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move to 3xnnn arrays

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This commit is contained in:
Jan Petykiewicz 2019-08-05 01:09:52 -07:00
parent 5951f2bdb1
commit 938c4c9a35
1 changed files with 25 additions and 18 deletions
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43
meanas/fdfd/functional.py
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@ -29,9 +29,13 @@ def curl_h(dxes: dx_lists_t) -> functional_matrix:
return (f - numpy.roll(f, 1, axis=ax)) / dxyz_b[ax] return (f - numpy.roll(f, 1, axis=ax)) / dxyz_b[ax]
def ch_fun(h: field_t) -> field_t: def ch_fun(h: field_t) -> field_t:
e = [dh(h[2], 1) - dh(h[1], 2), e = numpy.empty_like(h)
dh(h[0], 2) - dh(h[2], 0), e[0] = dh(h[2], 1)
dh(h[1], 0) - dh(h[0], 1)] e[0] -= dh(h[1], 2)
e[1] = dh(h[0], 2)
e[1] -= dh(h[2], 0)
e[2] = dh(h[1], 0)
e[2] -= dh(h[0], 1)
return e return e
return ch_fun return ch_fun
@ -50,9 +54,13 @@ def curl_e(dxes: dx_lists_t) -> functional_matrix:
return (numpy.roll(f, -1, axis=ax) - f) / dxyz_a[ax] return (numpy.roll(f, -1, axis=ax) - f) / dxyz_a[ax]
def ce_fun(e: field_t) -> field_t: def ce_fun(e: field_t) -> field_t:
h = [de(e[2], 1) - de(e[1], 2), h = numpy.empty_like(e)
de(e[0], 2) - de(e[2], 0), h[0] = de(e[2], 1)
de(e[1], 0) - de(e[0], 1)] h[0] -= de(e[1], 2)
h[1] = de(e[0], 2)
h[1] -= de(e[2], 0)
h[2] = de(e[1], 0)
h[2] -= de(e[0], 1)
return h return h
return ce_fun return ce_fun
@ -79,11 +87,11 @@ def e_full(omega: complex,
def op_1(e): def op_1(e):
curls = ch(ce(e)) curls = ch(ce(e))
return [c - omega ** 2 * e * x for c, e, x in zip(curls, epsilon, e)] return curls - omega ** 2 * epsilon * e
def op_mu(e): def op_mu(e):
curls = ch([m * y for m, y in zip(mu, ce(e))]) curls = ch(mu * ce(e))
return [c - omega ** 2 * p * x for c, p, x in zip(curls, epsilon, e)] return curls - omega ** 2 * epsilon * e
if numpy.any(numpy.equal(mu, None)): if numpy.any(numpy.equal(mu, None)):
return op_1 return op_1
@ -109,12 +117,12 @@ def eh_full(omega: complex,
ce = curl_e(dxes) ce = curl_e(dxes)
def op_1(e, h): def op_1(e, h):
return ([c - 1j * omega * p * x for c, p, x in zip(ch(h), epsilon, e)], return (ch(h) - 1j * omega * epsilon * e,
[c + 1j * omega * y for c, y in zip(ce(e), h)]) ce(e) + 1j * omega * h)
def op_mu(e, h): def op_mu(e, h):
return ([c - 1j * omega * p * x for c, p, x in zip(ch(h), epsilon, e)], return (ch(h) - 1j * omega * epsilon * e,
[c + 1j * omega * m * y for c, m, y in zip(ce(e), mu, h)]) ce(e) + 1j * omega * mu * h)
if numpy.any(numpy.equal(mu, None)): if numpy.any(numpy.equal(mu, None)):
return op_1 return op_1
@ -138,10 +146,10 @@ def e2h(omega: complex,
A2 = curl_e(dxes) A2 = curl_e(dxes)
def e2h_1_1(e): def e2h_1_1(e):
return [y / (-1j * omega) for y in A2(e)] return A2(e) / (-1j * omega)
def e2h_mu(e): def e2h_mu(e):
return [y / (-1j * omega * m) for y, m in zip(A2(e), mu)] return A2(e) / (-1j * omega * mu)
if numpy.any(numpy.equal(mu, None)): if numpy.any(numpy.equal(mu, None)):
return e2h_1_1 return e2h_1_1
@ -166,12 +174,11 @@ def m2j(omega: complex,
ch = curl_h(dxes) ch = curl_h(dxes)
def m2j_mu(m): def m2j_mu(m):
m_mu = [m[k] / mu[k] for k in range[3]] J = ch(m / mu) / (-1j * omega)
J = [Ji / (-1j * omega) for Ji in ch(m_mu)]
return J return J
def m2j_1(m): def m2j_1(m):
J = [Ji / (-1j * omega) for Ji in ch(m)] J = ch(m) / (-1j * omega)
return J return J
if numpy.any(numpy.equal(mu, None)): if numpy.any(numpy.equal(mu, None)):