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master
| Author | SHA1 | Date |
|---|---|---|
Jan Petykiewicz
|
03fc9e6d70 | 4 years ago |
@ -1,56 +1,52 @@
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# meanas
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# fdfd_tools
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**meanas** is a python package for electromagnetic simulations
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** DEPRECATED **
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This package is intended for building simulation inputs, analyzing
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simulation outputs, and running short simulations on unspecialized hardware.
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It is designed to provide tooling and a baseline for other, high-performance
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purpose- and hardware-specific solvers.
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The functionality in this module is now provided by [meanas](https://mpxd.net/code/jan/meanas).
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-----------------------
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**fdfd_tools** is a python package containing utilities for
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creating and analyzing 2D and 3D finite-difference frequency-domain (FDFD)
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electromagnetic simulations.
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**Contents**
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- Finite difference frequency domain (FDFD)
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* Library of sparse matrices for representing the electromagnetic wave
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equation in 3D, as well as auxiliary matrices for conversion between fields
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* Waveguide mode operators
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* Waveguide mode eigensolver
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* Stretched-coordinate PML boundaries (SCPML)
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* Functional versions of most operators
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* Anisotropic media (limited to diagonal elements eps_xx, eps_yy, eps_zz, mu_xx, ...)
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* Arbitrary distributions of perfect electric and magnetic conductors (PEC / PMC)
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- Finite difference time domain (FDTD)
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* Basic Maxwell time-steps
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* Poynting vector and energy calculation
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* Convolutional PMLs
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* Library of sparse matrices for representing the electromagnetic wave
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equation in 3D, as well as auxiliary matrices for conversion between fields
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* Waveguide mode solver and waveguide mode operators
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* Stretched-coordinate PML boundaries (SCPML)
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* Functional versions of most operators
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* Anisotropic media (eps_xx, eps_yy, eps_zz, mu_xx, ...)
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* Arbitrary distributions of perfect electric and magnetic conductors (PEC / PMC)
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This package does *not* provide a fast matrix solver, though by default
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`meanas.fdfd.solvers.generic(...)` will call
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`scipy.sparse.linalg.qmr(...)` to perform a solve.
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For 2D FDFD problems this should be fine; likewise, the waveguide mode
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```fdfd_tools.solvers.generic(...)``` will call
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```scipy.sparse.linalg.qmr(...)``` to perform a solve.
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For 2D problems this should be fine; likewise, the waveguide mode
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solver uses scipy's eigenvalue solver, with reasonable results.
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For solving large (or 3D) FDFD problems, I recommend a GPU-based iterative
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solver, such as [opencl_fdfd](https://mpxd.net/code/jan/opencl_fdfd) or
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For solving large (or 3D) problems, I recommend a GPU-based iterative
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solver, such as [opencl_fdfd](https://mpxd.net/gogs/jan/opencl_fdfd) or
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those included in [MAGMA](http://icl.cs.utk.edu/magma/index.html)). Your
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solver will need the ability to solve complex symmetric (non-Hermitian)
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linear systems, ideally with double precision.
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## Installation
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**Requirements:**
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* python 3 (tests require 3.7)
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* python 3 (written and tested with 3.5)
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* numpy
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* scipy
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Install with pip, via git:
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```bash
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pip install git+https://mpxd.net/code/jan/meanas.git@release
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pip install git+https://mpxd.net/gogs/jan/fdfd_tools.git@release
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```
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## Use
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See `examples/` for some simple examples; you may need additional
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packages such as [gridlock](https://mpxd.net/code/jan/gridlock)
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See examples/test.py for some simple examples; you may need additional
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packages such as [gridlock](https://mpxd.net/gogs/jan/gridlock)
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to run the examples.
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@ -1,90 +0,0 @@
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import importlib
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import numpy
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from numpy.linalg import norm
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from meanas import vec, unvec
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from meanas.fdfd import waveguide_mode, functional, scpml
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from meanas.fdfd.solvers import generic as generic_solver
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import gridlock
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from matplotlib import pyplot
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__author__ = 'Jan Petykiewicz'
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def test1(solver=generic_solver):
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dx = 20 # discretization (nm/cell)
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pml_thickness = 10 # (number of cells)
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wl = 1550 # Excitation wavelength
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omega = 2 * numpy.pi / wl
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# Device design parameters
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w = 800
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th = 220
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center = [0, 0, 0]
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r0 = 8e3
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# refractive indices
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n_wg = numpy.sqrt(12.6) # ~Si
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n_air = 1.0 # air
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# Half-dimensions of the simulation grid
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y_max = 1200
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z_max = 900
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xyz_max = numpy.array([800, y_max, z_max]) + (pml_thickness + 2) * dx
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# Coordinates of the edges of the cells.
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half_edge_coords = [numpy.arange(dx/2, m + dx/2, step=dx) for m in xyz_max]
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edge_coords = [numpy.hstack((-h[::-1], h)) for h in half_edge_coords]
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edge_coords[0] = numpy.array([-dx, dx])
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# #### Create the grid and draw the device ####
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grid = gridlock.Grid(edge_coords, initial=n_air**2, num_grids=3)
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grid.draw_cuboid(center=center, dimensions=[8e3, w, th], eps=n_wg**2)
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dxes = [grid.dxyz, grid.autoshifted_dxyz()]
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for a in (1, 2):
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for p in (-1, 1):
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dxes = scmpl.stretch_with_scpml(dxes, omega=omega, axis=a, polarity=p,
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thickness=pml_thickness)
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wg_args = {
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'omega': omega,
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'dxes': [(d[1], d[2]) for d in dxes],
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'epsilon': vec(g.transpose([1, 2, 0]) for g in grid.grids),
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'r0': r0,
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}
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wg_results = waveguide_mode.solve_waveguide_mode_cylindrical(mode_number=0, **wg_args)
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E = wg_results['E']
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n_eff = wl / (2 * numpy.pi / wg_results['wavenumber'])
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print('n =', n_eff)
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print('alpha (um^-1) =', -4 * numpy.pi * numpy.imag(n_eff) / (wl * 1e-3))
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'''
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Plot results
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'''
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def pcolor(v):
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vmax = numpy.max(numpy.abs(v))
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pyplot.pcolor(v.T, cmap='seismic', vmin=-vmax, vmax=vmax)
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pyplot.axis('equal')
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pyplot.colorbar()
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pyplot.figure()
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pyplot.subplot(2, 2, 1)
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pcolor(numpy.real(E[0][:, :]))
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pyplot.subplot(2, 2, 2)
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pcolor(numpy.real(E[1][:, :]))
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pyplot.subplot(2, 2, 3)
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pcolor(numpy.real(E[2][:, :]))
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pyplot.subplot(2, 2, 4)
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pyplot.show()
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if __name__ == '__main__':
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test1()
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@ -0,0 +1,25 @@
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"""
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Electromagnetic FDFD simulation tools
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Tools for 3D and 2D Electromagnetic Finite Difference Frequency Domain (FDFD)
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simulations. These tools handle conversion of fields to/from vector form,
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creation of the wave operator matrix, stretched-coordinate PMLs, PECs and PMCs,
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field conversion operators, waveguide mode operator, and waveguide mode
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solver.
