examples/fdtd.py

@@ -157,8 +157,7 @@ def main():
         e[1][tuple(grid.shape//2)] += field_source(t)
         update_H(e, h)
 
-        avg_rate = (t + 1)/(time.perf_counter() - start))
-        print(f'iteration {t}: average {avg_rate} iterations per sec')
+        print('iteration {}: average {} iterations per sec'.format(t, (t+1)/(time.perf_counter()-start)))
         sys.stdout.flush()
 
         if t % 20 == 0:

meanas/fdfd/bloch.py

@@ -696,12 +696,12 @@ def eigsolve(
 
             U_sZtD = U @ symZtD
 
-            dE = 2.0 * (_rtrace_AtB(U, symZtAD)
-                        - _rtrace_AtB(ZtAZU, U_sZtD))
+            dE = 2.0 * (_rtrace_AtB(U, symZtAD) -
+                        _rtrace_AtB(ZtAZU, U_sZtD))
 
-            d2E = 2 * (_rtrace_AtB(U, DtAD)
-                       - _rtrace_AtB(ZtAZU, U @ (DtD - 4 * symZtD @ U_sZtD))
-                       - 4 * _rtrace_AtB(U, symZtAD @ U_sZtD))
+            d2E = 2 * (_rtrace_AtB(U, DtAD) -
+                       _rtrace_AtB(ZtAZU, U @ (DtD - 4 * symZtD @ U_sZtD)) -
+                   4 * _rtrace_AtB(U, symZtAD @ U_sZtD))
 
             # Newton-Raphson to find a root of the first derivative:
             theta = -dE / d2E
@@ -781,7 +781,7 @@ def linmin(x_guess, f0, df0, x_max, f_tol=0.1, df_tol=min(tolerance, 1e-6), x_to
                                           x_min, x_max, isave, dsave)
         for i in range(int(1e6)):
             if task != 'F':
-                logging.info(f'search converged in {i} iterations')
+                logging.info('search converged in {} iterations'.format(i))
                 break
             fx = f(x, dfx)
             x, fx, dfx, task = minpack2.dsrch(x, fx, dfx, f_tol, df_tol, x_tol, task,

meanas/fdfd/solvers.py

@@ -43,8 +43,7 @@ def _scipy_qmr(
         nonlocal ii
         ii += 1
         if ii % 100 == 0:
-            cur_norm = norm(A @ xk - b)
-            logger.info(f'Solver residual at iteration {ii} : {cur_norm}')
+            logger.info('Solver residual at iteration {} : {}'.format(ii, norm(A @ xk - b)))
 
     if 'callback' in kwargs:
         def augmented_callback(xk: ArrayLike) -> None:

meanas/fdfd/waveguide_2d.py

@@ -253,8 +253,7 @@ 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
         )
@@ -322,8 +321,7 @@ 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
         )
@@ -422,7 +420,7 @@ def _normalized_fields(
     Sz_a = E[0] * numpy.conj(H[1] * phase) * dxes_real[0][1] * dxes_real[1][0]
     Sz_b = E[1] * numpy.conj(H[0] * phase) * dxes_real[0][0] * dxes_real[1][1]
     Sz_tavg = numpy.real(Sz_a.sum() - Sz_b.sum()) * 0.5       # 0.5 since E, H are assumed to be peak (not RMS) amplitudes
-    assert Sz_tavg > 0, f'Found a mode propagating in the wrong direction! {Sz_tavg=}'
+    assert Sz_tavg > 0, 'Found a mode propagating in the wrong direction! Sz_tavg={}'.format(Sz_tavg)
 
     energy = epsilon * e.conj() * e
 
@@ -720,109 +718,6 @@ def e_err(
     return float(norm(op) / norm(e))
 
