Fix t_starts not propagated to save memory.

Author: JoeZiminski

src/spikeinterface/core/baserecording.py

@@ -545,11 +545,11 @@ def _save(self, format="binary", verbose: bool = False, **save_kwargs):
             if kwargs.get("sharedmem", True):
                 from .numpyextractors import SharedMemoryRecording
 
-                cached = SharedMemoryRecording.from_recording(self, **job_kwargs)
+                cached = SharedMemoryRecording.from_recording(self, t_starts=t_starts, **job_kwargs)
             else:
                 from spikeinterface.core import NumpyRecording
 
-                cached = NumpyRecording.from_recording(self, **job_kwargs)
+                cached = NumpyRecording.from_recording(self, t_starts=t_starts, **job_kwargs)
 
         elif format == "zarr":
             from .zarrextractors import ZarrRecordingExtractor

src/spikeinterface/core/numpyextractors.py

@@ -85,7 +85,7 @@ def __init__(self, traces_list, sampling_frequency, t_starts=None, channel_ids=N
         }
 
     @staticmethod
-    def from_recording(source_recording, **job_kwargs):
+    def from_recording(source_recording, t_starts=None, **job_kwargs):
         traces_list, shms = write_memory_recording(source_recording, dtype=None, **job_kwargs)
         if shms[0] is not None:
             # if the computation was done in parallel then traces_list is shared array
@@ -95,13 +95,14 @@ def from_recording(source_recording, **job_kwargs):
             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):
@@ -211,18 +212,16 @@ def __del__(self):
                 shm.unlink()
 
     @staticmethod
-    def from_recording(source_recording, **job_kwargs):
+    def from_recording(source_recording, t_starts=None, **job_kwargs):
         traces_list, shms = write_memory_recording(source_recording, buffer_type="sharedmem", **job_kwargs)
 
-        # TODO later : propagte t_starts ?
-
         recording = SharedMemoryRecording(
             shm_names=[shm.name for shm in shms],
             shape_list=[traces.shape for traces in traces_list],
             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,
         )