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This package only contains a solver for the waveguide mode eigenproblem;
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if you want to solve 3D problems you can use your favorite iterative sparse
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matrix solver (so long as it can handle complex symmetric [non-Hermitian]
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matrices, ideally with double precision).
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Dependencies:
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- numpy
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- scipy
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"""
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from .vectorization import vec, unvec, field_t, vfield_t
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from .grid import dx_lists_t
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__author__ = 'Jan Petykiewicz'
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@ -0,0 +1,239 @@
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from typing import List, Callable, Tuple, Dict
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import numpy
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from . import dx_lists_t, field_t
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__author__ = 'Jan Petykiewicz'
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functional_matrix = Callable[[field_t], field_t]
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def curl_h(dxes: dx_lists_t = None) -> functional_matrix:
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"""
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Curl operator for use with the H field.
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:param dxes: Grid parameters [dx_e, dx_h] as described in fdfd_tools.operators header
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:return: Function for taking the discretized curl of the H-field, F(H) -> curlH
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"""
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if dxes:
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dxyz_b = numpy.meshgrid(*dxes[1], indexing='ij')
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def dh(f, ax):
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return (f - numpy.roll(f, 1, axis=ax)) / dxyz_b[ax]
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else:
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def dh(f, ax):
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return f - numpy.roll(f, 1, axis=ax)
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def ch_fun(h: field_t) -> field_t:
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e = [dh(h[2], 1) - dh(h[1], 2),
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dh(h[0], 2) - dh(h[2], 0),
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dh(h[1], 0) - dh(h[0], 1)]
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return e
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return ch_fun
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def curl_e(dxes: dx_lists_t = None) -> functional_matrix:
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"""
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Curl operator for use with the E field.
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:param dxes: Grid parameters [dx_e, dx_h] as described in fdfd_tools.operators header
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:return: Function for taking the discretized curl of the E-field, F(E) -> curlE
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"""
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if dxes is not None:
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dxyz_a = numpy.meshgrid(*dxes[0], indexing='ij')
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def de(f, ax):
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return (numpy.roll(f, -1, axis=ax) - f) / dxyz_a[ax]
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else:
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def de(f, ax):
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return numpy.roll(f, -1, axis=ax) - f
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def ce_fun(e: field_t) -> field_t:
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h = [de(e[2], 1) - de(e[1], 2),
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de(e[0], 2) - de(e[2], 0),
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de(e[1], 0) - de(e[0], 1)]
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return h
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return ce_fun
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def maxwell_e(dt: float, dxes: dx_lists_t = None) -> functional_matrix:
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curl_h_fun = curl_h(dxes)
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def me_fun(e: field_t, h: field_t, epsilon: field_t):
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ch = curl_h_fun(h)
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for ei, ci, epsi in zip(e, ch, epsilon):
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ei += dt * ci / epsi
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return e
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return me_fun
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def maxwell_h(dt: float, dxes: dx_lists_t = None) -> functional_matrix:
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curl_e_fun = curl_e(dxes)
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def mh_fun(e: field_t, h: field_t):
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ce = curl_e_fun(e)
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for hi, ci in zip(h, ce):
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hi -= dt * ci
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return h
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return mh_fun
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def conducting_boundary(direction: int,
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polarity: int
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) -> Tuple[functional_matrix, functional_matrix]:
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dirs = [0, 1, 2]
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if direction not in dirs:
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raise Exception('Invalid direction: {}'.format(direction))
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dirs.remove(direction)
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u, v = dirs
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if polarity < 0:
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boundary_slice = [slice(None)] * 3
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shifted1_slice = [slice(None)] * 3
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boundary_slice[direction] = 0
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shifted1_slice[direction] = 1
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def en(e: field_t):
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e[direction][boundary_slice] = 0
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e[u][boundary_slice] = e[u][shifted1_slice]
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e[v][boundary_slice] = e[v][shifted1_slice]
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return e
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def hn(h: field_t):
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h[direction][boundary_slice] = h[direction][shifted1_slice]
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h[u][boundary_slice] = 0
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h[v][boundary_slice] = 0
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return h
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return en, hn
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elif polarity > 0:
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boundary_slice = [slice(None)] * 3
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shifted1_slice = [slice(None)] * 3
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shifted2_slice = [slice(None)] * 3
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boundary_slice[direction] = -1
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shifted1_slice[direction] = -2
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shifted2_slice[direction] = -3
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def ep(e: field_t):
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e[direction][boundary_slice] = -e[direction][shifted2_slice]
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e[direction][shifted1_slice] = 0
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e[u][boundary_slice] = e[u][shifted1_slice]
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e[v][boundary_slice] = e[v][shifted1_slice]
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return e
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def hp(h: field_t):
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h[direction][boundary_slice] = h[direction][shifted1_slice]
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h[u][boundary_slice] = -h[u][shifted2_slice]
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h[u][shifted1_slice] = 0
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h[v][boundary_slice] = -h[v][shifted2_slice]
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h[v][shifted1_slice] = 0
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return h
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return ep, hp
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else:
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raise Exception('Bad polarity: {}'.format(polarity))
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def cpml(direction:int,
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polarity: int,
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dt: float,
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epsilon: field_t,
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thickness: int = 8,
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epsilon_eff: float = 1,
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dtype: numpy.dtype = numpy.float32,
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) -> Tuple[Callable, Callable, Dict[str, field_t]]:
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if direction not in range(3):
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raise Exception('Invalid direction: {}'.format(direction))
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if polarity not in (-1, 1):
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raise Exception('Invalid polarity: {}'.format(polarity))
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if thickness <= 2:
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raise Exception('It would be wise to have a pml with 4+ cells of thickness')
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if epsilon_eff <= 0:
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raise Exception('epsilon_eff must be positive')
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m = (3.5, 1)
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sigma_max = 0.8 * (m[0] + 1) / numpy.sqrt(epsilon_eff)
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alpha_max = 0 # TODO: Decide what to do about non-zero alpha
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transverse = numpy.delete(range(3), direction)
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u, v = transverse
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xe = numpy.arange(1, thickness+1, dtype=float)
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xh = numpy.arange(1, thickness+1, dtype=float)
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if polarity > 0:
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xe -= 0.5
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elif polarity < 0:
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xh -= 0.5
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xe = xe[::-1]
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xh = xh[::-1]
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else:
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raise Exception('Bad polarity!')
