# Copyright (C) 2015-2023: The University of Edinburgh # Authors: Craig Warren and Antonis Giannopoulos # # This file is part of gprMax. # # gprMax is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # gprMax is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with gprMax. If not, see . import os import sys from colorama import init, Fore, Style init() import h5py import numpy as np import matplotlib.pyplot as plt if sys.platform == 'linux': plt.switch_backend('agg') from gprMax.gprMax import api from gprMax.exceptions import GeneralError from tests.analytical_solutions import hertzian_dipole_fs """Compare field outputs Usage: cd gprMax python -m tests.test_models """ basepath = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'models_') # basepath += 'basic' # basepath += 'advanced' basepath += 'pmls' # List of available basic test models # testmodels = ['hertzian_dipole_fs_analytical', '2D_ExHyHz', '2D_EyHxHz', '2D_EzHxHy', 'cylinder_Ascan_2D', 'hertzian_dipole_fs', 'hertzian_dipole_hs', 'hertzian_dipole_dispersive', 'magnetic_dipole_fs', 'pmls'] # List of available advanced test models # testmodels = ['antenna_GSSI_1500_fs', 'antenna_MALA_1200_fs'] # List of available PML models testmodels = ['pml_x0', 'pml_y0', 'pml_z0', 'pml_xmax', 'pml_ymax', 'pml_zmax', 'pml_3D_pec_plate'] # Select a specific model if desired # testmodels = testmodels[:-1] testmodels = [testmodels[6]] testresults = dict.fromkeys(testmodels) path = '/rxs/rx1/' # Minimum value of difference to plot (dB) plotmin = -160 for i, model in enumerate(testmodels): testresults[model] = {} # Run model inputfile = os.path.join(basepath, model + os.path.sep + model + '.in') api(inputfile, gpu=[None]) # Special case for analytical comparison if model == 'hertzian_dipole_fs_analytical': # Get output for model file filetest = h5py.File(os.path.join(basepath, model + os.path.sep + model + '.out'), 'r') testresults[model]['Test version'] = filetest.attrs['gprMax'] # Get available field output component names outputstest = list(filetest[path].keys()) # Arrays for storing time floattype = filetest[path + outputstest[0]].dtype timetest = np.linspace(0, (filetest.attrs['Iterations'] - 1) * filetest.attrs['dt'], num=filetest.attrs['Iterations']) / 1e-9 timeref = timetest # Arrays for storing field data datatest = np.zeros((filetest.attrs['Iterations'], len(outputstest)), dtype=floattype) for ID, name in enumerate(outputstest): datatest[:, ID] = filetest[path + str(name)][:] if np.any(np.isnan(datatest[:, ID])): raise GeneralError('Test data contains NaNs') # Tx/Rx position to feed to analytical solution rxpos = filetest[path].attrs['Position'] txpos = filetest['/srcs/src1/'].attrs['Position'] rxposrelative = ((rxpos[0] - txpos[0]), (rxpos[1] - txpos[1]), (rxpos[2] - txpos[2])) # Analytical solution of a dipole in free space dataref = hertzian_dipole_fs(filetest.attrs['Iterations'], filetest.attrs['dt'], filetest.attrs['dx_dy_dz'], rxposrelative) filetest.close() else: # Get output for model and reference files fileref = h5py.File(os.path.join(basepath, model + os.path.sep + model + '_ref.out'), 'r') filetest = h5py.File(os.path.join(basepath, model + os.path.sep + model + '.out'), 'r') testresults[model]['Ref version'] = fileref.attrs['gprMax'] testresults[model]['Test version'] = filetest.attrs['gprMax'] # Get available field output component names outputsref = list(fileref[path].keys()) outputstest = list(filetest[path].keys()) if outputsref != outputstest: raise GeneralError('Field output components do not match reference solution') # Check that type of float used to store fields matches if filetest[path + outputstest[0]].dtype != fileref[path + outputsref[0]].dtype: print(Fore.RED + 'WARNING: Type of floating point number in test model ({}) does not match type in reference solution ({})\n'.format(filetest[path + outputstest[0]].dtype, fileref[path + outputsref[0]].dtype) + Style.RESET_ALL) floattyperef = fileref[path + outputsref[0]].dtype floattypetest = filetest[path + outputstest[0]].dtype # Arrays for storing time timeref = np.zeros((fileref.attrs['Iterations']), dtype=floattyperef) timeref = np.linspace(0, (fileref.attrs['Iterations'] - 1) * fileref.attrs['dt'], num=fileref.attrs['Iterations']) / 1e-9 timetest = np.zeros((filetest.attrs['Iterations']), dtype=floattypetest) timetest = np.linspace(0, (filetest.attrs['Iterations'] - 