 
-def sensitivity(
-        e_norm: vcfdfield_t,
-        h_norm: vcfdfield_t,
-        wavenumber: complex,
-        omega: complex,
-        dxes: dx_lists_t,
-        epsilon: vfdfield_t,
-        mu: vfdfield_t | None = None,
-        ) -> vcfdfield_t:
-    r"""
-      Given a waveguide structure (`dxes`, `epsilon`, `mu`) and mode fields
-    (`e_norm`, `h_norm`, `wavenumber`, `omega`), calculates the sensitivity of the wavenumber
-    $\beta$ to changes in the dielectric structure $\epsilon$.
-
-    The output is a vector of the same size as `vec(epsilon)`, with each element specifying the
-    sensitivity of `wavenumber` to changes in the corresponding element in `vec(epsilon)`, i.e.
-
-    $$sens_{i} = \frac{\partial\beta}{\partial\epsilon_i}$$
-
-    An adjoint approach is used to calculate the sensitivity; the derivation is provided here:
-
-    Starting with the eigenvalue equation
-
-    $$\beta^2 E_{xy} = A_E E_{xy}$$
-
-    where $A_E$ is the waveguide operator from `operator_e()`, and $E_{xy} = \begin{bmatrix} E_x \\
-                                                                                             E_y \end{bmatrix}$,
-    we can differentiate with respect to one of the $\epsilon$ elements (i.e. at one Yee grid point), $\epsilon_i$:
-
-    $$
-    (2 \beta) \partial_{\epsilon_i}(\beta) E_{xy} + \beta^2 \partial_{\epsilon_i} E_{xy}
-        = \partial_{\epsilon_i}(A_E) E_{xy} + A_E \partial_{\epsilon_i} E_{xy}
-    $$
-
-    We then multiply by $H_{yx}^\star = \begin{bmatrix}H_y^\star \\ -H_x^\star \end{bmatrix}$ from the left:
-
-    $$
-    (2 \beta) \partial_{\epsilon_i}(\beta) H_{yx}^\star E_{xy} + \beta^2 H_{yx}^\star \partial_{\epsilon_i} E_{xy}
-        = H_{yx}^\star \partial_{\epsilon_i}(A_E) E_{xy} + H_{yx}^\star A_E \partial_{\epsilon_i} E_{xy}
-    $$
-
-    However, $H_{yx}^\star$ is actually a left-eigenvector of $A_E$. This can be verified by inspecting
-    the form of `operator_h` ($A_H$) and comparing its conjugate transpose to `operator_e` ($A_E$). Also, note
-    $H_{yx}^\star \cdot E_{xy} = H^\star \times E$ recalls the mode orthogonality relation. See doi:10.5194/ars-9-85-201
-    for a similar approach. Therefore,
-
-    $$
-    H_{yx}^\star A_E \partial_{\epsilon_i} E_{xy} = \beta^2 H_{yx}^\star \partial_{\epsilon_i} E_{xy}
-    $$
-
-    and we can simplify to
-
-    $$
-    \partial_{\epsilon_i}(\beta)
-        = \frac{1}{2 \beta} \frac{H_{yx}^\star \partial_{\epsilon_i}(A_E) E_{xy} }{H_{yx}^\star E_{xy}}
-    $$
-
-    This expression can be quickly calculated for all $i$ by writing out the various terms of
-    $\partial_{\epsilon_i} A_E$ and recognizing that the vector-matrix-vector products (i.e. scalars)
-    $sens_i = \vec{v}_{left} \partial_{\epsilon_i} (\epsilon_{xyz}) \vec{v}_{right}$, indexed by $i$, can be expressed as
-    elementwise multiplications $\vec{sens} = \vec{v}_{left} \star \vec{v}_{right}$
-
-
-    Args:
-        e_norm: Normalized, vectorized E_xyz field for the mode. E.g. as returned by `normalized_fields_e`.
-        h_norm: Normalized, vectorized H_xyz field for the mode. E.g. as returned by `normalized_fields_e`.
-        wavenumber: Propagation constant for the mode. The z-axis is assumed to be continuous (i.e. without numerical dispersion).
-        omega: The angular frequency of the system.
-        dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
-        epsilon: Vectorized dielectric constant grid
-        mu: Vectorized magnetic permeability grid (default 1 everywhere)
-
-    Returns:
-        Sparse matrix representation of the operator.
-    """
-    if mu is None:
-        mu = numpy.ones_like(epsilon)
-
-    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_yx = sparse.diags(numpy.hstack((mu_y, mu_x)))
-
-    dv_e = dxes[0][0][:, None, None] * dxes[0][1][None, :, None] * dxes[0][2][None, None, :]
-    dv_h = dxes[1][0][:, None, None] * dxes[1][1][None, :, None] * dxes[1][2][None, None, :]
-    ev_xy = numpy.concatenate(numpy.split(e_norm, 3)[:2]) * dv_e
-    hx, hy, hz = numpy.split(h_norm, 3)
-    hv_yx_conj = numpy.conj(numpy.concatenate([hy, -hx])) * dv_h
-
-    sens_xy1 = (hv_yx_conj @ (omega * omega @ mu_yx)) * ev_xy
-    sens_xy2 = (hv_yx_conj @ sparse.vstack((Dfx, Dfy)) @ eps_z_inv @ sparse.hstack((Dbx, Dby))) * ev_xy
-    sens_z   = (hv_yx_conj @ sparse.vstack((Dfx, Dfy)) @ (-eps_z_inv * eps_z_inv)) * (sparse.hstack((Dbx, Dby)) @ eps_xy @ ev_xy)
-    norm = hv_yx_conj @ ev_xy
-
-    sens_tot = numpy.concatenate([sens_xy1 + sens_xy2, sens_z]) / (2 * wavenumber * norm)
-    return sens_tot
-
-
 def solve_modes(
         mode_numbers: list[int],
         omega: complex,