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expand_slice = [None] * 3
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expand_slice[direction] = slice(None)
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def par(x):
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sigma = ((x / thickness) ** m[0]) * sigma_max
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alpha = ((1 - x / thickness) ** m[1]) * alpha_max
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p0 = numpy.exp(-(sigma + alpha) * dt)
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p1 = sigma / (sigma + alpha) * (p0 - 1)
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return p0[expand_slice], p1[expand_slice]
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p0e, p1e = par(xe)
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p0h, p1h = par(xh)
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region = [slice(None)] * 3
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if polarity < 0:
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region[direction] = slice(None, thickness)
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elif polarity > 0:
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region[direction] = slice(-thickness, None)
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else:
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raise Exception('Bad polarity!')
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if direction == 1:
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se = 1
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else:
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se = -1
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# TODO check if epsilon is uniform?
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shape = list(epsilon[0].shape)
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shape[direction] = thickness
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psi_e = [numpy.zeros(shape, dtype=dtype), numpy.zeros(shape, dtype=dtype)]
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psi_h = [numpy.zeros(shape, dtype=dtype), numpy.zeros(shape, dtype=dtype)]
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fields = {
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'psi_e_u': psi_e[0],
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'psi_e_v': psi_e[1],
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'psi_h_u': psi_h[0],
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'psi_h_v': psi_h[1],
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}
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def pml_e(e: field_t, h: field_t, epsilon: field_t) -> Tuple[field_t, field_t]:
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psi_e[0] *= p0e
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psi_e[0] += p1e * (h[v][region] - numpy.roll(h[v], 1, axis=direction)[region])
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psi_e[1] *= p0e
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psi_e[1] += p1e * (h[u][region] - numpy.roll(h[u], 1, axis=direction)[region])
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e[u][region] += se * dt * psi_e[0] / epsilon[u][region]
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e[v][region] -= se * dt * psi_e[1] / epsilon[v][region]
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return e, h
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|
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def pml_h(e: field_t, h: field_t) -> Tuple[field_t, field_t]:
|
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psi_h[0] *= p0h
|
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psi_h[0] += p1h * (numpy.roll(e[v], -1, axis=direction)[region] - e[v][region])
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psi_h[1] *= p0h
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psi_h[1] += p1h * (numpy.roll(e[u], -1, axis=direction)[region] - e[u][region])
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h[u][region] -= se * dt * psi_h[0]
|
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h[v][region] += se * dt * psi_h[1]
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return e, h
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|
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return pml_e, pml_h, fields
|
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@ -1,48 +0,0 @@
|
||||
"""
|
||||
Electromagnetic simulation tools
|
||||
|
||||
This package is intended for building simulation inputs, analyzing
|
||||
simulation outputs, and running short simulations on unspecialized hardware.
|
||||
It is designed to provide tooling and a baseline for other, high-performance
|
||||
purpose- and hardware-specific solvers.
|
||||
|
||||
|
||||
**Contents**
|
||||
- Finite difference frequency domain (FDFD)
|
||||
* Library of sparse matrices for representing the electromagnetic wave
|
||||
equation in 3D, as well as auxiliary matrices for conversion between fields
|
||||
* Waveguide mode operators
|
||||
* Waveguide mode eigensolver
|
||||
* Stretched-coordinate PML boundaries (SCPML)
|
||||
* Functional versions of most operators
|
||||
* Anisotropic media (limited to diagonal elements eps_xx, eps_yy, eps_zz, mu_xx, ...)
|
||||
* Arbitrary distributions of perfect electric and magnetic conductors (PEC / PMC)
|
||||
- Finite difference time domain (FDTD)
|
||||
* Basic Maxwell time-steps
|
||||
* Poynting vector and energy calculation
|
||||
* Convolutional PMLs
|
||||
|
||||
This package does *not* provide a fast matrix solver, though by default
|
||||
```meanas.fdfd.solvers.generic(...)``` will call
|
||||
```scipy.sparse.linalg.qmr(...)``` to perform a solve.
|
||||
For 2D FDFD problems this should be fine; likewise, the waveguide mode
|
||||
solver uses scipy's eigenvalue solver, with reasonable results.
|
||||
|
||||
For solving large (or 3D) FDFD problems, I recommend a GPU-based iterative
|
||||
solver, such as [opencl_fdfd](https://mpxd.net/code/jan/opencl_fdfd) or
|
||||
those included in [MAGMA](http://icl.cs.utk.edu/magma/index.html)). Your
|
||||
solver will need the ability to solve complex symmetric (non-Hermitian)
|
||||
linear systems, ideally with double precision.
|
||||
|
||||
|
||||
Dependencies:
|
||||
- numpy
|
||||
- scipy
|
||||
|
||||
"""
|
||||
|
||||
from .types import dx_lists_t, field_t, vfield_t, field_updater
|
||||
from .vectorization import vec, unvec
|
||||
|
||||
__author__ = 'Jan Petykiewicz'
|
||||
version = '0.5'
|
||||
@ -1,9 +0,0 @@
|
||||
"""
|
||||
Basic FDTD functionality
|
||||
"""
|
||||
|
||||
from .base import maxwell_e, maxwell_h
|
||||
from .pml import cpml
|
||||
from .energy import (poynting, poynting_divergence, energy_hstep, energy_estep,
|
||||
delta_energy_h2e, delta_energy_h2e, delta_energy_j)
|
||||
from .boundaries import conducting_boundary
|
||||
@ -1,87 +0,0 @@
|
||||
"""
|
||||
Basic FDTD field updates
|
||||
"""
|
||||
from typing import List, Callable, Tuple, Dict
|
||||
import numpy
|
||||
|
||||
from .. import dx_lists_t, field_t, field_updater
|
||||
|
||||
__author__ = 'Jan Petykiewicz'
|
||||
|
||||
|
||||
def curl_h(dxes: dx_lists_t = None) -> field_updater:
|
||||
"""
|
||||
Curl operator for use with the H field.
|
||||
|
||||
:param dxes: Grid parameters [dx_e, dx_h] as described in meanas.types
|
||||
:return: Function for taking the discretized curl of the H-field, F(H) -> curlH
|
||||
"""
|
||||
if dxes:
|
||||
dxyz_b = numpy.meshgrid(*dxes[1], indexing='ij')
|
||||
|
||||
def dh(f, ax):
|
||||
return (f - numpy.roll(f, 1, axis=ax)) / dxyz_b[ax]
|
||||
else:
|
||||
def dh(f, ax):
|
||||
return f - numpy.roll(f, 1, axis=ax)
|
||||
|
||||
def ch_fun(h: field_t) -> field_t:
|
||||
output = numpy.empty_like(h)
|
||||
output[0] = dh(h[2], 1)
|
||||
output[1] = dh(h[0], 2)
|
||||
output[2] = dh(h[1], 0)
|
||||
output[0] -= dh(h[1], 2)
|
||||
output[1] -= dh(h[2], 0)
|
||||
output[2] -= dh(h[0], 1)
|
||||
return output
|
||||
|
||||
return ch_fun
|
||||
|
||||
|
||||
def curl_e(dxes: dx_lists_t = None) -> field_updater:
|
||||
"""
|
||||
Curl operator for use with the E field.
