#! /usr/bin/env python # Copyright 2021 Lorenzo Giacomel # # This file is part of WarpX. # # License: BSD-3-Clause-LBNL import yt import os, sys from scipy.constants import mu_0, pi, c import numpy as np sys.path.insert(1, '../../../../warpx/Regression/Checksum/') import checksumAPI # This is a script that analyses the simulation results from # the script `inputs_3d`. This simulates a TMmnp mode in a PEC cubic resonator rotated by pi/8. # The magnetic field in the simulation is given (in theory) by: # $$ B_x = \frac{-2\mu}{h^2}\, k_x k_z \sin(k_x x)\cos(k_y y)\cos(k_z z)\cos( \omega_p t)$$ # $$ B_y = \frac{-2\mu}{h^2}\, k_y k_z \cos(k_x x)\sin(k_y y)\cos(k_z z)\cos( \omega_p t)$$ # $$ B_z = \cos(k_x x)\cos(k_y y)\sin(k_z z)\sin( \omega_p t)$$ # with # $$ h^2 = k_x^2 + k_y^2 + k_z^2$$ # $$ k_x = \frac{m\pi}{L}$$ # $$ k_y = \frac{n\pi}{L}$$ # $$ k_z = \frac{p\pi}{L}$$ hi = [0.8, 0.8, 0.8] lo = [-0.8, -0.8, -0.8] m = 0 n = 1 p = 1 Lx = 1.06 Ly = 1.06 Lz = 1.06 h_2 = (m * pi / Lx) ** 2 + (n * pi / Ly) ** 2 + (p * pi / Lz) ** 2 theta = np.pi/8 # Open the right plot file filename = sys.argv[1] ds = yt.load(filename) data = ds.covering_grid(level=0, left_edge=ds.domain_left_edge, dims=ds.domain_dimensions) t = ds.current_time.to_value() rel_tol_err = 1e-2 my_grid = ds.index.grids[0] By_sim = my_grid['raw', 'By_fp'].squeeze().v Bz_sim = my_grid['raw', 'Bz_fp'].squeeze().v ncells = np.array(np.shape(By_sim[:, :, :, 0])) dx = (hi[0] - lo[0])/ncells[0] dy = (hi[1] - lo[1])/ncells[1] dz = (hi[2] - lo[2])/ncells[2] # Compute the analytic solution Bx_th = np.zeros(ncells) By_th = np.zeros(ncells) Bz_th = np.zeros(ncells) for i in range(ncells[0]): for j in range(ncells[1]): for k in range(ncells[2]): x0 = (i+0.5)*dx + lo[0] y0 = j*dy + lo[1] z0 = (k+0.5)*dz + lo[2] x = x0 y = y0*np.cos(-theta)-z0*np.sin(-theta) z = y0*np.sin(-theta)+z0*np.cos(-theta) By = -2/h_2*mu_0*(n * pi/Ly)*(p * pi/Lz) * (np.cos(m * pi/Lx * (x - Lx/2)) * np.sin(n * pi/Ly * (y - Ly/2)) * np.cos(p * pi/Lz * (z - Lz/2)) * np.cos(np.sqrt(2) * np.pi / Lx * c * t)) Bz = mu_0*(np.cos(m * pi/Lx * (x - Lx/2)) * np.cos(n * pi/Ly * (y - Ly/2)) * np.sin(p * pi/Lz * (z - Lz/2)) * np.cos(np.sqrt(2) * np.pi / Lx * c * t)) By_th[i, j, k] = (By*np.cos(theta) - Bz*np.sin(theta))*(By_sim[i, j, k, 0] != 0) x0 = (i+0.5)*dx + lo[0] y0 = (j+0.5)*dy + lo[1] z0 = k*dz + lo[2] x = x0 y = y0*np.cos(-theta)-z0*np.sin(-theta) z = y0*np.sin(-theta)+z0*np.cos(-theta) By = -2/h_2*mu_0*(n * pi/Ly)*(p * pi/Lz) * (np.cos(m * pi/Lx * (x - Lx/2)) * np.sin(n * pi/Ly * (y - Ly/2)) * np.cos(p * pi/Lz * (z - Lz/2)) * np.cos(np.sqrt(2) * np.pi / Lx * c * t)) Bz = mu_0*(np.cos(m * pi/Lx * (x - Lx/2)) * np.cos(n * pi/Ly * (y - Ly/2)) * np.sin(p * pi/Lz * (z - Lz/2)) * np.cos(np.sqrt(2) * np.pi / Lx * c * t)) Bz_th[i, j, k] = (By*np.sin(theta) + Bz*np.cos(theta))*(Bz_sim[i, j, k, 0] != 0) # Compute relative l^2 error on By rel_err_y = np.sqrt( np.sum(np.square(By_sim[:, :, :, 0] - By_th)) / np.sum(np.square(By_th))) assert(rel_err_y < rel_tol_err) # Compute relative l^2 error on Bz rel_err_z = np.sqrt( np.sum(np.square(Bz_sim[:, :, :, 0] - Bz_th)) / np.sum(np.square(Bz_th))) assert(rel_err_z < rel_tol_err) test_name = os.path.split(os.getcwd())[1] checksumAPI.evaluate_checksum(test_name, filename)