forked from jan/opencl_fdfd
move from fdfd_tools to meanas
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@ -34,11 +34,11 @@ generalization to multiple GPUs should be pretty straightforward
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## Installation
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**Dependencies:**
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* python 3 (written and tested with 3.5)
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* python 3 (written and tested with 3.7)
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* numpy
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* pyopencl
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* jinja2
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* [fdfd_tools](https://mpxd.net/code/jan/fdfd_tools) (>=0.2)
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* [meanas](https://mpxd.net/code/jan/meanas) (>=0.5)
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Install with pip, via git:
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@ -31,7 +31,7 @@
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Dependencies:
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- fdfd_tools ( https://mpxd.net/code/jan/fdfd_tools )
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- meanas ( https://mpxd.net/code/jan/meanas )
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- numpy
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- pyopencl
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- jinja2
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@ -15,7 +15,7 @@ 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.operators
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import meanas.fdfd.operators
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from . import ops
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@ -43,7 +43,7 @@ def cg_solver(omega: complex,
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OpenCL.
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All ndarray arguments should be 1D arrays. To linearize a list of 3 3D ndarrays,
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either use fdfd_tools.vec() or numpy:
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either use meanas.vec() or numpy:
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f_1D = numpy.hstack(tuple((fi.flatten(order='F') for fi in [f_x, f_y, f_z])))
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:param omega: Complex frequency to solve at.
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@ -104,7 +104,7 @@ def cg_solver(omega: complex,
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if mu is not None:
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mu = numpy.conj(mu)
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L, R = fdfd_tools.operators.e_full_preconditioners(dxes)
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L, R = meanas.fdfd.operators.e_full_preconditioners(dxes)
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if adjoint:
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b_preconditioned = R @ b
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@ -221,7 +221,7 @@ def cg_solver(omega: complex,
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logger.debug('final error {}'.format(errs[-1]))
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logger.debug('overhead {} sec'.format(start_time2 - start_time))
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A0 = fdfd_tools.operators.e_full(omega, dxes, epsilon, mu).tocsr()
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A0 = meanas.fdfd.operators.e_full(omega, dxes, epsilon, mu).tocsr()
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if adjoint:
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# Remember we conjugated all the contents of A earlier
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A0 = A0.T
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