|
||||
|
||||
:param dxes: Grid parameters [dx_e, dx_h] as described in meanas.types
|
||||
:return: Function for taking the discretized curl of the E-field, F(E) -> curlE
|
||||
"""
|
||||
if dxes is not None:
|
||||
dxyz_a = numpy.meshgrid(*dxes[0], indexing='ij')
|
||||
|
||||
def de(f, ax):
|
||||
return (numpy.roll(f, -1, axis=ax) - f) / dxyz_a[ax]
|
||||
else:
|
||||
def de(f, ax):
|
||||
return numpy.roll(f, -1, axis=ax) - f
|
||||
|
||||
def ce_fun(e: field_t) -> field_t:
|
||||
output = numpy.empty_like(e)
|
||||
output[0] = de(e[2], 1)
|
||||
output[1] = de(e[0], 2)
|
||||
output[2] = de(e[1], 0)
|
||||
output[0] -= de(e[1], 2)
|
||||
output[1] -= de(e[2], 0)
|
||||
output[2] -= de(e[0], 1)
|
||||
return output
|
||||
|
||||
return ce_fun
|
||||
|
||||
|
||||
def maxwell_e(dt: float, dxes: dx_lists_t = None) -> field_updater:
|
||||
curl_h_fun = curl_h(dxes)
|
||||
|
||||
def me_fun(e: field_t, h: field_t, epsilon: field_t):
|
||||
e += dt * curl_h_fun(h) / epsilon
|
||||
return e
|
||||
|
||||
return me_fun
|
||||
|
||||
|
||||
def maxwell_h(dt: float, dxes: dx_lists_t = None) -> field_updater:
|
||||
curl_e_fun = curl_e(dxes)
|
||||
|
||||
def mh_fun(e: field_t, h: field_t):
|
||||
h -= dt * curl_e_fun(e)
|
||||
return h
|
||||
|
||||
return mh_fun
|
||||
@ -1,68 +0,0 @@
|
||||
"""
|
||||
Boundary conditions
|
||||
"""
|
||||
|
||||
from typing import List, Callable, Tuple, Dict
|
||||
import numpy
|
||||
|
||||
from .. import dx_lists_t, field_t, field_updater
|
||||
|
||||
|
||||
def conducting_boundary(direction: int,
|
||||
polarity: int
|
||||
) -> Tuple[field_updater, field_updater]:
|
||||
dirs = [0, 1, 2]
|
||||
if direction not in dirs:
|
||||
raise Exception('Invalid direction: {}'.format(direction))
|
||||
dirs.remove(direction)
|
||||
u, v = dirs
|
||||
|
||||
if polarity < 0:
|
||||
boundary_slice = [slice(None)] * 3
|
||||
shifted1_slice = [slice(None)] * 3
|
||||
boundary_slice[direction] = 0
|
||||
shifted1_slice[direction] = 1
|
||||
|
||||
def en(e: field_t):
|
||||
e[direction][boundary_slice] = 0
|
||||
e[u][boundary_slice] = e[u][shifted1_slice]
|
||||
e[v][boundary_slice] = e[v][shifted1_slice]
|
||||
return e
|
||||
|
||||
def hn(h: field_t):
|
||||
h[direction][boundary_slice] = h[direction][shifted1_slice]
|
||||
h[u][boundary_slice] = 0
|
||||
h[v][boundary_slice] = 0
|
||||
return h
|
||||
|
||||
return en, hn
|
||||
|
||||
elif polarity > 0:
|
||||
boundary_slice = [slice(None)] * 3
|
||||
shifted1_slice = [slice(None)] * 3
|
||||
shifted2_slice = [slice(None)] * 3
|
||||
boundary_slice[direction] = -1
|
||||
shifted1_slice[direction] = -2
|
||||
shifted2_slice[direction] = -3
|
||||
|
||||
def ep(e: field_t):
|
||||
e[direction][boundary_slice] = -e[direction][shifted2_slice]
|
||||
e[direction][shifted1_slice] = 0
|
||||
e[u][boundary_slice] = e[u][shifted1_slice]
|
||||
e[v][boundary_slice] = e[v][shifted1_slice]
|
||||
return e
|
||||
|
||||
def hp(h: field_t):
|
||||
h[direction][boundary_slice] = h[direction][shifted1_slice]
|
||||
h[u][boundary_slice] = -h[u][shifted2_slice]
|
||||
h[u][shifted1_slice] = 0
|
||||
h[v][boundary_slice] = -h[v][shifted2_slice]
|
||||
h[v][shifted1_slice] = 0
|
||||
return h
|
||||
|
||||
return ep, hp
|
||||
|
||||
else:
|
||||
raise Exception('Bad polarity: {}'.format(polarity))
|
||||
|
||||
|
||||
@ -1,84 +0,0 @@
|
||||
from typing import List, Callable, Tuple, Dict
|
||||
import numpy
|
||||
|
||||
from .. import dx_lists_t, field_t, field_updater
|
||||
|
||||
|
||||
def poynting(e, h):
|
||||
s = (numpy.roll(e[1], -1, axis=0) * h[2] - numpy.roll(e[2], -1, axis=0) * h[1],
|
||||
numpy.roll(e[2], -1, axis=1) * h[0] - numpy.roll(e[0], -1, axis=1) * h[2],
|
||||
numpy.roll(e[0], -1, axis=2) * h[1] - numpy.roll(e[1], -1, axis=2) * h[0])
|
||||
return numpy.array(s)
|
||||
|
||||
|
||||
def poynting_divergence(s=None, *, e=None, h=None, dxes=None): # TODO dxes
|
||||
if dxes is None:
|
||||
dxes = tuple(tuple(numpy.ones(1) for _ in range(3)) for _ in range(2))
|
||||
|
||||
if s is None:
|
||||
s = poynting(e, h)
|
||||
|
||||
ds = ((s[0] - numpy.roll(s[0], 1, axis=0)) / numpy.sqrt(dxes[0][0] * dxes[1][0])[:, None, None] +
|
||||
(s[1] - numpy.roll(s[1], 1, axis=1)) / numpy.sqrt(dxes[0][1] * dxes[1][1])[None, :, None] +
|
||||
(s[2] - numpy.roll(s[2], 1, axis=2)) / numpy.sqrt(dxes[0][2] * dxes[1][2])[None, None, :] )
