| import numpy |
| import drjit as dr |
| import mitsuba as mi |
| mi.set_variant("cuda_ad_rgb") |
| import matplotlib.pyplot as plt |
| import sys |
| from PDE2D.Coefficient import * |
| from PDE2D.utils import * |
| from PDE2D.BoundaryShape import * |
| from PDE2D.Solver import * |
| from PDE2D import GreenSampling, Split, PATH |
| import argparse |
| import os |
|
|
| root_directory = os.path.join(PATH, "output2D", "finite_differences", "discrete-sdf", "normalder") |
|
|
| def create_path(path): |
| if not os.path.exists(path): |
| os.makedirs(path) |
|
|
| parser = argparse.ArgumentParser(description='''Normal derivative computation SDF.''') |
| parser.add_argument("--res", default = 1024, type = int) |
| parser.add_argument('--spp', default = 18, type=int) |
| parser.add_argument("--seed", default = 0, type = int) |
| parser.add_argument("--iter", default = 256, type = int) |
| parser.add_argument("--distance", default = 0.01, type = float) |
| parser.add_argument("--upsample", default = 1, type = int) |
| parser.add_argument("--epsilon", default = 5e-6, type = float) |
|
|
|
|
| args = parser.parse_args() |
| distance = args.distance |
| spp = 2**args.spp |
|
|
|
|
| sdf_array = np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1, 1, 1, 1, 1,-1, 1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1, 1,-1, 1,-1,-1,-1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1,-1,-1,-1,-1,-1,-1,-1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1,-1,-1,-1,-1, 1, 1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1,-1,-1,-1,-1, 1, 1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1,-1,-1,-1,-1, 1, 1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1, 1,-1,-1,-1, 1, 1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1, 1, 1,-1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], |
| [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]) |
|
|
| sdf_array = sdf_array.repeat(args.upsample, axis = 0).repeat(args.upsample, axis = 1) |
| box_length = 2.1 |
| box_center = [0,0] |
| in_boundary = SDFGrid(tensor_np= sdf_array, box_length=box_length, box_center=box_center, epsilon = args.epsilon, redistance = True) |
|
|
| out_boundary = CircleWithElectrodes(injection_confs = [[0,10]], is_delta = True, epsilon=args.epsilon) |
| shape = BoundaryWithDirichlets(out_boundary, [in_boundary], dirichlet_values = [[0]], epsilon = args.epsilon) |
| data_holder = DataHolder(shape) |
| wos = WostConstant(data_holder) |
|
|
| num_conf = out_boundary.num_confs |
| conf_numbers = [dr.opaque(UInt32, i, shape = (1)) for i in range(num_conf)] |
|
|
| path = root_directory |
| create_path(path) |
|
|
| for iter in range(args.iter): |
| seed_iter = iter + args.seed |
| wos.change_seed(seed_iter) |
| result, _ = wos.create_normal_derivative(args.res, spp, distance = distance, conf_numbers=conf_numbers) |
| np.save(os.path.join(path, f"res{args.res}-d{distance}-{seed_iter}"), result.numpy()) |
| print(f"Iteration {iter} finished.") |
| |
