Merge branch 'main' into fast-correlogram-merge

src/spikeinterface/core/node_pipeline.py

@@ -191,7 +191,7 @@ def __init__(
         self._dtype = spike_peak_dtype
 
         self.include_spikes_in_margin = include_spikes_in_margin
-        if include_spikes_in_margin is not None:
+        if include_spikes_in_margin:
             self._dtype = spike_peak_dtype + [("in_margin", "bool")]
 
         self.peaks = sorting_to_peaks(sorting, extremum_channel_inds, self._dtype)
@@ -228,12 +228,6 @@ def compute(self, traces, start_frame, end_frame, segment_index, max_margin, pea
         # get local peaks
         sl = self.segment_slices[segment_index]
         peaks_in_segment = self.peaks[sl]
-        # if self.include_spikes_in_margin:
-        #     i0, i1 = np.searchsorted(
-        #         peaks_in_segment["sample_index"], [start_frame - max_margin, end_frame + max_margin]
-        #     )
-        # else:
-        #     i0, i1 = np.searchsorted(peaks_in_segment["sample_index"], [start_frame, end_frame])
         i0, i1 = peak_slice
 
         local_peaks = peaks_in_segment[i0:i1]
@@ -435,21 +429,59 @@ def compute(self, traces, peaks):
         return sparse_wfs
 
 
-def find_parent_of_type(list_of_parents, parent_type, unique=True):
+def find_parent_of_type(list_of_parents, parent_type):
+    """
+    Find a single parent of a given type(s) in a list of parents.
+    If multiple parents of the given type are found, the first parent is returned.
+
+    Parameters
+    ----------
+    list_of_parents : list of PipelineNode
+        List of parents to search through.
+    parent_type : type | tuple of types
+        The type of parent to search for.
+
+    Returns
+    -------
+    parent : PipelineNode or None
+        The parent of the given type. Returns None if no parent of the given type is found.
+    """
     if list_of_parents is None:
         return None
 
+    parents = find_parents_of_type(list_of_parents, parent_type)
+
+    if len(parents) > 0:
+        return parents[0]
+    else:
+        return None
+
+
+def find_parents_of_type(list_of_parents, parent_type):
+    """
+    Find all parents of a given type(s) in a list of parents.
+
+    Parameters
+    ----------
+    list_of_parents : list of PipelineNode
+        List of parents to search through.
+    parent_type : type | tuple of types
+        The type(s) of parents to search for.
+
+    Returns
+    -------
+    parents : list of PipelineNode
+        List of parents of the given type(s). Returns an empty list if no parents of the given type(s) are found.
+    """
+    if list_of_parents is None:
+        return []
+
     parents = []
     for parent in list_of_parents:
         if isinstance(parent, parent_type):
             parents.append(parent)
 
-    if unique and len(parents) == 1:
-        return parents[0]
-    elif not unique and len(parents) > 1:
-        return parents[0]
-    else:
-        return None
+    return parents
 
 
 def check_graph(nodes):
@@ -471,7 +503,7 @@ def check_graph(nodes):
             assert parent in nodes, f"Node {node} has parent {parent} that was not passed in nodes"
             assert (
                 nodes.index(parent) < i
-            ), f"Node are ordered incorrectly: {node} before {parent} in the pipeline definition."
+            ), f"Nodes are ordered incorrectly: {node} before {parent} in the pipeline definition."
 
     return nodes
 
@@ -607,12 +639,16 @@ def _compute_peak_pipeline_chunk(segment_index, start_frame, end_frame, worker_c
     skip_after_n_peaks_per_worker = worker_ctx["skip_after_n_peaks_per_worker"]
 