|
||||
return ds
|
||||
|
||||
|
||||
def energy_hstep(e0, h1, e2, epsilon=None, mu=None, dxes=None):
|
||||
u = dxmul(e0 * e2, h1 * h1, epsilon, mu, dxes)
|
||||
return u
|
||||
|
||||
|
||||
def energy_estep(h0, e1, h2, epsilon=None, mu=None, dxes=None):
|
||||
u = dxmul(e1 * e1, h0 * h2, epsilon, mu, dxes)
|
||||
return u
|
||||
|
||||
|
||||
def delta_energy_h2e(dt, e0, h1, e2, h3, epsilon=None, mu=None, dxes=None):
|
||||
"""
|
||||
This is just from (e2 * e2 + h3 * h1) - (h1 * h1 + e0 * e2)
|
||||
"""
|
||||
de = e2 * (e2 - e0) / dt
|
||||
dh = h1 * (h3 - h1) / dt
|
||||
du = dxmul(de, dh, epsilon, mu, dxes)
|
||||
return du
|
||||
|
||||
|
||||
def delta_energy_e2h(dt, h0, e1, h2, e3, epsilon=None, mu=None, dxes=None):
|
||||
"""
|
||||
This is just from (h2 * h2 + e3 * e1) - (e1 * e1 + h0 * h2)
|
||||
"""
|
||||
de = e1 * (e3 - e1) / dt
|
||||
dh = h2 * (h2 - h0) / dt
|
||||
du = dxmul(de, dh, epsilon, mu, dxes)
|
||||
return du
|
||||
|
||||
|
||||
def delta_energy_j(j0, e1, dxes=None):
|
||||
if dxes is None:
|
||||
dxes = tuple(tuple(numpy.ones(1) for _ in range(3)) for _ in range(2))
|
||||
|
||||
du = ((j0 * e1).sum(axis=0) *
|
||||
dxes[0][0][:, None, None] *
|
||||
dxes[0][1][None, :, None] *
|
||||
dxes[0][2][None, None, :])
|
||||
return du
|
||||
|
||||
|
||||
def dxmul(ee, hh, epsilon=None, mu=None, dxes=None):
|
||||
if epsilon is None:
|
||||
epsilon = 1
|
||||
if mu is None:
|
||||
mu = 1
|
||||
if dxes is None:
|
||||
dxes = tuple(tuple(numpy.ones(1) for _ in range(3)) for _ in range(2))
|
||||
|
||||
result = ((ee * epsilon).sum(axis=0) *
|
||||
dxes[0][0][:, None, None] *
|
||||
dxes[0][1][None, :, None] *
|
||||
dxes[0][2][None, None, :] +
|
||||
(hh * mu).sum(axis=0) *
|
||||
dxes[1][0][:, None, None] *
|
||||
dxes[1][1][None, :, None] *
|
||||
dxes[1][2][None, None, :])
|
||||
return result
|
||||
@ -1,122 +0,0 @@
|
||||
"""
|
||||
PML implementations
|
||||
|
||||
"""
|
||||
# TODO retest pmls!
|
||||
|
||||
from typing import List, Callable, Tuple, Dict
|
||||
import numpy
|
||||
|
||||
from .. import dx_lists_t, field_t, field_updater
|
||||
|
||||
|
||||
__author__ = 'Jan Petykiewicz'
|
||||
|
||||
|
||||
def cpml(direction:int,
|
||||
polarity: int,
|
||||
dt: float,
|
||||
epsilon: field_t,
|
||||
thickness: int = 8,
|
||||
ln_R_per_layer: float = -1.6,
|
||||
epsilon_eff: float = 1,
|
||||
mu_eff: float = 1,
|
||||
m: float = 3.5,
|
||||
ma: float = 1,
|
||||
cfs_alpha: float = 0,
|
||||
dtype: numpy.dtype = numpy.float32,
|
||||
) -> Tuple[Callable, Callable, Dict[str, field_t]]:
|
||||
|
||||
if direction not in range(3):
|
||||
raise Exception('Invalid direction: {}'.format(direction))
|
||||
|
||||
if polarity not in (-1, 1):
|
||||
raise Exception('Invalid polarity: {}'.format(polarity))
|
||||
|
||||
if thickness <= 2:
|
||||
raise Exception('It would be wise to have a pml with 4+ cells of thickness')
|
||||
|
||||
if epsilon_eff <= 0:
|
||||
raise Exception('epsilon_eff must be positive')
|
||||
|
||||
sigma_max = -ln_R_per_layer / 2 * (m + 1)
|
||||
kappa_max = numpy.sqrt(epsilon_eff * mu_eff)
|
||||
alpha_max = cfs_alpha
|
||||
transverse = numpy.delete(range(3), direction)
|
||||
u, v = transverse
|
||||
|
||||
xe = numpy.arange(1, thickness+1, dtype=float)
|
||||
xh = numpy.arange(1, thickness+1, dtype=float)
|
||||
if polarity > 0:
|
||||
xe -= 0.5
|
||||
elif polarity < 0:
|
||||
xh -= 0.5
|
||||
xe = xe[::-1]
|
||||
xh = xh[::-1]
|
||||
else:
|
||||
raise Exception('Bad polarity!')
|
||||
|
||||
expand_slice = [None] * 3
|
||||
expand_slice[direction] = slice(None)
|
||||
|
||||
def par(x):
|
||||
scaling = (x / thickness) ** m
|
||||
sigma = scaling * sigma_max
|
||||
kappa = 1 + scaling * (kappa_max - 1)
|
||||
alpha = ((1 - x / thickness) ** ma) * alpha_max
|
||||
p0 = numpy.exp(-(sigma / kappa + alpha) * dt)
|
||||
p1 = sigma / (sigma + kappa * alpha) * (p0 - 1)
|
||||
p2 = 1 / kappa
|
||||
return p0[expand_slice], p1[expand_slice], p2[expand_slice]
|
||||
|
||||
p0e, p1e, p2e = par(xe)
|
||||
p0h, p1h, p2h = par(xh)
|
||||
|
||||
region = [slice(None)] * 3
|
||||
if polarity < 0:
|
||||
region[direction] = slice(None, thickness)
|
||||
elif polarity > 0:
|
||||
region[direction] = slice(-thickness, None)
|
||||
else:
|
||||
raise Exception('Bad polarity!')
