Fix silence period and job_kwargs

src/spikeinterface/preprocessing/silence_periods.py

@@ -5,8 +5,9 @@
 from spikeinterface.core.core_tools import define_function_handling_dict_from_class
 from .basepreprocessor import BasePreprocessor, BasePreprocessorSegment
 
-from spikeinterface.core import get_random_data_chunks, get_noise_levels
+from spikeinterface.core import get_noise_levels
 from spikeinterface.core.generate import NoiseGeneratorRecording
+from spikeinterface.core.job_tools import split_job_kwargs
 
 
 class SilencedPeriodsRecording(BasePreprocessor):
@@ -36,15 +37,23 @@ class SilencedPeriodsRecording(BasePreprocessor):
         - "noise": The periods are filled with a gaussion noise that has the
                    same variance that the one in the recordings, on a per channel
                    basis
-    **random_chunk_kwargs : Keyword arguments for `spikeinterface.core.get_random_data_chunk()` function
+    **noise_levels_kwargs : Keyword arguments for `spikeinterface.core.get_noise_levels()` function
 
     Returns
     -------
     silence_recording : SilencedPeriodsRecording
         The recording extractor after silencing some periods
     """
 
-    def __init__(self, recording, list_periods, mode="zeros", noise_levels=None, seed=None, **random_chunk_kwargs):
+    def __init__(
+        self,
+        recording,
+        list_periods,
+        mode="zeros",
+        noise_levels=None,
+        seed=None,
+        **noise_levels_kwargs,
+    ):
         available_modes = ("zeros", "noise")
         num_seg = recording.get_num_segments()
 
@@ -71,11 +80,9 @@ def __init__(self, recording, list_periods, mode="zeros", noise_levels=None, see
 
         if mode in ["noise"]:
             if noise_levels is None:
-                random_slices_kwargs = random_chunk_kwargs.copy()
-                random_slices_kwargs["seed"] = seed
-                noise_levels = get_noise_levels(
-                    recording, return_in_uV=False, random_slices_kwargs=random_slices_kwargs
-                )
+                noise_levels_kwargs["return_in_uV"] = False
+                noise_levels_kwargs["seed"] = seed
+                noise_levels = get_noise_levels(recording, **noise_levels_kwargs)
             noise_generator = NoiseGeneratorRecording(
                 num_channels=recording.get_num_channels(),
                 sampling_frequency=recording.sampling_frequency,
@@ -97,8 +104,10 @@ def __init__(self, recording, list_periods, mode="zeros", noise_levels=None, see
             rec_segment = SilencedPeriodsRecordingSegment(parent_segment, periods, mode, noise_generator, seg_index)
             self.add_recording_segment(rec_segment)
 
-        self._kwargs = dict(recording=recording, list_periods=list_periods, mode=mode, seed=seed)
-        self._kwargs.update(random_chunk_kwargs)
+        self._kwargs = dict(
+            recording=recording, list_periods=list_periods, mode=mode, seed=seed, noise_levels=noise_levels
+        )
+        self._kwargs.update(noise_levels_kwargs)
 
 
 class SilencedPeriodsRecordingSegment(BasePreprocessorSegment):

src/spikeinterface/sortingcomponents/peak_detection.py

@@ -111,6 +111,12 @@ def detect_peaks(
     method_kwargs, job_kwargs = split_job_kwargs(kwargs)
     job_kwargs["mp_context"] = method_class.preferred_mp_context
 
+    if method_class.need_noise_levels:
+        random_chunk_kwargs = method_kwargs.pop("random_chunk_kwargs", {})
+        method_kwargs["noise_levels"] = get_noise_levels(
+            recording, return_in_uV=False, **random_chunk_kwargs, **job_kwargs
+        )
+
     node0 = method_class(recording, **method_kwargs)
     nodes = [node0]
 
@@ -384,6 +390,7 @@ class DetectPeakByChannel(PeakDetectorWrapper):
 
     name = "by_channel"
     engine = "numpy"
+    need_noise_levels = True
     preferred_mp_context = None
     params_doc = """
     peak_sign: "neg" | "pos" | "both", default: "neg"
@@ -466,6 +473,7 @@ class DetectPeakByChannelTorch(PeakDetectorWrapper):
 
     name = "by_channel_torch"
     engine = "torch"
+    need_noise_levels = True
     preferred_mp_context = "spawn"
     params_doc = """
     peak_sign: "neg" | "pos" | "both", default: "neg"
@@ -509,7 +517,6 @@ def check_params(
         assert peak_sign in ("both", "neg", "pos")
         if device is None:
             device = "cuda" if torch.cuda.is_available() else "cpu"
-
         if noise_levels is None:
             noise_levels = get_noise_levels(recording, return_in_uV=False, **random_chunk_kwargs)
         abs_thresholds = noise_levels * detect_threshold
@@ -538,6 +545,7 @@ class DetectPeakLocallyExclusive(PeakDetectorWrapper):
 
     name = "locally_exclusive"
     engine = "numba"
+    need_noise_levels = True
     preferred_mp_context = None
     params_doc = (
         DetectPeakByChannel.params_doc
@@ -633,6 +641,7 @@ class DetectPeakMatchedFiltering(PeakDetector):
 
     name = "matched_filtering"
     engine = "numba"
+    need_noise_levels = False
     preferred_mp_context = None
     params_doc = (
         DetectPeakByChannel.params_doc
@@ -780,6 +789,7 @@ class DetectPeakLocallyExclusiveTorch(PeakDetectorWrapper):
 
     name = "locally_exclusive_torch"
     engine = "torch"
+    need_noise_levels = True
     preferred_mp_context = "spawn"
     params_doc = (
         DetectPeakByChannel.params_doc
@@ -1069,6 +1079,7 @@ def _torch_detect_peaks(traces, peak_sign, abs_thresholds, exclude_sweep_size=5,
 class DetectPeakLocallyExclusiveOpenCL(PeakDetectorWrapper):
     name = "locally_exclusive_cl"
     engine = "opencl"
+    need_noise_levels = True
     preferred_mp_context = None
     params_doc = (
         DetectPeakLocallyExclusive.params_doc