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depend on meanas instad of fdfd_tools

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This commit is contained in:
Jan Petykiewicz 2021-07-11 17:07:46 -07:00
parent 792b161753
commit 5861767a00
1 changed files with 8 additions and 8 deletions
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opencl_fdfd/csr.py
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@ -6,7 +6,7 @@ CSRMatrix sparse matrix representation.
The FDFD solver (fdfd_cg_solver()) solves an FDFD problem by The FDFD solver (fdfd_cg_solver()) solves an FDFD problem by
creating a sparse matrix representing the problem (using creating a sparse matrix representing the problem (using
fdfd_tools) and then passing it to cg(), which performs a meanas) and then passing it to cg(), which performs a
conjugate gradient solve. conjugate gradient solve.
cg() is capable of solving arbitrary sparse matrices which cg() is capable of solving arbitrary sparse matrices which
@ -23,7 +23,7 @@ from numpy.linalg import norm
import pyopencl import pyopencl
import pyopencl.array import pyopencl.array
import fdfd_tools.solvers import meanas.fdfd.solvers
from . import ops from . import ops
@ -158,7 +158,7 @@ def fdfd_cg_solver(solver_opts: Dict[str, Any] = None,
Conjugate gradient FDFD solver using CSR sparse matrices, mainly for Conjugate gradient FDFD solver using CSR sparse matrices, mainly for
testing and development since it's much slower than the solver in main.py. testing and development since it's much slower than the solver in main.py.
Calls fdfd_tools.solvers.generic(**fdfd_args, Calls meanas.fdfd.solvers.generic(**fdfd_args,
matrix_solver=opencl_fdfd.csr.cg, matrix_solver=opencl_fdfd.csr.cg,
matrix_solver_opts=solver_opts) matrix_solver_opts=solver_opts)
@ -172,7 +172,7 @@ def fdfd_cg_solver(solver_opts: Dict[str, Any] = None,
if solver_opts is None: if solver_opts is None:
solver_opts = dict() solver_opts = dict()
x = fdfd_tools.solvers.generic(matrix_solver=cg, x = meanas.fdfd.solvers.generic(matrix_solver=cg,
matrix_solver_opts=solver_opts, matrix_solver_opts=solver_opts,
**fdfd_args) **fdfd_args)