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已同步 2025-08-06 04:26:52 +08:00
autopep8 code cleanups.
这个提交包含在：
Craig Warren
@@ -25,7 +25,7 @@ outputfile = args.outputfile
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########################################
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# User configurable parameters
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# Pattern type (E or H)
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# Pattern type (E or H)
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type = 'H'
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# Antenna (true if using full antenna model; false for a theoretical Hertzian dipole
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@@ -116,21 +116,21 @@ for rx in range(0, nrx):
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f.close()
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# Plot traces for sanity checking
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#fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(num=outputfile, nrows=3, ncols=2, sharex=False, sharey='col', subplot_kw=dict(xlabel='Time [ns]'), figsize=(20, 10), facecolor='w', edgecolor='w')
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#ax1.plot(time, Ex[:, traceno],'r', lw=2)
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#ax1.set_ylabel('$E_x$, field strength [V/m]')
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#ax3.plot(time, Ey[:, traceno],'r', lw=2)
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#ax3.set_ylabel('$E_y$, field strength [V/m]')
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#ax5.plot(time, Ez[:, traceno],'r', lw=2)
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#ax5.set_ylabel('$E_z$, field strength [V/m]')
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#ax2.plot(time, Hx[:, traceno],'b', lw=2)
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#ax2.set_ylabel('$H_x$, field strength [A/m]')
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#ax4.plot(time, Hy[:, traceno],'b', lw=2)
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#ax4.set_ylabel('$H_y$, field strength [A/m]')
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#ax6.plot(time, Hz[:, traceno],'b', lw=2)
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#ax6.set_ylabel('$H_z$, field strength [A/m]')
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# fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(num=outputfile, nrows=3, ncols=2, sharex=False, sharey='col', subplot_kw=dict(xlabel='Time [ns]'), figsize=(20, 10), facecolor='w', edgecolor='w')
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# ax1.plot(time, Ex[:, traceno],'r', lw=2)
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# ax1.set_ylabel('$E_x$, field strength [V/m]')
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# ax3.plot(time, Ey[:, traceno],'r', lw=2)
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# ax3.set_ylabel('$E_y$, field strength [V/m]')
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# ax5.plot(time, Ez[:, traceno],'r', lw=2)
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# ax5.set_ylabel('$E_z$, field strength [V/m]')
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# ax2.plot(time, Hx[:, traceno],'b', lw=2)
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# ax2.set_ylabel('$H_x$, field strength [A/m]')
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# ax4.plot(time, Hy[:, traceno],'b', lw=2)
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# ax4.set_ylabel('$H_y$, field strength [A/m]')
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# ax6.plot(time, Hz[:, traceno],'b', lw=2)
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# ax6.set_ylabel('$H_z$, field strength [A/m]')
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# Turn on grid
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#[ax.grid() for ax in fig.axes]
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# [ax.grid() for ax in fig.axes]
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# plt.show()
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# Calculate fields for patterns
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@@ -18,15 +18,15 @@ from gprMax.constants import c, z0
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# Parse command line arguments
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parser = argparse.ArgumentParser(description='Plot field patterns from a simulation with receivers positioned in circles around an antenna. This module should be used after the field pattern data has been processed and stored using the initial_save.py module.', usage='cd gprMax; python -m user_libs.antenna_patterns.plot_fields numpyfile')
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parser.add_argument('numpyfile', help='name of numpy file including path')
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#parser.add_argument('hertzian', help='name of numpy file including path')
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# parser.add_argument('hertzian', help='name of numpy file including path')
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args = parser.parse_args()
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patterns = np.load(args.numpyfile)
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#hertzian = np.load(args.hertzian)
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# hertzian = np.load(args.hertzian)
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########################################
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# User configurable parameters
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# Pattern type (E or H)
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# Pattern type (E or H)
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type = 'H'
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# Relative permittivity of half-space for homogeneous materials (set to None for inhomogeneous)
