Merge pull request #2907 from samuelgarcia/fix_verbose

Fix more verbosity propagation

src/spikeinterface/core/analyzer_extension_core.py

@@ -49,9 +49,8 @@ class ComputeRandomSpikes(AnalyzerExtension):
     use_nodepipeline = False
     need_job_kwargs = False
 
-    def _run(
-        self,
-    ):
+    def _run(self, verbose=False):
+
         self.data["random_spikes_indices"] = random_spikes_selection(
             self.sorting_analyzer.sorting,
             num_samples=self.sorting_analyzer.rec_attributes["num_samples"],
@@ -145,7 +144,7 @@ def nbefore(self):
     def nafter(self):
         return int(self.params["ms_after"] * self.sorting_analyzer.sampling_frequency / 1000.0)
 
-    def _run(self, **job_kwargs):
+    def _run(self, verbose=False, **job_kwargs):
         self.data.clear()
 
         recording = self.sorting_analyzer.recording
@@ -183,6 +182,7 @@ def _run(self, **job_kwargs):
             sparsity_mask=sparsity_mask,
             copy=copy,
             job_name="compute_waveforms",
+            verbose=verbose,
             **job_kwargs,
         )
 
@@ -311,7 +311,7 @@ def _set_params(self, ms_before: float = 1.0, ms_after: float = 2.0, operators=N
         )
         return params
 
-    def _run(self, **job_kwargs):
+    def _run(self, verbose=False, **job_kwargs):
         self.data.clear()
 
         if self.sorting_analyzer.has_extension("waveforms"):
@@ -339,6 +339,7 @@ def _run(self, **job_kwargs):
                 self.nafter,
                 return_scaled=return_scaled,
                 return_std=return_std,
+                verbose=verbose,
                 **job_kwargs,
             )
 
@@ -581,7 +582,7 @@ def _select_extension_data(self, unit_ids):
         # this do not depend on units
         return self.data
 
-    def _run(self):
+    def _run(self, verbose=False):
         self.data["noise_levels"] = get_noise_levels(
             self.sorting_analyzer.recording, return_scaled=self.sorting_analyzer.return_scaled, **self.params
         )

src/spikeinterface/core/node_pipeline.py

@@ -473,6 +473,7 @@ def run_node_pipeline(
     squeeze_output=True,
     folder=None,
     names=None,
+    verbose=False,
 ):
     """
     Common function to run pipeline with peak detector or already detected peak.
@@ -499,6 +500,7 @@ def run_node_pipeline(
         init_args,
         gather_func=gather_func,
         job_name=job_name,
+        verbose=verbose,
         **job_kwargs,
     )
 

src/spikeinterface/core/sortinganalyzer.py

@@ -835,7 +835,7 @@ def get_num_units(self) -> int:
         return self.sorting.get_num_units()
 
     ## extensions zone
-    def compute(self, input, save=True, extension_params=None, **kwargs):
+    def compute(self, input, save=True, extension_params=None, verbose=False, **kwargs):
         """
         Compute one extension or several extensiosn.
         Internally calls compute_one_extension() or compute_several_extensions() depending on the input type.
@@ -883,11 +883,11 @@ def compute(self, input, save=True, extension_params=None, **kwargs):
         )
         """
         if isinstance(input, str):
-            return self.compute_one_extension(extension_name=input, save=save, **kwargs)
+            return self.compute_one_extension(extension_name=input, save=save, verbose=verbose, **kwargs)
         elif isinstance(input, dict):
             params_, job_kwargs = split_job_kwargs(kwargs)
             assert len(params_) == 0, "Too many arguments for SortingAnalyzer.compute_several_extensions()"
-            self.compute_several_extensions(extensions=input, save=save, **job_kwargs)
+            self.compute_several_extensions(extensions=input, save=save, verbose=verbose, **job_kwargs)
         elif isinstance(input, list):
             params_, job_kwargs = split_job_kwargs(kwargs)
             assert len(params_) == 0, "Too many arguments for SortingAnalyzer.compute_several_extensions()"
@@ -898,11 +898,11 @@ def compute(self, input, save=True, extension_params=None, **kwargs):
                         ext_name in input
                     ), f"SortingAnalyzer.compute(): Parameters specified for {ext_name}, which is not in the specified {input}"
                     extensions[ext_name] = ext_params
-            self.compute_several_extensions(extensions=extensions, save=save, **job_kwargs)
+            self.compute_several_extensions(extensions=extensions, save=save, verbose=verbose, **job_kwargs)
         else:
             raise ValueError("SortingAnalyzer.compute() need str, dict or list")
 
-    def compute_one_extension(self, extension_name, save=True, **kwargs):
+    def compute_one_extension(self, extension_name, save=True, verbose=False, **kwargs):
         """
         Compute one extension.
 
