diff --git a/.gitignore b/.gitignore index 2fe3d34..a5b0f68 100644 --- a/.gitignore +++ b/.gitignore @@ -3,6 +3,7 @@ *.rar *.docx /2022neimengdata +/sampledata_2022neimengdata - 副本 # Byte-compiled / optimized / DLL files diff --git a/merge_data.py b/merge_data.py index 0c2d693..030ebe3 100644 --- a/merge_data.py +++ b/merge_data.py @@ -1,8 +1,11 @@ import numpy as np import pandas as pd import os -from sklearn import datasets, linear_model +from datetime import datetime +import time +import math import argparse +import copy class GasAnalyzer(): @@ -21,25 +24,549 @@ class GasAnalyzer(): self.rowCountsBackup = [] for i, filename in enumerate(self.validFiles): names[f'df_{i}'] = pd.read_csv(self.folderPath + "\\" + filename, skiprows=1, sep=',') + # names[f'df_{i}'] = pd.read_csv(self.folderPath + "\\" + filename, skiprows=1, sep=',', usecols=[0, 1]) # ：读取csv的部分列 self.rowCountsBackup.append(names[f'df_{i}'].shape[0]) + # 将时间转换为秒，便于numpy向量化计算 + names[f'df_{i}'].insert(loc=2, column='time_inseconds', value=0) + for j in range(names[f'df_{i}'].shape[0]): + # time_tmp = names[f'df_{i}'].loc[j, " Time"].strip() + time_tmp = names[f'df_{i}'].iloc[j, 1].strip() + names[f'df_{i}'].loc[j, "time_inseconds"] = self.get_time_in_seconds(time_tmp) + for x in range(len(self.rowCountsBackup)): if(x==0): self.df_total = names[f'df_{0}'] else: - self.df_total = pd.concat([self.df_total, names[f'df_{x}']]) + self.df_total = pd.concat([self.df_total, names[f'df_{x}']], ignore_index=True) + # self.df_total = self.df_total.append(names[f'df_{x}']) if(sum(self.rowCountsBackup) != self.df_total.shape[0]): print("拼接气体分析仪数据失败！拼接前后行数不一致！") return 1 + self.data_numpy = np.array(self.df_total) + return 0 - def write_data(self): - a = 1 + def write_data(self, debug=False): + """ + 将气体分析仪数据写入到csv中 + :param debug: 控制是否写入调试信息（用于查看两个数据的时间是否匹配） + :return: + """ + # for file in self.validFiles: + # tmp = os.path.join(self.folderPath, "fixed", file) + # self.df_total.to_csv(self.folderPath, index=False, sep=',') + + if not debug: + del self.df_total['meteorologicalStation_newtime'] + del self.df_total['time_inseconds'] + tmp = os.path.join(self.folderPath, "all.csv") + + self.df_total.to_csv(tmp, index=False, sep=',') def merge_data(self, MeteorologicalStation): - a = 1 + """ + 根据时间融合气体分析仪和气象站的数据，第一步插入空的数据列，第二步为数据列填写数据 + :param MeteorologicalStation: 气象站数据，pandas的dataframe形式 + :return: + """ + self.insertMeteorologicalStationMetaData() + + # self.updateData_dataframe_enumerate(MeteorologicalStation) + # self.updateData_loc(MeteorologicalStation) + # self.updateData_list(MeteorologicalStation) + # self.updateData_list_time_delta(MeteorologicalStation) + # self.updateData_numpy(MeteorologicalStation.data_numpy) + self.updateData_numpy_vectorize(MeteorologicalStation.data_numpy) + + def insertMeteorologicalStationMetaData(self): + """ + 为气体分析仪数据（pandas的dataframe形式）插入一些列，用于保存对应的气象站数据 + :return: + """ + self.df_total.insert(loc=2, column='meteorologicalStation_newtime', value=0) + self.df_total.insert(loc=len(self.df_total.columns), column='humidity', value=0) + self.df_total.insert(loc=len(self.df_total.columns), column='temperature', value=0) + self.df_total.insert(loc=len(self.df_total.columns), column='pressure', value=0) + self.df_total.insert(loc=len(self.df_total.columns), column='windDirection', value=0) + self.df_total.insert(loc=len(self.df_total.columns), column='windVelocity_x', value=0) + self.df_total.insert(loc=len(self.df_total.columns), column='windVelocity_y', value=0) + + # 