)}!", exc_info=True)
+
+    # scoreFile = 'linear_score'
+
+    # custom score
+    # if config.scoreModel == "builtin":
+    #     scorePath = r"./"
+    #     scoreFile = "score_weight"
+    #     scoreModel = import_class(scorePath, scoreFile)
+    # else:
+    #     # customScorePath = r"C:\Users\cicv\Desktop\ads_evaluate_V4.1.0\customScore"
+    #     scoreFile = config.scoreModel
+    #     scoreModel = import_class(customScorePath, scoreFile)
+
+    scorePath = r"./"
+    scoreFile = "score_weight"
+    scoreModel = import_class(scorePath, scoreFile)
+
+    builtin_dimension_list = config.builtinDimensionList
+    dimension_name_dict = config.dimension_name
+
+    # initialization
+    case_dict = {}
+    case_dict["name"] = case_name
+
+    # score info
+    dimension_list = config.dimension_list
+    score_dimension_dict = {}
+    bad_dimension_list = []
+
+    # statistic the index by good or bad score
+    good_indicator_list = []
+    bad_indicator_list = []
+    # bad_indicator_count_list = []
+
+    dimension_dict = {}
+    # eval_data_list = []
+
+    try:
+        logger.info(f"[case:{case_name}] SINGLE_CASE_EVAL: Dimension evaluate start: ")
+        for dimension in dimension_list:
+            dimension_instance = globals()[dimension.capitalize()](data_processed, custom_data, scoreModel, path)
+
+            # if dimension in builtin_dimension_list:
+            #     dimension_instance = globals()[dimension.capitalize()](data_processed, custom_data, scoreModel)
+            # else:
+            #     dimension_instance = CustomDimension(dimension, data_processed, custom_data, scoreModel)
+
+            dimension_report_dict = dimension_instance.report_statistic()
+            dimension_dict[dimension] = dimension_report_dict
+            score_dimension_dict[dimension] = dimension_report_dict['score']
+
+            # dimension_eval_data = dimension_instance.get_eval_data()
+            # if not dimension_eval_data.empty:
+            #     eval_data_list.append(dimension_eval_data)
+            # eval_data_dict[dimension] = dimension_eval_data
+
+            if score_dimension_dict[dimension] < 80:
+                bad_dimension_list.append(dimension)
+
+            # if dimension == "function":
+            #     followStopCount = dimension_report_dict['followStopCount']
+            #     bad_indicator_list.append("跟停行为") if followStopCount != 0 else good_indicator_list.append("跟停行为")
+            # elif dimension == "compliance":
+            #     illegalCount = dimension_report_dict['illegalCount']
+            #     bad_indicator_list.append("违反交通规则行为") if illegalCount != 0 else good_indicator_list.append("违反交通规则行为")
+            # elif dimension == "comfort":
+            #     discomfortCount = dimension_report_dict['discomfortCount']
+            #     bad_indicator_list.append("不舒适行为") if discomfortCount != 0 else good_indicator_list.append("不舒适行为")
+
+    except Exception as e:
+        traceback.print_exc()
+        logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Dimension evaluate ERROR: {repr(e)}!", exc_info=True)
+        sys.exit(-1)
+
+    case_dict["details"] = dimension_dict
+    # calculate the score and grade of the case
+    weight_dimension_dict = config.dimension_weight
+    print("weight_dimension_dict is", weight_dimension_dict)
+    score_dimension = {key: score_dimension_dict[key] * weight_dimension_dict[key] for key in score_dimension_dict}
+
+    score_case = round(sum(score_dimension.values()), 2)
+    grade_case = score_grade(score_case)
+    case_dict['algorithmComprehensiveScore'] = score_case
+    case_dict['algorithmLevel'] = grade_case
+
+    case_dict['testMileage'] = data_processed.report_info['mileage']
+    case_dict['testDuration'] = data_processed.report_info['duration']
+
+    # generate algorithmResultDescription1 according to the actual case
+    # if bad_indicator_list:
+    #     for type in bad_indicator_list:
+    #         if type == '不舒适行为':
+    #             bad_indicator_count_list.append(f'{discomfortCount}次不舒适行为')
+    #         if type == '跟停行为':
+    #             bad_indicator_count_list.append(f'{followStopCount}次跟停行为')
+    #         if type == '违反交通规则行为':
+    #             bad_indicator_count_list.append(f'违反交通规则{illegalCount}次')
+    # str_bad_indicator_count = string_concatenate(bad_indicator_count_list) if bad_indicator_count_list else ""
+    # str_good_indicator = string_concatenate(good_indicator_list) if good_indicator_list else ""
+
+    # algorithmResultDescription1 = "车辆在本轮测试中，"
+    # if str_bad_indicator_count:
+    #     algorithmResultDescription1 += f"共出现{str_bad_indicator_count}，"
+    # if str_good_indicator:
+    #     algorithmResultDescription1 += f"未出现{str_good_indicator}。"
+    #
+    # if not str_good_indicator and not str_bad_indicator_count:
+    #     algorithmResultDescription1 += "【未进行舒适性、跟车及合规性评测】。"
+
+    # generate algorithmResultDescription2
+    if not bad_dimension_list:
+        algorithmResultDescription2 = '综上所述，算法在各个维度的表现俱佳。'
+    else:
+        str_bad_dimension = string_concatenate(bad_dimension_list)
+        # replace English to Chinese dimension name
