import numpy as np
from scipy import signal
from typing import Tuple

class FR3VitalSignsSimulator:
    """
    FR3频段生命体征感知仿真器
    
    论文核心方法实现：
    1. 建模胸腔运动（呼吸+心跳）
    2. 模拟FR3频段雷达回波
    3. 提取生命体征信号
    
    Args:
        freq: 载波频率 (Hz), 默认14GHz (FR3中间)
        tx_power: 发射功率 (dBm)
    """
    
    def __init__(self, freq=14e9, tx_power=10):
        self.freq = freq
        self.wavelength = 3e8 / freq  # ~2.14cm @14GHz
        self.tx_power = tx_power
        
        # 生理信号参数
        self.breathing_rate = 0.25  # 15 breaths/min = 0.25Hz
        self.heart_rate = 1.2       # 72 beats/min = 1.2Hz
        self.breathing_amplitude = 6e-3  # 6mm 胸腔起伏
        self.heart_amplitude = 0.3e-3    # 0.3mm 心脏微动
        
    def model_chest_displacement(self, duration=10, fs=1000):
        """
        建模胸腔位移（呼吸+心跳叠加）
        
        物理模型：
        - 呼吸：胸腔大幅度缓慢起伏
        - 心跳：胸壁微小快速振动
        
        Returns:
            displacement: 胸部位移 (m), shape=(N,)
        """
        t = np.arange(0, duration, 1/fs)
        
        # 呼吸信号（正弦+谐波）
        breathing = self.breathing_amplitude * (
            np.sin(2 * np.pi * self.breathing_rate * t) +
            0.3 * np.sin(2 * np.pi * 2 * self.breathing_rate * t)
        )
        
        # 心跳信号（更复杂波形）
        heartbeat = self.heart_amplitude * (
            np.sin(2 * np.pi * self.heart_rate * t) +
            0.5 * np.sin(2 * np.pi * 2 * self.heart_rate * t) +
            0.2 * np.sin(2 * np.pi * 3 * self.heart_rate * t)
        )
        
        # 叠加 + 少量随机运动
        noise = np.random.normal(0, 0.1e-4, len(t))
        displacement = breathing + heartbeat + noise
        
        return t, displacement
    
    def simulate_radar_echo(self, displacement, target_distance=1.0, 
                            snr_db=20):
        """
        模拟FR3频段雷达回波
        
        物理原理：
        - 胸腔位移导致相位变化: Δφ = 4π·Δd/λ
        - FR3波长~2cm, 0.3mm心跳 → Δφ ≈ 0.1 rad (可检测)
        - sub-6GHz波长~10cm, 0.3mm → Δφ ≈ 0.02 rad (难检测)
        
        Args:
            displacement: 胸部位移 (m)
            target_distance: 目标距离
            snr_db: 信噪比
        """
        # 相位调制
        phase_mod = 4 * np.pi * displacement / self.wavelength
        
        # 载波信号
        t = np.arange(len(displacement)) / 1000  # 1kHz采样
        carrier = np.exp(1j * (2 * np.pi * self.freq * t + phase_mod))
        
        # 添加多径（简化）
        multipath_delays = [0, 3e-9, 8e-9]  # 直射+2条多径
        multipath_gains = [1.0, 0.5, 0.3]
        
        received = np.zeros_like(carrier, dtype=complex)
        for delay, gain in zip(multipath_delays, multipath_gains):
            delayed = np.roll(carrier, int(delay * 1000))
            received += gain * delayed
        
        # 添加噪声
        signal_power = np.mean(np.abs(received)**2)
        noise_power = signal_power / (10 ** (snr_db / 10))
        noise = np.random.normal(0, np.sqrt(noise_power/2), len(received)) + \
                1j * np.random.normal(0, np.sqrt(noise_power/2), len(received))
        
        return received + noise
    
    def extract_vital_signs(self, received_signal, fs=1000):
        """
        从雷达回波提取生命体征
        