@@ -925,7 +925,7 @@ def compute_one_extension(self, extension_name, save=True, **kwargs):
         Returns
         -------
         result_extension: AnalyzerExtension
-            Return the extension instance.
+            Return the extension instance
 
         Examples
         --------
@@ -961,13 +961,16 @@ def compute_one_extension(self, extension_name, save=True, **kwargs):
 
         extension_instance = extension_class(self)
         extension_instance.set_params(save=save, **params)
-        extension_instance.run(save=save, **job_kwargs)
+        if extension_class.need_job_kwargs:
+            extension_instance.run(save=save, verbose=verbose, **job_kwargs)
+        else:
+            extension_instance.run(save=save, verbose=verbose)
 
         self.extensions[extension_name] = extension_instance
 
         return extension_instance
 
-    def compute_several_extensions(self, extensions, save=True, **job_kwargs):
+    def compute_several_extensions(self, extensions, save=True, verbose=False, **job_kwargs):
         """
         Compute several extensions
 
@@ -1021,9 +1024,9 @@ def compute_several_extensions(self, extensions, save=True, **job_kwargs):
         for extension_name, extension_params in extensions_without_pipeline.items():
             extension_class = get_extension_class(extension_name)
             if extension_class.need_job_kwargs:
-                self.compute_one_extension(extension_name, save=save, **extension_params, **job_kwargs)
+                self.compute_one_extension(extension_name, save=save, verbose=verbose, **extension_params, **job_kwargs)
             else:
-                self.compute_one_extension(extension_name, save=save, **extension_params)
+                self.compute_one_extension(extension_name, save=save, verbose=verbose, **extension_params)
         # then extensions with pipeline
         if len(extensions_with_pipeline) > 0:
             all_nodes = []
@@ -1053,6 +1056,7 @@ def compute_several_extensions(self, extensions, save=True, **job_kwargs):
                 job_name=job_name,
                 gather_mode="memory",
                 squeeze_output=False,
+                verbose=verbose,
             )
 
             for r, result in enumerate(results):
@@ -1071,9 +1075,9 @@ def compute_several_extensions(self, extensions, save=True, **job_kwargs):
         for extension_name, extension_params in extensions_post_pipeline.items():
             extension_class = get_extension_class(extension_name)
             if extension_class.need_job_kwargs:
-                self.compute_one_extension(extension_name, save=save, **extension_params, **job_kwargs)
+                self.compute_one_extension(extension_name, save=save, verbose=verbose, **extension_params, **job_kwargs)
             else:
-                self.compute_one_extension(extension_name, save=save, **extension_params)
+                self.compute_one_extension(extension_name, save=save, verbose=verbose, **extension_params)
 
     def get_saved_extension_names(self):
         """

src/spikeinterface/core/waveform_tools.py

@@ -221,6 +221,7 @@ def distribute_waveforms_to_buffers(
     mode="memmap",
     sparsity_mask=None,
     job_name=None,
+    verbose=False,
     **job_kwargs,
 ):
     """
@@ -281,7 +282,9 @@ def distribute_waveforms_to_buffers(
     )
     if job_name is None:
         job_name = f"extract waveforms {mode} multi buffer"
-    processor = ChunkRecordingExecutor(recording, func, init_func, init_args, job_name=job_name, **job_kwargs)
+    processor = ChunkRecordingExecutor(
+        recording, func, init_func, init_args, job_name=job_name, verbose=verbose, **job_kwargs
+    )
     processor.run()
 
 
@@ -410,6 +413,7 @@ def extract_waveforms_to_single_buffer(
     sparsity_mask=None,
     copy=True,
     job_name=None,
+    verbose=False,
     **job_kwargs,
 ):
     """
@@ -523,7 +527,9 @@ def extract_waveforms_to_single_buffer(
         if job_name is None:
             job_name = f"extract waveforms {mode} mono buffer"
 
