#!/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_us = float(time_str) * 1000  # 转换为微秒
            state = int(row['Ch01'])
            times.append(time_us)
            states.append(state)
    
    return times, states

def calculate_pulses(times, states):
    """计算脉冲时长"""
    pulses = []
    
    # 跳过第一个时间戳（通常是0或很大的初始值）
    # 从第二个开始计算差值
    for i in range(2, len(times)):
        duration = times[i] - times[i-1]
        # 只保留合理的脉冲时长（100μs - 20000μs）
        if 100 < duration < 20000:
            pulses.append(int(duration))
    
    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20个脉冲:")
    for i, pulse in enumerate(pulses[:20]):
        print(f"  [{i}]: {pulse}μs")
    
    # 统计脉冲时长分布
    pulse_counts = {}
    for pulse in 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）:")
    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个脉冲）
    if len(pulses) >= 2:
        print(f"\n引导码分析:")
        print(f"  引导码高: {pulses[0]}μs")
        print(f"  引导码低: {pulses[1]}μs")
    
    # 分析数据码（跳过引导码）
    data_pulses = pulses[2:-1]  # 跳过引导码和停止码
    
    # 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:")
    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")
    
    # 低电平有两种：短（0）和长（1）
    low_pulses_sorted = sorted(low_pulses)
    mid_point = len(low_pulses_sorted) // 2
    short_lows = low_pulses_sorted[:mid_point]
    long_lows = low_pulses_sorted[mid_point:]
    
    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")
    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")
    
    # 停止码
    if len(pulses) > 0:
        print(f"\n停止码: {pulses[-1]}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}")
        
        times, states = parse_csv(filename)
        pulses = calculate_pulses(times, states)
        analyze_pulses(pulses)

if __name__ == '__main__':
    main()