1) * filetest.attrs['dt'], num=filetest.attrs['Iterations']) / 1e-9 # Arrays for storing field data dataref = np.zeros((fileref.attrs['Iterations'], len(outputsref)), dtype=floattyperef) datatest = np.zeros((filetest.attrs['Iterations'], len(outputstest)), dtype=floattypetest) for ID, name in enumerate(outputsref): dataref[:, ID] = fileref[path + str(name)][:] datatest[:, ID] = filetest[path + str(name)][:] if np.any(np.isnan(datatest[:, ID])): raise GeneralError('Test data contains NaNs') fileref.close() filetest.close() # Diffs datadiffs = np.zeros(datatest.shape, dtype=np.float64) for i in range(len(outputstest)): max = np.amax(np.abs(dataref[:, i])) datadiffs[:, i] = np.divide(np.abs(dataref[:, i] - datatest[:, i]), max, out=np.zeros_like(dataref[:, i]), where=max != 0) # Replace any division by zero with zero # Calculate power (ignore warning from taking a log of any zero values) with np.errstate(divide='ignore'): datadiffs[:, i] = 20 * np.log10(datadiffs[:, i]) # Replace any NaNs or Infs from zero division datadiffs[:, i][np.invert(np.isfinite(datadiffs[:, i]))] = 0 # Store max difference maxdiff = np.amax(np.amax(datadiffs)) testresults[model]['Max diff'] = maxdiff # Plot datasets fig1, ((ex1, hx1), (ey1, hy1), (ez1, hz1)) = plt.subplots(nrows=3, ncols=2, sharex=False, sharey='col', subplot_kw=dict(xlabel='Time [ns]'), num=model + '.in', figsize=(20, 10), facecolor='w', edgecolor='w') ex1.plot(timetest, datatest[:, 0], 'r', lw=2, label=model) ex1.plot(timeref, dataref[:, 0], 'g', lw=2, ls='--', label=model + '(Ref)') ey1.plot(timetest, datatest[:, 1], 'r', lw=2, label=model) ey1.plot(timeref, dataref[:, 1], 'g', lw=2, ls='--', label=model + '(Ref)') ez1.plot(timetest, datatest[:, 2], 'r', lw=2, label=model) ez1.plot(timeref, dataref[:, 2], 'g', lw=2, ls='--', label=model + '(Ref)') hx1.plot(timetest, datatest[:, 3], 'r', lw=2, label=model) hx1.plot(timeref, dataref[:, 3], 'g', lw=2, ls='--', label=model + '(Ref)') hy1.plot(timetest, datatest[:, 4], 'r', lw=2, label=model) hy1.plot(timeref, dataref[:, 4], 'g', lw=2, ls='--', label=model + '(Ref)') hz1.plot(timetest, datatest[:, 5], 'r', lw=2, label=model) hz1.plot(timeref, dataref[:, 5], 'g', lw=2, ls='--', label=model + '(Ref)') ylabels = ['$E_x$, field strength [V/m]', '$H_x$, field strength [A/m]', '$E_y$, field strength [V/m]', '$H_y$, field strength [A/m]', '$E_z$, field strength [V/m]', '$H_z$, field strength [A/m]'] for i, ax in enumerate(fig1.axes): ax.set_ylabel(ylabels[i]) ax.set_xlim(0, np.amax(timetest)) ax.grid() ax.legend() # Plot diffs fig2, ((ex2, hx2), (ey2, hy2), (ez2, hz2)) = plt.subplots(nrows=3, ncols=2, sharex=False, sharey='col', subplot_kw=dict(xlabel='Time [ns]'), num='Diffs: ' + model + '.in', figsize=(20, 10), facecolor='w', edgecolor='w') ex2.plot(timeref, datadiffs[:, 0], 'r', lw=2, label='Ex') ey2.plot(timeref, datadiffs[:, 1], 'r', lw=2, label='Ey') ez2.plot(timeref, datadiffs[:, 2], 'r', lw=2, label='Ez') hx2.plot(timeref, datadiffs[:, 3], 'r', lw=2, label='Hx') hy2.plot(timeref, datadiffs[:, 4], 'r', lw=2, label='Hy') hz2.plot(timeref, datadiffs[:, 5], 'r', lw=2, label='Hz') ylabels = ['$E_x$, difference [dB]', '$H_x$, difference [dB]', '$E_y$, difference [dB]', '$H_y$, difference [dB]', '$E_z$, difference [dB]', '$H_z$, difference [dB]'] for i, ax in enumerate(fig2.axes): ax.set_ylabel(ylabels[i]) ax.set_xlim(0, np.amax(timetest)) ax.set_ylim([plotmin, np.amax(np.amax(datadiffs))]) ax.grid() # Save a PDF/PNG of the figure savename = os.path.join(basepath, model + os.path.sep + model) # fig1.savefig(savename + '.pdf', dpi=None, format='pdf', bbox_inches='tight', pad_inches=0.1) # fig2.savefig(savename + '_diffs.pdf', dpi=None, format='pdf', bbox_inches='tight', pad_inches=0.1) fig1.savefig(savename + '.png', dpi=150, format='png', bbox_inches='tight', pad_inches=0.1) fig2.savefig(savename + '_diffs.png', dpi=150, format='png', bbox_inches='tight', pad_inches=0.1) # Summary of results for name, data in sorted(testresults.items()): if 'analytical' in name: print(Fore.CYAN + "Test '{}.in' using v.{} compared to analytical solution. Max difference {:.2f}dB.".format(name, data['Test version'], data['Max diff']) + Style.RESET_ALL) else: print(Fore.CYAN + "Test '{}.in' using v.{} compared to reference solution using v.{}. Max difference {:.2f}dB.".format(name, data['Test version'], data['Ref version'], data['Max diff']) + Style.RESET_ALL)