-        processor = ChunkRecordingExecutor(recording, func, init_func, init_args, job_name=job_name, **job_kwargs)
+        processor = ChunkRecordingExecutor(
+            recording, func, init_func, init_args, job_name=job_name, verbose=verbose, **job_kwargs
+        )
         processor.run()
 
     if mode == "memmap":
@@ -783,6 +789,7 @@ def estimate_templates_with_accumulator(
     return_scaled: bool = True,
     job_name=None,
     return_std: bool = False,
+    verbose: bool = False,
     **job_kwargs,
 ):
     """
@@ -861,7 +868,9 @@ def estimate_templates_with_accumulator(
 
     if job_name is None:
         job_name = "estimate_templates_with_accumulator"
-    processor = ChunkRecordingExecutor(recording, func, init_func, init_args, job_name=job_name, **job_kwargs)
+    processor = ChunkRecordingExecutor(
+        recording, func, init_func, init_args, job_name=job_name, verbose=verbose, **job_kwargs
+    )
     processor.run()
 
     # average

src/spikeinterface/postprocessing/amplitude_scalings.py

@@ -181,7 +181,7 @@ def _get_pipeline_nodes(self):
         nodes = [spike_retriever_node, amplitude_scalings_node]
         return nodes
 
-    def _run(self, **job_kwargs):
+    def _run(self, verbose=False, **job_kwargs):
         job_kwargs = fix_job_kwargs(job_kwargs)
         nodes = self.get_pipeline_nodes()
         amp_scalings, collision_mask = run_node_pipeline(
@@ -190,6 +190,7 @@ def _run(self, **job_kwargs):
             job_kwargs=job_kwargs,
             job_name="amplitude_scalings",
             gather_mode="memory",
+            verbose=verbose,
         )
         self.data["amplitude_scalings"] = amp_scalings
         if self.params["handle_collisions"]:

src/spikeinterface/postprocessing/correlograms.py

@@ -70,7 +70,7 @@ def _select_extension_data(self, unit_ids):
         new_data = dict(ccgs=new_ccgs, bins=new_bins)
         return new_data
 
-    def _run(self):
+    def _run(self, verbose=False):
         ccgs, bins = compute_correlograms_on_sorting(self.sorting_analyzer.sorting, **self.params)
         self.data["ccgs"] = ccgs
         self.data["bins"] = bins

src/spikeinterface/postprocessing/isi.py

@@ -56,7 +56,7 @@ def _select_extension_data(self, unit_ids):
         new_extension_data = dict(isi_histograms=new_isi_hists, bins=new_bins)
         return new_extension_data
 
-    def _run(self):
+    def _run(self, verbose=False):
         isi_histograms, bins = _compute_isi_histograms(self.sorting_analyzer.sorting, **self.params)
         self.data["isi_histograms"] = isi_histograms
         self.data["bins"] = bins

src/spikeinterface/postprocessing/principal_component.py

@@ -256,7 +256,7 @@ def project_new(self, new_spikes, new_waveforms, progress_bar=True):
         new_projections = self._transform_waveforms(new_spikes, new_waveforms, pca_model, progress_bar=progress_bar)
         return new_projections
 
-    def _run(self, **job_kwargs):
+    def _run(self, verbose=False, **job_kwargs):
         """
         Compute the PCs on waveforms extacted within the by ComputeWaveforms.
         Projections are computed only on the waveforms sampled by the SortingAnalyzer.
@@ -295,7 +295,7 @@ def _run(self, **job_kwargs):
     def _get_data(self):
         return self.data["pca_projection"]
 
-    def run_for_all_spikes(self, file_path=None, **job_kwargs):
+    def run_for_all_spikes(self, file_path=None, verbose=False, **job_kwargs):
         """
         Project all spikes from the sorting on the PCA model.
         This is a long computation because waveform need to be extracted from each spikes.
@@ -359,7 +359,9 @@ def run_for_all_spikes(self, file_path=None, **job_kwargs):
             unit_channels,
             pca_model,
         )
-        processor = ChunkRecordingExecutor(recording, func, init_func, init_args, job_name="extract PCs", **job_kwargs)
+        processor = ChunkRecordingExecutor(
+            recording, func, init_func, init_args, job_name="extract PCs", verbose=verbose, **job_kwargs
+        )
         processor.run()
 
     def _fit_by_channel_local(self, n_jobs, progress_bar):

src/spikeinterface/postprocessing/spike_amplitudes.py

@@ -107,7 +107,7 @@ def _get_pipeline_nodes(self):
         nodes = [spike_retriever_node, spike_amplitudes_node]
         return nodes
 
