Repository: SpikeInterface/spikeinterface

Issue:
Times not propagated by `.save_to_memory()`
I was a bit surprised to see that the following is possible:

```
rec = si.read_binary_folder(...)
rec1 = rec.save_to_memory()
print(rec.sample_index_to_time(0), rec1.sample_index_to_time(0))
# => 2451.763033333333, 0.0
```

I'm working around this by `rec1.set_times(rec.get_times())`, but it seems to me that since `save()` maintains the times when saving to other formats (like the binary folder), it should do that for memory mode as well.

Discussion:

Patch:
diff --git a/src/spikeinterface/core/baserecording.py b/src/spikeinterface/core/baserecording.py
index 9a9747bf0..0ea942667 100644
--- a/src/spikeinterface/core/baserecording.py
+++ b/src/spikeinterface/core/baserecording.py
@@ -498,24 +498,35 @@ class BaseRecording(BaseRecordingSnippets):
         rs = self._recording_segments[segment_index]
         return rs.time_to_sample_index(time_s)
 
-    def _save(self, format="binary", verbose: bool = False, **save_kwargs):
+    def _get_t_starts(self):
         # handle t_starts
         t_starts = []
         has_time_vectors = []
-        for segment_index, rs in enumerate(self._recording_segments):
+        for rs in self._recording_segments:
             d = rs.get_times_kwargs()
             t_starts.append(d["t_start"])
-            has_time_vectors.append(d["time_vector"] is not None)
 
         if all(t_start is None for t_start in t_starts):
             t_starts = None
+        return t_starts
 
+    def _get_time_vectors(self):
+        time_vectors = []
+        for rs in self._recording_segments:
+            d = rs.get_times_kwargs()
+            time_vectors.append(d["time_vector"])
+        if all(time_vector is None for time_vector in time_vectors):
+            time_vectors = None
+        return time_vectors
+
+    def _save(self, format="binary", verbose: bool = False, **save_kwargs):
         kwargs, job_kwargs = split_job_kwargs(save_kwargs)
 
         if format == "binary":
             folder = kwargs["folder"]
             file_paths = [folder / f"traces_cached_seg{i}.raw" for i in range(self.get_num_segments())]
             dtype = kwargs.get("dtype", None) or self.get_dtype()
+            t_starts = self._get_t_starts()
 
             write_binary_recording(self, file_paths=file_paths, dtype=dtype, verbose=verbose, **job_kwargs)
 
@@ -572,11 +583,11 @@ class BaseRecording(BaseRecordingSnippets):
             probegroup = self.get_probegroup()
             cached.set_probegroup(probegroup)
 
-        for segment_index, rs in enumerate(self._recording_segments):
-            d = rs.get_times_kwargs()
-            time_vector = d["time_vector"]
-            if time_vector is not None:
-                cached._recording_segments[segment_index].time_vector = time_vector
+        time_vectors = self._get_time_vectors()
+        if time_vectors is not None:
+            for segment_index, time_vector in enumerate(time_vectors):
+                if time_vector is not None:
+                    cached.set_times(time_vector, segment_index=segment_index)
 
         return cached
 
diff --git a/src/spikeinterface/core/numpyextractors.py b/src/spikeinterface/core/numpyextractors.py
index 09ba743a8..f4790817a 100644
--- a/src/spikeinterface/core/numpyextractors.py
+++ b/src/spikeinterface/core/numpyextractors.py
@@ -83,6 +83,9 @@ class NumpyRecording(BaseRecording):
     @staticmethod
     def from_recording(source_recording, **job_kwargs):
         traces_list, shms = write_memory_recording(source_recording, dtype=None, **job_kwargs)
+
+        t_starts = source_recording._get_t_starts()
+
         if shms[0] is not None:
             # if the computation was done in parallel then traces_list is shared array
             # this can lead to problem
@@ -91,13 +94,14 @@ class NumpyRecording(BaseRecording):
             for shm in shms:
                 shm.close()
                 shm.unlink()
-        # TODO later : propagte t_starts ?
+
         recording = NumpyRecording(
             traces_list,
             source_recording.get_sampling_frequency(),
-            t_starts=None,
+            t_starts=t_starts,
             channel_ids=source_recording.channel_ids,
         )
+        return recording
 