     recording_segment = recording._recording_segments[segment_index]
-    node0 = nodes[0]
-
-    if isinstance(node0, (SpikeRetriever, PeakRetriever)):
-        # in this case PeakSource could have no peaks and so no need to load traces just skip
-        peak_slice = i0, i1 = node0.get_peak_slice(segment_index, start_frame, end_frame, max_margin)
-        load_trace_and_compute = i0 < i1
+    retrievers = find_parents_of_type(nodes, (SpikeRetriever, PeakRetriever))
+    # get peak slices once for all retrievers
+    peak_slice_by_retriever = {}
+    for retriever in retrievers:
+        peak_slice = i0, i1 = retriever.get_peak_slice(segment_index, start_frame, end_frame, max_margin)
+        peak_slice_by_retriever[retriever] = peak_slice
+
+    if len(peak_slice_by_retriever) > 0:
+        # in this case the retrievers could have no peaks, so we test if any spikes are in the chunk
+        load_trace_and_compute = any(i0 < i1 for i0, i1 in peak_slice_by_retriever.values())
     else:
         # PeakDetector always need traces
         load_trace_and_compute = True
@@ -646,7 +682,8 @@ def _compute_peak_pipeline_chunk(segment_index, start_frame, end_frame, worker_c
                 node_output = node.compute(trace_detection, start_frame, end_frame, segment_index, max_margin)
                 # set sample index to local
                 node_output[0]["sample_index"] += extra_margin
-            elif isinstance(node, PeakSource):
+            elif isinstance(node, (PeakRetriever, SpikeRetriever)):
+                peak_slice = peak_slice_by_retriever[node]
                 node_output = node.compute(traces_chunk, start_frame, end_frame, segment_index, max_margin, peak_slice)
             else:
                 # TODO later when in master: change the signature of all nodes (or maybe not!)

src/spikeinterface/core/sortinganalyzer.py

@@ -2033,19 +2033,18 @@ def load(cls, sorting_analyzer):
         return None
 
     def load_run_info(self):
+        run_info = None
         if self.format == "binary_folder":
             extension_folder = self._get_binary_extension_folder()
             run_info_file = extension_folder / "run_info.json"
             if run_info_file.is_file():
                 with open(str(run_info_file), "r") as f:
                     run_info = json.load(f)
-            else:
-                warnings.warn(f"Found no run_info file for {self.extension_name}, extension should be re-computed.")
-                run_info = None
 
         elif self.format == "zarr":
             extension_group = self._get_zarr_extension_group(mode="r")
             run_info = extension_group.attrs.get("run_info", None)
+
         if run_info is None:
             warnings.warn(f"Found no run_info file for {self.extension_name}, extension should be re-computed.")
         self.run_info = run_info

src/spikeinterface/postprocessing/template_metrics.py

@@ -327,13 +327,18 @@ def _run(self, verbose=False):
         )
 
         existing_metrics = []
-        tm_extension = self.sorting_analyzer.get_extension("template_metrics")
-        if (
-            delete_existing_metrics is False
-            and tm_extension is not None
-            and tm_extension.data.get("metrics") is not None
-        ):
-            existing_metrics = tm_extension.params["metric_names"]
+
+        # Check if we need to propogate any old metrics. If so, we'll do that.
+        # Otherwise, we'll avoid attempting to load an empty template_metrics.
+        if set(self.params["metrics_to_compute"]) != set(self.params["metric_names"]):
+
+            tm_extension = self.sorting_analyzer.get_extension("template_metrics")
+            if (
+                delete_existing_metrics is False
+                and tm_extension is not None
+                and tm_extension.data.get("metrics") is not None
+            ):
+                existing_metrics = tm_extension.params["metric_names"]
 
         existing_metrics = []
         # here we get in the loaded via the dict only (to avoid full loading from disk after params reset)

src/spikeinterface/sortingcomponents/motion/motion_utils.py

@@ -587,10 +587,14 @@ def ensure_time_bins(time_bin_centers_s=None, time_bin_edges_s=None):
     Going from centers to edges is done by taking midpoints and padding with the
     left and rightmost centers.
 
+    To handle multi segment, this function is working both:
+      * array/array input
+      * list[array]/list[array] input
+
     Parameters
     ----------
-    time_bin_centers_s : None or np.array
-    time_bin_edges_s : None or np.array
+    time_bin_centers_s : None or np.array or list[np.array]
+    time_bin_edges_s : None or np.array or list[np.array]
 
