Merge branch 'SpikeInterface:main' into meta_merging

src/spikeinterface/core/job_tools.py

@@ -149,12 +149,12 @@ def divide_segment_into_chunks(num_frames, chunk_size):
 
 
 def divide_recording_into_chunks(recording, chunk_size):
-    all_chunks = []
+    recording_slices = []
     for segment_index in range(recording.get_num_segments()):
         num_frames = recording.get_num_samples(segment_index)
         chunks = divide_segment_into_chunks(num_frames, chunk_size)
-        all_chunks.extend([(segment_index, frame_start, frame_stop) for frame_start, frame_stop in chunks])
-    return all_chunks
+        recording_slices.extend([(segment_index, frame_start, frame_stop) for frame_start, frame_stop in chunks])
+    return recording_slices
 
 
 def ensure_n_jobs(recording, n_jobs=1):
@@ -387,13 +387,13 @@ def __init__(
                 f"chunk_duration={chunk_duration_str}",
             )
 
-    def run(self, all_chunks=None):
+    def run(self, recording_slices=None):
         """
         Runs the defined jobs.
         """
 
-        if all_chunks is None:
-            all_chunks = divide_recording_into_chunks(self.recording, self.chunk_size)
+        if recording_slices is None:
+            recording_slices = divide_recording_into_chunks(self.recording, self.chunk_size)
 
         if self.handle_returns:
             returns = []
@@ -402,17 +402,17 @@ def run(self, all_chunks=None):
 
         if self.n_jobs == 1:
             if self.progress_bar:
-                all_chunks = tqdm(all_chunks, ascii=True, desc=self.job_name)
+                recording_slices = tqdm(recording_slices, ascii=True, desc=self.job_name)
 
             worker_ctx = self.init_func(*self.init_args)
-            for segment_index, frame_start, frame_stop in all_chunks:
+            for segment_index, frame_start, frame_stop in recording_slices:
                 res = self.func(segment_index, frame_start, frame_stop, worker_ctx)
                 if self.handle_returns:
                     returns.append(res)
                 if self.gather_func is not None:
                     self.gather_func(res)
         else:
-            n_jobs = min(self.n_jobs, len(all_chunks))
+            n_jobs = min(self.n_jobs, len(recording_slices))
 
             # parallel
             with ProcessPoolExecutor(
@@ -421,10 +421,10 @@ def run(self, all_chunks=None):
                 mp_context=mp.get_context(self.mp_context),
                 initargs=(self.func, self.init_func, self.init_args, self.max_threads_per_process),
             ) as executor:
-                results = executor.map(function_wrapper, all_chunks)
+                results = executor.map(function_wrapper, recording_slices)
 
                 if self.progress_bar:
-                    results = tqdm(results, desc=self.job_name, total=len(all_chunks))
+                    results = tqdm(results, desc=self.job_name, total=len(recording_slices))
 
                 for res in results:
                     if self.handle_returns:

src/spikeinterface/core/node_pipeline.py

@@ -489,6 +489,7 @@ def run_node_pipeline(
     names=None,
     verbose=False,
     skip_after_n_peaks=None,
+    recording_slices=None,
 ):
     """
     Machinery to compute in parallel operations on peaks and traces.
@@ -540,6 +541,10 @@ def run_node_pipeline(
     skip_after_n_peaks : None | int
         Skip the computation after n_peaks.
         This is not an exact because internally this skip is done per worker in average.
+    recording_slices : None | list[tuple]
+        Optionaly give a list of slices to run the pipeline only on some chunks of the recording.
+        It must be a list of (segment_index, frame_start, frame_stop).
+        If None (default), the function iterates over the entire duration of the recording.
 
     Returns
     -------
@@ -578,7 +583,7 @@ def run_node_pipeline(
         **job_kwargs,
     )
 
-    processor.run()
+    processor.run(recording_slices=recording_slices)
 
     outs = gather_func.finalize_buffers(squeeze_output=squeeze_output)
     return outs

src/spikeinterface/core/recording_tools.py

@@ -806,7 +806,7 @@ def append_noise_chunk(res):
             gather_func=append_noise_chunk,
             **job_kwargs,
         )
-        executor.run(all_chunks=recording_slices)
+        executor.run(recording_slices=recording_slices)
         noise_levels_chunks = np.stack(noise_levels_chunks)
         noise_levels = np.mean(noise_levels_chunks, axis=0)
 

src/spikeinterface/core/tests/test_node_pipeline.py

@@ -4,7 +4,7 @@
 import shutil
 
 from spikeinterface import create_sorting_analyzer, get_template_extremum_channel, generate_ground_truth_recording
-
+from spikeinterface.core.job_tools import divide_recording_into_chunks
 