 
 class NumpyRecordingSegment(BaseRecordingSegment):
@@ -206,7 +210,7 @@ class SharedMemoryRecording(BaseRecording):
     def from_recording(source_recording, **job_kwargs):
         traces_list, shms = write_memory_recording(source_recording, buffer_type="sharedmem", **job_kwargs)
 
-        # TODO later : propagte t_starts ?
+        t_starts = source_recording._get_t_starts()
 
         recording = SharedMemoryRecording(
             shm_names=[shm.name for shm in shms],
@@ -214,7 +218,7 @@ class SharedMemoryRecording(BaseRecording):
             dtype=source_recording.dtype,
             sampling_frequency=source_recording.sampling_frequency,
             channel_ids=source_recording.channel_ids,
-            t_starts=None,
+            t_starts=t_starts,
             main_shm_owner=True,
         )
 


Test patch:
diff --git a/src/spikeinterface/core/tests/test_time_handling.py b/src/spikeinterface/core/tests/test_time_handling.py
index 487a89309..049d5ab6e 100644
--- a/src/spikeinterface/core/tests/test_time_handling.py
+++ b/src/spikeinterface/core/tests/test_time_handling.py
@@ -1,69 +1,289 @@
+import copy
+
 import pytest
 import numpy as np
 
 from spikeinterface.core import generate_recording, generate_sorting
+import spikeinterface.full as si
+
 
+class TestTimeHandling:
+    """
+    This class tests how time is handled in SpikeInterface. Under the hood,
+    time can be represented as a full `time_vector` or only as
+    `t_start` attribute on segments from which a vector of times
+    is generated on the fly. Both time representations are tested here.
+    """
 
-def test_time_handling(create_cache_folder):
-    cache_folder = create_cache_folder
-    durations = [[10], [10, 5]]
+    # Fixtures #####
+    @pytest.fixture(scope="session")
+    def time_vector_recording(self):
+        """
+        Add time vectors to the recording, returning the
+        raw recording, recording with time vectors added to
+        segments, and list a the time vectors added to the recording.
+        """
+        durations = [10, 15, 20]
+        raw_recording = generate_recording(num_channels=4, durations=durations)
 
-    # test multi-segment
-    for i, dur in enumerate(durations):
-        rec = generate_recording(num_channels=4, durations=dur)
-        sort = generate_sorting(num_units=10, durations=dur)
+        return self._get_time_vector_recording(raw_recording)
 
-        for segment_index in range(rec.get_num_segments()):
-            original_times = rec.get_times(segment_index=segment_index)
-            new_times = original_times + 5
-            rec.set_times(new_times, segment_index=segment_index)
+    @pytest.fixture(scope="session")
+    def t_start_recording(self):
+        """
+        Add a t_starts to the recording, returning the
+        raw recording, recording with t_starts added to segments,
+        and a list of the time vectors generated from adding the
+        t_start to the recording times.
+        """
+        durations = [10, 15, 20]
+        raw_recording = generate_recording(num_channels=4, durations=durations)
 
-        sort.register_recording(rec)
-        assert sort.has_recording()
+        return self._get_t_start_recording(raw_recording)
 
-        rec_cache = rec.save(folder=cache_folder / f"rec{i}")
+    def _get_time_vector_recording(self, raw_recording):
+        """
+        Loop through all recording segments, adding a different time
+        vector to each segment. The time vector is the original times with
+        a t_start and irregularly spaced offsets to mimic irregularly
+        spaced timeseries data. Return the original recording,
+        recoridng with time vectors added and list including the added time vectors.
+        """
+        times_recording = copy.deepcopy(raw_recording)
+        all_time_vectors = []
+        for segment_index in range(raw_recording.get_num_segments()):
 
