alternate src formulation
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@ -169,9 +169,9 @@ def compute_source(E: field_t,
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M = numpy.zeros_like(E, dtype=complex)
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src_order = numpy.roll(range(3), -axis)
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exp_iphi = numpy.exp(1j * polarity * wavenumber * dxes[1][axis][slices[axis]])
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rollby = -1 if polarity > 0 else 0
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# exp_iphi = numpy.exp(1j * polarity * wavenumber * dxes[1][axis][slices[axis]])
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# rollby = -1 if polarity > 0 else 0
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# J[src_order[1]] = +exp_iphi * H[src_order[2]] * polarity / dxes[1][axis][slices[axis]]
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# J[src_order[2]] = -exp_iphi * H[src_order[1]] * polarity / dxes[1][axis][slices[axis]]
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# M[src_order[1]] = +numpy.roll(E[src_order[2]], rollby, axis=axis) / dxes[0][axis][slices[axis]]
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@ -181,14 +181,16 @@ def compute_source(E: field_t,
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s2[axis] = slice(slices[axis].start, slices[axis].stop)
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s2 = (src_order, *s2)
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J[s2] = numpy.roll(functional.curl_h(dxes=dxes)(H), rollby, axis=axis+1)[s2] * polarity * exp_iphi
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M[s2] = numpy.roll(functional.curl_e(dxes=dxes)(E), -rollby, axis=axis+1)[s2]
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rollby = 1 if polarity > 0 else 0
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exp_iphi = numpy.exp(-1j * polarity * wavenumber * dxes[1][axis][slices[axis]])
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J[s2] = numpy.roll(functional.curl_h(dxes=dxes)(H.conj()), -rollby, axis=axis+1)[s2] * polarity * exp_iphi
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M[s2] = -numpy.roll(functional.curl_e(dxes=dxes)(E.conj()), rollby, axis=axis+1)[s2]
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m2j = functional.m2j(omega, dxes, mu)
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Jm = m2j(M)
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Jtot = J + Jm
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return Jtot.conj()
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return Jtot
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def compute_overlap_e(E: field_t,
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@ -371,6 +373,17 @@ def compute_source_e(QE: field_t,
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else:
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start = slices[aa].start
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stop = slices[aa].stop
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# if start is not None or stop is not None:
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# if start is None:
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# start = 1
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# stop -= 1
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# elif stop is None:
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# stop = E.shape[aa + 1] - 1
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# start += 1
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# else:
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# start += 1
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# stop -= 1
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# slices_reduced[aa] = slice(start, stop)
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slices_reduced = (slice(None), *slices_reduced)
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# Don't actually need to mask out E here since it needs to be pre-masked (QE)
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