[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

nwbinspector/checks/ophys.py

@@ -34,36 +34,24 @@ def check_roi_response_series_dims(roi_response_series: RoiResponseSeries):
         )
 
 
-@register_check(
-    importance=Importance.BEST_PRACTICE_VIOLATION, neurodata_type=RoiResponseSeries
-)
+@register_check(importance=Importance.BEST_PRACTICE_VIOLATION, neurodata_type=RoiResponseSeries)
 def check_roi_response_series_link_to_plane_segmentation(
     roi_response_series: RoiResponseSeries,
 ):
     """Check that each ROI response series links to a plane segmentation."""
     if not isinstance(roi_response_series.rois.table, PlaneSegmentation):
-        return InspectorMessage(
-            message="rois field does not point to a PlaneSegmentation table."
-        )
+        return InspectorMessage(message="rois field does not point to a PlaneSegmentation table.")
 
 
-@register_check(
-    importance=Importance.BEST_PRACTICE_VIOLATION, neurodata_type=OpticalChannel
-)
+@register_check(importance=Importance.BEST_PRACTICE_VIOLATION, neurodata_type=OpticalChannel)
 def check_emission_lambda_in_nm(optical_channel: OpticalChannel):
     """Check that emission lambda is in feasible range for unit nanometers."""
     if optical_channel.emission_lambda < MIN_LAMBDA:
-        return InspectorMessage(
-            f"emission lambda of {optical_channel.emission_lambda} should be in units of nm."
-        )
+        return InspectorMessage(f"emission lambda of {optical_channel.emission_lambda} should be in units of nm.")
 
 
-@register_check(
-    importance=Importance.BEST_PRACTICE_VIOLATION, neurodata_type=ImagingPlane
-)
+@register_check(importance=Importance.BEST_PRACTICE_VIOLATION, neurodata_type=ImagingPlane)
 def check_excitation_lambda_in_nm(imaging_plane: ImagingPlane):
     """Check that emission lambda is in feasible range for unit nanometers."""
     if imaging_plane.excitation_lambda < MIN_LAMBDA:
-        return InspectorMessage(
-            f"excitation lambda of {imaging_plane.excitation_lambda} should be in units of nm."
-        )
+        return InspectorMessage(f"excitation lambda of {imaging_plane.excitation_lambda} should be in units of nm.")

nwbinspector/checks/tables.py

@@ -17,13 +17,9 @@
 
 
 @register_check(importance=Importance.CRITICAL, neurodata_type=DynamicTableRegion)
-def check_dynamic_table_region_data_validity(
-    dynamic_table_region: DynamicTableRegion, nelems=200
-):
+def check_dynamic_table_region_data_validity(dynamic_table_region: DynamicTableRegion, nelems=200):
     """Check if a DynamicTableRegion is valid."""
-    if np.any(
-        np.asarray(dynamic_table_region.data[:nelems]) > len(dynamic_table_region.table)
-    ):
+    if np.any(np.asarray(dynamic_table_region.data[:nelems]) > len(dynamic_table_region.table)):
         return InspectorMessage(
             message=(
                 f"Some elements of {dynamic_table_region.name} are out of range because they are greater than the "
@@ -36,18 +32,14 @@ def check_dynamic_table_region_data_validity(
         )
 
 
-@register_check(
-    importance=Importance.BEST_PRACTICE_VIOLATION, neurodata_type=DynamicTable
-)
+@register_check(importance=Importance.BEST_PRACTICE_VIOLATION, neurodata_type=DynamicTable)
 def check_empty_table(table: DynamicTable):
     """Check if a DynamicTable is empty."""
     if len(table.id) == 0:
         return InspectorMessage(message="This table has no data added to it.")
 
