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

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

mesa/experimental/cell_space/property_layer.py

@@ -69,24 +69,24 @@ def __init__(
             )
             self.__class__.propertylayer_experimental_warning_given = True
 
-
     @classmethod
-    def from_data(cls, name:str, data:np.ndarray):
-        """create a property layer from a NumPy array.
+    def from_data(cls, name: str, data: np.ndarray):
+        """Create a property layer from a NumPy array.
 
         Args:
             name: The name of the property layer.
             data: A NumPy array representing the grid data.
 
         """
-
-        layer = cls(name, data.shape, default_value=data[*[0 for _ in range(len(data.shape))]], dtype=data.dtype.type)
+        layer = cls(
+            name,
+            data.shape,
+            default_value=data[*[0 for _ in range(len(data.shape))]],
+            dtype=data.dtype.type,
+        )
         layer.set_cells(data)
         return layer
 
-
-
-
     def set_cells(self, value, condition: Callable | None = None):
         """Perform a batch update either on the entire grid or conditionally, in-place.
 
@@ -353,7 +353,9 @@ def select_cells(
 
         # Apply the empty mask if only_empty is True
         if only_empty:
-            combined_mask = np.logical_and(combined_mask, self._mesa_property_layers["empty"])
+            combined_mask = np.logical_and(
+                combined_mask, self._mesa_property_layers["empty"]
+            )
 
         # Apply conditions
         if conditions:

tests/test_cell_space.py

@@ -716,32 +716,43 @@ def test_select_cells():
     dimensions = (5, 5)
     grid = OrthogonalMooreGrid(dimensions, torus=False, random=random.Random(42))
 
-    data = np.random.default_rng(12456).random((5,5))
+    data = np.random.default_rng(12456).random((5, 5))
     grid.add_property_layer(PropertyLayer.from_data("elevation", data))
 
     # fixme, add an agent and update the np.all test accordingly
-    mask = grid.select_cells(conditions={"elevation": lambda x: x > 0.5}, return_list=False, only_empty=True)
+    mask = grid.select_cells(
+        conditions={"elevation": lambda x: x > 0.5}, return_list=False, only_empty=True
+    )
     assert mask.shape == (5, 5)
     assert np.all(mask == (data > 0.5))
 
-    mask = grid.select_cells(conditions={"elevation": lambda x: x > 0.5}, return_list=False, only_empty=False)
+    mask = grid.select_cells(
+        conditions={"elevation": lambda x: x > 0.5}, return_list=False, only_empty=False
+    )
     assert mask.shape == (5, 5)
     assert np.all(mask == (data > 0.5))
 
     # fixme add extreme_values heighest and lowest
-    mask = grid.select_cells(extreme_values={'elevation' : 'highest'}, return_list=False, only_empty=False)
+    mask = grid.select_cells(
+        extreme_values={"elevation": "highest"}, return_list=False, only_empty=False
+    )
     assert mask.shape == (5, 5)
-    assert np.all(mask == (data==data.max()))
+    assert np.all(mask == (data == data.max()))
 
-    mask = grid.select_cells(extreme_values={'elevation' : 'lowest'}, return_list=False, only_empty=False)
+    mask = grid.select_cells(
+        extreme_values={"elevation": "lowest"}, return_list=False, only_empty=False
+    )
     assert mask.shape == (5, 5)
-    assert np.all(mask == (data==data.min()))
+    assert np.all(mask == (data == data.min()))
 
     with pytest.raises(ValueError):
-        grid.select_cells(extreme_values={'elevation': 'weird'}, return_list=False, only_empty=False)
+        grid.select_cells(
+            extreme_values={"elevation": "weird"}, return_list=False, only_empty=False
+        )
 
     # fixme add pre-specified mask to any other option
 
+
 def test_property_layer_errors():
     """Test error handling for PropertyLayers."""
     dimensions = 5, 5