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October 23, 2018 17:29
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| import math | |
| import time | |
| from multiprocessing import Pool | |
| import boto3 | |
| import meep as mp | |
| from meep.materials import Al | |
| def get_results(fragment_stats, total_pixels, total_time): | |
| assert len(fragment_stats) == 1 | |
| stats = fragment_stats[0] | |
| results = "{},{},{},{},{},{},{},{:.4f}\n" | |
| return results.format(stats.num_anisotropic_eps_pixels, | |
| stats.num_anisotropic_mu_pixels, | |
| stats.num_nonlinear_pixels, | |
| stats.num_susceptibility_pixels, | |
| stats.num_nonzero_conductivity_pixels, | |
| stats.num_dft_pixels, | |
| total_pixels, | |
| total_time) | |
| def absorber_boundaries(s): | |
| resolution = 40 | |
| d = 1.0 | |
| abs_layers = [mp.Absorber(thickness=d)] | |
| cell_size = mp.Vector3(s+d, s+d, s+d) | |
| sources = [mp.Source(mp.GaussianSource(1, 0.2), | |
| component=mp.Ex, | |
| center=mp.Vector3())] | |
| start = time.time() | |
| sim = mp.Simulation(resolution=resolution, | |
| cell_size=cell_size, | |
| k_point=mp.Vector3(), | |
| sources=sources, | |
| boundary_layers=abs_layers) | |
| sim.run(until_after_sources=100) | |
| total_time = time.time() - start | |
| return get_results(sim.fragment_stats, s**3 * resolution, total_time) | |
| def aniso_medium(s): | |
| resolution = 40 | |
| cell_size = mp.Vector3(s, s, s) | |
| sources = [mp.Source(mp.GaussianSource(1, 0.2), | |
| component=mp.Ex, | |
| center=mp.Vector3())] | |
| aniso_mat = mp.Medium(epsilon_diag=mp.Vector3(5.56984083, 9.44501035, 2.09631405), | |
| epsilon_offdiag=mp.Vector3(1.20637481, 1.59461922, 1.41411461)) | |
| start = time.time() | |
| sim = mp.Simulation(resolution=resolution, | |
| cell_size=cell_size, | |
| k_point=mp.Vector3(), | |
| sources=sources, | |
| default_material=aniso_mat) | |
| sim.run(until_after_sources=100) | |
| total_time = time.time() - start | |
| return get_results(sim.fragment_stats, s**3 * resolution, total_time) | |
| def conductive_medium(s): | |
| resolution = 40 | |
| cell_size = mp.Vector3(s, s, s) | |
| sources = [mp.Source(mp.GaussianSource(1, 0.2), | |
| component=mp.Ex, | |
| center=mp.Vector3())] | |
| eps_real = 6.5 | |
| eps_imag = 0.2 | |
| cond_mat = mp.Medium(epsilon=eps_real, D_conductivity=2*math.pi*eps_imag/eps_real) | |
| start = time.time() | |
| sim = mp.Simulation(resolution=resolution, | |
| cell_size=cell_size, | |
| k_point=mp.Vector3(), | |
| sources=sources, | |
| default_material=cond_mat) | |
| sim.run(until_after_sources=100) | |
| total_time = time.time() - start | |
| return get_results(sim.fragment_stats, s**3 * resolution, total_time) | |
| def dft_flux(s): | |
| resolution = 40 | |
| cell_size = mp.Vector3(s, s, s) | |
| fcen = 1.0 | |
| df = 0.2 | |
| sources = [mp.Source(mp.GaussianSource(fcen, df), | |
| component=mp.Ex, | |
| center=mp.Vector3())] | |
| start = time.time() | |
| sim = mp.Simulation(resolution=resolution, | |
| cell_size=cell_size, | |
| k_point=mp.Vector3(), | |
| sources=sources) | |
| nfreq = 21 | |
| sim.add_flux(fcen, df, nfreq, mp.FluxRegion(center=mp.Vector3(z=0.4*s), | |
| size=mp.Vector3(s, s))) | |
| sim.add_flux(fcen, df, nfreq, mp.FluxRegion(center=mp.Vector3(z=-0.4*s), | |
| size=mp.Vector3(s, s), weight=-1.0)) | |
| sim.run(until_after_sources=100) | |
| total_time = time.time() - start | |
| return get_results(sim.fragment_stats, s**3 * resolution, total_time) | |
| def dispersive_medium(s): | |
| resolution = 40 | |
| cell_size = mp.Vector3(s, s, s) | |
| sources = [mp.Source(mp.GaussianSource(1, 0.2), | |
| component=mp.Ex, | |
| center=mp.Vector3())] | |
| start = time.time() | |
| sim = mp.Simulation(resolution=resolution, | |
| cell_size=cell_size, | |
| k_point=mp.Vector3(), | |
| sources=sources, | |
| default_material=Al) | |
| sim.run(until_after_sources=100) | |
| total_time = time.time() - start | |
| return get_results(sim.fragment_stats, s**3 * resolution, total_time) | |