+        algorithmResultDescription2 = f'综上所述，建议算法优化在{str_bad_dimension}指标上的表现。'
+
+    description = f"算法总体表现{grade_case}，高效性表现{dimension_dict['efficient']['level']}，平顺性表现{dimension_dict['comfort']['level']}。"
+
+    # case_dict['algorithmResultDescription1'] = algorithmResultDescription1
+    # case_dict['algorithmResultDescription2'] = algorithmResultDescription2
+    # case_dict['algorithmResultDescription1'] = replace_key_with_value(algorithmResultDescription1, dimension_name_dict)
+    # case_dict['algorithmResultDescription'] = replace_key_with_value(algorithmResultDescription2, dimension_name_dict)
+    case_dict['algorithmResultDescription'] = description
+
+    graph_dict = {
+        "线速度": os.path.join(path, "LinearAccelerate.png"),
+        "角速度": os.path.join(path, "AngularAccelerate.png"),
+        "速度": os.path.join(path, "Speed.png")
+    }
+
+    case_dict['graphPath'] = graph_dict
+
+    # for df in eval_data_list:
+    #     eval_data_df = pd.merge(eval_data_df, df, on=['simTime', 'simFrame', 'playerId'], how="left")
+    # eval_data_df = eval_data_df[eval_data_df['simFrame'] > 0]
+    #
+    # case_dict['evalData'] = eval_data_df
+    # case_dict['playbackData'] = playback(eval_data_df)
+
+    # report.json
+    # case_dict = {
+    #     'testMileage': mileage,
+    #     'testDuration': duration,
+    #     'algorithmResultDescription1': algorithmResultDescription1,
+    #     'algorithmResultDescription2': algorithmResultDescription2,
+    #     'algorithmComprehensiveScore': score_case,
+    #     'algorithmLevel': grade_case,
+    #
+    #     'safe': safe_report_dict,
+    #     'function': func_report_dict,
+    #     'compliance': comp_report_dict,
+    #     'comfort': comf_report_dict,
+    #     'efficient': effi_report_dict,
+    #
+    #     'commonData': common_report_dict
+    #
+    # }
+
+    return case_dict
+
+
+def playback(df):
+    """
+
+    Args:
+        df:
+
+    Returns:
+
+    """
+
+    target_id = 2
+    # choose_df = df[["simTime", "simFrame", "v", "lat_v_rel", "lon_v_rel", "lat_d", "lon_d", "curvHor", "rollRel", "pitchRel"]].copy()
+    ego_df = df[df['playerId'] == 1][["simTime", "simFrame", "v", "curvHor", "rollRel", "pitchRel"]].copy()
+    if "lat_v_rel" in df.columns:
+        rel_df = df[df['playerId'] == target_id][
+            ["simTime", "simFrame", "lat_v_rel", "lon_v_rel", "lat_d", "lon_d"]].copy()
+        result = pd.merge(ego_df, rel_df, on=['simTime', 'simFrame'], how='left')
+    else:
+        result = ego_df.copy()
+        result["lat_v_rel"] = ["-"] * len(result)
+        result["lon_v_rel"] = ["-"] * len(result)
+        result["lat_d"] = ["-"] * len(result)
+        result["lon_d"] = ["-"] * len(result)
+
+    result.rename(
+        columns={"v": "speed", "lat_v_rel": "latSpeedRel", "lon_v_rel": "lonSpeedRel", "lat_d": "latDistanceRel",
+                 "lon_d": "lonDistanceRel"}, inplace=True)
+
+    result = result[result['simFrame'] > 0]
+    # simTime, simFrame, speed, latSpeedRel, lonSpeedRel,
+    # latDistanceRel, lonDistanceRel, curvHor, rollRel, pitchRel
+
+    return result
+
+
+def single_case_statistic(case_dict):
+    """
+    This function add the basic infos to the case_dict.
+
+    Arguments:
+        case_dict: A dict of single case scores and descriptions.
+        config: A dict of config infos, contains basic infos.
+
+    Returns:
+        single_case_dict: A full dict of single case's performance.
+    """
+    single_case_dict = case_dict.copy()
+    single_case_dict['testMileage'] = mileage_format(single_case_dict['testMileage'])
+    single_case_dict['testDuration'] = duration_format(single_case_dict['testDuration'])
+
+    # single_case_dict = {
+    #     'testMileage': mileage,
+    #     'testDuration': duration,
+    #     'algorithmResultDescription1': algorithmResultDescription1,
+    #     'algorithmResultDescription2': algorithmResultDescription2,
+    #     'algorithmComprehensiveScore': score_case,
+    #     'algorithmLevel': grade_case,
+    #     'safe': safe_report_dict,
+    #     'function': func_report_dict,
+    #     'compliance': comp_report_dict,
+    #     'comfort': comf_report_dict,
+    #     'efficient': effi_report_dict
+    # }
+
+    return single_case_dict
+
+
+def single_report_post(single_case_dict, case_path):
+    """
+    This function generate the single case report based on single_case_dict.
+
+    Arguments:
+        single_case_dict: A dict of single case scores and descriptions.
+        case_path: A str of path of files, which says where the generated report is to be stored.
+
+    Returns:
+        None
+    """
+    print('准备发送请求：')
+    report_name = case_path.split('\\')[-2]  # get case name for report name
+    url_json = 'http://36.110.106.156:18080/report/generate'
+    data_json = json.dumps(single_case_dict)
+    response = requests.post(url_json, data_json, headers={'Content-Type': 'application/json; charset=utf-8'})
+    print("返回报告结果：", response)
+    runtime = time.strftime('%Y%m%d%H%M%S', time.localtime())
+    p = pathlib.Path(rf'{case_path}\{report_name}_{runtime}.pdf')
+    p.write_bytes(response.content)

