flake8 fixes

meanas/fdfd/bloch.py

@@ -696,12 +696,12 @@ def eigsolve(
 
             U_sZtD = U @ symZtD
 
-            dE = 2.0 * (_rtrace_AtB(U, symZtAD) -
+            dE = 2.0 * (_rtrace_AtB(U, symZtAD)
-                        _rtrace_AtB(ZtAZU, U_sZtD))
+                        - _rtrace_AtB(ZtAZU, U_sZtD))
 
-            d2E = 2 * (_rtrace_AtB(U, DtAD) -
+            d2E = 2 * (_rtrace_AtB(U, DtAD)
-                       _rtrace_AtB(ZtAZU, U @ (DtD - 4 * symZtD @ U_sZtD)) -
+                       - _rtrace_AtB(ZtAZU, U @ (DtD - 4 * symZtD @ U_sZtD))
-                   4 * _rtrace_AtB(U, symZtAD @ U_sZtD))
+                       - 4 * _rtrace_AtB(U, symZtAD @ U_sZtD))
 
             # Newton-Raphson to find a root of the first derivative:
             theta = -dE / d2E

meanas/fdfd/waveguide_2d.py

@@ -253,7 +253,8 @@ def operator_e(
     mu_yx = sparse.diags(numpy.hstack((mu_parts[1], mu_parts[0])))
     mu_z_inv = sparse.diags(1 / mu_parts[2])
 
-    op = (omega * omega * mu_yx @ eps_xy
+    op = (
+        omega * omega * mu_yx @ eps_xy
         + mu_yx @ sparse.vstack((-Dby, Dbx)) @ mu_z_inv @ sparse.hstack((-Dfy, Dfx))
         + sparse.vstack((Dfx, Dfy)) @ eps_z_inv @ sparse.hstack((Dbx, Dby)) @ eps_xy
         )
@@ -321,7 +322,8 @@ def operator_h(
     mu_xy = sparse.diags(numpy.hstack((mu_parts[0], mu_parts[1])))
     mu_z_inv = sparse.diags(1 / mu_parts[2])
 
-    op = (omega * omega * eps_yx @ mu_xy
+    op = (
+        omega * omega * eps_yx @ mu_xy
         + eps_yx @ sparse.vstack((-Dfy, Dfx)) @ eps_z_inv @ sparse.hstack((-Dby, Dbx))
         + sparse.vstack((Dbx, Dby)) @ mu_z_inv @ sparse.hstack((Dfx, Dfy)) @ mu_xy
         )
@@ -799,14 +801,12 @@ def sensitivity(
     Dfx, Dfy = deriv_forward(dxes[0])
     Dbx, Dby = deriv_back(dxes[1])
 
-
     eps_x, eps_y, eps_z = numpy.split(epsilon, 3)
     eps_xy = sparse.diags(numpy.hstack((eps_x, eps_y)))
     eps_z_inv = sparse.diags(1 / eps_z)
 
-    mu_x, mu_y, mu_z = numpy.split(mu, 3)
+    mu_x, mu_y, _mu_z = numpy.split(mu, 3)
     mu_yx = sparse.diags(numpy.hstack((mu_y, mu_x)))
-    mu_z_inv = sparse.diags(1 / mu_z)
 
     da_exxhyy = vec(dxes[1][0][:, None] * dxes[0][1][None, :])
     da_eyyhxx = vec(dxes[1][1][None, :] * dxes[0][0][:, None])

meanas/fdtd/boundaries.py

@@ -19,9 +19,13 @@ def conducting_boundary(
     dirs.remove(direction)
     u, v = dirs
 
+    boundary_slice: list[Any]
+    shifted1_slice: list[Any]
+    shifted2_slice: list[Any]
+
     if polarity < 0:
-        boundary_slice = [slice(None)] * 3      # type: list[Any]
+        boundary_slice = [slice(None)] * 3
-        shifted1_slice = [slice(None)] * 3      # type: list[Any]
+        shifted1_slice = [slice(None)] * 3
         boundary_slice[direction] = 0
         shifted1_slice[direction] = 1
 
@@ -42,7 +46,7 @@ def conducting_boundary(
     if polarity > 0:
         boundary_slice = [slice(None)] * 3
         shifted1_slice = [slice(None)] * 3
-        shifted2_slice = [slice(None)] * 3      # type: list[Any]
+        shifted2_slice = [slice(None)] * 3
         boundary_slice[direction] = -1
         shifted1_slice[direction] = -2
         shifted2_slice[direction] = -3