| def empty(s): | |
| resolution = 40 | |
| cell_size = mp.Vector3(s, s, s) | |
| sources = [mp.Source(mp.GaussianSource(1, 0.2), | |
| component=mp.Ex, | |
| center=mp.Vector3())] | |
| start = time.time() | |
| sim = mp.Simulation(resolution=resolution, | |
| cell_size=cell_size, | |
| k_point=mp.Vector3(), | |
| sources=sources) | |
| sim.run(until_after_sources=100) | |
| total_time = time.time() - start | |
| return get_results(sim.fragment_stats, s**3 * resolution, total_time) | |
| def empty_complex_fields(s): | |
| resolution = 40 | |
| cell_size = mp.Vector3(s, s, s) | |
| sources = [mp.Source(mp.GaussianSource(1, 0.2), | |
| component=mp.Ex, | |
| center=mp.Vector3())] | |
| start = time.time() | |
| sim = mp.Simulation(resolution=resolution, | |
| cell_size=cell_size, | |
| k_point=mp.Vector3(), | |
| force_complex_fields=True, | |
| sources=sources) | |
| sim.run(until_after_sources=100) | |
| total_time = time.time() - start | |
| return get_results(sim.fragment_stats, s**3 * resolution, total_time) | |
| def nonlinear_medium(s): | |
| resolution = 40 | |
| cell_size = mp.Vector3(s, s, s) | |
| sources = [mp.Source(mp.GaussianSource(1, 0.2), | |
| component=mp.Ex, | |
| center=mp.Vector3())] | |
| nonlinear_mat = mp.Medium(index=1, chi3=0.1) | |
| start = time.time() | |
| sim = mp.Simulation(resolution=resolution, | |
| cell_size=cell_size, | |
| k_point=mp.Vector3(), | |
| sources=sources, | |
| default_material=nonlinear_mat) | |
| sim.run(until_after_sources=100) | |
| total_time = time.time() - start | |
| return get_results(sim.fragment_stats, s**3 * resolution, total_time) | |
| def pml_boundaries(s): | |
| resolution = 40 | |
| d = 1.0 | |
| pml_layers = [mp.PML(thickness=d)] | |
| cell_size = mp.Vector3(s+d, s+d, s+d) | |
| sources = [mp.Source(mp.GaussianSource(1, 0.2), | |
| component=mp.Ex, | |
| center=mp.Vector3())] | |
| start = time.time() | |
| sim = mp.Simulation(resolution=resolution, | |
| cell_size=cell_size, | |
| k_point=mp.Vector3(), | |
| sources=sources, | |
| boundary_layers=pml_layers) | |
| sim.run(until_after_sources=100) | |
| total_time = time.time() - start | |
| return get_results(sim.fragment_stats, s**3 * resolution, total_time) | |
| def write_csv(fn, res): | |
| header = 'aniso_eps,aniso_mu,nonlinear,susceptibility,nonzer_cond,dft,total_pixels,total_time\n' | |
| with open(fn, 'a') as f: | |
| f.write(header) | |
| for line in res: | |
| f.write(line) | |
| def put_s3_file(fn, bucket): | |
| s3 = boto3.client('s3') | |
| s3.upload_file(fn, bucket, fn) | |
| print("Uploaded {} to S3".format(fn)) | |
| if __name__ == '__main__': | |
| bucket = 'hogan-fragment-stats' | |
| pool = Pool(processes=4) | |
| sizes = [1, 2, 3, 4] | |
| fn = 'absorber_boundaries.csv' | |
| res = pool.map(absorber_boundaries, sizes) | |
| write_csv(fn, res) | |
| put_s3_file(fn, bucket) | |
| fn = 'aniso_medium.csv' | |
| res = pool.map(aniso_medium, sizes) | |
| write_csv(fn, res) | |
| put_s3_file(fn, bucket) | |
| fn = 'conductive_medium.csv' | |
| res = pool.map(conductive_medium, sizes) | |
| write_csv(fn, res) | |
| put_s3_file(fn, bucket) | |
| fn = 'dft_flux.csv' | |
| res = pool.map(dft_flux, sizes) | |
| write_csv(fn, res) | |
| put_s3_file(fn, bucket) | |
| fn = 'dispersive_medium.csv' | |
| res = pool.map(dispersive_medium, sizes) | |
| write_csv(fn, res) | |
| put_s3_file(fn, bucket) | |
| fn = 'empty.csv' | |
| res = pool.map(empty, sizes) | |
| write_csv(fn, res) | |
| put_s3_file(fn, bucket) | |
| fn = 'empty_complex_fields.csv' | |
| res = pool.map(empty_complex_fields, sizes) | |
| write_csv(fn, res) | |
| put_s3_file(fn, bucket) | |
| fn = 'nonlinear_medium.csv' | |
| res = pool.map(nonlinear_medium, sizes) | |
| write_csv(fn, res) | |
| put_s3_file(fn, bucket) | |
| fn = 'pml_boundaries.csv' | |
| res = pool.map(pml_boundaries, sizes) | |
| write_csv(fn, res) | |
| put_s3_file(fn, bucket) |
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