black updates

containers/cleanair/cleanair_data/cleanair_data/inputs/rectgrid_writer.py

@@ -1,6 +1,7 @@
 """
 Get data from the AQE network via the API
 """
+
 import numpy as np
 from sqlalchemy.schema import CreateSchema
 from ..databases import DBWriter
@@ -83,9 +84,9 @@ def update_remote_tables(self):
         ]
         self.commit_records(meta_points, on_conflict="overwrite")
         for grid_cell, meta_point in zip(grid_cells, meta_points):
-            grid_cell[
-                "point_id"
-            ] = meta_point.id  # this will be None if the record was not inserted
+            grid_cell["point_id"] = (
+                meta_point.id
+            )  # this will be None if the record was not inserted
 
         # Commit the grid cell records to the database
         grid_records = [

containers/cleanair/cleanair_data/cleanair_data/mixins/availability_mixins/__init__.py

@@ -1,8 +1,13 @@
 """Mixins for checking what data is available and what is missing in database tables"""
+
 from .aqe_availability import AQEAvailabilityMixin
 from .laqn_availability import LAQNAvailabilityMixin
 from .breathe_availability import BreatheAvailabilityMixin
 from .satellite_availability import SatelliteAvailabilityMixin
 from .static_feature_availability import StaticFeatureAvailabilityMixin
 
-__all__ = ["SatelliteAvailabilityMixin", "BreatheAvailabilityMixin", "StaticFeatureAvailabilityMixin"]
+__all__ = [
+    "SatelliteAvailabilityMixin",
+    "BreatheAvailabilityMixin",
+    "StaticFeatureAvailabilityMixin",
+]

containers/cleanair/cleanair_data/cleanair_data/parsers/urbanair_db_parser/inputs/main.py

@@ -1,4 +1,5 @@
 """Inputs parser"""
+
 import typer
 from . import aqe_cli
 from . import laqn_cli
@@ -14,4 +15,4 @@
 app.add_typer(scoot_cli.app, name="scoot")
 
 if __name__ == "__main__":
-    app()
+    app()

containers/cleanair/gpjax_models/gpjax_models/models/predicting/utils.py

@@ -1,8 +1,12 @@
 """ Common functions that are usually required for runnign experiments. """
+
 import numpy as np
 from tqdm import tqdm
 
-def batch_predict(XS, prediction_fn=None, batch_size=1000, verbose=False, axis=0, ci=False):
+
+def batch_predict(
+    XS, prediction_fn=None, batch_size=1000, verbose=False, axis=0, ci=False
+):
     # Ensure batch is less than the number of test points
     if XS.shape[0] < batch_size:
         batch_size = XS.shape[0]
@@ -44,7 +48,7 @@ def batch_predict(XS, prediction_fn=None, batch_size=1000, verbose=False, axis=0
             ys_var_arr.append(y_var)
 
         if verbose:
-             bar.update(1)
+            bar.update(1)
 
     if ci:
         y_median = np.concatenate(ys_median_arr, axis=axis)