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已同步 2025-08-03 19:26:50 +08:00
206 行
9.9 KiB
Python
206 行
9.9 KiB
Python
# Copyright (C) 2015-2023: The University of Edinburgh
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# Authors: Craig Warren and Antonis Giannopoulos
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#
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# This file is part of gprMax.
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#
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# gprMax is free software: you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# gprMax is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with gprMax. If not, see <http://www.gnu.org/licenses/>.
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import os
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import sys
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from colorama import init, Fore, Style
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init()
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import h5py
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import numpy as np
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import matplotlib.pyplot as plt
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if sys.platform == 'linux':
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plt.switch_backend('agg')
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from gprMax.gprMax import api
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from gprMax.exceptions import GeneralError
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from tests.analytical_solutions import hertzian_dipole_fs
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"""Compare field outputs
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Usage:
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cd gprMax
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python -m tests.test_models
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"""
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basepath = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'models_')
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# basepath += 'basic'
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# basepath += 'advanced'
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basepath += 'pmls'
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# List of available basic test models
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# 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']
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# List of available advanced test models
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# testmodels = ['antenna_GSSI_1500_fs', 'antenna_MALA_1200_fs']
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# List of available PML models
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testmodels = ['pml_x0', 'pml_y0', 'pml_z0', 'pml_xmax', 'pml_ymax', 'pml_zmax', 'pml_3D_pec_plate']
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# Select a specific model if desired
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# testmodels = testmodels[:-1]
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testmodels = [testmodels[6]]
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testresults = dict.fromkeys(testmodels)
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path = '/rxs/rx1/'
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# Minimum value of difference to plot (dB)
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plotmin = -160
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for i, model in enumerate(testmodels):
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testresults[model] = {}
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# Run model
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inputfile = os.path.join(basepath, model + os.path.sep + model + '.in')
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api(inputfile, gpu=[None])
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# Special case for analytical comparison
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if model == 'hertzian_dipole_fs_analytical':
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# Get output for model file
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filetest = h5py.File(os.path.join(basepath, model + os.path.sep + model + '.out'), 'r')
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testresults[model]['Test version'] = filetest.attrs['gprMax']
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# Get available field output component names
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outputstest = list(filetest[path].keys())
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# Arrays for storing time
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floattype = filetest[path + outputstest[0]].dtype
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timetest = np.linspace(0, (filetest.attrs['Iterations'] - 1) * filetest.attrs['dt'], num=filetest.attrs['Iterations']) / 1e-9
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timeref = timetest
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# Arrays for storing field data
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datatest = np.zeros((filetest.attrs['Iterations'], len(outputstest)), dtype=floattype)
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for ID, name in enumerate(outputstest):
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datatest[:, ID] = filetest[path + str(name)][:]
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if np.any(np.isnan(datatest[:, ID])):
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raise GeneralError('Test data contains NaNs')
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# Tx/Rx position to feed to analytical solution
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rxpos = filetest[path].attrs['Position']
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txpos = filetest['/srcs/src1/'].attrs['Position']
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rxposrelative = ((rxpos[0] - txpos[0]), (rxpos[1] - txpos[1]), (rxpos[2] - txpos[2]))
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# Analytical solution of a dipole in free space
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dataref = hertzian_dipole_fs(filetest.attrs['Iterations'], filetest.attrs['dt'], filetest.attrs['dx_dy_dz'], rxposrelative)
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filetest.close()
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else:
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# Get output for model and reference files
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fileref = h5py.File(os.path.join(basepath, model + os.path.sep + model + '_ref.out'), 'r')
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filetest = h5py.File(os.path.join(basepath, model + os.path.sep + model + '.out'), 'r')
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testresults[model]['Ref version'] = fileref.attrs['gprMax']
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testresults[model]['Test version'] = filetest.attrs['gprMax']
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# Get available field output component names
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outputsref = list(fileref[path].keys())
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outputstest = list(filetest[path].keys())
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if outputsref != outputstest:
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raise GeneralError('Field output components do not match reference solution')
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# Check that type of float used to store fields matches
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if filetest[path + outputstest[0]].dtype != fileref[path + outputsref[0]].dtype:
