100 lines
3.9 KiB
Python
100 lines
3.9 KiB
Python
import re
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import pandas as pd
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from pathlib import Path
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from typing import List, Dict
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def parse_line(line: str) -> Dict:
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"""
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解析一行数据，返回一个字典
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例如输入:
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[300]心率:78,83,83,83,;标准差1:43.0491;标准差2:2.16506;均方根:82.2787;值:82;信任级别:1 血氧组:;标准差1:0;标准差:0;均方根:0;值:0;信任级别:0结果延迟:1 R组:;均方根:0; 仪器:60
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[325]心率:75,78,83,83,;标准差1:38.6641;标准差2:3.4187;均方根:80.3232;值:81;信任级别:1 血氧组:;标准差1:0;标准差:0;均方根:0;值:0;信任级别:0结果延迟:2 R组:;均方根:0; 仪器:60
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[350]心率:78,83,83,83,;标准差1:46.0893;标准差2:2.16506;均方根:82.2787;值:82;信任级别:1 血氧组:;标准差1:0;标准差:0;均方根:0;值:0;信任级别:0结果延迟:3 R组:;均方根:0; 仪器:60
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[375]心率:83,83,83,107,;标准差1:43.8422;标准差2:10.3923;均方根:90.1047;值:82;信任级别:0 血氧组:;标准差1:0;标准差:0;均方根:0;值:0;信任级别:0结果延迟:3 R组:;均方根:0; 仪器:60
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[400]心率:83,83,107,107,;标准差1:47.7501;标准差2:12;均方根:96.2549;值:82;信任级别:0 血氧组:;标准差1:0;标准差:0;均方根:0;值:0;信任级别:0结果延迟:3 R组:;均方根:0; 仪器:60
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"""
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line = line.strip()
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if not line:
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return None
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# 提取时间戳 [300] 这样的
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time_match = re.match(r'\[(\d+)\]', line)
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if not time_match:
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return None
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timestamp = int(time_match.group(1))
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# 去掉时间戳部分，后面全部是键值对
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content = line[time_match.end():]
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content = content.split(" ")
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result = {"timestamp": timestamp}
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for block in content:
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parsed_block = parse_block(block)
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result.update(parsed_block)
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return result
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def parse_block(block: str) -> Dict:
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"""
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解析一个数据块，返回一个字典
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例如输入:
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心率:78,83,83,83,;标准差1:43.0491;标准差2:2.16506;均方根:82.2787;值:82;信任级别:1
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血氧组:;标准差1:0;标准差:0;均方根:0;值:0;信任级别:0结果延迟:1
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R组:;均方根:0;
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仪器:60
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"""
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data = {}
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parts = block.split(';')
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if len(parts) == 1:
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key, value = parts[0].split(':', 1)
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data[key.strip()] = value.strip()
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else:
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key_0 = parts[0].split(':', 1)[0].strip()
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for part in parts:
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if len(part.strip()) == 0:
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continue
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if "结果延迟" in part:
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part0, key1, value1 = part.partition('结果延迟:')
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data[f"{key_0}_结果延迟"] = value1.strip()
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key2, _, value2 = part0.partition(':')
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if key2 and value2:
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data[f"{key_0}_{key2.strip()}"] = value2.strip()
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else:
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key, value = part.split(':', 1)
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data[f"{key_0}_{key.strip()}"] = value.strip()
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return data
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def parse_dat_file(file_path) -> pd.DataFrame:
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"""
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读取整个 .dat 文件并返回 pandas DataFrame
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"""
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data = []
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with open(file_path, 'r', encoding='utf-8') as f:
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for line_num, line in enumerate(f, 1):
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parsed = parse_line(line)
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if parsed:
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data.append(parsed)
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else:
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if line.strip():
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print(f"第 {line_num} 行解析失败，已跳过: {line.strip()[:80]}...")
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df = pd.DataFrame(data)
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# 按时间戳排序（一般已经是顺序，但保险起见）
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if not df.empty:
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df = df.sort_values("timestamp").reset_index(drop=True)
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return df
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if __name__ == '__main__':
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# file_path = "./data/spo2_alg_info[42-A3-C5-2F-F7-32]2025.11.17_17.01.14.dat"
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file_path = "./data/spo2_alg_info[42_A3_C5_2F_F7_32]2025.11.17_17.12.16.dat"
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file_path = Path(file_path)
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df = parse_dat_file(file_path)
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df.to_csv(file_path.with_suffix('.csv'), index=False, encoding='utf-8-sig') |