single_run.py

@@ -0,0 +1,105 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+##################################################################
+#
+# Copyright (c) 2023 CICV, Inc. All Rights Reserved
+#
+##################################################################
+"""
+@Authors:           yangzihao(yangzihao@china-icv.cn)
+@Data:              2024/03/27
+@Last Modified:     2024/03/27
+@Summary:           Evaluation entrance
+"""
+
+import os
+import sys
+import time
+from single_case_eval import single_case_eval
+from common import get_subfolders_name
+import log
+
+if __name__ == "__main__":
+
+    # if len(sys.argv) >= 6:
+    #     configPath = sys.argv[1]
+    #     dataPath = sys.argv[2]
+    #     reportPath = sys.argv[3]
+    #     csvPath = sys.argv[4]
+    #     customMetricPath = sys.argv[5]
+    #     customScorePath = sys.argv[6]
+    #
+    #     case_name = os.path.basename(os.path.dirname(dataPath))
+    #
+    #     if not os.path.exists(configPath):
+    #         print('Invalid configPath!')
+    #         logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Invalid configPath!")
+    #         sys.exit(-1)
+    #     elif not os.path.exists(dataPath):
+    #         print('Invalid dataPath!')
+    #         logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Invalid dataPath!")
+    #         sys.exit(-1)
+    #     elif not os.path.exists(customMetricPath):
+    #         print('Invalid customMetricPath!')
+    #         logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Invalid customMetricPath!")
+    #         sys.exit(-1)
+    #     elif not os.path.exists(customScorePath):
+    #         print('Invalid customScorePath!')
+    #         logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Invalid customScorePath!")
+    #         sys.exit(-1)
+    #     else:
+    #         try:
+    #             logger.info(f"[case:{case_name}] SINGLE_CASE_EVAL: Start evaluating:")
+    #             print("SINGLE_CASE_EVAL程序开始运行：")
+    #             print(f"   configPath: {configPath},\n   dataPath: {dataPath},\n   reportPath: {reportPath},\n   "
+    #                   f"csvPath: {csvPath},\n   customMetricPath: {customMetricPath}\n   customScorePath: {customScorePath}")
+    #             t1 = time.time()
+    #             single_case_eval(configPath, dataPath, reportPath, csvPath, customMetricPath, customScorePath)
+    #             t2 = time.time()
+    #             print(f"程序结束，执行时间：{int(t2 - t1)} s")
+    #             logger.info(f"[case:{case_name}] SINGLE_CASE_EVAL: End.")
+    #             sys.exit(0)
+    #         except Exception as e:
+    #             print("异常，退出...")
+    #             print(repr(e))
+    #             sys.exit(-1)
+    # else:
+    #     print('No enough arguments!')
+    #     logger.error(f"SINGLE_CASE_EVAL: No enough arguments!")
+    #     sys.exit(-1)
+
+
+    task_path = r"./task_0416"
+
+    cases = get_subfolders_name(task_path)
+
+    for case in cases:
+        dataPath = os.path.join(task_path, case)
+
+        # dataPath = r"./task/pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-01-35_obstacledetection_9"
+        configPath = r"./config.json"
+
+        # case_name = os.path.basename(os.path.dirname(dataPath))
+        case_name = dataPath.split('/')[-1]
+        casePath = os.path.join("./result", case_name)
+        if not os.path.exists(casePath):
+            os.makedirs(casePath)
+
+        reportPath = rf"{casePath}/report.json"
+        logFileName = './result/log.txt'
+
+        log.setup_logger(logFileName)
+        logger = log.get_logger()
+
+        if not os.path.exists(dataPath):
+            print('Invalid dataPath!')
+            logger.error(f"[case:{case_name}] SINGLE_CASE_EVAL: Invalid dataPath!")
+        else:
+            logger.info(f"[case:{case_name}] SINGLE_CASE_EVAL: Start evaluating:")
+            print("SINGLE_CASE_EVAL程序开始运行：")
+            print(f"  dataPath: {dataPath}, \n  reportPath: {reportPath}")
+            t1 = time.time()
+            single_case_eval(configPath, dataPath, reportPath, casePath)
+            t2 = time.time()
+            print(f"程序结束，执行时间：{int(t2 - t1)} s")
+            logger.info(f"[case:{case_name}] SINGLE_CASE_EVAL: End.")