 
-@register_check(
-    importance=Importance.BEST_PRACTICE_VIOLATION, neurodata_type=TimeIntervals
-)
+@register_check(importance=Importance.BEST_PRACTICE_VIOLATION, neurodata_type=TimeIntervals)
 def check_time_interval_time_columns(time_intervals: TimeIntervals, nelems: int = 200):
     """
     Check that time columns are in ascending order.
@@ -74,12 +66,8 @@ def check_time_interval_time_columns(time_intervals: TimeIntervals, nelems: int
         )
 
 
-@register_check(
-    importance=Importance.BEST_PRACTICE_VIOLATION, neurodata_type=TimeIntervals
-)
-def check_time_intervals_stop_after_start(
-    time_intervals: TimeIntervals, nelems: int = 200
-):
+@register_check(importance=Importance.BEST_PRACTICE_VIOLATION, neurodata_type=TimeIntervals)
+def check_time_intervals_stop_after_start(time_intervals: TimeIntervals, nelems: int = 200):
     """
     Check that all stop times on a TimeInterval object occur after their corresponding start times.
 
@@ -91,11 +79,7 @@ def check_time_intervals_stop_after_start(
         very long so you don't need to load the entire array into memory. Use None to
         load the entire arrays.
     """
-    if np.any(
-        np.asarray(time_intervals["stop_time"][:nelems])
-        - np.asarray(time_intervals["start_time"][:nelems])
-        < 0
-    ):
+    if np.any(np.asarray(time_intervals["stop_time"][:nelems]) - np.asarray(time_intervals["start_time"][:nelems]) < 0):
         return InspectorMessage(
             message=(
                 "stop_times should be greater than start_times. Make sure the stop times are with respect to the "
@@ -104,9 +88,7 @@ def check_time_intervals_stop_after_start(
         )
 
 
-@register_check(
-    importance=Importance.BEST_PRACTICE_SUGGESTION, neurodata_type=DynamicTable
-)
+@register_check(importance=Importance.BEST_PRACTICE_SUGGESTION, neurodata_type=DynamicTable)
 def check_column_binary_capability(table: DynamicTable, nelems: int = 200):
     """
     Check each column of a table to see if the data could be set as a boolean dtype.
@@ -130,13 +112,9 @@ def check_column_binary_capability(table: DynamicTable, nelems: int = 200):
             if unique_values.size != 2:
                 continue
             parsed_unique_values = np.array(unique_values)
-            if isinstance(
-                parsed_unique_values[0], Real
-            ):  # upcast to float for comparison
+            if isinstance(parsed_unique_values[0], Real):  # upcast to float for comparison
                 parsed_unique_values = parsed_unique_values.astype(float)
-            elif (
-                str(parsed_unique_values.dtype)[:2] == "<U"
-            ):  # parse strings as all lower-case
+            elif str(parsed_unique_values.dtype)[:2] == "<U":  # parse strings as all lower-case
                 for j in range(2):
                     parsed_unique_values[j] = parsed_unique_values[j].lower()
             pairs_to_check = [
@@ -168,9 +146,7 @@ def check_column_binary_capability(table: DynamicTable, nelems: int = 200):
                 )
 
 
-@register_check(
-    importance=Importance.BEST_PRACTICE_SUGGESTION, neurodata_type=DynamicTable
-)
+@register_check(importance=Importance.BEST_PRACTICE_SUGGESTION, neurodata_type=DynamicTable)
 def check_single_row(
     table: DynamicTable,
     exclude_types: Optional[list] = (Units,),
@@ -192,17 +168,11 @@ def check_single_row(
         )
 
 
-@register_check(
-    importance=Importance.BEST_PRACTICE_VIOLATION, neurodata_type=DynamicTable
-)
+@register_check(importance=Importance.BEST_PRACTICE_VIOLATION, neurodata_type=DynamicTable)
 def check_table_values_for_dict(table: DynamicTable, nelems: int = 200):
     """Check if any values in a row or column of a table contain a string casting of a Python dictionary."""
     for column in table.columns:
-        if (
-            not hasattr(column, "data")
-            or isinstance(column, VectorIndex)
-            or not isinstance(column.data[0], str)
-        ):
+        if not hasattr(column, "data") or isinstance(column, VectorIndex) or not isinstance(column.data[0], str):
             continue
         for string in column.data[:nelems]:
             if is_dict_in_string(string=string):