use f-strings in place of .format()
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@ -157,7 +157,8 @@ def main():
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e[1][tuple(grid.shape//2)] += field_source(t)
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update_H(e, h)
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print('iteration {}: average {} iterations per sec'.format(t, (t+1)/(time.perf_counter()-start)))
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avg_rate = (t + 1)/(time.perf_counter() - start))
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print(f'iteration {t}: average {avg_rate} iterations per sec')
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sys.stdout.flush()
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if t % 20 == 0:
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@ -781,7 +781,7 @@ def linmin(x_guess, f0, df0, x_max, f_tol=0.1, df_tol=min(tolerance, 1e-6), x_to
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x_min, x_max, isave, dsave)
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for i in range(int(1e6)):
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if task != 'F':
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logging.info('search converged in {} iterations'.format(i))
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logging.info(f'search converged in {i} iterations')
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break
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fx = f(x, dfx)
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x, fx, dfx, task = minpack2.dsrch(x, fx, dfx, f_tol, df_tol, x_tol, task,
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@ -43,7 +43,8 @@ def _scipy_qmr(
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nonlocal ii
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ii += 1
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if ii % 100 == 0:
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logger.info('Solver residual at iteration {} : {}'.format(ii, norm(A @ xk - b)))
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cur_norm = norm(A @ xk - b)
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logger.info(f'Solver residual at iteration {ii} : {cur_norm}')
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if 'callback' in kwargs:
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def augmented_callback(xk: ArrayLike) -> None:
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@ -420,7 +420,7 @@ def _normalized_fields(
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Sz_a = E[0] * numpy.conj(H[1] * phase) * dxes_real[0][1] * dxes_real[1][0]
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Sz_b = E[1] * numpy.conj(H[0] * phase) * dxes_real[0][0] * dxes_real[1][1]
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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
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assert Sz_tavg > 0, 'Found a mode propagating in the wrong direction! Sz_tavg={}'.format(Sz_tavg)
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assert Sz_tavg > 0, f'Found a mode propagating in the wrong direction! {Sz_tavg=}'
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energy = epsilon * e.conj() * e
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@ -29,9 +29,9 @@ def shift_circ(
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Sparse matrix for performing the circular shift.
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"""
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if len(shape) not in (2, 3):
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raise Exception('Invalid shape: {}'.format(shape))
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raise Exception(f'Invalid shape: {shape}')
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if axis not in range(len(shape)):
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raise Exception('Invalid direction: {}, shape is {}'.format(axis, shape))
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raise Exception(f'Invalid direction: {axis}, shape is {shape}')
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shifts = [abs(shift_distance) if a == axis else 0 for a in range(3)]
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shifted_diags = [(numpy.arange(n) + s) % n for n, s in zip(shape, shifts)]
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@ -69,12 +69,11 @@ def shift_with_mirror(
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Sparse matrix for performing the shift-with-mirror.
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"""
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if len(shape) not in (2, 3):
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raise Exception('Invalid shape: {}'.format(shape))
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raise Exception(f'Invalid shape: {shape}')
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if axis not in range(len(shape)):
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raise Exception('Invalid direction: {}, shape is {}'.format(axis, shape))
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raise Exception(f'Invalid direction: {axis}, shape is {shape}')
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if shift_distance >= shape[axis]:
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raise Exception('Shift ({}) is too large for axis {} of size {}'.format(
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shift_distance, axis, shape[axis]))
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raise Exception(f'Shift ({shift_distance}) is too large for axis {axis} of size {shape[axis]}')
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def mirrored_range(n: int, s: int) -> NDArray[numpy.int_]:
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v = numpy.arange(n) + s
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@ -198,7 +197,7 @@ def avg_forward(axis: int, shape: Sequence[int]) -> sparse.spmatrix:
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Sparse matrix for forward average operation.
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"""
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if len(shape) not in (2, 3):
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raise Exception('Invalid shape: {}'.format(shape))
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raise Exception(f'Invalid shape: {shape}')
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n = numpy.prod(shape)
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return 0.5 * (sparse.eye(n) + shift_circ(axis, shape))
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@ -15,7 +15,7 @@ def conducting_boundary(
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) -> tuple[fdfield_updater_t, fdfield_updater_t]:
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dirs = [0, 1, 2]
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if direction not in dirs:
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raise Exception('Invalid direction: {}'.format(direction))
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raise Exception(f'Invalid direction: {direction}')
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dirs.remove(direction)
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u, v = dirs
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@ -64,4 +64,4 @@ def conducting_boundary(
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return ep, hp
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raise Exception('Bad polarity: {}'.format(polarity))
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raise Exception(f'Bad polarity: {polarity}')
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@ -33,10 +33,10 @@ def cpml_params(
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) -> dict[str, Any]:
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if axis not in range(3):
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raise Exception('Invalid axis: {}'.format(axis))
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raise Exception(f'Invalid axis: {axis}')
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if polarity not in (-1, 1):
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raise Exception('Invalid polarity: {}'.format(polarity))
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raise Exception(f'Invalid polarity: {polarity}')
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if thickness <= 2:
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raise Exception('It would be wise to have a pml with 4+ cells of thickness')
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@ -101,7 +101,7 @@ def test_poynting_divergence(sim: 'TDResult') -> None:
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def test_poynting_planes(sim: 'TDResult') -> None:
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mask = (sim.js[0] != 0).any(axis=0)
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if mask.sum() > 1:
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pytest.skip('test_poynting_planes can only test single point sources, got {}'.format(mask.sum()))
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pytest.skip(f'test_poynting_planes can only test single point sources, got {mask.sum()}')
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args: dict[str, Any] = {
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'dxes': sim.dxes,
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