task/pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-01-35_obstacledetection_9/DriverCtrl_pji.csv

@@ -0,0 +1,84 @@
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task/pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-01-35_obstacledetection_9/EgoState_pji.csv

@@ -0,0 +1,897 @@
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task/pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-01-35_obstacledetection_9/log.txt

@@ -0,0 +1,126 @@
+[2024-09-20 10:07:32,968] - [INFO] [pji_run_test.py] [line:42] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-01-35_obstacledetection_9] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-09-20 10:07:33,289] - [ERROR] [single_case_evaluate.py] [line:57] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-01-35_obstacledetection_9] SINGLE_CASE_EVAL: Data processed ERROR: KeyError('travelDist')!]
+Traceback (most recent call last):
+  File "D:\Python\Lib\site-packages\pandas\core\indexes\base.py", line 3805, in get_loc
+    return self._engine.get_loc(casted_key)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "index.pyx", line 167, in pandas._libs.index.IndexEngine.get_loc
+  File "index.pyx", line 196, in pandas._libs.index.IndexEngine.get_loc
+  File "pandas\\_libs\\hashtable_class_helper.pxi", line 7081, in pandas._libs.hashtable.PyObjectHashTable.get_item
+  File "pandas\\_libs\\hashtable_class_helper.pxi", line 7089, in pandas._libs.hashtable.PyObjectHashTable.get_item
+KeyError: 'travelDist'
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\single_case_evaluate.py", line 54, in single_case_evaluate
+    data_processed = DataProcess(path, config)
+                     ^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\data_process.py", line 60, in __init__
+    self._process()
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\data_process.py", line 72, in _process
+    self.report_info = self._get_report_info(self.obj_data[1])
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\data_process.py", line 251, in _get_report_info
+    mileage = self._mileage_cal(df)
+              ^^^^^^^^^^^^^^^^^^^^^
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\data_process.py", line 216, in _mileage_cal
+    travelDist = df['travelDist'].values.tolist()
+                 ~~^^^^^^^^^^^^^^
+  File "D:\Python\Lib\site-packages\pandas\core\frame.py", line 4102, in __getitem__
+    indexer = self.columns.get_loc(key)
+              ^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "D:\Python\Lib\site-packages\pandas\core\indexes\base.py", line 3812, in get_loc
+    raise KeyError(key) from err
+KeyError: 'travelDist'
+[2024-09-20 10:07:33,290] - [INFO] [single_case_evaluate.py] [line:113] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-01-35_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-09-20 10:07:33,290] - [ERROR] [single_case_evaluate.py] [line:146] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-01-35_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate ERROR: UnboundLocalError("cannot access local variable 'data_processed' where it is not associated with a value")!]
+Traceback (most recent call last):
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\single_case_evaluate.py", line 115, in single_case_evaluate
+    dimension_instance = globals()[dimension.capitalize()](data_processed, custom_data, scoreModel, path)
+                                                           ^^^^^^^^^^^^^^
+UnboundLocalError: cannot access local variable 'data_processed' where it is not associated with a value
+[2024-09-23 09:17:05,429] - [INFO] [pji_run_test.py] [line:42] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-01-35_obstacledetection_9] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-09-23 09:17:05,697] - [ERROR] [single_case_evaluate.py] [line:57] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-01-35_obstacledetection_9] SINGLE_CASE_EVAL: Data processed ERROR: KeyError('travelDist')!]
+Traceback (most recent call last):
+  File "D:\Python\Lib\site-packages\pandas\core\indexes\base.py", line 3805, in get_loc
+    return self._engine.get_loc(casted_key)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "index.pyx", line 167, in pandas._libs.index.IndexEngine.get_loc
+  File "index.pyx", line 196, in pandas._libs.index.IndexEngine.get_loc
+  File "pandas\\_libs\\hashtable_class_helper.pxi", line 7081, in pandas._libs.hashtable.PyObjectHashTable.get_item
+  File "pandas\\_libs\\hashtable_class_helper.pxi", line 7089, in pandas._libs.hashtable.PyObjectHashTable.get_item
+KeyError: 'travelDist'
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\single_case_evaluate.py", line 54, in single_case_evaluate
+    data_processed = DataProcess(path, config)
+                     ^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\data_process.py", line 60, in __init__
+    self._process()
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\data_process.py", line 72, in _process
+    self.report_info = self._get_report_info(self.obj_data[1])
