formatting

sortingview/SpikeSortingView/prepare_spikesortingview_data.py

@@ -20,7 +20,7 @@ def prepare_spikesortingview_data(
     bandpass_filter: bool = False,
 ) -> str:
     # NOTE(DS): for data longer than 25hours with fs = 20000; num_frame is too large for int32
-    if recording.get_num_frames() > (2 ** 31 - 1):
+    if recording.get_num_frames() > (2**31 - 1):
         int_type = np.int64
     else:
         int_type = np.int32
@@ -104,7 +104,7 @@ def prepare_spikesortingview_data(
                 start_frame_with_padding = max(start_frame - snippet_len[0], 0)
                 end_frame_with_padding = min(end_frame + snippet_len[1], num_frames)
                 traces_with_padding = recording.get_traces(start_frame=start_frame_with_padding, end_frame=end_frame_with_padding)
-                traces_sample = traces_with_padding[start_frame - start_frame_with_padding: start_frame - start_frame_with_padding + int(sampling_frequency * 1), :]
+                traces_sample = traces_with_padding[start_frame - start_frame_with_padding : start_frame - start_frame_with_padding + int(sampling_frequency * 1), :]
                 f.create_dataset(f"segment/{iseg}/traces_sample", data=traces_sample)
                 all_subsampled_spike_trains = []
                 for unit_id in unit_ids:
@@ -137,7 +137,7 @@ def prepare_spikesortingview_data(
                     channel_neighborhood = unit_channel_neighborhoods[str(unit_id)]
                     channel_neighborhood_indices = [channel_ids.tolist().index(ch_id) for ch_id in channel_neighborhood]
                     num = len(all_subsampled_spike_trains[ii])
-                    spike_snippets = spike_snippets_concat[index: index + num, :, channel_neighborhood_indices]
+                    spike_snippets = spike_snippets_concat[index : index + num, :, channel_neighborhood_indices]
                     index = index + num
                     f.create_dataset(f"segment/{iseg}/unit/{unit_id}/subsampled_spike_snippets", data=spike_snippets)
         uri = kcl.store_file_local(output_file_name)
@@ -165,7 +165,7 @@ def subsample(x: np.ndarray, num: int):
     if num >= len(x):
         return x
     stride = math.floor(len(x) / num)
-    return x[0: stride * num: stride]
+    return x[0 : stride * num : stride]
 
 
 def extract_spike_snippets(*, traces: np.ndarray, times: np.ndarray, snippet_len: Tuple[int, int]):

sortingview/version.py

@@ -1,2 +1,2 @@
 # This file was automatically generated by jinjaroot. Do not edit directly.
-__version__ = '0.13.3'
+__version__ = "0.13.3"

sortingview/views/TabLayout.py

@@ -13,7 +13,7 @@ class TabLayout(View):
     Tab layout
     """
 
-    def __init__(self, items: List[TabLayoutItem], tab_bar_layout: Literal['horizontal', 'vertical'] = 'horizontal', **kwargs) -> None:
+    def __init__(self, items: List[TabLayoutItem], tab_bar_layout: Literal["horizontal", "vertical"] = "horizontal", **kwargs) -> None:
         super().__init__("TabLayout", is_layout=True, **kwargs)
         self._items = items
         self._tab_bar_layout = tab_bar_layout
@@ -22,7 +22,7 @@ def to_dict(self) -> dict:
         ret = {
             "type": self.type,
             "items": [item.view.to_dict() if item.view.is_layout else {"type": "View", "viewId": item.view.id} for item in self._items],
-            "itemProperties": [{"label": item.label} for item in self._items]
+            "itemProperties": [{"label": item.label} for item in self._items],
         }
         if self._tab_bar_layout != "horizontal":
             ret["tabBarLayout"] = self._tab_bar_layout