Edit motion interpolator for 1d case

src/spikeinterface/sortingcomponents/motion/motion_interpolation.py

@@ -120,20 +120,22 @@ def interpolate_motion_on_traces(
     time_bins = interpolation_time_bin_centers_s
     if time_bins is None:
         time_bins = motion.temporal_bins_s[segment_index]
-    bin_s = (
-        time_bins[1] - time_bins[0] if time_bins.size > 1 else time_bins * 2
-    )  # TODO: check this is * 2 but yes must be because its in the middle NO ITS NOT if first time is not 0
-    # must use a different stragery
-    bins_start = time_bins[0] - 0.5 * bin_s
-    # nearest bin center for each frame?
-    bin_inds = (times - bins_start) // bin_s
-    bin_inds = bin_inds.astype(int)
-    # the time bins may not cover the whole set of times in the recording,
-    # so we need to clip these indices to the valid range
-    np.clip(bin_inds, 0, time_bins.size, out=bin_inds)
-
-    # -- what are the possibilities here anyway?
-    bins_here = np.arange(bin_inds[0], bin_inds[-1] + 1)  # TODO: just replace this with 0
+
+    if time_bins.size == 1:
+        bins_here = [0]
+    else:
+        bin_s = time_bins[1] - time_bins[0]
+        # must use a different stragery
+        bins_start = time_bins[0] - 0.5 * bin_s
+        # nearest bin center for each frame?
+        bin_inds = (times - bins_start) // bin_s
+        bin_inds = bin_inds.astype(int)
+        # the time bins may not cover the whole set of times in the recording,
+        # so we need to clip these indices to the valid range
+        np.clip(bin_inds, 0, time_bins.size, out=bin_inds)
+
+        # -- what are the possibilities here anyway?
+        bins_here = np.arange(bin_inds[0], bin_inds[-1] + 1)
 
     # inperpolation kernel will be the same per temporal bin
     interp_times = np.empty(total_num_chans)
@@ -168,16 +170,19 @@ def interpolate_motion_on_traces(
         # plt.show()
 
         # quickly find the end of this bin, which is also the start of the next
-        next_start_index = current_start_index + np.searchsorted(
-            bin_inds[current_start_index:], bin_ind + 1, side="left"
-        )
-        in_bin = slice(current_start_index, next_start_index)
+        if time_bins.size == 1:
+            in_bin = None
+        else:
+            next_start_index = current_start_index + np.searchsorted(
+                bin_inds[current_start_index:], bin_ind + 1, side="left"
+            )
+            in_bin = slice(current_start_index, next_start_index)
+            current_start_index = next_start_index
 
         # here we use a simple np.matmul even if dirft_kernel can be super sparse.
         # because the speed for a sparse matmul is not so good when we disable multi threaad (due multi processing
         # in ChunkRecordingExecutor)
         np.matmul(traces[in_bin], drift_kernel, out=traces_corrected[in_bin])
-        current_start_index = next_start_index
 
     return traces_corrected