|
||||
|
||||
se = 1 if direction == 1 else -1
|
||||
|
||||
# TODO check if epsilon is uniform in pml region?
|
||||
shape = list(epsilon[0].shape)
|
||||
shape[direction] = thickness
|
||||
psi_e = [numpy.zeros(shape, dtype=dtype), numpy.zeros(shape, dtype=dtype)]
|
||||
psi_h = [numpy.zeros(shape, dtype=dtype), numpy.zeros(shape, dtype=dtype)]
|
||||
|
||||
fields = {
|
||||
'psi_e_u': psi_e[0],
|
||||
'psi_e_v': psi_e[1],
|
||||
'psi_h_u': psi_h[0],
|
||||
'psi_h_v': psi_h[1],
|
||||
}
|
||||
|
||||
# Note that this is kinda slow -- would be faster to reuse dHv*p2h for the original
|
||||
# H update, but then you have multiple arrays and a monolithic (field + pml) update operation
|
||||
def pml_e(e: field_t, h: field_t, epsilon: field_t) -> Tuple[field_t, field_t]:
|
||||
dHv = h[v][region] - numpy.roll(h[v], 1, axis=direction)[region]
|
||||
dHu = h[u][region] - numpy.roll(h[u], 1, axis=direction)[region]
|
||||
psi_e[0] *= p0e
|
||||
psi_e[0] += p1e * dHv * p2e
|
||||
psi_e[1] *= p0e
|
||||
psi_e[1] += p1e * dHu * p2e
|
||||
e[u][region] += se * dt / epsilon[u][region] * (psi_e[0] + (p2e - 1) * dHv)
|
||||
e[v][region] -= se * dt / epsilon[v][region] * (psi_e[1] + (p2e - 1) * dHu)
|
||||
return e, h
|
||||
|
||||
def pml_h(e: field_t, h: field_t) -> Tuple[field_t, field_t]:
|
||||
dEv = (numpy.roll(e[v], -1, axis=direction)[region] - e[v][region])
|
||||
dEu = (numpy.roll(e[u], -1, axis=direction)[region] - e[u][region])
|
||||
psi_h[0] *= p0h
|
||||
psi_h[0] += p1h * dEv * p2h
|
||||
psi_h[1] *= p0h
|
||||
psi_h[1] += p1h * dEu * p2h
|
||||
h[u][region] -= se * dt * (psi_h[0] + (p2h - 1) * dEv)
|
||||
h[v][region] += se * dt * (psi_h[1] + (p2h - 1) * dEu)
|
||||
return e, h
|
||||
|
||||
return pml_e, pml_h, fields
|
||||
@ -1,293 +0,0 @@
|
||||
import numpy
|
||||
import pytest
|
||||
import dataclasses
|
||||
from typing import List, Tuple
|
||||
from numpy.testing import assert_allclose, assert_array_equal
|
||||
|
||||
from meanas import fdtd
|
||||
|
||||
|
||||
prng = numpy.random.RandomState(12345)
|
||||
|
||||
|
||||
def assert_fields_close(a, b, *args, **kwargs):
|
||||
numpy.testing.assert_allclose(a, b, verbose=False, err_msg='Fields did not match:\n{}\n{}'.format(numpy.rollaxis(a, -1),
|
||||
numpy.rollaxis(b, -1)), *args, **kwargs)
|
||||
|
||||
def assert_close(a, b, *args, **kwargs):
|
||||
numpy.testing.assert_allclose(a, b, *args, **kwargs)
|
||||
|
||||
|
||||
def test_initial_fields(sim):
|
||||
# Make sure initial fields didn't change
|
||||
e0 = sim.es[0]
|
||||
h0 = sim.hs[0]
|
||||
j0 = sim.js[0]
|
||||
mask = (j0 != 0)
|
||||
|
||||
assert_fields_close(e0[mask], j0[mask] / sim.epsilon[mask])
|
||||
assert not e0[~mask].any()
|
||||
assert not h0.any()
|
||||
|
||||
|
||||
def test_initial_energy(sim):
|
||||
"""
|
||||
Assumes fields start at 0 before J0 is added
|
||||
"""
|
||||
j0 = sim.js[0]
|
||||
e0 = sim.es[0]
|
||||
h0 = sim.hs[0]
|
||||
h1 = sim.hs[1]
|
||||
mask = (j0 != 0)
|
||||
dV = numpy.prod(numpy.meshgrid(*sim.dxes[0], indexing='ij'), axis=0)
|
||||
u0 = (j0 * j0.conj() / sim.epsilon * dV).sum(axis=0)
|
||||
args = {'dxes': sim.dxes,
|
||||
'epsilon': sim.epsilon}
|
||||
|
||||
# Make sure initial energy and E dot J are correct
|
||||
energy0 = fdtd.energy_estep(h0=h0, e1=e0, h2=h1, **args)
|
||||
e0_dot_j0 = fdtd.delta_energy_j(j0=j0, e1=e0, dxes=sim.dxes)
|
||||
assert_fields_close(energy0, u0)
|
||||
assert_fields_close(e0_dot_j0, u0)
|
||||
|
||||
|
||||
def test_energy_conservation(sim):
|
||||
"""
|
||||
Assumes fields start at 0 before J0 is added
|
||||
"""
|
||||
e0 = sim.es[0]
|
||||
j0 = sim.js[0]
|
||||
u = fdtd.delta_energy_j(j0=j0, e1=e0, dxes=sim.dxes).sum()
|
||||
args = {'dxes': sim.dxes,
|
||||
'epsilon': sim.epsilon}
|
||||
|
||||
for ii in range(1, 8):
|
||||
u_hstep = fdtd.energy_hstep(e0=sim.es[ii-1], h1=sim.hs[ii], e2=sim.es[ii], **args)
|
||||
u_estep = fdtd.energy_estep(h0=sim.hs[ii], e1=sim.es[ii], h2=sim.hs[ii + 1], **args)
|
||||
delta_j_A = fdtd.delta_energy_j(j0=sim.js[ii], e1=sim.es[ii-1], dxes=sim.dxes)
|
||||
delta_j_B = fdtd.delta_energy_j(j0=sim.js[ii], e1=sim.es[ii], dxes=sim.dxes)
|
||||
|
||||
u += delta_j_A.sum()
|
||||
assert_close(u_hstep.sum(), u)
|
||||
u += delta_j_B.sum()
|
||||
assert_close(u_estep.sum(), u)
|
||||
|
||||
|
||||
def test_poynting_divergence(sim):
|
||||
args = {'dxes': sim.dxes,
|
||||
'epsilon': sim.epsilon}
|
||||
|
||||
dV = numpy.prod(numpy.meshgrid(*sim.dxes[0], indexing='ij'), axis=0)
|
||||
|
||||
u_eprev = None
|
||||
for ii in range(1, 8):
|
||||
u_hstep = fdtd.energy_hstep(e0=sim.es[ii-1], h1=sim.hs[ii], e2=sim.es[ii], **args)
|
||||