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\data_process.py", line 251, in _get_report_info
+    mileage = self._mileage_cal(df)
+              ^^^^^^^^^^^^^^^^^^^^^
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\data_process.py", line 216, in _mileage_cal
+    travelDist = df['travelDist'].values.tolist()
+                 ~~^^^^^^^^^^^^^^
+  File "D:\Python\Lib\site-packages\pandas\core\frame.py", line 4102, in __getitem__
+    indexer = self.columns.get_loc(key)
+              ^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "D:\Python\Lib\site-packages\pandas\core\indexes\base.py", line 3812, in get_loc
+    raise KeyError(key) from err
+KeyError: 'travelDist'
+[2024-09-23 09:17:05,698] - [INFO] [single_case_evaluate.py] [line:113] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-01-35_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-09-23 09:17:05,698] - [ERROR] [single_case_evaluate.py] [line:146] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-01-35_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate ERROR: UnboundLocalError("cannot access local variable 'data_processed' where it is not associated with a value")!]
+Traceback (most recent call last):
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\single_case_evaluate.py", line 115, in single_case_evaluate
+    dimension_instance = globals()[dimension.capitalize()](data_processed, custom_data, scoreModel, path)
+                                                           ^^^^^^^^^^^^^^
+UnboundLocalError: cannot access local variable 'data_processed' where it is not associated with a value
+[2024-09-23 09:31:19,444] - [INFO] [pji_run_test.py] [line:41] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-01-35_obstacledetection_9] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-09-23 09:31:19,766] - [ERROR] [single_case_evaluate.py] [line:57] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-01-35_obstacledetection_9] SINGLE_CASE_EVAL: Data processed ERROR: KeyError('travelDist')!]
+Traceback (most recent call last):
+  File "D:\Python\Lib\site-packages\pandas\core\indexes\base.py", line 3805, in get_loc
+    return self._engine.get_loc(casted_key)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "index.pyx", line 167, in pandas._libs.index.IndexEngine.get_loc
+  File "index.pyx", line 196, in pandas._libs.index.IndexEngine.get_loc
+  File "pandas\\_libs\\hashtable_class_helper.pxi", line 7081, in pandas._libs.hashtable.PyObjectHashTable.get_item
+  File "pandas\\_libs\\hashtable_class_helper.pxi", line 7089, in pandas._libs.hashtable.PyObjectHashTable.get_item
+KeyError: 'travelDist'
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\single_case_evaluate.py", line 54, in single_case_evaluate
+    data_processed = DataProcess(path, config)
+                     ^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\data_process.py", line 60, in __init__
+    self._process()
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\data_process.py", line 72, in _process
+    self.report_info = self._get_report_info(self.obj_data[1])
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\data_process.py", line 251, in _get_report_info
+    mileage = self._mileage_cal(df)
+              ^^^^^^^^^^^^^^^^^^^^^
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\data_process.py", line 216, in _mileage_cal
+    travelDist = df['travelDist'].values.tolist()
+                 ~~^^^^^^^^^^^^^^
+  File "D:\Python\Lib\site-packages\pandas\core\frame.py", line 4102, in __getitem__
+    indexer = self.columns.get_loc(key)
+              ^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "D:\Python\Lib\site-packages\pandas\core\indexes\base.py", line 3812, in get_loc
+    raise KeyError(key) from err
+KeyError: 'travelDist'
+[2024-09-23 09:31:19,767] - [INFO] [single_case_evaluate.py] [line:113] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-01-35_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-09-23 09:31:19,767] - [ERROR] [single_case_evaluate.py] [line:146] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-01-35_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate ERROR: UnboundLocalError("cannot access local variable 'data_processed' where it is not associated with a value")!]
+Traceback (most recent call last):
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\single_case_evaluate.py", line 115, in single_case_evaluate
+    dimension_instance = globals()[dimension.capitalize()](data_processed, custom_data, scoreModel, path)
+                                                           ^^^^^^^^^^^^^^
+UnboundLocalError: cannot access local variable 'data_processed' where it is not associated with a value

task/pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9/DriverCtrl_pji.csv

@@ -0,0 +1,74 @@
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task/pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9/EgoState_pji.csv

@@ -0,0 +1,896 @@
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task/pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9/log.txt