使用enumerate生成迭代器进行遍历pandas的dataframe的每一行 + def updateData_dataframe_enumerate(self, df): + time_start = time.time() # 记录开始时间 + index_last_loop = 0 + delta_in_seconds_first_loop = 0 # 记录第一行完全遍历取得的最小时间差 + delta_backup = [] + for index_GasAnalyzer, row_GasAnalyzer in enumerate(self.df_total.itertuples()): + time_GasAnalyzer = row_GasAnalyzer[2].strip() # 是从1开始 + date_GasAnalyzer = datetime.strptime(time_GasAnalyzer, '%m/%d/%Y %H:%M:%S.%f') + + if(index_GasAnalyzer == 400): + sdf = 0 + + index_min_delta = 0 + delta_in_seconds_min = 10000000 + delta_in_seconds_last_loop = 0 + for index_MeteorologicalStation, row_MeteorologicalStation in enumerate(df.itertuples()): # 寻找最近的时间 + if index_MeteorologicalStation < index_last_loop: + continue + + time_MeteorologicalStation = row_MeteorologicalStation[1] # 是从1开始 + # print(time_MeteorologicalStation) + date_MeteorologicalStation = datetime.strptime(time_MeteorologicalStation, '%m/%d/%Y %H:%M:%S.%f') + delta_in_seconds_tmp = abs((date_GasAnalyzer - date_MeteorologicalStation).total_seconds()) + + if index_MeteorologicalStation == index_last_loop: # 第一次循环时，为delta_in_seconds_last_loop赋初值 + delta_in_seconds_last_loop = delta_in_seconds_tmp + + if delta_in_seconds_tmp - delta_in_seconds_last_loop > 0: # + delta_in_seconds_min = delta_in_seconds_last_loop + delta_backup.append(delta_in_seconds_min) + index_min_delta = index_MeteorologicalStation - 1 + index_last_loop = index_min_delta + break + + if delta_in_seconds_min > max(delta_backup)*3 and index_GasAnalyzer != 0: # 如果时间偏差太离谱，就放弃给此行写入数据 + continue + + # 进行数据插入 + # self.df_total.loc[index_GasAnalyzer, 'meteorologicalStation_newtime'] = df.loc[index_min_delta, 0] + self.df_total.loc[index_GasAnalyzer, 'humidity'] = df.loc[index_min_delta, 1] + self.df_total.loc[index_GasAnalyzer, 'temperature'] = df.loc[index_min_delta, 2] + self.df_total.loc[index_GasAnalyzer, 'pressure'] = df.loc[index_min_delta, 5] + + windDirection = self.df_total.loc[index_GasAnalyzer, 'Vehicle Heading (degrees)'] + df.loc[index_min_delta, 3] + if windDirection >= 360: + windDirection = windDirection - 360 + self.df_total.loc[index_GasAnalyzer, 'windDirection'] = windDirection + + windVelocity = df.loc[index_min_delta, 4] + if 0 <= windDirection < 90: + windVelocity_x = math.sin(math.radians(windDirection)) * windVelocity + windVelocity_y = math.cos(math.radians(windDirection)) * windVelocity + if 90 <= windDirection < 180: + windDirection = windDirection - 90 + windVelocity_x = math.cos(math.radians(windDirection)) * windVelocity + windVelocity_y = math.sin(math.radians(windDirection)) * windVelocity + if 180 <= windDirection < 270: + windDirection = windDirection - 180 + windVelocity_x = math.sin(math.radians(windDirection)) * windVelocity + windVelocity_y = math.cos(math.radians(windDirection)) * windVelocity + if 270 <= windDirection < 360: + windDirection = windDirection - 270 + windVelocity_x = math.cos(math.radians(windDirection)) * windVelocity + windVelocity_y = math.sin(math.radians(windDirection)) * windVelocity + self.df_total.loc[index_GasAnalyzer, 'windVelocity_x'] = windVelocity_x + self.df_total.loc[index_GasAnalyzer, 'windVelocity_y'] = windVelocity_y + + time_end = time.time() # 记录结束时间 + time_sum = time_end - time_start # 计算的时间差为程序的执行时间，单位为秒/s + print(time_sum) + + # 不使用迭代器，直接通过df.loc存取pandas的dataframe的元素 + def updateData_loc(self, df): + time_start = time.time() # 记录开始时间 + index_last_loop = 0 + delta_backup = [] + + for index_GasAnalyzer in range(self.df_total.shape[0]): + time_GasAnalyzer = self.df_total.loc[index_GasAnalyzer, " Time"].strip() + date_GasAnalyzer = datetime.strptime(time_GasAnalyzer, '%m/%d/%Y %H:%M:%S.