 # from spikeinterface.sortingcomponents.peak_detection import detect_peaks
 from spikeinterface.core.node_pipeline import (
@@ -191,8 +191,8 @@ def test_run_node_pipeline(cache_folder_creation):
             unpickled_node = pickle.loads(pickled_node)
 
 
-def test_skip_after_n_peaks():
-    recording, sorting = generate_ground_truth_recording(num_channels=10, num_units=10, durations=[10.0])
+def test_skip_after_n_peaks_and_recording_slices():
+    recording, sorting = generate_ground_truth_recording(num_channels=10, num_units=10, durations=[10.0], seed=2205)
 
     # job_kwargs = dict(chunk_duration="0.5s", n_jobs=2, progress_bar=False)
     job_kwargs = dict(chunk_duration="0.5s", n_jobs=1, progress_bar=False)
@@ -211,18 +211,27 @@ def test_skip_after_n_peaks():
     node1 = AmplitudeExtractionNode(recording, parents=[node0], param0=6.6, return_output=True)
     nodes = [node0, node1]
 
+    # skip
     skip_after_n_peaks = 30
     some_amplitudes = run_node_pipeline(
         recording, nodes, job_kwargs, gather_mode="memory", skip_after_n_peaks=skip_after_n_peaks
     )
-
     assert some_amplitudes.size >= skip_after_n_peaks
     assert some_amplitudes.size < spikes.size
 
+    # slices : 1 every 4
+    recording_slices = divide_recording_into_chunks(recording, 10_000)
+    recording_slices = recording_slices[::4]
+    some_amplitudes = run_node_pipeline(
+        recording, nodes, job_kwargs, gather_mode="memory", recording_slices=recording_slices
+    )
+    tolerance = 1.2
+    assert some_amplitudes.size < (spikes.size // 4) * tolerance
+
 
 # the following is for testing locally with python or ipython. It is not used in ci or with pytest.
 if __name__ == "__main__":
     # folder = Path("./cache_folder/core")
     # test_run_node_pipeline(folder)
 
-    test_skip_after_n_peaks()
+    test_skip_after_n_peaks_and_recording_slices()

src/spikeinterface/preprocessing/whiten.py

@@ -19,6 +19,8 @@ class WhitenRecording(BasePreprocessor):
     recording : RecordingExtractor
         The recording extractor to be whitened.
     dtype : None or dtype, default: None
+        Datatype of the output recording (covariance matrix estimation
+        and whitening are performed in float32).
         If None the the parent dtype is kept.
         For integer dtype a int_scale must be also given.
     mode : "global" | "local", default: "global"
@@ -74,7 +76,9 @@ def __init__(
         dtype_ = fix_dtype(recording, dtype)
 
         if dtype_.kind == "i":
-            assert int_scale is not None, "For recording with dtype=int you must set dtype=float32 OR set a int_scale"
+            assert (
+                int_scale is not None
+            ), "For recording with dtype=int you must set the output dtype to float OR set a int_scale"
 
         if W is not None:
             W = np.asarray(W)
@@ -124,7 +128,7 @@ def __init__(self, parent_recording_segment, W, M, dtype, int_scale):
     def get_traces(self, start_frame, end_frame, channel_indices):
         traces = self.parent_recording_segment.get_traces(start_frame, end_frame, slice(None))
         traces_dtype = traces.dtype
-        # if uint --> force int
+        # if uint --> force float
         if traces_dtype.kind == "u":
             traces = traces.astype("float32")
 
@@ -185,6 +189,7 @@ def compute_whitening_matrix(
 
     """
     random_data = get_random_data_chunks(recording, concatenated=True, return_scaled=False, **random_chunk_kwargs)
+    random_data = random_data.astype(np.float32)
 
     regularize_kwargs = regularize_kwargs if regularize_kwargs is not None else {"method": "GraphicalLassoCV"}
 

src/spikeinterface/sortingcomponents/peak_detection.py

@@ -57,6 +57,7 @@ def detect_peaks(
     folder=None,
     names=None,
     skip_after_n_peaks=None,
+    recording_slices=None,
     **kwargs,
 ):
     """Peak detection based on threshold crossing in term of k x MAD.
@@ -83,6 +84,10 @@ def detect_peaks(
     skip_after_n_peaks : None | int
         Skip the computation after n_peaks.
         This is not an exact because internally this skip is done per worker in average.
+    recording_slices : None | list[tuple]
+        Optionaly give a list of slices to run the pipeline only on some chunks of the recording.
+        It must be a list of (segment_index, frame_start, frame_stop).
+        If None (default), the function iterates over the entire duration of the recording.
 
     {method_doc}
     {job_doc}
@@ -135,6 +140,7 @@ def detect_peaks(
         folder=folder,
         names=names,
         skip_after_n_peaks=skip_after_n_peaks,
+        recording_slices=recording_slices,
     )
     return outs