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@@ -87,11 +87,11 @@ for patt in range(0, len(radii)):
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# Add Hertzian dipole plot
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# hertzplot1 = np.append(hertzian[0, :], hertzian[0, 0]) # Append start value to close circle
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#hertzplot1 = hertzplot1 / np.max(np.max(hertzian))
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#ax.plot(theta, 10 * np.log10(hertzplot1), label='Inf. dipole, 0.1m', color='black', ls='-.', lw=3)
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# hertzplot1 = hertzplot1 / np.max(np.max(hertzian))
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# ax.plot(theta, 10 * np.log10(hertzplot1), label='Inf. dipole, 0.1m', color='black', ls='-.', lw=3)
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# hertzplot2 = np.append(hertzian[-1, :], hertzian[-1, 0]) # Append start value to close circle
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#hertzplot2 = hertzplot2 / np.max(np.max(hertzian))
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#ax.plot(theta, 10 * np.log10(hertzplot2), label='Inf. dipole, 0.58m', color='black', ls='--', lw=3)
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# hertzplot2 = hertzplot2 / np.max(np.max(hertzian))
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# ax.plot(theta, 10 * np.log10(hertzplot2), label='Inf. dipole, 0.58m', color='black', ls='--', lw=3)
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# Theta axis options
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ax.set_theta_zero_location('N')
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@@ -110,13 +110,13 @@ ax.set_yticklabels(yticks)
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ax.grid(True)
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handles, existlabels = ax.get_legend_handles_labels()
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leg = ax.legend([handles[0], handles[-1]], [existlabels[0], existlabels[-1]], ncol=2, loc=(0.27, -0.12), frameon=False) # Plot just first and last legend entries
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#leg = ax.legend([handles[0], handles[-3], handles[-2], handles[-1]], [existlabels[0], existlabels[-3], existlabels[-2], existlabels[-1]], ncol=4, loc=(-0.13,-0.12), frameon=False)
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# leg = ax.legend([handles[0], handles[-3], handles[-2], handles[-1]], [existlabels[0], existlabels[-3], existlabels[-2], existlabels[-1]], ncol=4, loc=(-0.13,-0.12), frameon=False)
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[legobj.set_linewidth(2) for legobj in leg.legendHandles]
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# Save a pdf of the plot
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savename = os.path.splitext(args.numpyfile)[0] + '.pdf'
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fig.savefig(savename, dpi=None, format='pdf', bbox_inches='tight', pad_inches=0.1)
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#savename = os.path.splitext(args.numpyfile)[0] + '.png'
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#fig.savefig(savename, dpi=150, format='png', bbox_inches='tight', pad_inches=0.1)
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# savename = os.path.splitext(args.numpyfile)[0] + '.png'
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# fig.savefig(savename, dpi=150, format='png', bbox_inches='tight', pad_inches=0.1)
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plt.show()
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@@ -52,13 +52,13 @@ def antenna_like_GSSI_1500(x, y, z, resolution=0.001, rotate90=False, **kwargs):
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else:
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# excitationfreq = 1.5e9 # GHz
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# sourceresistance = 50 # Ohms
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#absorberEr = 1.7
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#absorbersig = 0.59
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# absorberEr = 1.7
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# absorbersig = 0.59
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# Values from http://hdl.handle.net/1842/4074
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excitationfreq = 1.71e9
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#sourceresistance = 4
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sourceresistance = 230 # Correction for old (< 123) GprMax3D bug
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# sourceresistance = 4
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sourceresistance = 230 # Correction for old (< 123) GprMax3D bug
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absorberEr = 1.58
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absorbersig = 0.428
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rxres = 925 # Resistance at Rx bowtie
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@@ -157,8 +157,8 @@ def antenna_like_GSSI_1500(x, y, z, resolution=0.001, rotate90=False, **kwargs):
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box(x, y, z, x + casesize[0], y + casesize[1], z + skidthickness, 'hdpe', rotate90origin=rotate90origin)
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# Geometry views
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#geometry_view(x - dx, y - dy, z - dz, x + casesize[0] + dx, y + casesize[1] + dy, z + skidthickness + casesize[2] + dz, dx, dy, dz, 'antenna_like_GSSI_1500')
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#geometry_view(x, y, z, x + casesize[0], y + casesize[1], z + 0.010, dx, dy, dz, 'antenna_like_GSSI_1500_pcb', type='f')
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# geometry_view(x - dx, y - dy, z - dz, x + casesize[0] + dx, y + casesize[1] + dy, z + skidthickness + casesize[2] + dz, dx, dy, dz, 'antenna_like_GSSI_1500')