u_estep = fdtd.energy_estep(h0=sim.hs[ii], e1=sim.es[ii], h2=sim.hs[ii + 1], **args)
|
||||
delta_j_B = fdtd.delta_energy_j(j0=sim.js[ii], e1=sim.es[ii], dxes=sim.dxes)
|
||||
|
||||
du_half_h2e = u_estep - u_hstep - delta_j_B
|
||||
div_s_h2e = sim.dt * fdtd.poynting_divergence(e=sim.es[ii], h=sim.hs[ii], dxes=sim.dxes) * dV
|
||||
assert_fields_close(du_half_h2e, -div_s_h2e)
|
||||
|
||||
if u_eprev is None:
|
||||
u_eprev = u_estep
|
||||
continue
|
||||
|
||||
# previous half-step
|
||||
delta_j_A = fdtd.delta_energy_j(j0=sim.js[ii], e1=sim.es[ii-1], dxes=sim.dxes)
|
||||
|
||||
du_half_e2h = u_hstep - u_eprev - delta_j_A
|
||||
div_s_e2h = sim.dt * fdtd.poynting_divergence(e=sim.es[ii-1], h=sim.hs[ii], dxes=sim.dxes) * dV
|
||||
assert_fields_close(du_half_e2h, -div_s_e2h)
|
||||
u_eprev = u_estep
|
||||
|
||||
|
||||
def test_poynting_planes(sim):
|
||||
mask = (sim.js[0] != 0)
|
||||
if mask.sum() > 1:
|
||||
pytest.skip('test_poynting_planes can only test single point sources')
|
||||
|
||||
args = {'dxes': sim.dxes,
|
||||
'epsilon': sim.epsilon}
|
||||
dV = numpy.prod(numpy.meshgrid(*sim.dxes[0], indexing='ij'), axis=0)
|
||||
|
||||
mx = numpy.roll(mask, (-1, -1), axis=(0, 1))
|
||||
my = numpy.roll(mask, -1, axis=2)
|
||||
mz = numpy.roll(mask, (+1, -1), axis=(0, 3))
|
||||
px = numpy.roll(mask, -1, axis=0)
|
||||
py = mask.copy()
|
||||
pz = numpy.roll(mask, +1, axis=0)
|
||||
|
||||
u_eprev = None
|
||||
for ii in range(1, 8):
|
||||
u_hstep = fdtd.energy_hstep(e0=sim.es[ii-1], h1=sim.hs[ii], e2=sim.es[ii], **args)
|
||||
u_estep = fdtd.energy_estep(h0=sim.hs[ii], e1=sim.es[ii], h2=sim.hs[ii + 1], **args)
|
||||
|
||||
s_h2e = -fdtd.poynting(e=sim.es[ii], h=sim.hs[ii]) * sim.dt
|
||||
s_h2e[0] *= sim.dxes[0][1][None, :, None] * sim.dxes[0][2][None, None, :]
|
||||
s_h2e[1] *= sim.dxes[0][0][:, None, None] * sim.dxes[0][2][None, None, :]
|
||||
s_h2e[2] *= sim.dxes[0][0][:, None, None] * sim.dxes[0][1][None, :, None]
|
||||
planes = [s_h2e[px].sum(), -s_h2e[mx].sum(),
|
||||
s_h2e[py].sum(), -s_h2e[my].sum(),
|
||||
s_h2e[pz].sum(), -s_h2e[mz].sum()]
|
||||
assert_close(sum(planes), (u_estep - u_hstep).sum())
|
||||
if u_eprev is None:
|
||||
u_eprev = u_estep
|
||||
continue
|
||||
|
||||
s_e2h = -fdtd.poynting(e=sim.es[ii - 1], h=sim.hs[ii]) * sim.dt
|
||||
s_e2h[0] *= sim.dxes[0][1][None, :, None] * sim.dxes[0][2][None, None, :]
|
||||
s_e2h[1] *= sim.dxes[0][0][:, None, None] * sim.dxes[0][2][None, None, :]
|
||||
s_e2h[2] *= sim.dxes[0][0][:, None, None] * sim.dxes[0][1][None, :, None]
|
||||
planes = [s_e2h[px].sum(), -s_e2h[mx].sum(),
|
||||
s_e2h[py].sum(), -s_e2h[my].sum(),
|
||||
s_e2h[pz].sum(), -s_e2h[mz].sum()]
|
||||
assert_close(sum(planes), (u_hstep - u_eprev).sum())
|
||||
|
||||
# previous half-step
|
||||
u_eprev = u_estep
|
||||
|
||||
|
||||
#####################################
|
||||
# Test fixtures
|
||||
#####################################
|
||||
|
||||
@pytest.fixture(scope='module',
|
||||
params=[(5, 5, 1),
|
||||
(5, 1, 5),
|
||||
(5, 5, 5),
|
||||
# (7, 7, 7),
|
||||
])
|
||||
def shape(request):
|
||||
yield (3, *request.param)
|
||||
|
||||
|
||||
@pytest.fixture(scope='module', params=[0.3])
|
||||
def dt(request):
|
||||
yield request.param
|
||||
|
||||
|
||||
@pytest.fixture(scope='module', params=[1.0, 1.5])
|
||||
def epsilon_bg(request):
|
||||
yield request.param
|
||||
|
||||
|
||||
@pytest.fixture(scope='module', params=[1.0, 2.5])
|
||||
def epsilon_fg(request):
|
||||
yield request.param
|
||||
|
||||
|
||||
@pytest.fixture(scope='module', params=['center', '000', 'random'])
|
||||
def epsilon(request, shape, epsilon_bg, epsilon_fg):
|
||||
is3d = (numpy.array(shape) == 1).sum() == 0
|
||||
if is3d:
|
||||
if request.param == '000':
|
||||
pytest.skip('Skipping 000 epsilon because test is 3D (for speed)')
|
||||
if epsilon_bg != 1:
|
||||
pytest.skip('Skipping epsilon_bg != 1 because test is 3D (for speed)')
|
||||
if epsilon_fg not in (1.0, 2.0):
|
||||
pytest.skip('Skipping epsilon_fg not in (1, 2) because test is 3D (for speed)')
|
||||
|
||||
epsilon = numpy.full(shape, epsilon_bg, dtype=float)
|
||||
if request.param == 'center':
|
||||
epsilon[:, shape[1]//2, shape[2]//2, shape[3]//2] = epsilon_fg
|
||||
elif request.param == '000':
|
||||
epsilon[:, 0, 0, 0] = epsilon_fg
|
||||
elif request.param == 'random':
|
||||
epsilon[:] = prng.uniform(low=min(epsilon_bg, epsilon_fg),
|
||||
high=max(epsilon_bg, epsilon_fg),
|
||||
size=shape)
|
||||
|
||||
yield epsilon
|
||||
|
||||
|
||||
@pytest.fixture(scope='module', params=[1.0])#, 1.5])
|
||||
def j_mag(request):
|
||||
yield request.param
|
||||
|
||||
|
||||
@pytest.fixture(scope='module', params=['center', 'random'])
