jan/meanas
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Compare commits

base: jan:950a5831ec35869a195d351391f7cfb73157b20d
Branches Tags
jan:master
jan:wgsens
jan:spar
jan:v0.10
jan:v0.9
jan:release
jan:v0.8
jan:v0.7
jan:v0.6
..
compare: jan:1b3d322fc6263d7c7b4d741fd9029b26aaf1d416
Branches Tags
jan:master
jan:wgsens
jan:spar
jan:v0.10
jan:v0.9
jan:release
jan:v0.8
jan:v0.7
jan:v0.6

No commits in common. "950a5831ec35869a195d351391f7cfb73157b20d" and "1b3d322fc6263d7c7b4d741fd9029b26aaf1d416" have entirely different histories.
950a5831ec ... 1b3d322fc6

2 changed files with 2 additions and 3 deletions
Show Stats Expand all files Collapse all files

2
meanas/fdfd/functional.py
View File

@ -200,7 +200,7 @@ def poynting_e_cross_h(dxes: dx_lists_t) -> Callable[[cfdfield_t, cfdfield_t], c
Note: Note:
If `E` and `H` are peak amplitudes as assumed elsewhere in this code, If `E` and `H` are peak amplitudes as assumed elsewhere in this code,
the time-average of the poynting vector is `<S> = Re(S)/2 = Re(E x H*) / 2`. the time-average of the poynting vector is `<S> = Re(S)/2 = Re(E x H) / 2`.
The factor of `1/2` can be omitted if root-mean-square quantities are used The factor of `1/2` can be omitted if root-mean-square quantities are used
instead. instead.

3
meanas/fdfd/operators.py
View File

@ -324,7 +324,6 @@ def poynting_e_cross(e: vcfdfield_t, dxes: dx_lists_t) -> sparse.spmatrix:
fx, fy, fz = [shift_circ(i, shape, 1) for i in range(3)] fx, fy, fz = [shift_circ(i, shape, 1) for i in range(3)]
dxag = [dx.ravel(order='C') for dx in numpy.meshgrid(*dxes[0], indexing='ij')] dxag = [dx.ravel(order='C') for dx in numpy.meshgrid(*dxes[0], indexing='ij')]
dxbg = [dx.ravel(order='C') for dx in numpy.meshgrid(*dxes[1], indexing='ij')]
Ex, Ey, Ez = [ei * da for ei, da in zip(numpy.split(e, 3), dxag)] Ex, Ey, Ez = [ei * da for ei, da in zip(numpy.split(e, 3), dxag)]
block_diags = [[ None, fx @ -Ez, fx @ Ey], block_diags = [[ None, fx @ -Ez, fx @ Ey],
@ -332,7 +331,7 @@ def poynting_e_cross(e: vcfdfield_t, dxes: dx_lists_t) -> sparse.spmatrix:
[ fz @ -Ey, fz @ Ex, None]] [ fz @ -Ey, fz @ Ex, None]]
block_matrix = sparse.bmat([[sparse.diags(x) if x is not None else None for x in row] block_matrix = sparse.bmat([[sparse.diags(x) if x is not None else None for x in row]
for row in block_diags]) for row in block_diags])
P = block_matrix @ sparse.diags(numpy.concatenate(dxbg)) P = block_matrix @ sparse.diags(numpy.concatenate(dxag))
return P return P