使用python實現自動化拉取壓縮包并處理流程

實現成果展示
使用說明
實現成果展示

最終文件
圖片文件
拉取壓縮文件解壓后的lcm文件以及解析文件目錄
使用說明

執行./run.sh
腳本中的內容主要功能是：
1、從遠程服務器上下拉制定時間更新的數據
2、將數據中的zip拷貝到指定文件夾內
3、解壓后刪除所有除了lcm之外的文件
4、新建一個out文件夾執行解析lcm可執行文件，保存所有的解析后文件
5、新建一個analysis文件夾并執行analysis腳本，對out文件夾中的內容進行analysis，并把生成的5個圖片以及三個文檔保存到analysis中
6、生成的5個圖片是：
gnss狀態，gnss狀態解釋，中心線差值對比，gnssQC，車道線數據
#!/usr/bin/python
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import sys
import numpy as np
import re
import math
import scipy.spatial as spt  # Scipy庫空間算法，使用kd樹查找最近點
import csv  # 調用數據保存文件
import pandas as pd
import cv2
import os
import time
import datetime
from datetime import datetime, timedelta
from collections import Counter
import statisticsdef write_conclution(data_path, status, last_time_list):value_counts = Counter(status)# 計算每個值所占的比例total_count = len(status)value_ratios = {value: count / total_count for value,count in value_counts.items()}# last time mode nummode = statistics.mode(last_time_list)minutes = mode // 60000seconds = (mode // 1000) % 60milliseconds = mode % 1000mode = "{:02d}'{:02d}''{:03d}'''".format(minutes, seconds, milliseconds)# last time min numminimum = min(last_time_list)minutes = minimum // 60000seconds = (minimum // 1000) % 60milliseconds = minimum % 1000minimum = "{:02d}'{:02d}''{:03d}'''".format(minutes, seconds, milliseconds)# last time mean nummean_time = int(statistics.mean(last_time_list))minutes = mean_time // 60000seconds = (mean_time // 1000) % 60milliseconds = mean_time % 1000mean_time = "{:02d}'{:02d}''{:03d}'''".format(minutes, seconds, milliseconds)# last time max nummaxmum = max(last_time_list)minutes = maxmum // 60000seconds = (maxmum // 1000) % 60milliseconds = maxmum % 1000#處理成文件形式maxmum = "{:02d}'{:02d}''{:03d}'''".format(minutes, seconds, milliseconds)with open(data_path + "conclution.csv", '+w') as loc_f:loc_f.write("1、概述" "\n""1.1、路線以及情況概述" "\n"'\n'"本路線是             共計      km;" "\n""全程定位狀態較為正常,絕大部分時間為4(狀態持續時間占全程的  % )" "\n""定位狀態變化描述:車輛穿過高架橋時容易跳變為1, 在經過較短距離的橫跨道路廣告牌時出現定位狀態變換為4->5->4" "\n""在短時間內(一般認為從上一個橋或其他的物體下出到進入下一個橋或者空中物體下的時間不超過5s)經過多次橋下,定位狀態會出現多次4->5(2)->1->0->1->5(2)->4跳變,時間不超過6秒" "\n""定位狀態在經過隧道(一般是指除入口和出口之外封閉的環境)時,狀態會持續保持0""\n""\n""1.2 異常情況描述""\n"'\n'"localization_conclution""\n""\n""1.3、異常點匯總表格""\n"'\n'"附件為全程定位狀態變化表格""\n"'\n'"gnss status 表示含義為：""\n""解狀態是4,是最高精度的解狀態,是通過地基增強方式達到的,精度最高;"  "\n""解狀態是5或2,是精度稍低一點的解狀態,也是通過地基地基增強方式達到,一般是可用衛星數少一點,差分齡期大一些,可能會有這樣的現象,多在環境不是很理想的條件下,如林蔭路、城市峽谷;" "\n""解狀態是1,是達到衛星定位,不依賴地基增強就能達到的解狀態,如果差分數據傳輸正常,那應該是搜星數特別少,少于10顆,或者差分齡期特別大,大于30S,會出現解狀態是1的;"  "\n""解狀態是0,是表示沒有定位,搜星數不足以解算出位置。" "\n""\n""\n""1.4、該路段定位總結：" "\n"'\n')with open(data_path + "conclution.csv", '+a') as loc_f:for value, ratio in value_ratios.items():loc_f.write("\t定位狀態為" + f"{value}" +"占全程的百分比為:" + f"{ratio:.2%}" + "\n")with open(data_path + "conclution.csv", '+a') as loc_f:loc_f.write("\t 1.4.2、定位狀態為0或1時, 多數情況是因為經過（橋梁,廣告牌,龍門架,隧道,大車跟隨）" "\n""\t 1.4.3、定位狀態跳變最長的時長為: " + str(maxmum) + "\n""\t 1.4.4、定位狀態跳變時長最短為: " + str(minimum) + "\n""\t 1.4.5、定位狀態跳變平均時長為:" +str(mean_time) + '\t' + "眾位數為:  " + str(mode) + "\n""該路線下的定位狀態(gnss status)跳變時間較短,ins定位精度誤差保持在(    ,    )范圍(m)內,定位精度良好." "\n""\n""1.5、該路段下的感知識別車道線與聯編程序結合四維高精地圖輸出的車道線對比情況:   " "\n"'\n'"\t 1.5.1 車道線與與聯編程序結合四維高精地圖輸出的車道線基本擬合占比為：     \n""\t 1.5.2 車道線與與聯編程序結合四維高精地圖輸出的車道線擬合較好占比為：     \n""\t 1.5.3 車道線與與聯編程序結合四維高精地圖輸出的車道線擬合較差占比為：     \n""\t 1.5.4 車道線與與聯編程序結合四維高精地圖輸出的車道線沒有擬合占比為：     \n""\t 該路線下的感知穩定,可以安全使用NOA     \n""\t 該路線下的感知比較穩定,可以在晴天且車道線清晰場景下使用NOA     \n""\t 該路線下的感知穩定性較差,請酌情使用NOA     \n""\n""\n""2、定位異常點分析:\n""\t 2.1 異常點解釋 \n"'\n'"\t 2.2 異常點圖片\n")def get_all_change_data(gnss_status):pairs = []for i in range(len(gnss_status)):if i == 0 or gnss_status[i] != gnss_status[i-1]:pairs.append((gnss_status[i], i))# print("Pairs:", pairs)index_ranges = []start_index = Nonepair0_sets = []pair1_sets = []for i in range(len(pairs)):if pairs[i][0] == 4:if start_index is not None:index_ranges.append((start_index, i))start_index = Noneelse:if start_index is None:start_index = iif len(pair0_sets) < start_index + 1:pair0_sets.append(set())pair1_sets.append(set())pair0_sets[start_index].add(pairs[i][0])pair1_sets[start_index].add(pairs[i][1])# 處理最后一個區間if start_index is not None:index_ranges.append((start_index, len(pairs)))# print("索引區間：", index_ranges)# print("pair[i][0]集合：", pair0_sets)# print("pair[i][1]集合：", pair1_sets)def get_all_file_name(data_path):img_list = os.listdir(data_path)# print(img_list)img_list = sorted(img_list)# print(img_list)return img_listdef check_number(lst, num):result = []for sub_lst in lst:if num in sub_lst:result.append(True)else:result.append(False)return resultdef update_index_ranges(lst, result_check, index_ranges):result = []for sub_lst, check in zip(lst, result_check):if check:reversed_lst = sub_lst[::-1]last_index = len(sub_lst) - reversed_lst.index(4) - 1result.append(last_index)else:result.append(0)update_index_range = []for idx, sub_lst, res in zip(index_ranges, lst, result):if len(sub_lst) > res:change_pair = idxfirst_element = change_pair[0]result_change = first_element + reschange_pair = (result_change, change_pair[1])idx = change_pairupdate_index_range.append(idx)# print(result, "\n", update_index_range)return result, update_index_rangedef get_last_time(start_utc_time, end_time):dt1 = datetime.fromtimestamp(start_utc_time / 1000000)dt2 = datetime.fromtimestamp(end_time / 1000000)diff = dt2 - dt1# 提取小時、分鐘和秒數year = dt1.yearmonth = dt1.monthday = dt1.dayhour = dt1.hourminute = dt1.minutesecond = dt1.second# 打印時分秒# print(f"{year}年{month}月{day}日 {hour:02d}:{minute:02d}:{second:02d}")start_time = f"{year}年{month}月{day}日 {hour:02d}:{minute:02d}:{second:02d}"year = dt2.yearmonth = dt2.monthday = dt2.dayhour = dt2.hourminute = dt2.minutesecond = dt2.second# 打印時分秒# print(f"{year}年{month}月{day}日 {hour:02d}:{minute:02d}:{second:02d}")end_time = f"{year}年{month}月{day}日 {hour:02d}:{minute:02d}:{second:02d}"last_time = int(diff.total_seconds()*10000)last_time_origin = last_timeminutes = last_time // 60000seconds = (last_time // 1000) % 60milliseconds = last_time % 1000last_time = "{:02d}'{:02d}''{:03d}'''".format(minutes, seconds, milliseconds)return last_time, start_time, end_time, last_time_origindef localization_error_time_filter(status, vp31_lon, vp31_lat, log_time, data_path, file_name):file_save_path = data_path+file_namestatus_4_x, status_4_y = [], []status_5_x, status_5_y = [], []status_1_x, status_1_y = [], []status_0_x, status_0_y = [], []status_e_x, status_e_y = [], []res_no4_time, res_5_time, res_1_time, res_0_time, res_e_time = [], [], [], [], []# 20230916 add analysis loclaization gnss status part from difference and save start&end time last timewith open(file_save_path + "analysis_localization_origin.csv", '+w') as loc_f:loc_f.write("error_start_time,error_end_time,error_status,last_time(ms)\n")with open(file_save_path + "analysis_localization_new.csv", '+w') as loc_f:loc_f.write("序號,起始時間,終止時間,狀態變化,持續時間(min'sec''ms'''),解釋,圖例\n")# 20230920 add analysis localization gnss status change# loc_f.closefor i in range(0, len(status)):if status[i] == 4:status_4_x.append(vp31_lon[i])status_4_y.append(vp31_lat[i])elif status[i] == 5:status_5_x.append(vp31_lon[i])status_5_y.append(vp31_lat[i])elif status[i] == 1:status_1_x.append(vp31_lon[i])status_1_y.append(vp31_lat[i])elif status[i] == 0:status_0_x.append(vp31_lon[i])status_0_y.append(vp31_lat[i])else:status_e_x.append(vp31_lon[i])status_e_y.append(vp31_lat[i])# 20230920 add new part to analysis gnss status changesplit_indices = [i for i in range(1, len(status)) if status[i] == 4 and status[i-1] != 4]sublists = []start = 0for index in split_indices:sublists.append(status[start:index])start = indexsublists.append(status[start:])# print("分割后的子列表:", sublists)index_ranges = [(start, start + len(sublist) - 1) for start,sublist in zip([0] + [index + 1 for index in split_indices], sublists)]last_pair = index_ranges[-1]second_element = last_pair[1]result = second_element - 1updated_pair = (last_pair[0], result)index_ranges[-1] = updated_pair# print("每個子列表的索引區間集合::", index_ranges)unique_sublists = [[x for x, y in zip(sublist, sublist[1:]) if x != y] + [sublist[-1]] for sublist in sublists]# print("刪除相鄰重復字符后的子列表:", unique_sublists)last_time_list = []num = 4result_check = check_number(sublists, num)# print(result_check)index_status_change = 1four_result, updated_index_ranges = update_index_ranges(sublists, result_check, index_ranges)with open(file_save_path + "analysis_localization_new.csv", '+a') as loc_f:for idx_range, sublist in zip(updated_index_ranges, unique_sublists):if idx_range[1] < len(log_time) and idx_range[0] < len(log_time):last_time, start_time, end_time, last_time_origin = get_last_time(log_time[idx_range[0]], log_time[idx_range[1]])loc_f.write(str(index_status_change)+","+str(start_time) + "," + str(end_time) + ","+ " ".join(map(str, sublist)) + "," + str(last_time) + "\n")last_time_list.append(last_time_origin)elif idx_range[0] >= len(log_time):continueelse:last_time, start_time, end_time, last_time_origin = get_last_time(log_time[idx_range[0]], log_time[len(log_time)-1])loc_f.write(str(index_status_change)+","+str(start_time) + "," + str(end_time) + ","+ " ".join(map(str, sublist)) + "," + str(last_time) + "\n")last_time_list.append(last_time_origin)index_status_change += 1# add last time list:write_conclution(file_save_path, status, last_time_list)# 20230916 addzero_indices = [i for i, x in enumerate(status) if x == 0]one_indices = [i for i, x in enumerate(status) if x == 1]four_indices = [i for i, x in enumerate(status) if x == 4]five_indices = [i for i, x in enumerate(status) if x == 5]other_indices = [i for i, x in enumerate(status) if x not in [0, 1, 4, 5]]zero_ranges = []one_ranges = []four_ranges = []five_ranges = []other_ranges = []if zero_indices:start = zero_indices[0]for i in range(1, len(zero_indices)):if zero_indices[i] != zero_indices[i-1] + 1:zero_ranges.append((start, zero_indices[i-1]))start = zero_indices[i]zero_ranges.append((start, zero_indices[-1]))for r in zero_ranges:last_time, start_time, end_time, last_time_origin = get_last_time(log_time[r[0]], log_time[r[1]])with open(file_save_path + "analysis_localization_origin.csv", '+a') as loc_f:loc_f.write(start_time + ',' + end_time +',' + '0' + ',' + str(last_time) + '\n')# print("0值的索引區間", zero_ranges)if one_indices:start = one_indices[0]for i in range(1, len(one_indices)):if one_indices[i] != one_indices[i-1] + 1:one_ranges.append((start, one_indices[i-1]))start = one_indices[i]one_ranges.append((start, one_indices[-1]))for r in one_ranges:last_time, start_time, end_time, last_time_origin = get_last_time(log_time[r[0]], log_time[r[1]])with open(file_save_path + "analysis_localization_origin.csv", '+a') as loc_f:loc_f.write(start_time + ',' + end_time +',' + '1' + ',' + str(last_time) + '\n')# print("1值的索引區間", one_ranges)if four_indices:start = four_indices[0]for i in range(1, len(four_indices)):if four_indices[i] != four_indices[i-1] + 1:four_ranges.append((start, four_indices[i-1]))start = four_indices[i]four_ranges.append((start, four_indices[-1]))for r in four_ranges:last_time, start_time, end_time, last_time_origin = get_last_time(log_time[r[0]], log_time[r[1]])with open(file_save_path + "analysis_localization_origin.csv", '+a') as loc_f:loc_f.write(start_time + ',' + end_time +',' + '4' + ',' + str(last_time) + '\n')# print("4值的索引區間", four_ranges)if five_indices:start = five_indices[0]for i in range(1, len(five_indices)):if five_indices[i] != five_indices[i-1] + 1:five_ranges.append((start, five_indices[i-1]))start = five_indices[i]five_ranges.append((start, five_indices[-1]))for r in five_ranges:last_time, start_time, end_time, last_time_origin = get_last_time(log_time[r[0]], log_time[r[1]])with open(file_save_path + "analysis_localization_origin.csv", '+a') as loc_f:loc_f.write(start_time + ',' + end_time +',' + '5' + ',' + str(last_time) + '\n')# print("5值的索引區間", five_ranges)if other_indices:start = other_indices[0]for i in range(1, len(other_indices)):if other_indices[i] != other_indices[i-1] + 1:other_ranges.append((start, other_indices[i-1]))start = other_indices[i]other_ranges.append((start, other_indices[-1]))for r in other_ranges:last_time, start_time, end_time, last_time_origin = get_last_time(log_time[r[0]], log_time[r[1]])with open(file_save_path + "analysis_localization_origin.csv", '+a') as loc_f:loc_f.write(start_time + ',' + end_time +',' + '2' + ',' + str(last_time) + '\n')# print("e值的索引區間", other_ranges)return res_no4_time, res_5_time, res_1_time, res_0_time, res_e_timedef show_status(status, vp31_lon, vp31_lat):status_4_x, status_4_y, status_4_idx = [], [], []status_5_x, status_5_y, status_5_idx = [], [], []status_1_x, status_1_y, status_1_idx = [], [], []status_0_x, status_0_y, status_0_idx = [], [], []status_e_x, status_e_y, status_e_idx = [], [], []for i in range(0, len(status)):if status[i] == 4:status_4_x.append(vp31_lon[i])status_4_y.append(vp31_lat[i])status_4_idx.append(i)elif status[i] == 5:status_5_x.append(vp31_lon[i])status_5_y.append(vp31_lat[i])status_5_idx.append(i)elif status[i] == 1:status_1_x.append(vp31_lon[i])status_1_y.append(vp31_lat[i])status_1_idx.append(i)elif status[i] == 0:status_0_x.append(vp31_lon[i])status_0_y.append(vp31_lat[i])status_0_idx.append(i)else:status_e_x.append(vp31_lon[i])status_e_y.append(vp31_lat[i])status_e_idx.append(i)plt.scatter(status_4_x, status_4_y, marker='o',color='green', label='status = 4')  # map headingplt.scatter(status_5_x, status_5_y, marker='o',color='blue', label='status = 5')  # ins_yawplt.scatter(status_1_x, status_1_y, marker='o',color='red', label='status = 1')  # map headingplt.scatter(status_0_x, status_0_y, marker='o',color='black', label='status = 0')  # ins_yaw5plt.scatter(status_e_x, status_e_y, marker='o',color='yellow', label='status else')def GPS_data_filter(x, y, z, x1, y1, z1):x_res, y_res, z_res = [], [], []x1_res, y1_res, z1_res = [], [], []for i in range(0, len(x)):if x1[i] != 0 and y1[i] != 0:x_res.append(x[i])y_res.append(y[i])z_res.append(z[i])x1_res.append(x1[i])y1_res.append(y1[i])z1_res.append(z1[i])return x_res, y_res, z_res, x1_res, y1_res, z1_resdef cma_data_filter(diff):diff_res = []for i in range(0, len(diff)):if diff[i] <= 5:diff_res.append(diff[i])else:diff_res.append(-1)return diff_resdef line_filter(l):l_res = []for i in range(0, len(l)):if l[i] >= 0:l_res.append(l[i])else:l_res.append(-1)return l_resdef cma_filter(str):if str == 'nan':return '-1'else:return strdef show_counter(lst, data_path):counter = Counter(lst)# 計算總數量total_count = sum(counter.values())# 計算每個數字的占比percentage = {num: count / total_count for num, count in counter.items()}# 打印結果with open(data_path + "conclution.csv", '+a') as loc_f:loc_f.write("3、定位QC結果查詢:\n")for num, count in counter.items():loc_f.write("數字 {} 出現的次數：{}，占比：{:.2%}".format(num, count, percentage[num]) + "\n")# 提取數字和對應的次數和占比numbers = list(counter.keys())counts = list(counter.values())percentages = [percentage[num] for num in numbers]plt.figure()# 繪制條形圖plt.bar(numbers, counts)# 添加標題和標簽plt.title('Number Counts')plt.xlabel('Numbers')plt.ylabel('Counts')plt.savefig(data_path + "localization_bar"+'.png', dpi=512)# 顯示圖形# plt.show()def localization_QC(gnss_lon, gnss_lat, ins_lon,ins_lat, gnss_status, ins_status, data_path):cnter_status = 1cnter_lonlat = 1res_cnter = []for gnss_lon, gnss_lat, ins_lon, ins_lat, gnss_status, ins_status in zip(gnss_lon, gnss_lat, ins_lon,ins_lat, gnss_status, ins_status):if gnss_status == 4 and ins_status == 1:cnter_status *= 1elif gnss_status == 5 and ins_status == 1:cnter_status *= 1.5elif gnss_status == 5 and ins_status == 0:cnter_status *= 2elif gnss_status == 4 and ins_status == 0:cnter_status *= 2.5else:cnter_status *= 0if abs(gnss_lon - ins_lon) < 2e-6 and abs(gnss_lat - ins_lat) < 2e-6:cnter_lonlat *= 1elif abs(gnss_lon - ins_lon) < 4e-6 and abs(gnss_lat - ins_lat) < 4e-6:cnter_lonlat *= 2else:cnter_lonlat *= -1res_cnter.append(cnter_status * cnter_lonlat)cnter_status = 1cnter_lonlat = 1show_counter(res_cnter, data_path)def Ablines_QC(line1_length, line2_length, diff1, diff2, diff3):cnter_ab1, cnter_ab2 = 1, 1cnter_vp1, cnter_vp2, cnter_vp3 = 1, 1, 1res_cnt_ab1, res_cnt_ab2 = [], []res_cnt_vp = []for line1_length, line2_length, diff1, diff2, diff3 in zip(line1_length,line2_length, diff1, diff2, diff3):if line1_length >= 100:cnter_ab1 *= 1elif line1_length >= 50:cnter_ab1 *= 1.5elif line1_length >= 20:cnter_ab1 *= 2else:cnter_ab1 *= -1if line2_length >= 100:cnter_ab2 *= 1elif line2_length >= 50:cnter_ab2 *= 1.5elif line2_length >= 20:cnter_ab2 *= 2else:cnter_ab2 *= 0res_cnt_ab1.append(cnter_ab1)res_cnt_ab2.append(cnter_ab2)if diff1 < 0.1:cnter_vp1 *= 1elif diff1 < 0.3:cnter_vp1 *= 1.5elif diff1 < 0.5:cnter_vp1 *= 2elif diff1 < 0.7:cnter_vp1 *= 2.5elif diff1 < 0.9:cnter_vp1 *= 3else:cnter_vp1 *= 0if diff2 < 0.1:cnter_vp2 *= 1elif diff2 < 0.3:cnter_vp2 *= 1.5elif diff2 < 0.5:cnter_vp2 *= 2elif diff2 < 0.7:cnter_vp2 *= 2.5elif diff2 < 0.9:cnter_vp2 *= 3else:cnter_vp2 *= -1if diff3 < 0.1:cnter_vp3 *= 1elif diff3 < 0.3:cnter_vp3 *= 1.5elif diff3 < 0.5:cnter_vp3 *= 2elif diff3 < 0.7:cnter_vp3 *= 2.5elif diff3 < 0.9:cnter_vp3 *= 3else:cnter_vp3 *= 0.000001cnter_vp = cnter_vp3*cnter_vp1*cnter_vp2res_cnt_vp.append(cnter_vp)cnter_ab1, cnter_ab2 = 1, 1cnter_vp1, cnter_vp2, cnter_vp3 = 1, 1, 1show_counter(res_cnt_ab1)show_counter(res_cnt_ab2)show_counter(res_cnt_vp)return res_cnt_ab1, res_cnt_ab2, res_cnt_vp# def Ablines_QC(line1, line2, line3, line4, vp_center_line, ab_center_line):def save_string_after_backslash(string):index = string.rfind('/')if index != -1:result = string[index+1:]else:result = stringreturn resultdef Prepare_data(file_name1, file_name2, data_path, analysis_data_path):file_name = save_string_after_backslash(file_name1)print("filename: ", file_name)# print_dis()file_name1 = str(data_path + file_name1)file_name2 = str(data_path + file_name2)data_line1 = [l.split('\n')[0].split(',')for l in open(file_name1, "r").readlines()[1:]]data_line2 = [l.split('\n')[0].split(',')for l in open(file_name2, "r").readlines()[1:]]gnss_timestamps = [int(l[0]) for l in data_line1]vp31_lon_ins = [float(l[2]) for l in data_line2]vp31_lat_ins = [float(l[3]) for l in data_line2]vp31_lon_gnss = [float(l[4]) for l in data_line2]vp31_lat_gnss = [float(l[5]) for l in data_line2]vp31_gnss_status = [int(l[8]) for l in data_line2]# print(vp31_gnss_status)vp31_ins_status = [int(l[9]) for l in data_line2]vp31_lon_gnss_f, vp31_lat_gnss_f, vp31_gnss_status_f, vp31_lon_ins_f, vp31_lat_ins_f, vp31_ins_status_f = GPS_data_filter(vp31_lon_gnss,vp31_lat_gnss, vp31_gnss_status, vp31_lon_ins, vp31_lat_ins, vp31_ins_status)# vp31_lon_ins, vp31_lat_ins, vp31_ins_status = GPS_data_filter(#     vp31_lon_ins, vp31_lat_ins, vp31_ins_status)log_time = [int(l[12]) for l in data_line2]diff1 = [float(cma_filter(l[0]))for l in data_line1]diff1 = cma_data_filter(diff1)diff2 = cma_data_filter([float(cma_filter(l[1]))for l in data_line1])diff3 = cma_data_filter([float(cma_filter(l[2]))for l in data_line1])l2 = line_filter([float(l[4]) for l in data_line1])l3 = line_filter([float(l[5]) for l in data_line1])idx = np.array([i for i in range(0, len(diff1))])idx_ins = np.array([i for i in range(0, len(vp31_ins_status))])idx_gnss = np.array([i for i in range(0, len(vp31_gnss_status))])idx3 = np.array([i for i in range(0, len(l2))])file_name = file_name[:-7]print("update name : ", file_name)data_path = analysis_data_path+'analysis/'print("analysis data path is:", data_path)# 記錄所有數據信息localization_error_time_filter(vp31_gnss_status, vp31_lon_ins, vp31_lat_ins, log_time, data_path, file_name)# GNSS status &　gnss filterplt.figure(1)plt.subplot(1, 2, 1)show_status(vp31_gnss_status, vp31_lon_ins, vp31_lat_ins)plt.legend(loc="upper right")plt.title("gnss status origin")plt.xlabel("lon")  # 橫軸名稱plt.ylabel("lat")  # 縱軸名稱plt.subplot(1, 2, 2)show_status(vp31_gnss_status_f, vp31_lon_ins_f, vp31_lat_ins_f)plt.legend(loc="upper right")plt.title("gnss status filer")plt.xlabel("lon")  # 橫軸名稱plt.ylabel("lat")  # 縱軸名稱plt.savefig(os.path.join(data_path, file_name +"gnss_status"+'.png'), dpi=1024)# plt.subplot(2, 2, 2)plt.clf()plt.figure(2)plt.plot(idx, diff1, ls='-', lw=2, label='center_line_diff',color='black')  # map headingplt.plot(idx, diff1, ls='-', lw=2, label='left_line_diff',color='blue')  # map headingplt.plot(idx, diff3, ls='-', lw=2, label='right_line_diff',color='red')  # map headingplt.plot()plt.legend()plt.title("line_diff")plt.xlabel("idx")  # 橫軸名稱plt.ylabel("diff(m)")  # 縱軸名稱plt.savefig(os.path.join(data_path, file_name + "diff"+'.png'), dpi=512)plt.clf()plt.figure(3)plt.plot(idx_ins, vp31_ins_status, ls='-', lw=2, label='ins_status',color='red')plt.plot(idx_gnss, vp31_gnss_status, ls='-', lw=2, label='gnss_status',color='blue')plt.legend()plt.title("ins_status")plt.xlabel("idx")  # 橫軸名稱plt.ylabel("ins_status")plt.savefig(os.path.join(data_path, file_name +"gnss_status_explian"+'.png'),dpi=512)# plt.subplot(2, 2, 4)plt.clf()plt.figure(4)plt.plot(idx3, l2, ls='-', lw=2, label='line2_length',color='blue')plt.plot(idx3, l3, ls='-', lw=2, label='line3_length',color='black')plt.savefig(os.path.join(data_path, file_name + '.png'), dpi=512)# plt.legend()# plt.show()data_save_path = data_path+file_namelocalization_QC(vp31_lon_gnss, vp31_lat_gnss, vp31_lon_ins,vp31_lat_ins, vp31_gnss_status, vp31_ins_status, data_save_path)if __name__ == "__main__":if len(sys.argv) < 1:print(" please add file name!! ")sys.exit(1)data_path = sys.argv[1]analysis_data_path = data_path[:-4]print("analysis file save path is : ", analysis_data_path, '\n')# Prepare_data(file_name1, file_name2)file_name_list = get_all_file_name(data_path)for i in range(1, len(file_name_list), 2):Prepare_data(file_name_list[i-1], file_name_list[i], data_path, analysis_data_path)time.sleep(1)# if len(sys.argv) < 2:#     print(" please add file name!! ")#     sys.exit(1)# file_name1, file_name2 = sys.argv[1], sys.argv[2]# data_path = "路徑"# Prepare_data(file_name1, file_name2, data_path)