@@ -0,0 +1,160 @@
+[2024-09-23 09:42:38,653] - [INFO] [pji_run_test.py] [line:41] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-09-23 09:42:38,837] - [ERROR] [single_case_evaluate.py] [line:57] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Data processed ERROR: KeyError('travelDist')!]
+Traceback (most recent call last):
+  File "D:\Python\Lib\site-packages\pandas\core\indexes\base.py", line 3805, in get_loc
+    return self._engine.get_loc(casted_key)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "index.pyx", line 167, in pandas._libs.index.IndexEngine.get_loc
+  File "index.pyx", line 196, in pandas._libs.index.IndexEngine.get_loc
+  File "pandas\\_libs\\hashtable_class_helper.pxi", line 7081, in pandas._libs.hashtable.PyObjectHashTable.get_item
+  File "pandas\\_libs\\hashtable_class_helper.pxi", line 7089, in pandas._libs.hashtable.PyObjectHashTable.get_item
+KeyError: 'travelDist'
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\single_case_evaluate.py", line 54, in single_case_evaluate
+    data_processed = DataProcess(path, config)
+                     ^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\data_process.py", line 60, in __init__
+    self._process()
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\data_process.py", line 72, in _process
+    self.report_info = self._get_report_info(self.obj_data[1])
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\data_process.py", line 251, in _get_report_info
+    mileage = self._mileage_cal(df)
+              ^^^^^^^^^^^^^^^^^^^^^
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\data_process.py", line 216, in _mileage_cal
+    travelDist = df['travelDist'].values.tolist()
+                 ~~^^^^^^^^^^^^^^
+  File "D:\Python\Lib\site-packages\pandas\core\frame.py", line 4102, in __getitem__
+    indexer = self.columns.get_loc(key)
+              ^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "D:\Python\Lib\site-packages\pandas\core\indexes\base.py", line 3812, in get_loc
+    raise KeyError(key) from err
+KeyError: 'travelDist'
+[2024-09-23 09:42:38,839] - [INFO] [single_case_evaluate.py] [line:113] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-09-23 09:42:38,839] - [ERROR] [single_case_evaluate.py] [line:146] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate ERROR: UnboundLocalError("cannot access local variable 'data_processed' where it is not associated with a value")!]
+Traceback (most recent call last):
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\single_case_evaluate.py", line 115, in single_case_evaluate
+    dimension_instance = globals()[dimension.capitalize()](data_processed, custom_data, scoreModel, path)
+                                                           ^^^^^^^^^^^^^^
+UnboundLocalError: cannot access local variable 'data_processed' where it is not associated with a value
+[2024-09-23 09:43:30,882] - [INFO] [pji_run_test.py] [line:41] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-09-23 09:43:31,066] - [INFO] [single_case_evaluate.py] [line:113] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-09-23 09:43:31,495] - [INFO] [pji_run_test.py] [line:50] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: End.]
+[2024-09-23 10:01:40,813] - [INFO] [pji_run_test.py] [line:41] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-09-23 10:01:41,019] - [INFO] [single_case_evaluate.py] [line:113] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-09-23 10:01:41,425] - [INFO] [pji_run_test.py] [line:50] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: End.]
+[2024-09-23 10:03:17,564] - [INFO] [pji_run_test.py] [line:47] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-09-23 10:03:17,744] - [INFO] [single_case_evaluate.py] [line:113] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-09-23 10:03:18,133] - [INFO] [pji_run_test.py] [line:56] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: End.]
+[2024-09-23 10:19:08,796] - [INFO] [pji_run_test.py] [line:47] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-09-23 10:19:09,100] - [INFO] [single_case_evaluate.py] [line:113] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-09-23 10:19:09,508] - [INFO] [pji_run_test.py] [line:56] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: End.]
+[2024-09-24 13:42:56,282] - [INFO] [pji_run_test.py] [line:47] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-09-24 13:42:56,541] - [INFO] [single_case_evaluate.py] [line:113] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-09-24 13:42:56,953] - [ERROR] [single_case_eval.py] [line:73] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Evaluate single case ERROR: PermissionError(13, 'Permission denied')!]
+Traceback (most recent call last):
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\single_case_eval.py", line 69, in single_case_eval
+    report_generate(reportDict, reportPdf)
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\report_generate.py", line 229, in report_generate
+    doc.build(content)
+  File "C:\Users\34739\AppData\Roaming\Python\Python312\site-packages\reportlab\platypus\doctemplate.py", line 1318, in build
+    BaseDocTemplate.build(self,flowables, canvasmaker=canvasmaker)
+  File "C:\Users\34739\AppData\Roaming\Python\Python312\site-packages\reportlab\platypus\doctemplate.py", line 1109, in build
+    self._endBuild()
+  File "C:\Users\34739\AppData\Roaming\Python\Python312\site-packages\reportlab\platypus\doctemplate.py", line 1044, in _endBuild
+    if getattr(self,'_doSave',1): self.canv.save()
+                                  ^^^^^^^^^^^^^^^^
+  File "C:\Users\34739\AppData\Roaming\Python\Python312\site-packages\reportlab\pdfgen\canvas.py", line 1298, in save
+    self._doc.SaveToFile(self._filename, self)
+  File "C:\Users\34739\AppData\Roaming\Python\Python312\site-packages\reportlab\pdfbase\pdfdoc.py", line 208, in SaveToFile
+    f = open(filename, "wb")
+        ^^^^^^^^^^^^^^^^^^^^
+PermissionError: [Errno 13] Permission denied: './task/pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9/report.pdf'
+[2024-09-24 13:43:03,301] - [INFO] [pji_run_test.py] [line:47] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-09-24 13:43:03,490] - [INFO] [single_case_evaluate.py] [line:113] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-09-24 13:43:03,877] - [INFO] [pji_run_test.py] [line:56] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: End.]
+[2024-09-24 13:59:42,948] - [INFO] [pji_run_test.py] [line:47] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-09-24 13:59:43,153] - [INFO] [single_case_evaluate.py] [line:113] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-09-24 13:59:43,532] - [ERROR] [single_case_eval.py] [line:73] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Evaluate single case ERROR: PermissionError(13, 'Permission denied')!]
+Traceback (most recent call last):
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\single_case_eval.py", line 69, in single_case_eval
+    report_generate(reportDict, reportPdf)
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\report_generate.py", line 229, in report_generate
+    doc.build(content)
+  File "C:\Users\34739\AppData\Roaming\Python\Python312\site-packages\reportlab\platypus\doctemplate.py", line 1318, in build