-        for segment_index in range(sort.get_num_segments()):
-            assert rec.has_time_vector(segment_index=segment_index)
-            assert sort.has_time_vector(segment_index=segment_index)
+            t_start = segment_index + 1 * 100
 
-            # times are correctly saved by the recording
-            assert np.allclose(
-                rec.get_times(segment_index=segment_index), rec_cache.get_times(segment_index=segment_index)
+            some_small_increasing_numbers = np.arange(times_recording.get_num_samples(segment_index)) * (
+                1 / times_recording.get_sampling_frequency()
             )
 
-            # spike times are correctly adjusted
-            for u in sort.get_unit_ids():
-                spike_times = sort.get_unit_spike_train(u, segment_index=segment_index, return_times=True)
-                rec_times = rec.get_times(segment_index=segment_index)
-                assert np.all(spike_times >= rec_times[0])
-                assert np.all(spike_times <= rec_times[-1])
+            offsets = np.cumsum(some_small_increasing_numbers)
+            time_vector = t_start + times_recording.get_times(segment_index) + offsets
+
+            all_time_vectors.append(time_vector)
+            times_recording.set_times(times=time_vector, segment_index=segment_index)
+
+            assert np.array_equal(
+                times_recording._recording_segments[segment_index].time_vector,
+                time_vector,
+            ), "time_vector was not properly set during test setup"
+
+        return (raw_recording, times_recording, all_time_vectors)
+
+    def _get_t_start_recording(self, raw_recording):
+        """
+        For each segment in the recording, add a different `t_start`.
+        Return a list of time vectors generating from the recording times
+        + the t_starts.
+        """
+        t_start_recording = copy.deepcopy(raw_recording)
+
+        all_t_starts = []
+        for segment_index in range(raw_recording.get_num_segments()):
+
+            t_start = (segment_index + 1) * 100
+
+            all_t_starts.append(t_start + t_start_recording.get_times(segment_index))
+            t_start_recording._recording_segments[segment_index].t_start = t_start
+
+        return (raw_recording, t_start_recording, all_t_starts)
+
+    def _get_fixture_data(self, request, fixture_name):
+        """
+        A convenience function to get the data from a fixture
+        based on the name. This is used to allow parameterising
+        tests across fixtures.
+        """
+        time_recording_fixture = request.getfixturevalue(fixture_name)
+        raw_recording, times_recording, all_times = time_recording_fixture
+        return (raw_recording, times_recording, all_times)
+
+    # Tests #####
+    def test_has_time_vector(self, time_vector_recording):
+        """
+        Test the `has_time_vector` function returns `False` before
+        a time vector is added and `True` afterwards.
+        """
+        raw_recording, times_recording, _ = time_vector_recording
+
+        for segment_idx in range(raw_recording.get_num_segments()):
+
+            assert raw_recording.has_time_vector(segment_idx) is False
+            assert times_recording.has_time_vector(segment_idx) is True
+
+    @pytest.mark.parametrize("mode", ["binary", "zarr"])
+    @pytest.mark.parametrize("fixture_name", ["time_vector_recording", "t_start_recording"])
+    def test_times_propagated_to_save_folder(self, request, fixture_name, mode, tmp_path):
+        """
+        Test `t_start` or `time_vector` is propagated to a saved recording,
+        by saving, reloading, and checking times are correct.
+        """
+        _, times_recording, all_times = self._get_fixture_data(request, fixture_name)
+
+        folder_name = "recording"
+        recording_cache = times_recording.save(format=mode, folder=tmp_path / folder_name)
+
+        if mode == "zarr":
+            folder_name += ".zarr"
+        recording_load = si.load_extractor(tmp_path / folder_name)
+
+        self._check_times_match(recording_cache, all_times)
+        self._check_times_match(recording_load, all_times)
+
+    @pytest.mark.parametrize("sharedmem", [True, False])
+    @pytest.mark.parametrize("fixture_name", ["time_vector_recording", "t_start_recording"])
+    def test_times_propagated_to_save_memory(self, request, fixture_name, sharedmem):
+        """
+        Test t_start and time_vector are propagated to recording saved into memory.
+        """
+        _, times_recording, all_times = self._get_fixture_data(request, fixture_name)
+
+        recording_load = times_recording.save(format="memory", sharedmem=sharedmem)
 
