unwhitened template amplitudes, saved normalized templates

Author: jacobpennington

kilosort/io.py

@@ -156,14 +156,18 @@ def save_to_phy(st, clu, tF, Wall, probe, ops, imin, results_dir=None,
     np.save((results_dir / 'channel_positions.npy'), channel_positions)
 
     # whitening matrix ** saving real whitening matrix doesn't work with phy currently
-    whitening_mat = ops['Wrot'].cpu().numpy()
-    np.save((results_dir / 'whitening_mat_dat.npy'), whitening_mat)
+    whitening_mat = ops['Wrot']
+    np.save((results_dir / 'whitening_mat_dat.npy'), whitening_mat.cpu())
     # NOTE: commented out for reference, this was different in KS 2.5 because
     #       the binary file was already whitened.
     # whitening_mat = 0.005 * np.eye(len(chan_map), dtype='float32')
-    whitening_mat_inv = np.linalg.inv(whitening_mat + 1e-5 * np.eye(whitening_mat.shape[0]))
-    np.save((results_dir / 'whitening_mat.npy'), whitening_mat)
-    np.save((results_dir / 'whitening_mat_inv.npy'), whitening_mat_inv)
+    whitening_mat_inv = torch.inverse(
+        whitening_mat
+        + 1e-5 * torch.eye(whitening_mat.shape[0]).to(whitening_mat.device)
+        )
+    #whitening_mat_inv = np.linalg.inv(whitening_mat + 1e-5 * np.eye(whitening_mat.shape[0]))
+    np.save((results_dir / 'whitening_mat.npy'), whitening_mat.cpu())
+    np.save((results_dir / 'whitening_mat_inv.npy'), whitening_mat_inv.cpu())
 
     # spike properties
     spike_times = st[:,0].astype('int64') + imin  # shift by minimum sample index
@@ -191,13 +195,19 @@ def save_to_phy(st, clu, tF, Wall, probe, ops, imin, results_dir=None,
 
     # template properties
     similar_templates = CCG.similarity(Wall, ops['wPCA'].contiguous(), nt=ops['nt'])
-    template_amplitudes = ((Wall**2).sum(axis=(-2,-1))**0.5).cpu().numpy()
+    temp_amplitudes = ((Wall**2).sum(axis=(-2,-1))**0.5).cpu().numpy()
     templates = (Wall.unsqueeze(-1).cpu() * ops['wPCA'].cpu()).sum(axis=-2).numpy()
     templates = templates.transpose(0,2,1)
+    # normalize templates by amplitude
+    templates = templates / temp_amplitudes[:, np.newaxis, np.newaxis]
     templates_ind = np.tile(np.arange(Wall.shape[1])[np.newaxis, :], (templates.shape[0],1))
     np.save((results_dir / 'similar_templates.npy'), similar_templates)
     np.save((results_dir / 'templates.npy'), templates)
     np.save((results_dir / 'templates_ind.npy'), templates_ind)
+    # get unwhitened template amplitudes to use as cluster_Amplitudes
+    iwrot = whitening_mat_inv.to(Wall.device)
+    unwhitened = torch.einsum('jk, ikl -> ijl', iwrot, Wall)
+    template_amplitudes = ((unwhitened**2).sum(axis=(-2,-1))**0.5).cpu().numpy()
 
     # contamination ratio
     acg_threshold = ops['settings']['acg_threshold']