-    def _run(self, **job_kwargs):
+    def _run(self, verbose=False, **job_kwargs):
         job_kwargs = fix_job_kwargs(job_kwargs)
         nodes = self.get_pipeline_nodes()
         amps = run_node_pipeline(
@@ -116,6 +116,7 @@ def _run(self, **job_kwargs):
             job_kwargs=job_kwargs,
             job_name="spike_amplitudes",
             gather_mode="memory",
+            verbose=False,
         )
         self.data["amplitudes"] = amps
 

src/spikeinterface/postprocessing/spike_locations.py

@@ -120,7 +120,7 @@ def _get_pipeline_nodes(self):
         )
         return nodes
 
-    def _run(self, **job_kwargs):
+    def _run(self, verbose=False, **job_kwargs):
         job_kwargs = fix_job_kwargs(job_kwargs)
         nodes = self.get_pipeline_nodes()
         spike_locations = run_node_pipeline(
@@ -129,6 +129,7 @@ def _run(self, **job_kwargs):
             job_kwargs=job_kwargs,
             job_name="spike_locations",
             gather_mode="memory",
+            verbose=verbose,
         )
         self.data["spike_locations"] = spike_locations
 

src/spikeinterface/postprocessing/template_metrics.py

@@ -150,7 +150,7 @@ def _select_extension_data(self, unit_ids):
         new_metrics = self.data["metrics"].loc[np.array(unit_ids)]
         return dict(metrics=new_metrics)
 
-    def _run(self):
+    def _run(self, verbose=False):
         import pandas as pd
         from scipy.signal import resample_poly
 

src/spikeinterface/postprocessing/template_similarity.py

@@ -42,7 +42,7 @@ def _select_extension_data(self, unit_ids):
         new_similarity = self.data["similarity"][unit_indices][:, unit_indices]
         return dict(similarity=new_similarity)
 
-    def _run(self):
+    def _run(self, verbose=False):
         templates_array = get_dense_templates_array(
             self.sorting_analyzer, return_scaled=self.sorting_analyzer.return_scaled
         )

src/spikeinterface/postprocessing/unit_localization.py

@@ -64,7 +64,7 @@ def _select_extension_data(self, unit_ids):
         new_unit_location = self.data["unit_locations"][unit_inds]
         return dict(unit_locations=new_unit_location)
 
-    def _run(self):
+    def _run(self, verbose=False):
         method = self.params["method"]
         method_kwargs = self.params["method_kwargs"]
 

src/spikeinterface/sortingcomponents/matching/main.py

@@ -7,7 +7,9 @@
 from spikeinterface.core import get_chunk_with_margin
 
 
-def find_spikes_from_templates(recording, method="naive", method_kwargs={}, extra_outputs=False, **job_kwargs):
+def find_spikes_from_templates(
+    recording, method="naive", method_kwargs={}, extra_outputs=False, verbose=False, **job_kwargs
+):
     """Find spike from a recording from given templates.
 
     Parameters
@@ -53,7 +55,14 @@ def find_spikes_from_templates(recording, method="naive", method_kwargs={}, extr
     init_func = _init_worker_find_spikes
     init_args = (recording, method, method_kwargs_seralized)
     processor = ChunkRecordingExecutor(
-        recording, func, init_func, init_args, handle_returns=True, job_name=f"find spikes ({method})", **job_kwargs
+        recording,
+        func,
+        init_func,
+        init_args,
+        handle_returns=True,
+        job_name=f"find spikes ({method})",
+        verbose=verbose,
+        **job_kwargs,
     )
     spikes = processor.run()
 

src/spikeinterface/sortingcomponents/peak_detection.py

@@ -9,10 +9,8 @@
 import numpy as np
 
 from spikeinterface.core.job_tools import (
-    ChunkRecordingExecutor,
     _shared_job_kwargs_doc,
     split_job_kwargs,
-    fix_job_kwargs,
 )
 from spikeinterface.core.recording_tools import get_noise_levels, get_channel_distances, get_random_data_chunks