lint

python/adjoint/filters.py

@@ -330,7 +330,9 @@ def conic_filter(
     """
     Nx, Ny, X, Y = mesh_grid(radius, Lx, Ly, resolution, periodic_axes)
     x = x.reshape(Nx, Ny)  # Ensure the input is 2d
-    h = npa.where(X**2 + Y**2 < radius**2, (1 - np.sqrt(abs(X**2 + Y**2)) / radius), 0)
+    h = npa.where(
+        X**2 + Y**2 < radius**2, (1 - np.sqrt(abs(X**2 + Y**2)) / radius), 0
+    )
     return convolve_design_weights_and_kernel(x, h, periodic_axes)
 
 

python/examples/waveguide_crossing.py

@@ -1,5 +1,5 @@
 """waveguide_crossing.py - Using meep's adjoint solver, designs a waveguide
-crossing that maximizes transmission at a single frequency. 
+crossing that maximizes transmission at a single frequency.
 
 Two approaches are demonstrated: (1) start with the nominal crossing shape and
 perform shape optimization via meep's smoothed projection feature; (2) run a
@@ -611,12 +611,7 @@ def analyze_FOM_convergence(
         damping_factor=damping_factor,
     )
     print("Running shape optimization WITH smoothing...")
-    (
-        _,
-        results_smoothed,
-        _,
-        _,
-    ) = run_shape_optimization(
+    (_, results_smoothed, _, _,) = run_shape_optimization(
         beta=beta,
         resolution=resolution,
         maxeval=maxeval,