Add batch processing methods for CSI data in CSIProcessor and PhaseSanitizer

src/core/csi_processor.py

@@ -44,4 +44,36 @@ class CSIProcessor:
         if processed.std() > 0:
             processed = (processed - processed.mean()) / processed.std()
         
-        return processed
+        return processed
+    
+    def process_csi_batch(self, csi_data: np.ndarray) -> torch.Tensor:
+        """Process a batch of CSI data for neural network input.
+        
+        Args:
+            csi_data: Complex CSI data array of shape (batch, antennas, subcarriers, time)
+            
+        Returns:
+            Processed CSI tensor ready for neural network input
+        """
+        if csi_data.ndim != 4:
+            raise ValueError(f"Expected 4D input (batch, antennas, subcarriers, time), got {csi_data.ndim}D")
+        
+        batch_size, num_antennas, num_subcarriers, time_samples = csi_data.shape
+        
+        # Extract amplitude and phase
+        amplitude = np.abs(csi_data)
+        phase = np.angle(csi_data)
+        
+        # Process each component
+        processed_amplitude = self.process_raw_csi(amplitude)
+        processed_phase = self.process_raw_csi(phase)
+        
+        # Stack amplitude and phase as separate channels
+        processed_data = np.stack([processed_amplitude, processed_phase], axis=1)
+        
+        # Reshape to (batch, channels, antennas, subcarriers, time)
+        # Then flatten spatial dimensions for CNN input
+        processed_data = processed_data.reshape(batch_size, 2 * num_antennas, num_subcarriers, time_samples)
+        
+        # Convert to tensor
+        return torch.from_numpy(processed_data).float()