import numpy as np
import torch
import torch.nn as nn

class PhysicsInformedWiFiPose(nn.Module):
    """
    物理信息WiFi感知3D人体姿态估计
    
    论文: arXiv 2608.23995
    核心: 多径感知注意力 + 解耦表示学习
    
    输入: WiFi CSI (Channel State Information)
    输出: 3D人体关节坐标
    
    优势: 跨环境泛化, 无需摄像头, 隐私保护
    """
    
    def __init__(self, 
                 num_joints=17,
                 csi_shape=(3, 30, 114),  # (antennas, subcarriers, time)
                 hidden_dim=256):
        super().__init__()
        
        # 物理传播建模模块
        self.phys_module = MultipathModeling(
            num_antennas=csi_shape[0],
            num_subcarriers=csi_shape[1]
        )
        
        # 多径感知注意力
        self.multipath_attention = MultipathAttention(
            input_dim=csi_shape[0] * csi_shape[1],
            hidden_dim=hidden_dim
        )
        
        # 解耦表示学习
        self.pose_encoder = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim)
        )
        
        self.env_encoder = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim // 2),
            nn.ReLU(),
            nn.Linear(hidden_dim // 2, hidden_dim // 4)  # 环境编码更紧凑
        )
        
        # 姿态解码器
        self.pose_decoder = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, num_joints * 3)  # 3D坐标
        )
        
        # 环境判别器（对抗训练）
        self.env_discriminator = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim // 2),
            nn.ReLU(),
            nn.Linear(hidden_dim // 2, 1),
            nn.Sigmoid()
        )
    
    def forward(self, csi: torch.Tensor) -> dict:
        """
        前向传播
        
        Args:
            csi: (B, 3, 30, T) CSI信号
            
        Returns:
            dict: {
                'pose_3d': (B, 17, 3) 3D关节坐标,
                'pose_feat': (B, hidden_dim) 姿态特征,
                'env_feat': (B, hidden_dim//4) 环境特征
            }
        """
        B = csi.shape[0]
        
        # 1. 物理传播建模
        phys_features = self.phys_module(csi)
        
        # 2. 多径感知注意力
        attended = self.multipath_attention(phys_features)
        
        # 3. 解耦: 姿态 vs 环境
        pose_feat = self.pose_encoder(attended)
        env_feat = self.env_encoder(attended)
        
        # 4. 解码3D姿态
        pose_3d = self.pose_decoder(pose_feat).view(B, -1, 3)
        
        return {
            'pose_3d': pose_3d,
            'pose_feat': pose_feat,
            'env_feat': env_feat
        }


class MultipathModeling(nn.Module):
    """多径传播物理建模"""
    
    def __init__(self, num_antennas, num_subcarriers):
        super().__init__()
        self.num_antennas = num_antennas
        self.num_subcarriers = num_subcarriers
        
        # 可学习的多径参数
        self.num_paths = 5  # 假设5条主要多径
        self.path_gains = nn.Parameter(torch.randn(self.num_paths))
        self.path_delays = nn.Parameter(torch.randn(self.num_paths))
        self.path_angles = nn.Parameter(torch.randn(self.num_paths, num_antennas))
        
        # CSI到特征的CNN
        self.cnn = nn.Sequential(
            nn.Conv2d(num_antennas, 32, 3, padding=1),
            nn.ReLU(),
            nn.Conv2d(32, 64, 3, padding=1),
            nn.ReLU(),
            nn.AdaptiveAvgPool2d((1, None))  # 时间维度保留
        )
        
    def forward(self, csi: torch.Tensor) -> torch.Tensor:
        """
        物理建模的前向传播
        
        CSI模型: H(f,t) = Σ_k α_k * exp(-j2πf*τ_k) * a(θ_k)
        其中: α_k=路径增益, τ_k=延迟, θ_k=到达角
        """
        B = csi.shape[0]
        
        # 物理模型: 多径叠加
        # 在实际实现中会显式计算多径模型
        # 这里用CNN近似提取物理相关特征
        features = self.cnn(csi)  # (B, 64, 1, T)
        T = features.shape[-1]
        
        # 展平
        return features.view(B, -1, T).permute(0, 2, 1)  # (B, T, 64)


class MultipathAttention(nn.Module):
    """多径感知注意力"""
    
    def __init__(self, input_dim, hidden_dim, num_heads=4):
        super().__init__()
        self.input_proj = nn.Linear(input_dim, hidden_dim)
        self.attention = nn.MultiheadAttention(
            embed_dim=hidden_dim,
            num_heads=num_heads,
            batch_first=True
        )
        self.norm = nn.LayerNorm(hidden_dim)
        
    def forward(self, x):
        x = self.input_proj(x)
        attended, _ = self.attention(x, x, x)
        # 残差连接
        x = self.norm(x + attended)
        # 全局平均池化
        return x.mean(dim=1)  # (B, hidden_dim)


# 测试
if __name__ == "__main__":
    model = PhysicsInformedWiFiPose()
    
    # 模拟WiFi CSI
    csi = torch.randn(4, 3, 30, 114)  # (B, antennas, subcarriers, time)
    
    output = model(csi)
    
    print("=== Physics-Informed WiFi Pose 测试 ===")
    print(f"输入CSI: {csi.shape}")
    print(f"3D姿态: {output['pose_3d'].shape} (B, 17关节, 3D坐标)")
    print(f"姿态特征: {output['pose_feat'].shape}")
    print(f"环境特征: {output['env_feat'].shape}")
    print()
    print("论文核心价值:")
    print("1. 物理建模WiFi多径传播 → 跨环境泛化")
    print("2. 解耦姿态和环境特征 → 新环境无需重训练")
    print("3. 无需摄像头 → 隐私保护")

class CabinWiFiSensing:
    """
    座舱WiFi感知方案
    
    利用现有车载WiFi/蓝牙硬件
    通过CSI信号变化检测乘员状态
    
    无需额外传感器，软件升级即可
    """
    
    def __init__(self):
        # 使用车载WiFi的CSI
        self.num_antennas = 4  # 车载MIMO
        self.num_subcarriers = 56  # 802.11n/ac
        self.sample_rate = 100  # Hz
        
    def detect_occupancy(self, csi_buffer):
        """检测座椅占用"""
        # CSI方差 > 阈值 → 有人
        variance = np.var(csi_buffer, axis=-1)
        occupied = np.any(variance > 0.5, axis=0)
        return occupied
    
    def detect_breathing(self, csi_buffer):
        """检测呼吸率（CPD核心）"""
        # 呼吸频率0.1-0.5Hz
        from scipy import signal as sig
        fft = np.fft.fft(np.mean(csi_buffer, axis=(0,1)))
        freqs = np.fft.fftfreq(len(fft), 1/self.sample_rate)
        
        # 0.1-0.5Hz范围峰值
        mask = (freqs >= 0.1) & (freqs <= 0.5)
        breathing_rate = freqs[mask][np.argmax(np.abs(fft[mask]))] * 60
        
        return breathing_rate  # breaths/min