+        self._check_times_match(recording_load, all_times)
 
-def test_frame_slicing():
-    duration = [10]
+    @pytest.mark.parametrize("fixture_name", ["time_vector_recording", "t_start_recording"])
+    def test_time_propagated_to_select_segments(self, request, fixture_name):
+        """
+        Test that when `recording.select_segments()` is used, the times
+        are propagated to the new recoridng object.
+        """
+        _, times_recording, all_times = self._get_fixture_data(request, fixture_name)
 
-    rec = generate_recording(num_channels=4, durations=duration)
-    sort = generate_sorting(num_units=10, durations=duration)
+        for segment_index in range(times_recording.get_num_segments()):
+            segment = times_recording.select_segments(segment_index)
+            assert np.array_equal(segment.get_times(), all_times[segment_index])
 
-    original_times = rec.get_times()
-    new_times = original_times + 5
-    rec.set_times(new_times)
+    @pytest.mark.parametrize("fixture_name", ["time_vector_recording", "t_start_recording"])
+    def test_times_propagated_to_sorting(self, request, fixture_name):
+        """
+        Check that when attached to a sorting object, the times are propagated
+        to the object. This means that all spike times should respect the
+        `t_start` or `time_vector` added.
+        """
+        raw_recording, times_recording, all_times = self._get_fixture_data(request, fixture_name)
+        sorting = self._get_sorting_with_recording_attached(
+            recording_for_durations=raw_recording, recording_to_attach=times_recording
+        )
+        for segment_index in range(raw_recording.get_num_segments()):
 
-    sort.register_recording(rec)
+            if fixture_name == "time_vector_recording":
+                assert sorting.has_time_vector(segment_index=segment_index)
 
