Tidy up tests and (slightly) improve num_iter handling.

src/spikeinterface/preprocessing/inter_session_alignment/alignment_utils.py

@@ -268,8 +268,10 @@ def compute_histogram_crosscorrelation(
     center_bin = np.floor((num_bins * 2 - 1) / 2).astype(int)
 
     # Create the (num windows, num_bins) matrix for this pair of sessions
-    num_iter = num_bins * 2 - 1 if not num_shifts else num_shifts * 2
-    shifts_array = np.arange(-(num_iter // 2), num_iter // 2 + 1)
+    if num_shifts is None:
+        num_shifts = num_bins - 1
+    shifts_array = np.arange(-(num_shifts), num_shifts + 1)
+    num_iter = shifts_array.size
 
     for i in range(num_sessions):
         for j in range(i, num_sessions):
@@ -291,7 +293,7 @@ def compute_histogram_crosscorrelation(
                     window_i = windowed_histogram_i - np.mean(windowed_histogram_i, axis=1)[:, np.newaxis]
                     window_j = windowed_histogram_j - np.mean(windowed_histogram_j, axis=1)[:, np.newaxis]
 
-                    xcorr = np.zeros(num_iter + 1)
+                    xcorr = np.zeros(num_iter)
 
                     for idx, shift in enumerate(shifts_array):
                         shifted_i = shift_array_fill_zeros(window_i, shift)
@@ -309,7 +311,7 @@ def compute_histogram_crosscorrelation(
                         mode="full",
                     )
                     if num_shifts:
-                        window_indices = np.arange(center_bin - num_shifts, center_bin + num_shifts)
+                        window_indices = np.arange(center_bin - num_shifts, center_bin + num_shifts + 1)
                         xcorr = xcorr[window_indices]
 
                 xcorr_matrix[win_idx, :] = xcorr
@@ -322,7 +324,6 @@ def compute_histogram_crosscorrelation(
             if num_windows > 1 and smoothing_sigma_window:
                 xcorr_matrix = gaussian_filter(xcorr_matrix, smoothing_sigma_window, axes=0)
 
-            shifts_array = np.arange(-(num_iter // 2), num_iter // 2 + 1)  # TODO: double check
             # Upsample the cross-correlation
             if interpolate:
 
@@ -337,44 +338,16 @@ def compute_histogram_crosscorrelation(
                     kriging_d,
                 )
 
-                #    breakpoint()
-
-                xcorr_matrix_old = np.matmul(xcorr_matrix, K, axes=[(-2, -1), (-2, -1), (-2, -1)])
-                xcorr_matrix_ = np.zeros(
-                    (xcorr_matrix.shape[0], shifts_upsampled.size)
-                )  # TODO: check in nonlinear case
-                for i_ in range(xcorr_matrix.shape[0]):
-                    xcorr_matrix_[i_, :] = np.matmul(xcorr_matrix[i_, :], K)
-
-                # breakpoint()
-
-                plt.plot(shifts_array, xcorr_matrix.T)
-                plt.show
-                plt.plot(shifts_upsampled, xcorr_matrix_.T)
-                plt.show()
-
-                xcorr_matrix = xcorr_matrix_
-
-                #    plt.plot(xcorr_matrix.T)
-                #   plt.plot(xcorr_matrix_old.T)
-                #  plt.show()
-                #
+                xcorr_matrix = np.matmul(xcorr_matrix, K, axes=[(-2, -1), (-2, -1), (-2, -1)])
 
                 xcorr_peak = np.argmax(xcorr_matrix, axis=1)
                 shift = shifts_upsampled[xcorr_peak]
-
-            #   breakpoint()
-
             else:
                 xcorr_peak = np.argmax(xcorr_matrix, axis=1)
                 shift = shifts_array[xcorr_peak]
 
-            # x=i;y=j
-            # breakpoint()
             shift_matrix[i, j, :] = shift
 
-    breakpoint()
-
     # As xcorr shifts are symmetric, the shift matrix is skew symmetric, so fill
     # the (empty) lower triangular with the negative (already computed) upper triangular to save computation
     for k in range(shift_matrix.shape[2]):