+    BaseDocTemplate.build(self,flowables, canvasmaker=canvasmaker)
+  File "C:\Users\34739\AppData\Roaming\Python\Python312\site-packages\reportlab\platypus\doctemplate.py", line 1109, in build
+    self._endBuild()
+  File "C:\Users\34739\AppData\Roaming\Python\Python312\site-packages\reportlab\platypus\doctemplate.py", line 1044, in _endBuild
+    if getattr(self,'_doSave',1): self.canv.save()
+                                  ^^^^^^^^^^^^^^^^
+  File "C:\Users\34739\AppData\Roaming\Python\Python312\site-packages\reportlab\pdfgen\canvas.py", line 1298, in save
+    self._doc.SaveToFile(self._filename, self)
+  File "C:\Users\34739\AppData\Roaming\Python\Python312\site-packages\reportlab\pdfbase\pdfdoc.py", line 208, in SaveToFile
+    f = open(filename, "wb")
+        ^^^^^^^^^^^^^^^^^^^^
+PermissionError: [Errno 13] Permission denied: './task/pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9/report.pdf'
+[2024-09-24 14:00:11,421] - [INFO] [pji_run_test.py] [line:47] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-09-24 14:00:11,601] - [INFO] [single_case_evaluate.py] [line:113] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-09-24 14:00:11,991] - [INFO] [pji_run_test.py] [line:56] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: End.]
+[2024-09-24 14:07:14,302] - [INFO] [pji_run_test.py] [line:47] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-09-24 14:07:14,484] - [INFO] [single_case_evaluate.py] [line:113] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-09-24 14:07:14,950] - [INFO] [pji_run_test.py] [line:56] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: End.]
+[2024-09-24 14:09:33,792] - [INFO] [pji_run_test.py] [line:47] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-09-24 14:09:33,986] - [INFO] [single_case_evaluate.py] [line:113] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-09-24 14:09:34,381] - [INFO] [pji_run_test.py] [line:56] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: End.]
+[2024-09-24 14:17:34,597] - [INFO] [pji_run_test.py] [line:47] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-09-24 14:17:34,793] - [INFO] [single_case_evaluate.py] [line:113] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-09-24 14:17:35,176] - [INFO] [pji_run_test.py] [line:56] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: End.]
+[2024-09-24 14:18:55,100] - [INFO] [pji_run_test.py] [line:47] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-09-24 14:18:55,287] - [INFO] [single_case_evaluate.py] [line:113] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-09-24 14:18:55,669] - [INFO] [pji_run_test.py] [line:56] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: End.]
+[2024-09-24 14:24:54,242] - [INFO] [pji_run_test.py] [line:47] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-09-24 14:24:54,469] - [INFO] [single_case_evaluate.py] [line:113] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-09-24 14:24:54,908] - [INFO] [pji_run_test.py] [line:56] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: End.]
+[2024-09-24 17:36:52,862] - [INFO] [pji_run_test.py] [line:47] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-09-24 17:36:53,109] - [INFO] [single_case_evaluate.py] [line:113] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-09-24 17:36:53,478] - [INFO] [pji_run_test.py] [line:56] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: End.]
+[2024-09-24 17:46:10,712] - [INFO] [pji_run_test.py] [line:47] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-09-24 17:46:10,892] - [INFO] [single_case_evaluate.py] [line:113] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-09-24 17:46:11,267] - [INFO] [pji_run_test.py] [line:56] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: End.]
+[2024-09-24 18:00:09,463] - [INFO] [pji_run_test.py] [line:47] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-09-24 18:00:09,685] - [INFO] [single_case_evaluate.py] [line:113] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-09-24 18:00:10,058] - [ERROR] [single_case_eval.py] [line:73] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Evaluate single case ERROR: PermissionError(13, 'Permission denied')!]
+Traceback (most recent call last):
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\single_case_eval.py", line 69, in single_case_eval
+    report_generate(reportDict, reportPdf)
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\report_generate.py", line 229, in report_generate
+    doc.build(content)
+  File "C:\Users\34739\AppData\Roaming\Python\Python312\site-packages\reportlab\platypus\doctemplate.py", line 1318, in build
+    BaseDocTemplate.build(self,flowables, canvasmaker=canvasmaker)
+  File "C:\Users\34739\AppData\Roaming\Python\Python312\site-packages\reportlab\platypus\doctemplate.py", line 1109, in build
+    self._endBuild()
+  File "C:\Users\34739\AppData\Roaming\Python\Python312\site-packages\reportlab\platypus\doctemplate.py", line 1044, in _endBuild
+    if getattr(self,'_doSave',1): self.canv.save()
+                                  ^^^^^^^^^^^^^^^^
+  File "C:\Users\34739\AppData\Roaming\Python\Python312\site-packages\reportlab\pdfgen\canvas.py", line 1298, in save
+    self._doc.SaveToFile(self._filename, self)
+  File "C:\Users\34739\AppData\Roaming\Python\Python312\site-packages\reportlab\pdfbase\pdfdoc.py", line 208, in SaveToFile
+    f = open(filename, "wb")
+        ^^^^^^^^^^^^^^^^^^^^
+PermissionError: [Errno 13] Permission denied: './task/pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9/report.pdf'
+[2024-09-24 18:00:16,623] - [INFO] [pji_run_test.py] [line:47] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-09-24 18:00:16,798] - [INFO] [single_case_evaluate.py] [line:113] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-09-24 18:00:17,158] - [INFO] [pji_run_test.py] [line:56] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: End.]
+[2024-09-24 18:18:22,492] - [INFO] [pji_run_test.py] [line:47] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-09-24 18:18:22,697] - [INFO] [single_case_evaluate.py] [line:113] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-09-24 18:18:22,981] - [ERROR] [single_case_eval.py] [line:73] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Evaluate single case ERROR: TypeError('ReportPDF.draw_text() takes 1 positional argument but 2 were given')!]
+Traceback (most recent call last):
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\single_case_eval.py", line 69, in single_case_eval
+    report_generate(reportDict, reportPdf)
+  File "D:\Cicv\朴津项目\pji_indoor_robot_evaluate_real_0920\report_generate.py", line 223, in report_generate
+    content.append(ReportPDF.draw_text(key, 'Heading5'))
+                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+TypeError: ReportPDF.draw_text() takes 1 positional argument but 2 were given
+[2024-09-24 18:21:40,289] - [INFO] [pji_run_test.py] [line:47] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Start evaluating:]
+[2024-09-24 18:21:40,497] - [INFO] [single_case_evaluate.py] [line:113] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: Dimension evaluate start: ]
+[2024-09-24 18:21:40,930] - [INFO] [pji_run_test.py] [line:56] - [[case:pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-11_obstacledetection_9] SINGLE_CASE_EVAL: End.]