        信号处理管道：
        1. 相位解调
        2. 带通滤波分离呼吸/心跳
        3. 频谱分析
        """
        # 1. 相位解调
        phase = np.angle(received_signal)
        phase_unwrapped = np.unwrap(phase)
        
        # 2. 去趋势
        phase_detrended = signal.detrend(phase_unwrapped)
        
        # 3. 带通滤波
        # 呼吸: 0.1-0.5 Hz (6-30 breaths/min)
        breathing_filter = signal.butter(4, [0.1, 0.5], btype='band', fs=fs)
        breathing_signal = signal.filtfilt(*breathing_filter, phase_detrended)
        
        # 心跳: 0.8-2.0 Hz (48-120 bpm)
        heart_filter = signal.butter(4, [0.8, 2.0], btype='band', fs=fs)
        heart_signal = signal.filtfilt(*heart_filter, phase_detrended)
        
        # 4. 频谱分析
        freqs_b, psd_b = signal.welch(breathing_signal, fs=fs, nperseg=1024)
        freqs_h, psd_h = signal.welch(heart_signal, fs=fs, nperseg=1024)
        
        breathing_freq = freqs_b[np.argmax(psd_b)]
        heart_freq = freqs_h[np.argmax(psd_h)]
        
        return {
            'breathing_signal': breathing_signal,
            'heart_signal': heart_signal,
            'breathing_rate_hz': breathing_freq,
            'heart_rate_hz': heart_freq,
            'breathing_bpm': breathing_freq * 60,
            'heart_bpm': heart_freq * 60
        }


# 频段对比分析
def compare_frequency_bands():
    """对比FR1/FR3/FR2频段性能"""
    
    bands = {
        'FR1 (5GHz)': {'freq': 5e9, 'wavelength': 0.06},
        'FR3 (14GHz)': {'freq': 14e9, 'wavelength': 0.0214},
        'FR3 (24GHz)': {'freq': 24e9, 'wavelength': 0.0125},
        'FR2 (60GHz)': {'freq': 60e9, 'wavelength': 0.005},
    }
    
    print("=" * 75)
    print("频段对比：心跳微动(0.3mm)的相位变化")
    print("=" * 75)
    print(f"{'频段':<15} {'波长(mm)':<12} {'相位变化(rad)':<15} {'可检测性':<10}")
    print("-" * 75)
    
    heart_displacement = 0.3e-3  # 0.3mm
    
    for name, params in bands.items():
        phase_change = 4 * np.pi * heart_displacement / params['wavelength']
        detectable = "✅" if phase_change > 0.05 else "⚠️"
        print(f"{name:<15} {params['wavelength']*1e3:<12.1f} {phase_change:<15.4f} {detectable:<10}")
    
    print("\n关键结论：FR3频段（7-24GHz）在心跳检测方面具有最优性价比")
    print("  - 相位灵敏度远高于FR1")
    print("  - 多径丰富度优于FR2（mmWave）")
    print("  - 穿透力优于FR2")


if __name__ == "__main__":
    # 运行仿真
    simulator = FR3VitalSignsSimulator(freq=14e9)
    
    # 1. 建模胸部位移
    t, displacement = simulator.model_chest_displacement(duration=10)
    print(f"胸部位移: 范围 [{displacement.min()*1e3:.2f}mm, {displacement.max()*1e3:.2f}mm]")
    
    # 2. 模拟雷达回波
    echo = simulator.simulate_radar_echo(displacement, snr_db=20)
    
    # 3. 提取生命体征
    results = simulator.extract_vital_signs(echo)
    print(f"\n提取结果:")
    print(f"  呼吸频率: {results['breathing_bpm']:.1f} bpm (真值: 15.0)")
    print(f"  心率: {results['heart_bpm']:.1f} bpm (真值: 72.0)")
    
    # 4. 频段对比
    print()
    compare_frequency_bands()