Implement CSI processing and phase sanitization modules; add unit tests for DensePose and modality translation networks

src/core/csi_processor.py

@@ -0,0 +1,46 @@
+"""CSI (Channel State Information) processor for WiFi-DensePose system."""
+
+import numpy as np
+from typing import Dict, Any, Optional
+
+
+class CSIProcessor:
+    """Processes raw CSI data for neural network input."""
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialize CSI processor with configuration.
+        
+        Args:
+            config: Configuration dictionary with processing parameters
+        """
+        self.config = config or {}
+        self.sample_rate = self.config.get('sample_rate', 1000)
+        self.num_subcarriers = self.config.get('num_subcarriers', 56)
+        self.num_antennas = self.config.get('num_antennas', 3)
+    
+    def process_raw_csi(self, raw_data: np.ndarray) -> np.ndarray:
+        """Process raw CSI data into normalized format.
+        
+        Args:
+            raw_data: Raw CSI data array
+            
+        Returns:
+            Processed CSI data ready for neural network input
+        """
+        if raw_data.size == 0:
+            raise ValueError("Raw CSI data cannot be empty")
+        
+        # Basic processing: normalize and reshape
+        processed = raw_data.astype(np.float32)
+        
+        # Handle NaN values by replacing with mean of non-NaN values
+        if np.isnan(processed).any():
+            nan_mask = np.isnan(processed)
+            non_nan_mean = np.nanmean(processed)
+            processed[nan_mask] = non_nan_mean
+        
+        # Simple normalization
+        if processed.std() > 0:
+            processed = (processed - processed.mean()) / processed.std()
+        
+        return processed