where_fi/cli/__init__.py

@@ -50,8 +50,8 @@ def heatmap() -> None:
     def callback(antenna_data: npt.NDArray[np.complex64]) -> None:
         logger.info(f"Got final CSI data with shape {antenna_data.shape}")
         processed = preprocessor.preprocess(antenna_data)
+        logger.info(f"Processed CSI data with shape {processed.shape}")
         processed_tensor = torch.tensor(processed, device=device)
-        # visualise.add_data(all_data, processed)
         aoa.update(processed_tensor)
 
         if not webapp_queue.full():

where_fi/processing/aoa.py

@@ -52,6 +52,7 @@ class AoA:
     def update(self, data: torch.Tensor) -> None:
         self.timestamp = datetime.now()
         H_sm = self.smooth(data)
+        logger.debug(f"Calculated smoothed CSI matrix: {H_sm.shape}")
 
         auto_corr = H_sm @ torch.conj(H_sm).T
         # This matrix is by definition Hermitian.
@@ -68,6 +69,8 @@ class AoA:
         if self.historical_autocorr.shape[0] > WINDOW_SIZE:
             self.historical_autocorr = self.historical_autocorr[-WINDOW_SIZE:]
 
+        logger.debug("Finished updating autocorrelation matrix")
+
     def steering_vector(self, theta: torch.Tensor, tof: torch.Tensor) -> torch.Tensor:
         assert theta.shape == tof.shape
         assert len(theta.shape) == 1
@@ -83,7 +86,6 @@ class AoA:
             / 299_792_458
         )
         assert omega_t.shape == phi_theta.shape == (N,)
-        print(omega_t, phi_theta)
 
         omega_t = torch.unsqueeze(omega_t, dim=-1)
         phi_theta = torch.unsqueeze(phi_theta, dim=-1)
@@ -114,20 +116,22 @@ class AoA:
 
         # The smallest eigenvectors span the noise subspace,
         # and the largest span the signal subspace.
+        logger.debug(f"Calculating eigenvectors of R: {R.shape}")
         eigvals, eigvecs = torch.linalg.eig(R)
         assert isinstance(eigvals, torch.Tensor)
         assert isinstance(eigvecs, torch.Tensor)
         logger.info(f"Eigenvalues: {eigvals}")
         E_n = eigvecs[:, torch.abs(eigvals) < config.music.eigval_threshold]
 
-        logger.info(f"Signal subspace: {E_n.shape}")
+        logger.debug(f"Signal subspace: {E_n.shape}")
         steering = torch.unsqueeze(self.steering_vector(theta, tof), dim=-1)
         steering_h = torch.conj(steering).permute(0, 2, 1)
 
         E_n = E_n.unsqueeze(0)
         E_n_H = torch.conj(E_n).permute(0, 2, 1)
-        logger.info(
-            f"Heatmap multiplication: {steering_h.shape}, {E_n.shape}, {E_n_H.shape}, {steering.shape}"
+        logger.debug(
+            f"Heatmap multiplication: {steering_h.shape}, {E_n.shape}, "
+            f"{E_n_H.shape}, {steering.shape}"
         )
         c: torch.Tensor = 1 / (0.001 + (steering_h @ E_n @ E_n_H @ steering))
         return torch.abs(c.real)
@@ -143,17 +147,18 @@ class AoA:
             dtype=np.float32,
         )
         thetas_mesh, tofs_mesh = np.meshgrid(thetas, tofs)
-        heatmap: npt.NDArray[np.float32] = (
-            self.evaluate(
-                torch.tensor(thetas_mesh.reshape(-1)),
-                torch.tensor(tofs_mesh.reshape(-1)),
-            )
-            .reshape(
-                config.music.heatmap.theta_resolution,
-                config.music.heatmap.tof_resolution,
-            )
-            .numpy(force=True)
+        logger.debug(
+            f"Calculating heatmap with {thetas_mesh.shape} and {tofs_mesh.shape}"
         )
+        evaluated = self.evaluate(
+            torch.tensor(thetas_mesh.reshape(-1)),
+            torch.tensor(tofs_mesh.reshape(-1)),
+        )
+        logger.debug(f"Evaluated heatmap: {evaluated.shape}")
+        heatmap: npt.NDArray[np.float32] = evaluated.reshape(
+            config.music.heatmap.theta_resolution,
+            config.music.heatmap.tof_resolution,
+        ).numpy(force=True)
         return heatmap