Merge branch 'SpikeInterface:main' into sc2_recording_slices

src/spikeinterface/core/baserecording.py

@@ -1,4 +1,5 @@
 from __future__ import annotations
+
 import warnings
 from pathlib import Path
 
@@ -7,14 +8,9 @@
 
 from .base import BaseSegment
 from .baserecordingsnippets import BaseRecordingSnippets
-from .core_tools import (
-    convert_bytes_to_str,
-    convert_seconds_to_str,
-)
-from .recording_tools import write_binary_recording
-
-
+from .core_tools import convert_bytes_to_str, convert_seconds_to_str
 from .job_tools import split_job_kwargs
+from .recording_tools import write_binary_recording
 
 
 class BaseRecording(BaseRecordingSnippets):
@@ -950,11 +946,11 @@ def time_to_sample_index(self, time_s):
                 sample_index = time_s * self.sampling_frequency
             else:
                 sample_index = (time_s - self.t_start) * self.sampling_frequency
-            sample_index = round(sample_index)
+            sample_index = np.round(sample_index).astype(int)
         else:
             sample_index = np.searchsorted(self.time_vector, time_s, side="right") - 1
 
-        return int(sample_index)
+        return sample_index
 
     def get_num_samples(self) -> int:
         """Returns the number of samples in this signal segment

src/spikeinterface/sortingcomponents/motion/motion_interpolation.py

@@ -6,6 +6,8 @@
 from spikeinterface.preprocessing.basepreprocessor import BasePreprocessor, BasePreprocessorSegment
 from spikeinterface.preprocessing.filter import fix_dtype
 
+from .motion_utils import ensure_time_bin_edges, ensure_time_bins
+
 
 def correct_motion_on_peaks(peaks, peak_locations, motion, recording) -> np.ndarray:
     """
@@ -54,14 +56,19 @@ def interpolate_motion_on_traces(
     segment_index=None,
     channel_inds=None,
     interpolation_time_bin_centers_s=None,
+    interpolation_time_bin_edges_s=None,
     spatial_interpolation_method="kriging",
     spatial_interpolation_kwargs={},
     dtype=None,
 ):
     """
     Apply inverse motion with spatial interpolation on traces.
 
-    Traces can be full traces, but also waveforms snippets.
+    Traces can be full traces, but also waveforms snippets. Times used for looking up
+    displacements are controlled by interpolation_time_bin_edges_s or
+    interpolation_time_bin_centers_s, or fall back to the Motion object's time bins
+    by default; times in the recording outside these time bins use the closest edge
+    bin's displacement value during interpolation.
 
     Parameters
     ----------
@@ -80,6 +87,9 @@ def interpolate_motion_on_traces(
     interpolation_time_bin_centers_s : None or np.array
         Manually specify the time bins which the interpolation happens
         in for this segment. If None, these are the motion estimate's time bins.
+    interpolation_time_bin_edges_s : None or np.array
+        If present, interpolation chunks will be the time bins defined by these edges
+        rather than interpolation_time_bin_centers_s or the motion's bins.
     spatial_interpolation_method : "idw" | "kriging", default: "kriging"
         The spatial interpolation method used to interpolate the channel locations:
             * idw : Inverse Distance Weighing
@@ -119,26 +129,33 @@ def interpolate_motion_on_traces(
     total_num_chans = channel_locations.shape[0]
 
     # -- determine the blocks of frames that will land in the same interpolation time bin
-    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]
-    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)
+    if interpolation_time_bin_centers_s is None and interpolation_time_bin_edges_s is None:
+        interpolation_time_bin_centers_s = motion.temporal_bins_s[segment_index]
+        interpolation_time_bin_edges_s = motion.temporal_bin_edges_s[segment_index]
+    else:
+        interpolation_time_bin_centers_s, interpolation_time_bin_edges_s = ensure_time_bins(
+            interpolation_time_bin_centers_s, interpolation_time_bin_edges_s
+        )
+
+    # bin the frame times according to the interpolation time bins.
+    # searchsorted(b, t, side="right") == i means that b[i-1] <= t < b[i]
+    # hence the -1. doing it with "left" is not as nice -- we want t==b[0]
+    # to lead to i=1 (rounding down).
+    interpolation_bin_inds = np.searchsorted(interpolation_time_bin_edges_s, times, side="right") - 1
+
     # 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)
+    n_bins = interpolation_time_bin_edges_s.shape[0] - 1
+    np.clip(interpolation_bin_inds, 0, n_bins - 1, out=interpolation_bin_inds)
 