%f') + + if (index_GasAnalyzer == 100): + sdf = 0 + + index_min_delta = 0 + delta_in_seconds_min = 10000000 + delta_in_seconds_last_loop = 0 + for index_MeteorologicalStation in range(index_last_loop, df.shape[0]): + # if index_MeteorologicalStation < index_last_loop: + # continue + + time_MeteorologicalStation = df.loc[index_MeteorologicalStation, 0] + date_MeteorologicalStation = datetime.strptime(time_MeteorologicalStation, '%m/%d/%Y %H:%M:%S.%f') + delta_in_seconds_tmp = abs((date_GasAnalyzer - date_MeteorologicalStation).total_seconds()) + + if index_MeteorologicalStation == index_last_loop: # 第一次循环时，为delta_in_seconds_last_loop赋初值 + delta_in_seconds_last_loop = delta_in_seconds_tmp + + if delta_in_seconds_tmp - delta_in_seconds_last_loop > 0: # ????????????????????? + delta_in_seconds_min = delta_in_seconds_last_loop + delta_backup.append(delta_in_seconds_min) + index_min_delta = index_MeteorologicalStation - 1 + index_last_loop = index_min_delta + break + + if delta_in_seconds_min > max(delta_backup)*3 and index_GasAnalyzer != 0: # 如果时间偏差太离谱，就放弃给此行写入数据 + continue + + # 进行数据插入 + # self.df_total.loc[index_GasAnalyzer, 'meteorologicalStation_newtime'] = df.loc[index_min_delta, 0] + # self.df_total.loc[index_GasAnalyzer, 'humidity'] = df.loc[index_min_delta, 1] + # self.df_total.loc[index_GasAnalyzer, 'temperature'] = df.loc[index_min_delta, 2] + # self.df_total.loc[index_GasAnalyzer, 'pressure'] = df.loc[index_min_delta, 5] + # + # windDirection = self.df_total.loc[index_GasAnalyzer, 'Vehicle Heading (degrees)'] + df.loc[index_min_delta, 3] + # if windDirection >= 360: + # windDirection = windDirection - 360 + # self.df_total.loc[index_GasAnalyzer, 'windDirection'] = windDirection + # + # windVelocity = df.loc[index_min_delta, 4] + # if 0 <= windDirection < 90: + # windVelocity_x = math.sin(math.radians(windDirection)) * windVelocity + # windVelocity_y = math.cos(math.radians(windDirection)) * windVelocity + # if 90 <= windDirection < 180: + # windDirection = windDirection - 90 + # windVelocity_x = math.cos(math.radians(windDirection)) * windVelocity + # windVelocity_y = math.sin(math.radians(windDirection)) * windVelocity + # if 180 <= windDirection < 270: + # windDirection = windDirection - 180 + # windVelocity_x = math.sin(math.radians(windDirection)) * windVelocity + # windVelocity_y = math.cos(math.radians(windDirection)) * windVelocity + # if 270 <= windDirection < 360: + # windDirection = windDirection - 270 + # windVelocity_x = math.cos(math.radians(windDirection)) * windVelocity + # windVelocity_y = math.sin(math.radians(windDirection)) * windVelocity + # self.df_total.loc[index_GasAnalyzer, 'windVelocity_x'] = windVelocity_x + # self.df_total.loc[index_GasAnalyzer, 'windVelocity_y'] = windVelocity_y + + time_end = time.time() # 记录结束时间 + time_sum = time_end - time_start # 计算的时间差为程序的执行时间，单位为秒/s + print(time_sum) + + # 获取pandas的dataframe的时间列并返回成列表 + def