shuren/fdfd_tools
1
0
Fork 0
forked from jan/fdfd_tools
Code Issues Pull Requests Releases Wiki Activity

Use ravel instead of flatten where possible

Browse Source
This commit is contained in:
Jan Petykiewicz 2017-05-20 21:22:28 -07:00
parent 43d1464258
commit 5033472342
1 changed files with 8 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

16
fdfd_tools/operators.py
View File

@ -304,7 +304,7 @@ def rotation(axis: int, shape: List[int], shift_distance: int=1) -> sparse.spmat
i_ind = numpy.arange(n) i_ind = numpy.arange(n)
j_ind = numpy.ravel_multi_index(ijk, shape, order='C') j_ind = numpy.ravel_multi_index(ijk, shape, order='C')
vij = (numpy.ones(n), (i_ind, j_ind.flatten(order='C'))) vij = (numpy.ones(n), (i_ind, j_ind.ravel(order='C')))
d = sparse.csr_matrix(vij, shape=(n, n)) d = sparse.csr_matrix(vij, shape=(n, n))
@ -348,7 +348,7 @@ def shift_with_mirror(axis: int, shape: List[int], shift_distance: int=1) -> spa
if len(shape) == 3: if len(shape) == 3:
j_ind += ijk[2] * shape[0] * shape[1] j_ind += ijk[2] * shape[0] * shape[1]
vij = (numpy.ones(n), (i_ind, j_ind.flatten(order='C'))) vij = (numpy.ones(n), (i_ind, j_ind.ravel(order='C')))
d = sparse.csr_matrix(vij, shape=(n, n)) d = sparse.csr_matrix(vij, shape=(n, n))
return d return d
@ -369,7 +369,7 @@ def deriv_forward(dx_e: List[numpy.ndarray]) -> List[sparse.spmatrix]:
def deriv(axis): def deriv(axis):
return rotation(axis, shape, 1) - sparse.eye(n) return rotation(axis, shape, 1) - sparse.eye(n)
Ds = [sparse.diags(+1 / dx.flatten(order='C')) @ deriv(a) Ds = [sparse.diags(+1 / dx.ravel(order='C')) @ deriv(a)
for a, dx in enumerate(dx_e_expanded)] for a, dx in enumerate(dx_e_expanded)]
return Ds return Ds
@ -390,7 +390,7 @@ def deriv_back(dx_h: List[numpy.ndarray]) -> List[sparse.spmatrix]:
def deriv(axis): def deriv(axis):
return rotation(axis, shape, -1) - sparse.eye(n) return rotation(axis, shape, -1) - sparse.eye(n)
Ds = [sparse.diags(-1 / dx.flatten(order='C')) @ deriv(a) Ds = [sparse.diags(-1 / dx.ravel(order='C')) @ deriv(a)
for a, dx in enumerate(dx_h_expanded)] for a, dx in enumerate(dx_h_expanded)]
return Ds return Ds
@ -461,8 +461,8 @@ def poynting_e_cross(e: vfield_t, dxes: dx_lists_t) -> sparse.spmatrix:
fx, fy, fz = [avgf(i, shape) for i in range(3)] fx, fy, fz = [avgf(i, shape) for i in range(3)]
bx, by, bz = [avgb(i, shape) for i in range(3)] bx, by, bz = [avgb(i, shape) for i in range(3)]
dxag = [dx.flatten(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')]
dbgx, dbgy, dbgz = [sparse.diags(dx.flatten(order='C')) dbgx, dbgy, dbgz = [sparse.diags(dx.ravel(order='C'))
for dx in numpy.meshgrid(*dxes[1], indexing='ij')] for dx in numpy.meshgrid(*dxes[1], indexing='ij')]
Ex, Ey, Ez = [sparse.diags(ei * da) for ei, da in zip(numpy.split(e, 3), dxag)] Ex, Ey, Ez = [sparse.diags(ei * da) for ei, da in zip(numpy.split(e, 3), dxag)]
@ -490,8 +490,8 @@ def poynting_h_cross(h: vfield_t, dxes: dx_lists_t) -> sparse.spmatrix:
fx, fy, fz = [avgf(i, shape) for i in range(3)] fx, fy, fz = [avgf(i, shape) for i in range(3)]
bx, by, bz = [avgb(i, shape) for i in range(3)] bx, by, bz = [avgb(i, shape) for i in range(3)]
dxbg = [dx.flatten(order='C') for dx in numpy.meshgrid(*dxes[1], indexing='ij')] dxbg = [dx.ravel(order='C') for dx in numpy.meshgrid(*dxes[1], indexing='ij')]
dagx, dagy, dagz = [sparse.diags(dx.flatten(order='C')) dagx, dagy, dagz = [sparse.diags(dx.ravel(order='C'))
for dx in numpy.meshgrid(*dxes[0], indexing='ij')] for dx in numpy.meshgrid(*dxes[0], indexing='ij')]
Hx, Hy, Hz = [sparse.diags(hi * db) for hi, db in zip(numpy.split(h, 3), dxbg)] Hx, Hy, Hz = [sparse.diags(hi * db) for hi, db in zip(numpy.split(h, 3), dxbg)]