     # -- what are the possibilities here anyway?
-    bins_here = np.arange(bin_inds[0], bin_inds[-1] + 1)
+    interpolation_bins_here = np.arange(interpolation_bin_inds[0], interpolation_bin_inds[-1] + 1)
 
     # inperpolation kernel will be the same per temporal bin
     interp_times = np.empty(total_num_chans)
     current_start_index = 0
-    for bin_ind in bins_here:
-        bin_time = time_bins[bin_ind]
+    for interp_bin_ind in interpolation_bins_here:
+        bin_time = interpolation_time_bin_centers_s[interp_bin_ind]
         interp_times.fill(bin_time)
         channel_motions = motion.get_displacement_at_time_and_depth(
             interp_times,
@@ -166,16 +183,17 @@ def interpolate_motion_on_traces(
         # ax.set_title(f"bin_ind {bin_ind} - {bin_time}s - {spatial_interpolation_method}")
         # plt.show()
 
+        # quick search logic to find frames corresponding to this interpolation bin in the recording
         # 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"
+            interpolation_bin_inds[current_start_index:], interp_bin_ind + 1, side="left"
         )
-        in_bin = slice(current_start_index, next_start_index)
+        frames_in_bin = slice(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])
+        np.matmul(traces[frames_in_bin], drift_kernel, out=traces_corrected[frames_in_bin])
         current_start_index = next_start_index
 
     return traces_corrected
@@ -297,6 +315,7 @@ def __init__(
         p=1,
         num_closest=3,
         interpolation_time_bin_centers_s=None,
+        interpolation_time_bin_edges_s=None,
         interpolation_time_bin_size_s=None,
         dtype=None,
         **spatial_interpolation_kwargs,
@@ -363,9 +382,14 @@ def __init__(
 
         # handle manual interpolation_time_bin_centers_s
         # the case where interpolation_time_bin_size_s is set is handled per-segment below
-        if interpolation_time_bin_centers_s is None:
+        if interpolation_time_bin_centers_s is None and interpolation_time_bin_edges_s is None:
             if interpolation_time_bin_size_s is None:
                 interpolation_time_bin_centers_s = motion.temporal_bins_s
+                interpolation_time_bin_edges_s = motion.temporal_bin_edges_s
+        else:
+            interpolation_time_bin_centers_s, interpolation_time_bin_edges_s = ensure_time_bins(
+                interpolation_time_bin_centers_s, interpolation_time_bin_edges_s
+            )
 
         for segment_index, parent_segment in enumerate(recording._recording_segments):
             # finish the per-segment part of the time bin logic
@@ -375,8 +399,13 @@ def __init__(
                 t_start, t_end = parent_segment.sample_index_to_time(np.array([0, s_end]))
                 halfbin = interpolation_time_bin_size_s / 2.0
                 segment_interpolation_time_bins_s = np.arange(t_start + halfbin, t_end, interpolation_time_bin_size_s)
+                segment_interpolation_time_bin_edges_s = np.arange(
+                    t_start, t_end + halfbin, interpolation_time_bin_size_s
+                )
+                assert segment_interpolation_time_bin_edges_s.shape == (segment_interpolation_time_bins_s.shape[0] + 1,)
             else:
                 segment_interpolation_time_bins_s = interpolation_time_bin_centers_s[segment_index]
+                segment_interpolation_time_bin_edges_s = interpolation_time_bin_edges_s[segment_index]
 
