forked from jan/opencl_fdfd
depend on meanas instad of fdfd_tools
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@ -6,7 +6,7 @@ CSRMatrix sparse matrix representation.
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The FDFD solver (fdfd_cg_solver()) solves an FDFD problem by
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creating a sparse matrix representing the problem (using
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fdfd_tools) and then passing it to cg(), which performs a
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meanas) and then passing it to cg(), which performs a
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conjugate gradient solve.
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cg() is capable of solving arbitrary sparse matrices which
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@ -23,7 +23,7 @@ from numpy.linalg import norm
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import pyopencl
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import pyopencl.array
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import fdfd_tools.solvers
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import meanas.fdfd.solvers
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from . import ops
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@ -158,9 +158,9 @@ def fdfd_cg_solver(solver_opts: Dict[str, Any] = None,
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Conjugate gradient FDFD solver using CSR sparse matrices, mainly for
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testing and development since it's much slower than the solver in main.py.
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Calls fdfd_tools.solvers.generic(**fdfd_args,
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matrix_solver=opencl_fdfd.csr.cg,
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matrix_solver_opts=solver_opts)
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Calls meanas.fdfd.solvers.generic(**fdfd_args,
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matrix_solver=opencl_fdfd.csr.cg,
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matrix_solver_opts=solver_opts)
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:param solver_opts: Passed as matrix_solver_opts to fdfd_tools.solver.generic(...).
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Default {}.
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@ -172,8 +172,8 @@ def fdfd_cg_solver(solver_opts: Dict[str, Any] = None,
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if solver_opts is None:
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solver_opts = dict()
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x = fdfd_tools.solvers.generic(matrix_solver=cg,
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matrix_solver_opts=solver_opts,
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**fdfd_args)
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x = meanas.fdfd.solvers.generic(matrix_solver=cg,
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matrix_solver_opts=solver_opts,
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**fdfd_args)
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return x
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