updateData_list(self, df): + time_start = time.time() # 记录开始时间 + index_last_loop = 0 + delta_backup = [] + + time_GasAnalyzer = self.df_total[" Time"].to_list() + vehicle_Heading_GasAnalyzer = self.df_total["Vehicle Heading (degrees)"].to_list() + + time_MeteorologicalStation_tmp = df[0].to_list() + time_MeteorologicalStation = copy.deepcopy(time_MeteorologicalStation_tmp) + humidity_MeteorologicalStation = df[0].to_list() + temperature_MeteorologicalStation = df[0].to_list() + windDirection_MeteorologicalStation = df[0].to_list() + windVelocity_MeteorologicalStation = df[0].to_list() + pressure_MeteorologicalStation = df[0].to_list() + + for index_GasAnalyzer in range(len(time_GasAnalyzer)): + time_GasAnalyzer_tmp = time_GasAnalyzer[index_GasAnalyzer].strip() + # date_GasAnalyzer = datetime.strptime(time_GasAnalyzer_tmp, '%m/%d/%Y %H:%M:%S.%f') + + if (index_GasAnalyzer == 13): + sdf = 0 + + index_min_delta = 0 + delta_in_seconds_min = 10000000 + delta_in_seconds_last_loop = 0 + + time_start2 = time.time() # 记录开始时间 + for index_MeteorologicalStation in range(index_last_loop, len(time_MeteorologicalStation)): + # if index_MeteorologicalStation < index_last_loop: + # continue + + time_MeteorologicalStation_tmp = time_MeteorologicalStation[index_MeteorologicalStation] + + # date_MeteorologicalStation = datetime.strptime(time_MeteorologicalStation_tmp, '%m/%d/%Y %H:%M:%S.%f') + # delta_in_seconds_tmp = abs((date_GasAnalyzer - date_MeteorologicalStation).total_seconds()) + + delta_in_seconds_tmp = self.delta_in_seconds(time_GasAnalyzer_tmp, time_MeteorologicalStation_tmp) + + if index_MeteorologicalStation == index_last_loop: # 第一次循环时，为delta_in_seconds_last_loop赋初值 + delta_in_seconds_last_loop = delta_in_seconds_tmp + + if delta_in_seconds_tmp - delta_in_seconds_last_loop > 0: # ????????????????????? + delta_in_seconds_min = delta_in_seconds_last_loop + delta_backup.append(delta_in_seconds_min) + index_min_delta = index_MeteorologicalStation - 1 + index_last_loop = index_min_delta + break + time_end2 = time.time() # 记录结束时间 + time_sum2 = time_end2 - time_start2 # 计算的时间差为程序的执行时间，单位为秒/s + if index_GasAnalyzer % 100 == 0: + print("第%d次内层循环用时：%d" % (index_GasAnalyzer, time_sum2)) + + # if delta_in_seconds_min > max(delta_backup)*3 and index_GasAnalyzer != 0: # 如果时间偏差太离谱，就放弃给此行写入数据 + # continue + + # 进行数据插入 + # self.df_total.loc[index_GasAnalyzer, 'meteorologicalStation_newtime'] = df.loc[index_min_delta, 0] # ?????????????????????????????????????????????????????????? + # self.df_total.loc[index_GasAnalyzer, 'humidity'] = df.loc[index_min_delta, 1] + # self.df_total.loc[index_GasAnalyzer, 'temperature'] = df.loc[index_min_delta, 2] + # self.df_total.loc[index_GasAnalyzer, 'pressure'] = df.loc[index_min_delta, 5] + # + # windDirection = self.df_total.loc[index_GasAnalyzer, 'Vehicle Heading (degrees)'] + df.loc[index_min_delta, 3] + # if windDirection >= 360: + # windDirection = windDirection - 360 + # self.df_total.loc[index_GasAnalyzer, 'windDirection'] = windDirection + # + # windVelocity = df.loc[index_min_delta, 4] + # if 0 <= windDirection < 90: + # windVelocity_x = math.sin(math.radians(windDirection)) * windVelocity + # windVelocity_y = math.cos(math.radians(windDirection)) * windVelocity + # if 90 <= windDirection < 180: + # windDirection = windDirection - 90 + # windVelocity_x = math.cos(math.radians(windDirection)) * windVelocity + # windVelocity_y = math.sin(math.radians(windDirection)) * windVelocity + # if 180 <= windDirection < 270: + # windDirection = windDirection - 180 + # windVelocity_x = math.sin(math.radians(windDirection)) * windVelocity + # windVelocity_y = math.cos(math.radians(windDirection)) * windVelocity + # if 270 <= windDirection < 360: + # windDirection = windDirection - 270 + # windVelocity_x = math.cos(math.radians(windDirection)) * windVelocity + # windVelocity_y = math.sin(math.radians(windDirection)) * windVelocity + # self.df_total.loc[index_GasAnalyzer, 'windVelocity_x'] = windVelocity_x + # self.df_total.loc[index_GasAnalyzer, 'windVelocity_y'] = windVelocity_y + + time_end = time.time() # 记录结束时间 + time_sum = time_end - time_start # 计算的时间差为程序的执行时间，单位为秒/s + print("外层循环用时：%d" % time_sum) + + # 不使用库datetime，自己写函数计算时间差 + def updateData_list_time_delta(self, df): + time_start = time.time() # 记录开始时间 + index_last_loop = 0 + delta_backup = [] + + time_GasAnalyzer = self.df_total[" Time"].to_list() + time_MeteorologicalStation = df[0].to_list() + + for index_GasAnalyzer in range(len(time_GasAnalyzer)): + time_GasAnalyzer_tmp = time_GasAnalyzer[index_GasAnalyzer].strip() + # date_GasAnalyzer = datetime.strptime(time_GasAnalyzer_tmp, '%m/%d/%Y %H:%M:%S.%f') + + if (index_GasAnalyzer == 13): + sdf = 0 + + # continue + + index_min_delta = 0 + delta_in_seconds_min = 10000000 + delta_in_seconds_last_loop = 0 + + time_start2 = time.time() # 记录开始时间 + for index_MeteorologicalStation in range(index_last_loop, len(time_MeteorologicalStation)): + # if index_MeteorologicalStation < index_last_loop: + # continue + + # continue + + time_MeteorologicalStation_tmp = time_MeteorologicalStation[index_MeteorologicalStation] + + # time_start3 = time.time() # 记录开始时间 + + # date_MeteorologicalStation = datetime.strptime(time_MeteorologicalStation_tmp, '%m/%d/%Y %H:%M:%S.%f') + # delta_in_seconds_tmp = abs((date_GasAnalyzer - date_MeteorologicalStation).total_seconds()) + delta_in_seconds_tmp = self.delta_in_seconds(time_GasAnalyzer_tmp, time_MeteorologicalStation_tmp) + + # time_end3 = time.time() # 记录结束时间 + # time_sum3 = time_end3 - time_start2 # 计算的时间差为程序的执行时间，单位为秒/s + # if index_GasAnalyzer % 100 == 0: + # print("第%d次内层循环时间转换用时：%d" % (index_MeteorologicalStation, time_sum3)) + + if index_MeteorologicalStation == index_last_loop: # 第一次循环时，为delta_in_seconds_last_loop赋初值 + delta_in_seconds_last_loop = delta_in_seconds_tmp + + if delta_in_seconds_tmp - delta_in_seconds_last_loop > 0: # ????????????????????? + delta_in_seconds_min = delta_in_seconds_last_loop + delta_backup.append(delta_in_seconds_min) + index_min_delta = index_MeteorologicalStation - 1 + index_last_loop = index_min_delta + break + time_end2 = time.time() # 记录结束时间 + time_sum2 = time_end2 - time_start2 # 计算的时间差为程序的执行时间，单位为秒/s + if index_GasAnalyzer % 100 == 0: + print("第%d次内层循环用时：%d" % (index_GasAnalyzer, time_sum2)) + + # if delta_in_seconds_min > max(delta_backup)*3 and index_GasAnalyzer != 0: # 如果时间偏差太离谱，就放弃给此行写入数据 + # continue + + # 进行数据插入 + self.df_total.loc[index_GasAnalyzer, 'meteorologicalStation_newtime'] = df.loc[index_min_delta, 0] + self.df_total.loc[index_GasAnalyzer, 'humidity'] = df.loc[index_min_delta, 1] + self.df_total.loc[index_GasAnalyzer, 'temperature'] = df.loc[index_min_delta, 2] + self.df_total.loc[index_GasAnalyzer, 'pressure'] = df.loc[index_min_delta, 