|
||||
def j_distribution(request, shape, j_mag):
|
||||
j = numpy.zeros(shape)
|
||||
if request.param == 'center':
|
||||
j[:, shape[1]//2, shape[2]//2, shape[3]//2] = j_mag
|
||||
elif request.param == '000':
|
||||
j[:, 0, 0, 0] = j_mag
|
||||
elif request.param == 'random':
|
||||
j[:] = prng.uniform(low=-j_mag, high=j_mag, size=shape)
|
||||
yield j
|
||||
|
||||
|
||||
@pytest.fixture(scope='module', params=[1.0, 1.5])
|
||||
def dx(request):
|
||||
yield request.param
|
||||
|
||||
|
||||
@pytest.fixture(scope='module', params=['uniform'])
|
||||
def dxes(request, shape, dx):
|
||||
if request.param == 'uniform':
|
||||
dxes = [[numpy.full(s, dx) for s in shape[1:]] for _ in range(2)]
|
||||
yield dxes
|
||||
|
||||
|
||||
@pytest.fixture(scope='module',
|
||||
params=[(0,),
|
||||
(0, 4, 8),
|
||||
]
|
||||
)
|
||||
def j_steps(request):
|
||||
yield request.param
|
||||
|
||||
|
||||
@dataclasses.dataclass()
|
||||
class SimResult:
|
||||
shape: Tuple[int]
|
||||
dt: float
|
||||
dxes: List[List[numpy.ndarray]]
|
||||
epsilon: numpy.ndarray
|
||||
j_distribution: numpy.ndarray
|
||||
j_steps: Tuple[int]
|
||||
es: List[numpy.ndarray] = dataclasses.field(default_factory=list)
|
||||
hs: List[numpy.ndarray] = dataclasses.field(default_factory=list)
|
||||
js: List[numpy.ndarray] = dataclasses.field(default_factory=list)
|
||||
|
||||
|
||||
@pytest.fixture(scope='module')
|
||||
def sim(request, shape, epsilon, dxes, dt, j_distribution, j_steps):
|
||||
is3d = (numpy.array(shape) == 1).sum() == 0
|
||||
if is3d:
|
||||
if dt != 0.3:
|
||||
pytest.skip('Skipping dt != 0.3 because test is 3D (for speed)')
|
||||
|
||||
sim = SimResult(
|
||||
shape=shape,
|
||||
dt=dt,
|
||||
dxes=dxes,
|
||||
epsilon=epsilon,
|
||||
j_distribution=j_distribution,
|
||||
j_steps=j_steps,
|
||||
)
|
||||
|
||||
e = numpy.zeros_like(epsilon)
|
||||
h = numpy.zeros_like(epsilon)
|
||||
|
||||
assert 0 in j_steps
|
||||
j_zeros = numpy.zeros_like(j_distribution)
|
||||
|
||||
eh2h = fdtd.maxwell_h(dt=dt, dxes=dxes)
|
||||
eh2e = fdtd.maxwell_e(dt=dt, dxes=dxes)
|
||||
for tt in range(10):
|
||||
e = e.copy()
|
||||
h = h.copy()
|
||||
eh2h(e, h)
|
||||
eh2e(e, h, epsilon)
|
||||
if tt in j_steps:
|
||||
e += j_distribution / epsilon
|
||||
sim.js.append(j_distribution)
|
||||
else:
|
||||
sim.js.append(j_zeros)
|
||||
sim.es.append(e)
|
||||
sim.hs.append(h)
|
||||
return sim
|
||||
|
||||
|
||||
@ -1,22 +0,0 @@
|
||||
"""
|
||||
Types shared across multiple submodules
|
||||
"""
|
||||
import numpy
|
||||
from typing import List, Callable
|
||||
|
||||
|
||||
# Field types
|
||||
field_t = numpy.ndarray # vector field with shape (3, X, Y, Z) (e.g. [E_x, E_y, E_z])
|
||||
vfield_t = numpy.ndarray # linearized vector field (vector of length 3*X*Y*Z)
|
||||
|
||||
'''
|
||||
'dxes' datastructure which contains grid cell width information in the following format:
|
||||
[[[dx_e_0, dx_e_1, ...], [dy_e_0, ...], [dz_e_0, ...]],
|
||||
[[dx_h_0, dx_h_1, ...], [dy_h_0, ...], [dz_h_0, ...]]]
|
||||
where dx_e_0 is the x-width of the x=0 cells, as used when calculating dE/dx,
|
||||
and dy_h_0 is the y-width of the y=0 cells, as used when calculating dH/dy, etc.
|
||||
'''
|
||||
dx_lists_t = List[List[numpy.ndarray]]
|
||||
|
||||
|
||||
field_updater = Callable[[field_t], field_t]
|
||||
@ -1,36 +1,18 @@
|
||||
#!/usr/bin/env python3
|
||||
#!/usr/bin/env python
|
||||
|
||||
from setuptools import setup, find_packages
|
||||
import meanas
|
||||
|
||||
with open('README.md', 'r') as f:
|
||||
long_description = f.read()
|
||||
|
||||
setup(name='meanas',
|
||||
version=meanas.version,
|
||||
description='Electromagnetic simulation tools',
|
||||
long_description=long_description,
|
||||
long_description_content_type='text/markdown',
|
||||
setup(name='fdfd_tools',
|
||||
version='0.4',
|
||||
description='FDFD Electromagnetic simulation tools',
|
||||
author='Jan Petykiewicz',
|
||||
author_email='anewusername@gmail.com',
|
||||
url='https://mpxd.net/code/jan/fdfd_tools',
|
||||
url='https://mpxd.net/gogs/jan/fdfd_tools',
|
||||
packages=find_packages(),
|
||||
install_requires=[
|
||||
'numpy',
|
||||
'scipy',
|
||||
],
|
||||
extras_require={
|
||||
'test': [
|
||||
'pytest',
|
||||
'dataclasses',
|
||||
],
|
||||
},
|
||||
classifiers=[
|
||||
'Programming Language :: Python :: 3',
|
||||
'Development Status :: 4 - Beta',
|
||||
'Intended Audience :: Developers',
|
||||
'Intended Audience :: Science/Research',
|
||||
'License :: OSI Approved :: GNU Affero General Public License v3',
|
||||
'Topic :: Scientific/Engineering :: Physics',
|
||||
],
|
||||
)
|
||||
|
||||
Loading…
Reference in New Issue