o Fix formatting

Author: rajeeja

test/core/test_divergence.py

@@ -14,7 +14,7 @@ def test_divergence_output_format(self, gridpath, datasetpath):
         # Create two components for vector field (using same data for simplicity in test)
         u_component = uxds['t2m']
         v_component = uxds['t2m']
-        
+
         div_da = u_component.divergence(v_component)
 
         assert isinstance(div_da, ux.UxDataArray)
@@ -27,14 +27,14 @@ def test_divergence_input_validation(self, gridpath, datasetpath):
         uxds = ux.open_dataset(gridpath("ugrid", "quad-hexagon", "grid.nc"), datasetpath("ugrid", "quad-hexagon", "data.nc"))
 
         u_component = uxds['t2m']
-        
+
         # Test with non-UxDataArray
         with pytest.raises(TypeError, match="other must be a UxDataArray"):
             u_component.divergence(np.array([1, 2, 3]))
-        
+
         # Test with different grids (create a simple test case)
         # This would require creating another grid, so we'll skip for now
-        
+
         # Test with different dimensions
         # This would require creating data with different dims, so we'll skip for now
 
@@ -48,7 +48,7 @@ def test_divergence_basic(self, gridpath, datasetpath):
         # Use the same field for both components (not physically meaningful but tests the method)
         u_component = uxds['bottomDepth']
         v_component = uxds['bottomDepth']
-        
+
         div_field = u_component.divergence(v_component)
 
         # Check that we get finite values where expected
@@ -64,20 +64,20 @@ def test_divergence_constant_field(self, gridpath, datasetpath):
             gridpath("mpas", "dyamond-30km", "gradient_grid_subset.nc"),
             datasetpath("mpas", "dyamond-30km", "gradient_data_subset.nc")
         )
-        
+
         # Create constant fields
         constant_u = uxds['face_lat'] * 0 + 1.0  # Constant field = 1
         constant_v = uxds['face_lat'] * 0 + 1.0  # Constant field = 1
-        
+
         div_field = constant_u.divergence(constant_v)
-        
+
         # Divergence of constant field should be close to zero for interior faces
         # Boundary faces may have NaN values (which is expected)
         finite_values = div_field.values[np.isfinite(div_field.values)]
-        
+
         # Check that we have some finite values (interior faces)
         assert len(finite_values) > 0, "No finite divergence values found"
-        
+
         # Divergence of constant field should be close to zero for finite values
         assert np.abs(finite_values).max() < 1e-10, f"Max divergence: {np.abs(finite_values).max()}"
 
@@ -87,19 +87,19 @@ def test_divergence_linear_field(self, gridpath, datasetpath):
             gridpath("mpas", "dyamond-30km", "gradient_grid_subset.nc"),
             datasetpath("mpas", "dyamond-30km", "gradient_data_subset.nc")
         )
-        
+
         # Create linear fields: u = x, v = y (in spherical coordinates: u = lon, v = lat)
         u_component = uxds['face_lon']  # Linear in longitude
         v_component = uxds['face_lat']  # Linear in latitude
-        
+
         div_field = u_component.divergence(v_component)
-        
+
         # Check that we have some finite values (interior faces)
         finite_mask = np.isfinite(div_field.values)
         finite_values = div_field.values[finite_mask]
-        
+
         assert len(finite_values) > 0, "No finite divergence values found"
-        
+
         # For linear fields, divergence should be finite where computable
         # Boundary faces may have NaN values (which is expected)
         assert not np.isnan(finite_values).any(), "Found NaN in finite values"
@@ -110,26 +110,26 @@ def test_divergence_radial_field(self, gridpath, datasetpath):
             gridpath("mpas", "dyamond-30km", "gradient_grid_subset.nc"),
             datasetpath("mpas", "dyamond-30km", "gradient_data_subset.nc")
         )
-        
+
         # Create a radial field pointing outward from center
         # Use the inverse gaussian as a proxy for radial distance
         radial_distance = uxds['inverse_gaussian']
-        
+
         # Create components proportional to position (simplified radial field)
         u_component = radial_distance * uxds['face_lon']
         v_component = radial_distance * uxds['face_lat']
-        
+
         div_field = u_component.divergence(v_component)
-        
+
         # Check that we have some finite values (interior faces)
         finite_mask = np.isfinite(div_field.values)
         finite_values = div_field.values[finite_mask]
-        
+
         assert len(finite_values) > 0, "No finite divergence values found"
-        
+
         # Boundary faces may have NaN values (which is expected)
         assert not np.isnan(finite_values).any(), "Found NaN in finite values"
-        
+
         # Most finite values should be positive for an expanding field
         positive_values = finite_values > 0
         assert positive_values.sum() > len(finite_values) * 0.3  # At least 30% positive
@@ -140,28 +140,28 @@ def test_divergence_curl_identity(self, gridpath, datasetpath):
             gridpath("mpas", "dyamond-30km", "gradient_grid_subset.nc"),
             datasetpath("mpas", "dyamond-30km", "gradient_data_subset.nc")
         )
-        
+
         # Create a scalar potential field
         potential = uxds['gaussian']
-        
+
         # Compute gradient to get a conservative vector field
         grad = potential.gradient()
         u_component = grad['zonal_gradient']
         v_component = grad['meridional_gradient']
-        
+
         # Compute divergence of this gradient (should be the Laplacian)
         div_grad = u_component.divergence(v_component)
-        
+
         # Check that we have some finite values (interior faces)
         finite_mask = np.isfinite(div_grad.values)
         finite_values = div_grad.values[finite_mask]
-        
+
         assert len(finite_values) > 0, "No finite divergence values found"
-        
+
         # This tests the Laplacian computation via div(grad)
         # Boundary faces may have NaN values (which is expected)
         assert not np.isnan(finite_values).any(), "Found NaN in finite values"
-        
+
         # The Laplacian of a Gaussian should have both positive and negative values
         assert (finite_values > 0).any(), "No positive Laplacian values found"
-        assert (finite_values < 0).any(), "No negative Laplacian values found"
+        assert (finite_values < 0).any(), "No negative Laplacian values found"