5] + + windDirection = self.df_total.loc[index_GasAnalyzer, 'Vehicle Heading (degrees)'] + df.loc[index_min_delta, 3] + if windDirection >= 360: + windDirection = windDirection - 360 + self.df_total.loc[index_GasAnalyzer, 'windDirection'] = windDirection + + windVelocity = df.loc[index_min_delta, 4] + if 0 <= windDirection < 90: + windVelocity_x = math.sin(math.radians(windDirection)) * windVelocity + windVelocity_y = math.cos(math.radians(windDirection)) * windVelocity + if 90 <= windDirection < 180: + windDirection = windDirection - 90 + windVelocity_x = math.cos(math.radians(windDirection)) * windVelocity + windVelocity_y = math.sin(math.radians(windDirection)) * windVelocity + if 180 <= windDirection < 270: + windDirection = windDirection - 180 + windVelocity_x = math.sin(math.radians(windDirection)) * windVelocity + windVelocity_y = math.cos(math.radians(windDirection)) * windVelocity + if 270 <= windDirection < 360: + windDirection = windDirection - 270 + windVelocity_x = math.cos(math.radians(windDirection)) * windVelocity + windVelocity_y = math.sin(math.radians(windDirection)) * windVelocity + self.df_total.loc[index_GasAnalyzer, 'windVelocity_x'] = windVelocity_x + self.df_total.loc[index_GasAnalyzer, 'windVelocity_y'] = windVelocity_y + + time_end = time.time() # 记录结束时间 + time_sum = time_end - time_start # 计算的时间差为程序的执行时间，单位为秒/s + print("外层循环用时：%d" % time_sum) + + # 遍历时使用numpy + def updateData_numpy(self, array_numpy): + time_start = time.time() # 记录开始时间 + index_last_loop = 0 + delta_backup = [] + + for index_GasAnalyzer in range(self.data_numpy.shape[0]): + time_GasAnalyzer = self.data_numpy[index_GasAnalyzer, 1].strip() + date_GasAnalyzer = datetime.strptime(time_GasAnalyzer, '%m/%d/%Y %H:%M:%S.%f') + + if (index_GasAnalyzer == 100): + sdf = 0 + + index_min_delta = 0 + delta_in_seconds_min = 10000000 + delta_in_seconds_last_loop = 0 + + time_start2 = time.time() # 记录开始时间 + for index_MeteorologicalStation in range(index_last_loop, array_numpy.shape[0]): + # if index_MeteorologicalStation < index_last_loop: + # continue + + time_MeteorologicalStation = array_numpy[index_MeteorologicalStation, 0] + date_MeteorologicalStation = datetime.strptime(time_MeteorologicalStation, '%m/%d/%Y %H:%M:%S.%f') + + delta_in_seconds_tmp = abs((date_GasAnalyzer - date_MeteorologicalStation).total_seconds()) + + if index_MeteorologicalStation == index_last_loop: # 第一次循环时，为delta_in_seconds_last_loop赋初值 + delta_in_seconds_last_loop = delta_in_seconds_tmp + + if delta_in_seconds_tmp - delta_in_seconds_last_loop > 0: # ????????????????????? + delta_in_seconds_min = delta_in_seconds_last_loop + delta_backup.append(delta_in_seconds_min) + index_min_delta = index_MeteorologicalStation - 1 + index_last_loop = index_min_delta + break + + time_end2 = time.time() # 记录结束时间 + time_sum2 = time_end2 - time_start2 # 计算的时间差为程序的执行时间，单位为秒/s + if index_GasAnalyzer % 100 == 0: + print("第%d次内层循环用时：%d" % (index_GasAnalyzer, time_sum2)) + + if delta_in_seconds_min > max(delta_backup)*3 and index_GasAnalyzer != 0: # 如果时间偏差太离谱，就放弃给此行写入数据 + continue + + # 进行数据插入 + + time_end = time.time() # 记录结束时间 + time_sum = time_end - time_start # 计算的时间差为程序的执行时间，单位为秒/s + print(time_sum) + + # numpy向量化：最快 + def updateData_numpy_vectorize(self, array_numpy): + """ + 气体分析仪和气象站数据每一行都有时间 + 根据每一行气体分析仪的数据中的时间，找到时间差最小的气象站数据的那一行，并将气象站数据插入到气体分析仪数据中 + :param array_numpy: 气象站数据的numpy形式 + :return: + """ + time_start = time.time() # 记录开始时间 + for index_GasAnalyzer in range(self.data_numpy.shape[0]): + delta_in_seconds = abs(array_numpy[:, 6] - self.data_numpy[index_GasAnalyzer, 2]) + index_min = np.argmin(delta_in_seconds) + + # if delta_in_seconds_min > max(delta_backup) * 3 and index_GasAnalyzer != 0: # 如果时间偏差太离谱，就放弃给此行写入数据 + # continue + + # 进行数据插入 + self.df_total.loc[index_GasAnalyzer, 'meteorologicalStation_newtime'] = array_numpy[index_min, 0] + self.df_total.loc[index_GasAnalyzer, 'humidity'] = array_numpy[index_min, 1] + self.df_total.loc[index_GasAnalyzer, 'temperature'] = array_numpy[index_min, 2] + self.df_total.loc[index_GasAnalyzer, 'pressure'] = array_numpy[index_min, 5] + + windDirection = self.df_total.loc[index_GasAnalyzer, 'Vehicle Heading (degrees)'] + array_numpy[index_min, 3] + if windDirection >= 360: + windDirection = windDirection - 360 + self.df_total.loc[index_GasAnalyzer, 'windDirection'] = windDirection + + windVelocity = array_numpy[index_min, 4] + if 0 <= windDirection < 90: + windVelocity_x = math.sin(math.radians(windDirection)) * windVelocity + windVelocity_y = math.cos(math.radians(windDirection)) * windVelocity + if 90 <= windDirection < 180: + windDirection = windDirection - 90 + windVelocity_x = math.cos(math.radians(windDirection)) * windVelocity + windVelocity_y = math.sin(math.radians(windDirection)) * windVelocity + if 180 <= windDirection < 270: + windDirection = windDirection - 180 + windVelocity_x = math.sin(math.radians(windDirection)) * windVelocity + windVelocity_y = math.cos(math.radians(windDirection)) * windVelocity + if 270 <= windDirection < 360: + windDirection = windDirection - 270 + windVelocity_x = math.cos(math.radians(windDirection)) * windVelocity + windVelocity_y = math.sin(math.radians(windDirection)) * windVelocity + self.df_total.loc[index_GasAnalyzer, 'windVelocity_x'] = windVelocity_x + self.df_total.loc[index_GasAnalyzer, 'windVelocity_y'] = windVelocity_y + + time_end = time.time() # 记录结束时间 + time_sum = time_end - time_start # 计算的时间差为程序的执行时间，单位为秒/s + print(time_sum) + + def delta_in_seconds(self, time1, time2): + """ + 输入两个日期，返回两个日期的时间差（单位为秒） + :param time1: 字符串，形如：08/13/2022 10:24:26.022 + :param time2: 字符串，形如：08/13/2022 10:24:26.022 + :return: + """ + month_day_year1, hour_minute_second1 = self.get_time(time1) + month_day_year2, hour_minute_second2 = self.get_time(time2) + + tmp0 = abs((int(month_day_year1[0]) - int(month_day_year2[0])) * 30 * 24 * 60 * 60) + tmp1 = abs((int(month_day_year1[1]) - int(month_day_year2[1])) * 24 * 60 * 60) + tmp2 = abs((int(month_day_year1[2]) - int(month_day_year2[2])) * 12 * 30 * 24 * 60 * 60) + + tmp3 = abs((int(hour_minute_second1[0]) - int(hour_minute_second2[0])) * 60 * 60) + tmp4 = abs((int(hour_minute_second1[1]) - int(hour_minute_second2[1])) * 60) + tmp5 = abs((float(hour_minute_second1[2]) - float(hour_minute_second2[2]))) + + return tmp0 + tmp1 + tmp2 + tmp3 + tmp4 + tmp5 + + def get_time(self, time1): + """ + 输入时间字符串，输出分割后的时间 + :param time1: 字符串，形如：08/13/2022 10:24:26.022 + :return: + """ + tmp1 = time1.split(sep=" ") # tmp = ['08/13/2022', '10:24:26.022'] + month_day_year = tmp1[0].split(sep="/") # ['08', '13', '2022'] + hour_minute_second = tmp1[1].split(sep=":") # ['10', '24', '26.022'] + + return month_day_year, hour_minute_second + + def