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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)
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floattyperef = fileref[path + outputsref[0]].dtype
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floattypetest = filetest[path + outputstest[0]].dtype
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# Arrays for storing time
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timeref = np.zeros((fileref.attrs['Iterations']), dtype=floattyperef)
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timeref = np.linspace(0, (fileref.attrs['Iterations'] - 1) * fileref.attrs['dt'], num=fileref.attrs['Iterations']) / 1e-9
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timetest = np.zeros((filetest.attrs['Iterations']), dtype=floattypetest)
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timetest = np.linspace(0, (filetest.attrs['Iterations'] - 1) * filetest.attrs['dt'], num=filetest.attrs['Iterations']) / 1e-9
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# Arrays for storing field data
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dataref = np.zeros((fileref.attrs['Iterations'], len(outputsref)), dtype=floattyperef)
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datatest = np.zeros((filetest.attrs['Iterations'], len(outputstest)), dtype=floattypetest)
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for ID, name in enumerate(outputsref):
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dataref[:, ID] = fileref[path + str(name)][:]
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datatest[:, ID] = filetest[path + str(name)][:]
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if np.any(np.isnan(datatest[:, ID])):
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raise GeneralError('Test data contains NaNs')
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fileref.close()
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filetest.close()
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# Diffs
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datadiffs = np.zeros(datatest.shape, dtype=np.float64)
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for i in range(len(outputstest)):
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max = np.amax(np.abs(dataref[:, i]))
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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
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# Calculate power (ignore warning from taking a log of any zero values)
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with np.errstate(divide='ignore'):
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datadiffs[:, i] = 20 * np.log10(datadiffs[:, i])
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# Replace any NaNs or Infs from zero division
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datadiffs[:, i][np.invert(np.isfinite(datadiffs[:, i]))] = 0
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# Store max difference
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maxdiff = np.amax(np.amax(datadiffs))
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testresults[model]['Max diff'] = maxdiff
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# Plot datasets
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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')
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ex1.plot(timetest, datatest[:, 0], 'r', lw=2, label=model)
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ex1.plot(timeref, dataref[:, 0], 'g', lw=2, ls='--', label=model + '(Ref)')
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ey1.plot(timetest, datatest[:, 1], 'r', lw=2, label=model)
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ey1.plot(timeref, dataref[:, 1], 'g', lw=2, ls='--', label=model + '(Ref)')
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ez1.plot(timetest, datatest[:, 2], 'r', lw=2, label=model)
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ez1.plot(timeref, dataref[:, 2], 'g', lw=2, ls='--', label=model + '(Ref)')
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hx1.plot(timetest, datatest[:, 3], 'r', lw=2, label=model)
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hx1.plot(timeref, dataref[:, 3], 'g', lw=2, ls='--', label=model + '(Ref)')
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hy1.plot(timetest, datatest[:, 4], 'r', lw=2, label=model)
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hy1.plot(timeref, dataref[:, 4], 'g', lw=2, ls='--', label=model + '(Ref)')
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hz1.plot(timetest, datatest[:, 5], 'r', lw=2, label=model)
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hz1.plot(timeref, dataref[:, 5], 'g', lw=2, ls='--', label=model + '(Ref)')
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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]']
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for i, ax in enumerate(fig1.axes):
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ax.set_ylabel(ylabels[i])
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ax.set_xlim(0, np.amax(timetest))
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ax.grid()
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ax.legend()
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# Plot diffs
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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')
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ex2.plot(timeref, datadiffs[:, 0], 'r', lw=2, label='Ex')
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ey2.plot(timeref, datadiffs[:, 1], 'r', lw=2, label='Ey')
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ez2.plot(timeref, datadiffs[:, 2], 'r', lw=2, label='Ez')
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hx2.plot(timeref, datadiffs[:, 3], 'r', lw=2, label='Hx')
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hy2.plot(timeref, datadiffs[:, 4], 'r', lw=2, label='Hy')
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hz2.plot(timeref, datadiffs[:, 5], 'r', lw=2, label='Hz')
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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]']
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for i, ax in enumerate(fig2.axes):
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ax.set_ylabel(ylabels[i])
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ax.set_xlim(0, np.amax(timetest))
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ax.set_ylim([plotmin, np.amax(np.amax(datadiffs))])
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ax.grid()
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# Save a PDF/PNG of the figure
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savename = os.path.join(basepath, model + os.path.sep + model)
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# fig1.savefig(savename + '.pdf', dpi=None, format='pdf', bbox_inches='tight', pad_inches=0.1)
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# fig2.savefig(savename + '_diffs.pdf', dpi=None, format='pdf', bbox_inches='tight', pad_inches=0.1)
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fig1.savefig(savename + '.png', dpi=150, format='png', bbox_inches='tight', pad_inches=0.1)
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fig2.savefig(savename + '_diffs.png', dpi=150, format='png', bbox_inches='tight', pad_inches=0.1)
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# Summary of results
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for name, data in sorted(testresults.items()):
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if 'analytical' in name:
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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)
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else:
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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)
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