#!/usr/bin/env python3
"""
音频转换测试脚本
用于测试高性能音频格式转换
"""

import time
import struct
from audio_converter import AudioConverter

def test_conversion_performance():
    """测试转换性能"""
    print("=== 音频转换性能测试 ===")
    
    converter = AudioConverter()
    
    # 生成测试数据（1秒的24kHz Float32音频）
    sample_rate = 24000
    duration = 1.0  # 1秒
    num_samples = int(sample_rate * duration)
    
    # 生成正弦波测试数据
    test_data = bytearray()
    for i in range(num_samples):
        # 生成440Hz正弦波
        value = 0.5 * (i / sample_rate * 440 * 2 * 3.14159)
        sample = (value).astype('float32') if hasattr(value, 'astype') else float(value)
        test_data.extend(struct.pack('f', sample))
    
    test_data = bytes(test_data)
    print(f"生成了 {len(test_data)} 字节的测试数据")
    
    # 测试转换性能
    start_time = time.time()
    converted_data = converter.float32_to_int16_fast(test_data)
    end_time = time.time()
    
    conversion_time = end_time - start_time
    data_ratio = len(converted_data) / len(test_data)
    
    print(f"转换结果:")
    print(f"  原始数据: {len(test_data)} 字节")
    print(f"  转换后: {len(converted_data)} 字节")
    print(f"  数据比例: {data_ratio:.2f}")
    print(f"  转换时间: {conversion_time:.4f} 秒")
    print(f"  转换速度: {len(test_data) / conversion_time / 1024:.1f} KB/s")
    
    # 验证转换质量
    print("\n=== 转换质量验证 ===")
    
    # 检查一些样本值
    original_samples = struct.unpack('10f', test_data[:40])
    converted_samples = struct.unpack('10h', converted_data[:20])
    
    print("前10个样本的转换结果:")
    for i, (orig, conv) in enumerate(zip(original_samples, converted_samples)):
        expected = int(orig * 32767)
        print(f"  样本{i}: {orig:.6f} -> {conv} (期望: {expected})")
    
    # 检查是否有明显错误
    errors = 0
    for orig, conv in zip(original_samples, converted_samples):
        expected = int(orig * 32767)
        if abs(conv - expected) > 1:  # 允许1的误差
            errors += 1
    
    if errors == 0:
        print("✓ 转换质量验证通过")
    else:
        print(f"✗ 转换质量验证失败，{errors}个样本有误差")

def test_numpy_vs_python():
    """测试numpy和纯Python实现的性能差异"""
    print("\n=== NumPy vs Python 性能对比 ===")
    
    converter = AudioConverter()
    
    # 生成较大的测试数据
    sample_rate = 24000
    duration = 2.0  # 2秒
    num_samples = int(sample_rate * duration)
    
    # 生成测试数据
    import random
    test_data = bytearray()
    for _ in range(num_samples):
        sample = random.uniform(-1.0, 1.0)
        test_data.extend(struct.pack('f', sample))
    
    test_data = bytes(test_data)
    
    # 测试NumPy版本
    if converter._numpy_available:
        print("测试NumPy版本...")
        start_time = time.time()
        for _ in range(10):  # 重复10次
            converter.float32_to_int16_fast(test_data)
        numpy_time = time.time() - start_time
        print(f"NumPy版本: {numpy_time:.4f} 秒 (10次)")
    else:
        print("NumPy不可用")
        numpy_time = None
    
    # 测试纯Python版本
    print("测试纯Python版本...")
    start_time = time.time()
    for _ in range(10):  # 重复10次
        AudioConverter.float32_to_int16(test_data)
    python_time = time.time() - start_time
    print(f"纯Python版本: {python_time:.4f} 秒 (10次)")
    
    if numpy_time:
        speedup = python_time / numpy_time
        print(f"NumPy加速比: {speedup:.2f}x")

if __name__ == "__main__":
    test_conversion_performance()
    test_numpy_vs_python()