-    start_frame = 3 * rec.get_sampling_frequency()
-    end_frame = 7 * rec.get_sampling_frequency()
+                self._check_spike_times_are_correct(sorting, times_recording, segment_index)
+
+    @pytest.mark.parametrize("fixture_name", ["time_vector_recording", "t_start_recording"])
+    def test_time_sample_converters(self, request, fixture_name):
+        """
+        Test the `recording.sample_time_to_index` and
+        `recording.time_to_sample_index` convenience functions.
+        """
+        raw_recording, times_recording, all_times = self._get_fixture_data(request, fixture_name)
+        with pytest.raises(ValueError) as e:
+            times_recording.sample_index_to_time(0)
+        assert "Provide 'segment_index'" in str(e)
+
+        for segment_index in range(times_recording.get_num_segments()):
+
+            sample_index = np.random.randint(low=0, high=times_recording.get_num_samples(segment_index))
+            time_ = times_recording.sample_index_to_time(sample_index, segment_index=segment_index)
+
+            assert time_ == all_times[segment_index][sample_index]
+
+            new_sample_index = times_recording.time_to_sample_index(time_, segment_index=segment_index)
+
+            assert new_sample_index == sample_index
+
+    @pytest.mark.parametrize("time_type", ["time_vector", "t_start"])
+    @pytest.mark.parametrize("bounds", ["start", "middle", "end"])
+    def test_slice_recording(self, time_type, bounds):
+        """
+        Test times are correct after applying `frame_slice` or `time_slice`
+        to a recording or sorting (for `frame_slice`). The the recording times
+        should be correct with respect to the set `t_start` or `time_vector`.
+        """
+        raw_recording = generate_recording(num_channels=4, durations=[10])
+
+        if time_type == "time_vector":
+            raw_recording, times_recording, all_times = self._get_time_vector_recording(raw_recording)
+        else:
+            raw_recording, times_recording, all_times = self._get_t_start_recording(raw_recording)
+
+        sorting = self._get_sorting_with_recording_attached(
+            recording_for_durations=raw_recording, recording_to_attach=times_recording
+        )
+
+        # Take some different times, including min and max bounds of
+        # the recording, and some arbitaray times in the middle (20% and 80%).
+        if bounds == "start":
+            start_frame = 0
+            end_frame = int(times_recording.get_num_samples(0) * 0.8)
+        elif bounds == "end":
+            start_frame = int(times_recording.get_num_samples(0) * 0.2)
+            end_frame = times_recording.get_num_samples(0) - 1
+        elif bounds == "middle":
+            start_frame = int(times_recording.get_num_samples(0) * 0.2)
+            end_frame = int(times_recording.get_num_samples(0) * 0.8)
+
+        # Slice the recording and get the new times are correct
+        rec_frame_slice = times_recording.frame_slice(start_frame=start_frame, end_frame=end_frame)
+        sort_frame_slice = sorting.frame_slice(start_frame=start_frame, end_frame=end_frame)
+
+        assert np.allclose(rec_frame_slice.get_times(0), all_times[0][start_frame:end_frame], rtol=0, atol=1e-8)
+
+        self._check_spike_times_are_correct(sort_frame_slice, rec_frame_slice, segment_index=0)
+
+        # Test `time_slice`
+        start_time = times_recording.sample_index_to_time(start_frame)
+        end_time = times_recording.sample_index_to_time(end_frame)
+
+        rec_slice = times_recording.time_slice(start_time=start_time, end_time=end_time)
+
+        assert np.allclose(rec_slice.get_times(0), all_times[0][start_frame:end_frame], rtol=0, atol=1e-8)
+
+    # Helpers ####
+    def _check_times_match(self, recording, all_times):
+        """
+        For every segment in a recording, check the `get_times()`
+        match the expected times in the list of time vectors, `all_times`.
+        """
+        for segment_index in range(recording.get_num_segments()):
+            assert np.array_equal(recording.get_times(segment_index), all_times[segment_index])
+
+    def _check_spike_times_are_correct(self, sorting, times_recording, segment_index):
+        """
+        For every unit in the `sorting`, for a particular segment, check that
+        the unit times match the times of the original recording as
+        retrieved with `get_times()`.
+        """
+        for unit_id in sorting.get_unit_ids():
+            spike_times = sorting.get_unit_spike_train(unit_id, segment_index=segment_index, return_times=True)
+            spike_indexes = sorting.get_unit_spike_train(unit_id, segment_index=segment_index)
+            rec_times = times_recording.get_times(segment_index=segment_index)
+
+            assert np.array_equal(
+                spike_times,
+                rec_times[spike_indexes],
+            )
 
-    rec_slice = rec.frame_slice(start_frame=start_frame, end_frame=end_frame)
-    sort_slice = sort.frame_slice(start_frame=start_frame, end_frame=end_frame)
+    def _get_sorting_with_recording_attached(self, recording_for_durations, recording_to_attach):
+        """
+        Convenience function to create a sorting object with
+        a recording attached. Typically use the raw recordings
+        for the durations of which to make the sorter, as
+        the generate_sorter is not setup to handle the
+        (strange) edge case of the irregularly spaced
+        test time vectors.
+        """
+        durations = [
+            recording_for_durations.get_duration(idx) for idx in range(recording_for_durations.get_num_segments())
+        ]
 
-    for u in sort_slice.get_unit_ids():
-        spike_times = sort_slice.get_unit_spike_train(u, return_times=True)
-        rec_times = rec_slice.get_times()
-        assert np.all(spike_times >= rec_times[0])
-        assert np.all(spike_times <= rec_times[-1])
+        sorting = generate_sorting(num_units=10, durations=durations)
 
+        sorting.register_recording(recording_to_attach)
+        assert sorting.has_recording()
 
-if __name__ == "__main__":
-    test_frame_slicing()
+        return sorting