import os import re import pandas as pd from tqdm import tqdm from datetime import datetime, timedelta # 从分地点csv转换为分变量csv def process_csv_files(input_dir, output_dir): """ 遍历指定目录下的所有.csv文件，并按要求处理数据。 :param input_dir: 输入目录，包含原始.csv文件 :param output_dir: 输出目录，保存处理后的.csv文件 """ # 定义关键词列表 KEYWORDS = ["表面位移", "表面裂缝", "深部位移", "温度", "雨量"] # 遍历输入目录中的所有文件 for file_name in os.listdir(input_dir): # if file_name == "辰溪孝坪镇江东村山体滑坡.csv": if file_name.endswith(".csv"): # 确保是.csv文件 # 获取文件名前4个字作为A A = file_name[:4] print(f"Processing file: {file_name}, A = {A}") # 读取CSV文件 file_path = os.path.join(input_dir, file_name) df = pd.read_csv(file_path) # 遍历每个关键词 for keyword in KEYWORDS: print(f"processing {keyword}") # 创建一个空的字典，用于存储每个设备名称对应的数据 device_data_dict = {} device_counter = 0 # 用于记录每个关键词下的设备名称编号 # 遍历每一行数据 for index, row in tqdm(df.iterrows(), total=len(df), desc=f"file_name={file_name}, keyword={keyword}"): # 检查设备名称是否包含当前关键词 if re.search(keyword, row["设备名称"]): # 如果设备名称包含关键词，提取设备名称、时间、采集值x、y、z device_name = row["设备名称"] data = row[["时间", "采集值x", "采集值y", "采集值z"]] # 如果设备名称第一次出现，分配一个编号 if device_name not in device_data_dict: device_data_dict[device_name] = {"data": [], "id": device_counter} device_counter += 1 # 获取设备名称的编号 device_id = device_data_dict[device_name]["id"] # 追加数据到对应设备名称的列表中 device_data_dict[device_name]["data"].append(data) # 保存每个设备名称编号对应的数据为新的CSV文件 for device_name, info in device_data_dict.items(): device_id = info["id"] data_list = info["data"] # 将数据列表转换为DataFrame device_df = pd.DataFrame(data_list, columns=["时间", "采集值x", "采集值y", "采集值z"]) # 确保输出目录存在 keyword_output_dir = os.path.join(output_dir, keyword) os.makedirs(keyword_output_dir, exist_ok=True) # 保存为新的CSV文件 output_file_name = f"{keyword}_{device_id}_{A}.csv" output_file_path = os.path.join(keyword_output_dir, output_file_name) device_df.to_csv(output_file_path, index=False) print(f"Saved file: {output_file_path}") # 函数1：删除完全相同的重复记录 def remove_duplicates(data_list): seen = set() result = [] duplicate_count = 0 # 用于统计删除的重复记录数 for item in data_list: if item not in seen: seen.add(item) result.append(item) else: duplicate_count += 1 print(f"origin_len:{len(data_list)}") print(f"after_duplication_len:{len(result)}") print(f"Removed duplicates: {duplicate_count}") return result, duplicate_count # 函数2：对时间不连续的部分进行平滑过渡填充 def smooth_data(data_list): if not data_list or len(data_list) < 2: return data_list, 0 # 解析时间戳和值 def parse_timestamp_and_value(record): parts = record.strip().split(',') timestamp_str = parts[0] values = [float(x) if x != 'NaN' and x != '' else 0.0 for x in parts[1:]] # 如果是NaN，转换为0 timestamp = datetime.fromisoformat(timestamp_str.replace('+08', '+0800')) return timestamp, values # 格式化为字符串 def format_record(timestamp, values): timestamp_str = timestamp.strftime('%Y-%m-%d %H:%M:%S%z').replace('+0800', '+08') values_str = ','.join(f"{v:.10f}" for v in values) # 使用通用格式化，保留足够的精度 return f"{timestamp_str},{values_str}\n" header = data_list[0] # 提取表头 data_list = data_list[1:] result = [header] supply_count = 0 # 用于统计添加的平滑数据数 for i in tqdm(range(len(data_list) - 1), desc="Processing data", unit="step"): current_timestamp, current_values = parse_timestamp_and_value(data_list[i]) next_timestamp, next_values = parse_timestamp_and_value(data_list[i + 1]) # 确保 current_values 和 next_values 的长度一致 if len(current_values) != len(next_values): raise ValueError(f"数据行 {i} 和 {i+1} 的列数不一致") result.append(format_record(current_timestamp, current_values)) # 如果时间差超过10分钟，进行平滑过渡填充 time_diff = (next_timestamp - current_timestamp).total_seconds() / 60 if time_diff > 10: steps = int(time_diff / 10) supply_count += steps - 1 value_steps = [(next_values[j] - current_values[j]) / steps for j in range(len(current_values))] for step in range(1, steps): new_timestamp = current_timestamp + timedelta(minutes=step * 10) new_values = [current_values[j] + step * value_steps[j] for j in range(len(current_values))] result.append(format_record(new_timestamp, new_values)) # 添加最后一个数据点 last_timestamp, last_values = parse_timestamp_and_value(data_list[-1]) result.append(format_record(last_timestamp, last_values)) print(f"supply_num={supply_count}") return result, supply_count # 对单个文件：读取文件、调用处理函数、写回文件 def dep_and_smooth(input_file, output_file): try: # 读取文件内容 with open(input_file, 'r') as file: data_list = file.readlines() # 删除重复记录 print("Removing duplicates...") data_list, duplicate_count = remove_duplicates(data_list) # 对时间不连续的部分进行平滑过渡填充 print("Smoothing data...") data_list, supply_count = smooth_data(data_list) # 写回文件 with open(output_file, 'w') as file: file.writelines(data_list) print(f"处理完成，结果已写入 {output_file}") return duplicate_count, supply_count, len(data_list) except Exception as e: print(f"处理过程中发生错误：{e}") return 0, 0, 0 # 函数4：遍历文件夹并处理所有文件 def process_folder(input_folder, output_folder): total_duplicate_count = 0 total_supply_count = 0 total_final_row_count = 0 # 确保输出文件夹存在 if not os.path.exists(output_folder): os.makedirs(output_folder) # 遍历输入文件夹 for root, dirs, files in os.walk(input_folder): for file in files: if file.endswith('.csv'): # 构建输入文件路径 input_file_path = os.path.join(root, file) # 构建输出文件路径 relative_path = os.path.relpath(root, input_folder) output_subfolder = os.path.join(output_folder, relative_path) if not os.path.exists(output_subfolder): os.makedirs(output_subfolder) output_file_path = os.path.join(output_subfolder, file) # 处理文件 print(f"Processing file: {input_file_path}") duplicate_count, supply_count, final_row_count = dep_and_smooth(input_file_path, output_file_path) total_duplicate_count += duplicate_count total_supply_count += supply_count total_final_row_count += final_row_count print(f"Total removed duplicates: {total_duplicate_count}") print(f"Total added smooth data: {total_supply_count}") print(f"Total final rows: {total_final_row_count}") if __name__ == "__main__": process_folder( input_folder="/home/mby/time-series-transformer-demo/datasets/category_data", output_folder="/home/mby/time-series-transformer-demo/datasets/category_data_processed" ) # dep_and_smooth( # input_file="/home/mby/time-series-transformer-demo/datasets/category_data/表面裂缝/表面裂缝_0_辰溪孝坪.csv", # output_file="/home/mby/time-series-transformer-demo/datasets/category_data/out.csv")