Merge branch 'main' of github.com:cwindolf/spike-psvae into main

requirements.txt

@@ -1,4 +1,5 @@
 pytest
 ibl-neuropixel
 spikeinterface
-cloudpickle
+cloudpickle
+hdbscan

src/dartsort/cluster/split.py

@@ -74,19 +74,15 @@ def split_clusters(
             new_labels = split_result.new_labels
             triaged = split_result.new_labels < 0
             labels[in_unit[triaged]] = new_labels[triaged]
-            labels[in_unit[new_labels > 0]] = (
-                cur_max_label + new_labels[new_labels > 0]
-            )
+            labels[in_unit[new_labels > 0]] = cur_max_label + new_labels[new_labels > 0]
             new_untriaged_labels = labels[in_unit[new_labels >= 0]]
             cur_max_label = new_untriaged_labels.max()
 
             # submit recursive jobs to the pool, if any
             if recursive:
                 new_units = np.unique(new_untriaged_labels)
                 for i in new_units:
-                    jobs.append(
-                        pool.submit(_split_job, np.flatnonzero(labels == i))
-                    )
+                    jobs.append(pool.submit(_split_job, np.flatnonzero(labels == i)))
                 if show_progress:
                     iterator.total += len(new_units)
 
@@ -151,7 +147,7 @@ def __init__(
         min_cluster_size=25,
         min_samples=25,
         cluster_selection_epsilon=25,
-        reassign_outliers=True,
+        reassign_outliers=False,
         random_state=0,
         **dataset_name_kwargs,
     ):
@@ -241,18 +237,14 @@ def split_cluster(self, in_unit):
         is_split = np.setdiff1d(np.unique(hdb_labels), [-1]).size > 1
 
         if is_split and self.reassign_outliers:
-            hdb_labels = cluster_util.knn_reassign_outliers(
-                hdb_labels, features
-            )
+            hdb_labels = cluster_util.knn_reassign_outliers(hdb_labels, features)
 
         new_labels = None
         if is_split:
             new_labels = np.full(n_spikes, -1)
             new_labels[kept] = hdb_labels
 
-        return SplitResult(
-            is_split=is_split, in_unit=in_unit, new_labels=new_labels
-        )
+        return SplitResult(is_split=is_split, in_unit=in_unit, new_labels=new_labels)
 
     def pca_features(self, in_unit):
         """Compute relocated PCA features on a drift-invariant channel set"""
@@ -316,12 +308,8 @@ def pca_features(self, in_unit):
             return False, no_nan, None
 
         # fit pca and embed
-        pca = PCA(
-            self.n_pca_features, random_state=self.random_state, whiten=True
-        )
-        pca_projs = np.full(
-            (n, self.n_pca_features), np.nan, dtype=waveforms.dtype
-        )
+        pca = PCA(self.n_pca_features, random_state=self.random_state, whiten=True)
+        pca_projs = np.full((n, self.n_pca_features), np.nan, dtype=waveforms.dtype)
         pca_projs[no_nan] = pca.fit_transform(waveforms[no_nan])
 
         return True, no_nan, pca_projs
@@ -386,9 +374,7 @@ def initialize_from_h5(
 
 # this is to help split_clusters take a string argument
 all_split_strategies = [FeatureSplit]
-split_strategies_by_class_name = {
-    cls.__name__: cls for cls in all_split_strategies
-}
+split_strategies_by_class_name = {cls.__name__: cls for cls in all_split_strategies}
 
 # -- parallelism widgets
 
@@ -404,9 +390,7 @@ def __init__(self, split_strategy):
 def _split_job_init(split_strategy_class_name, split_strategy_kwargs):
     global _split_job_context
     split_strategy = split_strategies_by_class_name[split_strategy_class_name]
-    _split_job_context = SplitJobContext(
-        split_strategy(**split_strategy_kwargs)
-    )
+    _split_job_context = SplitJobContext(split_strategy(**split_strategy_kwargs))
 
 
 def _split_job(in_unit):