Sc2 fix

src/spikeinterface/comparison/groundtruthstudy.py

@@ -180,7 +180,7 @@ def run_sorters(self, case_keys=None, engine="loop", engine_kwargs={}, keep=True
 
             if sorting_exists:
                 # delete older sorting + log before running sorters
-                shutil.rmtree(sorting_exists)
+                shutil.rmtree(sorting_folder)
                 log_file = self.folder / "sortings" / "run_logs" / f"{self.key_to_str(key)}.json"
                 if log_file.exists():
                     log_file.unlink()

src/spikeinterface/sorters/internal/spyking_circus2.py

@@ -118,8 +118,6 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
         if clustering_folder.exists():
             shutil.rmtree(clustering_folder)
 
-        sorting = sorting.save(folder=clustering_folder)
-
         ## We get the templates our of such a clustering
         waveforms_params = params["waveforms"].copy()
         waveforms_params.update(job_kwargs)
@@ -131,6 +129,7 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
             mode = "memory"
             waveforms_folder = None
         else:
+            sorting = sorting.save(folder=clustering_folder)
             mode = "folder"
             waveforms_folder = sorter_output_folder / "waveforms"
 

src/spikeinterface/sortingcomponents/clustering/clustering_tools.py

@@ -574,6 +574,8 @@ def remove_duplicates_via_matching(
     if tmp_folder is None:
         tmp_folder = get_global_tmp_folder()
 
+    tmp_folder.mkdir(parents=True, exist_ok=True)
+
     tmp_filename = tmp_folder / "tmp.raw"
 
     f = open(tmp_filename, "wb")
@@ -583,8 +585,8 @@ def remove_duplicates_via_matching(
     f.close()
 
     recording = BinaryRecordingExtractor(tmp_filename, num_channels=num_chans, sampling_frequency=fs, dtype="float32")
-    recording.annotate(is_filtered=True)
     recording = recording.set_probe(waveform_extractor.recording.get_probe())
+    recording.annotate(is_filtered=True)
 
     margin = 2 * max(waveform_extractor.nbefore, waveform_extractor.nafter)
     half_marging = margin // 2
@@ -608,7 +610,6 @@ def remove_duplicates_via_matching(
         t_stop = padding + (i + 1) * duration
 
         sub_recording = recording.frame_slice(t_start - half_marging, t_stop + half_marging)
-
         method_kwargs.update({"ignored_ids": ignore_ids + [i]})
         spikes, computed = find_spikes_from_templates(
             sub_recording, method=method, method_kwargs=method_kwargs, extra_outputs=True, **job_kwargs
@@ -660,7 +661,7 @@ def remove_duplicates_via_matching(
     labels = np.unique(new_labels)
     labels = labels[labels >= 0]
 
-    del recording, sub_recording
+    del recording, sub_recording, method_kwargs
     os.remove(tmp_filename)
 
     return labels, new_labels

src/spikeinterface/sortingcomponents/clustering/random_projections.py

@@ -181,17 +181,20 @@ def sigmoid(x, L, x0, k, b):
         else:
             tmp_folder = Path(params["tmp_folder"])
 
+        tmp_folder.mkdir(parents=True, exist_ok=True)
+
+        sorting_folder = tmp_folder / "sorting"
+        unit_ids = np.arange(len(np.unique(spikes["unit_index"])))
+        sorting = NumpySorting(spikes, fs, unit_ids=unit_ids)
+
         if params["shared_memory"]:
             waveform_folder = None
             mode = "memory"
         else:
             waveform_folder = tmp_folder / "waveforms"
             mode = "folder"
+            sorting = sorting.save(folder=sorting_folder)
 
-        sorting_folder = tmp_folder / "sorting"
-        unit_ids = np.arange(len(np.unique(spikes["unit_index"])))
-        sorting = NumpySorting(spikes, fs, unit_ids=unit_ids)
-        sorting = sorting.save(folder=sorting_folder)
         we = extract_waveforms(
             recording,
             sorting,
@@ -219,12 +222,14 @@ def sigmoid(x, L, x0, k, b):
             we, noise_levels, peak_labels, job_kwargs=cleaning_matching_params, **cleaning_params
         )
 
+        del we, sorting
+
         if params["tmp_folder"] is None:
             shutil.rmtree(tmp_folder)
         else:
             if not params["shared_memory"]:
                 shutil.rmtree(tmp_folder / "waveforms")
-            shutil.rmtree(tmp_folder / "sorting")
+                shutil.rmtree(tmp_folder / "sorting")
 
         if verbose:
             print("We kept %d non-duplicated clusters..." % len(labels))

src/spikeinterface/sortingcomponents/matching/circus.py

@@ -592,6 +592,7 @@ def _prepare_templates(cls, d):
         d["spatial"] = np.moveaxis(d["spatial"], [0, 1, 2], [1, 0, 2])
         d["temporal"] = np.moveaxis(d["temporal"], [0, 1, 2], [1, 2, 0])
         d["singular"] = d["singular"].T[:, :, np.newaxis]
+
         return d
 
     @classmethod