task/pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-20_obstacledetection_9/DriverCtrl_pji.csv

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task/pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-20_obstacledetection_9/EgoState_pji.csv

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task/pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-25_obstacledetection_9/EgoState_pji.csv

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task/pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-31_obstacledetection_9/EgoState_pji.csv

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task/pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-45_obstacledetection_9/DriverCtrl_pji.csv

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task/pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-32-45_obstacledetection_9/EgoState_pji.csv

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task/pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-33-00_obstacledetection_9/DriverCtrl_pji.csv

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task/pjibot_pjibot-P1YNYD1M228000127_data_merge_2024-04-07-02-33-00_obstacledetection_9/EgoState_pji.csv

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track.py

@@ -0,0 +1,21 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# 创建一个dataframe
+file = r"C:\Users\cicv\Desktop\PJI_robot_evaluate_V1.1.0\task_0416\2024-04-16-10-20-45_obstacledetection_30\EgoState_pji.csv"
+
+df = pd.read_csv(file)
+
+# 绘制散点图
+
+plt.scatter(df['posX'], df['posY'], c=df['simTime'], s=0.1)
+plt.axis('equal')
+
+# 添加坐标轴标签和标题
+plt.xlabel('posX')
+plt.ylabel('posY')
+plt.title('Trajectory')
+
+# 显示图形
+plt.savefig("./track.png")
+plt.close()