     Returns
     -------
@@ -600,17 +604,34 @@ def ensure_time_bins(time_bin_centers_s=None, time_bin_edges_s=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 isinstance(time_bin_edges_s, list):
+            # multi segment cas
+            time_bin_centers_s = []
+            for be in time_bin_edges_s:
+                bc, _ = ensure_time_bins(time_bin_centers_s=None, time_bin_edges_s=be)
+                time_bin_centers_s.append(bc)
+        else:
+            # simple segment
+            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])
+        if isinstance(time_bin_centers_s, list):
+            # multi segment cas
+            time_bin_edges_s = []
+            for bc in time_bin_centers_s:
+                _, be = ensure_time_bins(time_bin_centers_s=bc, time_bin_edges_s=None)
+                time_bin_edges_s.append(be)
+        else:
+            # simple segment
+            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]

src/spikeinterface/sortingcomponents/motion/tests/test_motion_interpolation.py

@@ -10,18 +10,26 @@
     interpolate_motion_on_traces,
 )
 from spikeinterface.sortingcomponents.tests.common import make_dataset
-
+from spikeinterface.core import generate_ground_truth_recording
 
 def make_fake_motion(rec):
     # make a fake motion object
-    duration = rec.get_total_duration()
+
     locs = rec.get_channel_locations()
-    temporal_bins = np.arange(0.5, duration - 0.49, 0.5)
     spatial_bins = np.arange(locs[:, 1].min(), locs[:, 1].max(), 100)
-    displacement = np.zeros((temporal_bins.size, spatial_bins.size))
-    displacement[:, :] = np.linspace(-30, 30, temporal_bins.size)[:, None]
 
-    motion = Motion([displacement], [temporal_bins], spatial_bins, direction="y")
+    displacement = []
+    temporal_bins = []
+    for segment_index in range(rec.get_num_segments()):
+        duration = rec.get_duration(segment_index=segment_index)
+        seg_time_bins = np.arange(0.5, duration - 0.49, 0.5)
+        seg_disp = np.zeros((seg_time_bins.size, spatial_bins.size))
+        seg_disp[:, :] = np.linspace(-30, 30, seg_time_bins.size)[:, None]
+
+        temporal_bins.append(seg_time_bins)
+        displacement.append(seg_disp)
+
+    motion = Motion(displacement, temporal_bins, spatial_bins, direction="y")
 
     return motion
 
@@ -176,7 +184,27 @@ def test_cross_band_interpolation():
 
 
 def test_InterpolateMotionRecording():
-    rec, sorting = make_dataset()
+    # rec, sorting = make_dataset()
+
+    # 2 segments
+    rec, sorting = generate_ground_truth_recording(
+        durations=[30.0],
+        sampling_frequency=30000.0,
+        num_channels=32,
+        num_units=10,
+        generate_probe_kwargs=dict(
+            num_columns=2,
+            xpitch=20,
+            ypitch=20,
+            contact_shapes="circle",
+            contact_shape_params={"radius": 6},
+        ),
+        generate_sorting_kwargs=dict(firing_rates=6.0, refractory_period_ms=4.0),
+        noise_kwargs=dict(noise_levels=5.0, strategy="on_the_fly"),
+        seed=2205,
+    )
+
+
     motion = make_fake_motion(rec)
 
     rec2 = InterpolateMotionRecording(rec, motion, border_mode="force_extrapolate")
@@ -187,15 +215,19 @@ def test_InterpolateMotionRecording():
 
     rec2 = InterpolateMotionRecording(rec, motion, border_mode="remove_channels")
     assert rec2.channel_ids.size == 24
-    for ch_id in (0, 1, 14, 15, 16, 17, 30, 31):
+    for ch_id in ("0", "1", "14", "15", "16", "17", "30", "31"):
         assert ch_id not in rec2.channel_ids
 
     traces = rec2.get_traces(segment_index=0, start_frame=0, end_frame=30000)
     assert traces.shape == (30000, 24)
 
-    traces = rec2.get_traces(segment_index=0, start_frame=0, end_frame=30000, channel_ids=[3, 4])
+    traces = rec2.get_traces(segment_index=0, start_frame=0, end_frame=30000, channel_ids=["3", "4"])
     assert traces.shape == (30000, 2)
 
+    # test dump.load when multi segments
+    rec2.dump("rec_motion_interp.pickle")
+    rec3 = sc.load("rec_motion_interp.pickle")
+
     # import matplotlib.pyplot as plt
     # import spikeinterface.widgets as sw
     # fig, ax = plt.subplots()
@@ -207,7 +239,7 @@ def test_InterpolateMotionRecording():
 
 if __name__ == "__main__":
     # test_correct_motion_on_peaks()
-    test_interpolate_motion_on_traces()
+    # test_interpolate_motion_on_traces()
     # test_interpolation_simple()
-    # test_InterpolateMotionRecording()
-    test_cross_band_interpolation()
+    test_InterpolateMotionRecording()
+    # test_cross_band_interpolation()