             rec_segment = InterpolateMotionRecordingSegment(
                 parent_segment,
@@ -387,6 +416,7 @@ def __init__(
                 channel_inds,
                 segment_index,
                 segment_interpolation_time_bins_s,
+                segment_interpolation_time_bin_edges_s,
                 dtype=dtype_,
             )
             self.add_recording_segment(rec_segment)
@@ -420,6 +450,7 @@ def __init__(
         channel_inds,
         segment_index,
         interpolation_time_bin_centers_s,
+        interpolation_time_bin_edges_s,
         dtype="float32",
     ):
         BasePreprocessorSegment.__init__(self, parent_recording_segment)
@@ -429,13 +460,11 @@ def __init__(
         self.channel_inds = channel_inds
         self.segment_index = segment_index
         self.interpolation_time_bin_centers_s = interpolation_time_bin_centers_s
+        self.interpolation_time_bin_edges_s = interpolation_time_bin_edges_s
         self.dtype = dtype
         self.motion = motion
 
     def get_traces(self, start_frame, end_frame, channel_indices):
-        if self.time_vector is not None:
-            raise NotImplementedError("InterpolateMotionRecording does not yet support recordings with time_vectors.")
-
         if start_frame is None:
             start_frame = 0
         if end_frame is None:
@@ -453,7 +482,7 @@ def get_traces(self, start_frame, end_frame, channel_indices):
             channel_inds=self.channel_inds,
             spatial_interpolation_method=self.spatial_interpolation_method,
             spatial_interpolation_kwargs=self.spatial_interpolation_kwargs,
-            interpolation_time_bin_centers_s=self.interpolation_time_bin_centers_s,
+            interpolation_time_bin_edges_s=self.interpolation_time_bin_edges_s,
         )
 
         if channel_indices is not None:

src/spikeinterface/sortingcomponents/motion/motion_utils.py

@@ -1,5 +1,5 @@
-import warnings
 import json
+import warnings
 from pathlib import Path
 
 import numpy as np
@@ -54,6 +54,7 @@ def __init__(self, displacement, temporal_bins_s, spatial_bins_um, direction="y"
         self.direction = direction
         self.dim = ["x", "y", "z"].index(direction)
         self.check_properties()
+        self.temporal_bin_edges_s = [ensure_time_bin_edges(tbins) for tbins in self.temporal_bins_s]
 
     def check_properties(self):
         assert all(d.ndim == 2 for d in self.displacement)
@@ -576,3 +577,40 @@ def make_3d_motion_histograms(
         motion_histograms = np.log2(1 + motion_histograms)
 
     return motion_histograms, temporal_bin_edges, spatial_bin_edges
+
+
+def ensure_time_bins(time_bin_centers_s=None, time_bin_edges_s=None):
+    """Ensure that both bin edges and bin centers are present
+
+    If either of the inputs are None but not both, the missing is reconstructed
+    from the present. Going from edges to centers is done by taking midpoints.
+    Going from centers to edges is done by taking midpoints and padding with the
+    left and rightmost centers.
+
+    Parameters
+    ----------
+    time_bin_centers_s : None or np.array
+    time_bin_edges_s : None or np.array
+
+    Returns
+    -------
+    time_bin_centers_s, time_bin_edges_s
+    """
+    if time_bin_centers_s is None and time_bin_edges_s is None:
+        raise ValueError("Need at least one of time_bin_centers_s or time_bin_edges_s.")
+
+    if time_bin_centers_s is None:
+        assert time_bin_edges_s.ndim == 1 and time_bin_edges_s.size >= 2
+        time_bin_centers_s = 0.5 * (time_bin_edges_s[1:] + time_bin_edges_s[:-1])
+
+    if time_bin_edges_s is None:
+        time_bin_edges_s = np.empty(time_bin_centers_s.shape[0] + 1, dtype=time_bin_centers_s.dtype)
+        time_bin_edges_s[[0, -1]] = time_bin_centers_s[[0, -1]]
+        if time_bin_centers_s.size > 2:
+            time_bin_edges_s[1:-1] = 0.5 * (time_bin_centers_s[1:] + time_bin_centers_s[:-1])
+
+    return time_bin_centers_s, time_bin_edges_s
+
+
+def ensure_time_bin_edges(time_bin_centers_s=None, time_bin_edges_s=None):
+    return ensure_time_bins(time_bin_centers_s, time_bin_edges_s)[1]