meanas/fdmath/operators.py

@@ -29,9 +29,9 @@ def shift_circ(
         Sparse matrix for performing the circular shift.
     """
     if len(shape) not in (2, 3):
-        raise Exception(f'Invalid shape: {shape}')
+        raise Exception('Invalid shape: {}'.format(shape))
     if axis not in range(len(shape)):
-        raise Exception(f'Invalid direction: {axis}, shape is {shape}')
+        raise Exception('Invalid direction: {}, shape is {}'.format(axis, shape))
 
     shifts = [abs(shift_distance) if a == axis else 0 for a in range(3)]
     shifted_diags = [(numpy.arange(n) + s) % n for n, s in zip(shape, shifts)]
@@ -69,11 +69,12 @@ def shift_with_mirror(
         Sparse matrix for performing the shift-with-mirror.
     """
     if len(shape) not in (2, 3):
-        raise Exception(f'Invalid shape: {shape}')
+        raise Exception('Invalid shape: {}'.format(shape))
     if axis not in range(len(shape)):
-        raise Exception(f'Invalid direction: {axis}, shape is {shape}')
+        raise Exception('Invalid direction: {}, shape is {}'.format(axis, shape))
     if shift_distance >= shape[axis]:
-        raise Exception(f'Shift ({shift_distance}) is too large for axis {axis} of size {shape[axis]}')
+        raise Exception('Shift ({}) is too large for axis {} of size {}'.format(
+                        shift_distance, axis, shape[axis]))
 
     def mirrored_range(n: int, s: int) -> NDArray[numpy.int_]:
         v = numpy.arange(n) + s
@@ -197,7 +198,7 @@ def avg_forward(axis: int, shape: Sequence[int]) -> sparse.spmatrix:
         Sparse matrix for forward average operation.
     """
     if len(shape) not in (2, 3):
-        raise Exception(f'Invalid shape: {shape}')
+        raise Exception('Invalid shape: {}'.format(shape))
 
     n = numpy.prod(shape)
     return 0.5 * (sparse.eye(n) + shift_circ(axis, shape))

meanas/fdtd/boundaries.py

@@ -15,17 +15,13 @@ def conducting_boundary(
         ) -> tuple[fdfield_updater_t, fdfield_updater_t]:
     dirs = [0, 1, 2]
     if direction not in dirs:
-        raise Exception(f'Invalid direction: {direction}')
+        raise Exception('Invalid direction: {}'.format(direction))
     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
-        shifted1_slice = [slice(None)] * 3
+        boundary_slice = [slice(None)] * 3      # type: list[Any]
+        shifted1_slice = [slice(None)] * 3      # type: list[Any]
         boundary_slice[direction] = 0
         shifted1_slice[direction] = 1
 
@@ -46,7 +42,7 @@ def conducting_boundary(
     if polarity > 0:
         boundary_slice = [slice(None)] * 3
         shifted1_slice = [slice(None)] * 3
-        shifted2_slice = [slice(None)] * 3
+        shifted2_slice = [slice(None)] * 3      # type: list[Any]
         boundary_slice[direction] = -1
         shifted1_slice[direction] = -2
         shifted2_slice[direction] = -3
@@ -68,4 +64,4 @@ def conducting_boundary(
 
         return ep, hp
 
-    raise Exception(f'Bad polarity: {polarity}')
+    raise Exception('Bad polarity: {}'.format(polarity))

meanas/fdtd/pml.py

@@ -33,10 +33,10 @@ def cpml_params(
         ) -> dict[str, Any]:
 
     if axis not in range(3):
-        raise Exception(f'Invalid axis: {axis}')
+        raise Exception('Invalid axis: {}'.format(axis))
 
     if polarity not in (-1, 1):
-        raise Exception(f'Invalid polarity: {polarity}')
+        raise Exception('Invalid polarity: {}'.format(polarity))
 
     if thickness <= 2:
         raise Exception('It would be wise to have a pml with 4+ cells of thickness')

meanas/test/test_fdtd.py

@@ -101,7 +101,7 @@ def test_poynting_divergence(sim: 'TDResult') -> None:
 def test_poynting_planes(sim: 'TDResult') -> None:
     mask = (sim.js[0] != 0).any(axis=0)
     if mask.sum() > 1:
-        pytest.skip(f'test_poynting_planes can only test single point sources, got {mask.sum()}')
+        pytest.skip('test_poynting_planes can only test single point sources, got {}'.format(mask.sum()))
 
     args: dict[str, Any] = {
         'dxes': sim.dxes,