get_time_in_seconds(self, time1): + """ + 计算日期的秒数（除去年月日，只保留时分秒） + :param time1: 字符串，形如：08/13/2022 10:24:26.022 + :return:日期的秒数 + """ + month_day_year1, hour_minute_second1 = self.get_time(time1) + + tmp3 = int(hour_minute_second1[0]) * 60 * 60 + tmp4 = int(hour_minute_second1[1]) * 60 + tmp5 = float(hour_minute_second1[2]) + + return tmp3 + tmp4 + tmp5 class MeteorologicalStation(): @@ -60,35 +587,56 @@ class MeteorologicalStation(): names[f'df_{i}'] = pd.read_csv(self.folderPath + "\\" + filename, header=None, sep=',') self.rowCountsBackup.append(names[f'df_{i}'].shape[0]) + # 将时间转换为秒，并插入到最右边的那一列（原本共6列，从0开始） + names[f'df_{i}'].insert(loc=6, column='time_inseconds', value=0) + for j in range(names[f'df_{i}'].shape[0]): + time_tmp = names[f'df_{i}'].iloc[j, 0].strip() + names[f'df_{i}'].loc[j, "time_inseconds"] = self.get_time_in_seconds(time_tmp) + for x in range(len(self.rowCountsBackup)): if(x==0): self.df_total = names[f'df_{0}'] else: - self.df_total = pd.concat([self.df_total, names[f'df_{x}']]) + self.df_total = pd.concat([self.df_total, names[f'df_{x}']], ignore_index=True) if(sum(self.rowCountsBackup) != self.df_total.shape[0]): print("拼接气体分析仪数据失败！拼接前后行数不一致！") return 1 + self.data_numpy = np.array(self.df_total) + return 0 + def get_time(self, time1): + tmp1 = time1.split(sep=" ") # tmp = ['08/13/2022', '10:24:26.022'] + month_day_year = tmp1[0].split(sep="/") # ['08', '13', '2022'] + hour_minute_second = tmp1[1].split(sep=":") # ['10', '24', '26.022'] + + return month_day_year, hour_minute_second + + def get_time_in_seconds(self, time1): + month_day_year1, hour_minute_second1 = self.get_time(time1) + + tmp3 = int(hour_minute_second1[0]) * 60 * 60 + tmp4 = int(hour_minute_second1[1]) * 60 + tmp5 = float(hour_minute_second1[2]) + + return tmp3 + tmp4 + tmp5 + if __name__ == "__main__": - # parser = argparse.ArgumentParser() - # parser.add_argument("csv_path", help="Path of csv file which contains wavelength.") - # parser.add_argument("start_row", help="Start row of coning 410 sensor.") - # args = parser.parse_args() + parser = argparse.ArgumentParser() + parser.add_argument("GasAnalyzer", help="气体分析仪数据路径。") + parser.add_argument("MeteorologicalStation", help="气象站路径。") + args = parser.parse_args() - # row_bin1, wave_bin1, row_bin2, wave_bin2 = read_data(args.csv_path, int(args.start_row)) + gas_analyzer = GasAnalyzer(args.GasAnalyzer) + gas_analyzer.read_data() - GasAnalyzer_folderPath = r"D:\PycharmProjects\weatherInstrument\2022neimengdata\气体分析仪\2022-08-13" + meteorological_station = MeteorologicalStation(args.MeteorologicalStation) + meteorological_station.read_data() - MeteorologicalStation_folderPath = r"D:\PycharmProjects\weatherInstrument\2022neimengdata\气象站\2022_08_13" - - tmp1 = GasAnalyzer(GasAnalyzer_folderPath) - tmp1.read_data() - - tmp2 = MeteorologicalStation(MeteorologicalStation_folderPath) - tmp2.read_data() + gas_analyzer.merge_data(meteorological_station) + gas_analyzer.write_data(debug=True) print("completed!!") diff --git a/sampledata/气体分析仪/2022-08-13/mgga_170100000233_2022-08-13_f0000.txt.zip b/sampledata/气体分析仪/2022-08-13/mgga_170100000233_2022-08-13_f0000.txt.zip new file mode 100644 index 0000000..a27ae2f Binary files /dev/null and b/sampledata/气体分析仪/2022-08-13/mgga_170100000233_2022-08-13_f0000.txt.zip differ diff --git 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