#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
分析官方APP的CSV文件，提取真实的脉冲时长
"""

import csv
import statistics

def parse_csv(filename):
    """解析CSV文件，提取脉冲时长"""
    times = []
    states = []
    
    with open(filename, 'r', encoding='utf-8') as f:
        reader = csv.DictReader(f)
        for row in reader:
            time_str = row['Time'].replace(' ms', '')
            time_ms = float(time_str)
            times.append(time_ms)
            state = int(row['Ch01'])
            states.append(state)
    
    return times, states

def calculate_pulses(times, states):
    """计算脉冲时长（微秒）"""
    pulses = []
    
    # 从第二个时间戳开始计算差值
    for i in range(1, len(times)):
        duration_ms = times[i] - times[i-1]
        duration_us = duration_ms * 1000  # 转换为微秒
        pulses.append(int(duration_us))
    
    return pulses

def analyze_pulses(pulses):
    """分析脉冲时长，找出引导码、数据码等"""
    print(f"总脉冲数: {len(pulses)}")
    print(f"\n前30个脉冲:")
    for i, pulse in enumerate(pulses[:30]):
        print(f"  [{i}]: {pulse}μs")
    
    # 过滤掉异常大的脉冲（可能是信号间隔）
    normal_pulses = [p for p in pulses if p < 10000]
    
    # 统计脉冲时长分布
    pulse_counts = {}
    for pulse in normal_pulses:
        # 四舍五入到最近的50μs
        rounded = round(pulse / 50) * 50
        pulse_counts[rounded] = pulse_counts.get(rounded, 0) + 1
    
    print(f"\n脉冲时长分布（出现次数 > 5，< 10000μs）:")
    for duration in sorted(pulse_counts.keys()):
        count = pulse_counts[duration]
        if count > 5:
            print(f"  {duration}μs: {count}次")
    
    # 找出最常见的脉冲时长
    common_pulses = sorted(pulse_counts.items(), key=lambda x: x[1], reverse=True)[:10]
    print(f"\n最常见的10个脉冲时长:")
    for duration, count in common_pulses:
        print(f"  {duration}μs: {count}次")
    
    # 分析引导码（前2个正常脉冲）
    normal_start = [p for p in pulses[:10] if p < 10000]
    if len(normal_start) >= 2:
        print(f"\n引导码分析:")
        print(f"  引导码高: {normal_start[0]}μs")
        print(f"  引导码低: {normal_start[1]}μs")
    
    # 分析数据码（跳过引导码，只看正常脉冲）
    data_pulses = [p for p in pulses[2:] if p < 10000]
    
    if len(data_pulses) > 0:
        # 找出高电平脉冲（偶数位置）
        high_pulses = [data_pulses[i] for i in range(0, len(data_pulses), 2)]
        # 找出低电平脉冲（奇数位置）
        low_pulses = [data_pulses[i] for i in range(1, len(data_pulses), 2)]
        
        print(f"\n数据码分析:")
        if high_pulses:
            print(f"  数据高电平: 平均{int(statistics.mean(high_pulses))}μs, "
                  f"中位数{int(statistics.median(high_pulses))}μs, "
                  f"最小{min(high_pulses)}μs, 最大{max(high_pulses)}μs")
        
        if low_pulses:
            # 低电平有两种：短（0）和长（1）
            # 使用聚类方法分离
            low_pulses_sorted = sorted(low_pulses)
            
            # 找到分界点（最大的跳跃）
            max_gap = 0
            split_idx = len(low_pulses_sorted) // 2
            for i in range(1, len(low_pulses_sorted)):
                gap = low_pulses_sorted[i] - low_pulses_sorted[i-1]
                if gap > max_gap:
                    max_gap = gap
                    split_idx = i
            
            short_lows = low_pulses_sorted[:split_idx]
            long_lows = low_pulses_sorted[split_idx:]
            
            if short_lows:
                print(f"  数据0低电平: 平均{int(statistics.mean(short_lows))}μs, "
                      f"中位数{int(statistics.median(short_lows))}μs, "
                      f"范围{min(short_lows)}-{max(short_lows)}μs")
            if long_lows:
                print(f"  数据1低电平: 平均{int(statistics.mean(long_lows))}μs, "
                      f"中位数{int(statistics.median(long_lows))}μs, "
                      f"范围{min(long_lows)}-{max(long_lows)}μs")

def main():
    files = [
        '正确的csv文档/官方app制冷.csv',
        '正确的csv文档/官方app制热.csv',
        '正确的csv文档/官方app送风模式.csv',
        '正确的csv文档/官方app抽湿.csv',
    ]
    
    for filename in files:
        print(f"\n{'='*60}")
        print(f"分析文件: {filename}")
        print(f"{'='*60}")
        
        try:
            times, states = parse_csv(filename)
            pulses = calculate_pulses(times, states)
            analyze_pulses(pulses)
        except Exception as e:
            print(f"错误: {e}")
            import traceback
            traceback.print_exc()

if __name__ == '__main__':
    main()