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# geometry_view(x, y, z, x + casesize[0], y + casesize[1], z + 0.010, dx, dy, dz, 'antenna_like_GSSI_1500_pcb', type='f')
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# Excitation - custom pulse
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# print('#excitation_file: {}'.format(os.path.join(moduledirectory, 'GSSIgausspulse1.txt')))
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@@ -395,8 +395,8 @@ def antenna_like_MALA_1200(x, y, z, resolution=0.001, rotate90=False, **kwargs):
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box(x, y, z + polypropylenethickness, x + casesize[0], y + casesize[1], z + polypropylenethickness + hdpethickness, 'hdpe', rotate90origin=rotate90origin)
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# Geometry views
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#geometry_view(x - dx, y - dy, z - dz, x + casesize[0] + dx, y + casesize[1] + dy, z + casesize[2] + skidthickness + dz, dx, dy, dz, 'antenna_like_MALA_1200')
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#geometry_view(x, y, z, x + casesize[0], y + casesize[1], z + 0.010, dx, dy, dz, 'antenna_like_MALA_1200_pcb', type='f')
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# geometry_view(x - dx, y - dy, z - dz, x + casesize[0] + dx, y + casesize[1] + dy, z + casesize[2] + skidthickness + dz, dx, dy, dz, 'antenna_like_MALA_1200')
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# geometry_view(x, y, z, x + casesize[0], y + casesize[1], z + 0.010, dx, dy, dz, 'antenna_like_MALA_1200_pcb', type='f')
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# Excitation
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print('#waveform: gaussian 1.0 {} myGaussian'.format(excitationfreq))
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@@ -107,31 +107,31 @@ def xcorr(filename, args):
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raise GeneralError('No outputs matching {} were found'.format(args['outputs']))
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# Normalise reference respose and response from output file
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# refresp /= np.amax(np.abs(refresp))
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# modelresp /= np.amax(np.abs(modelresp))
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# refresp /= np.amax(np.abs(refresp))
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# modelresp /= np.amax(np.abs(modelresp))
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# Make both responses the same length in time
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# if reftime[-1] > modeltime[-1]:
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# reftime = np.arange(0, f.attrs['dt'] * f.attrs['Iterations'], reftime[-1] / len(reftime))
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# refresp = refresp[0:len(reftime)]
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# elif modeltime[-1] > reftime[-1]:
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# modeltime = np.arange(0, reftime[-1], f.attrs['dt'])
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# modelresp = modelresp[0:len(modeltime)]
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#
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# # Downsample the response with the higher sampling rate
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# if len(modeltime) < len(reftime):
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# refresp = signal.resample(refresp, len(modelresp))
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# elif len(reftime) < len(modeltime):
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# modelresp = signal.resample(modelresp, len(refresp))
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# if reftime[-1] > modeltime[-1]:
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# reftime = np.arange(0, f.attrs['dt'] * f.attrs['Iterations'], reftime[-1] / len(reftime))
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# refresp = refresp[0:len(reftime)]
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# elif modeltime[-1] > reftime[-1]:
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# modeltime = np.arange(0, reftime[-1], f.attrs['dt'])
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# modelresp = modelresp[0:len(modeltime)]
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# Downsample the response with the higher sampling rate
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# if len(modeltime) < len(reftime):
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# refresp = signal.resample(refresp, len(modelresp))
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# elif len(reftime) < len(modeltime):
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# modelresp = signal.resample(modelresp, len(refresp))
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# Prepare data for normalized cross-correlation
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refresp = (refresp - np.mean(refresp)) / (np.std(refresp) * len(refresp))
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modelresp = (modelresp - np.mean(modelresp)) / np.std(modelresp)
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# Plots responses for checking
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#fig, ax = plt.subplots(subplot_kw=dict(xlabel='Iterations', ylabel='Voltage [V]'), figsize=(20, 10), facecolor='w', edgecolor='w')
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#ax.plot(refresp,'r', lw=2, label='refresp')
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#ax.plot(modelresp,'b', lw=2, label='modelresp')
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# fig, ax = plt.subplots(subplot_kw=dict(xlabel='Iterations', ylabel='Voltage [V]'), figsize=(20, 10), facecolor='w', edgecolor='w')
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# ax.plot(refresp,'r', lw=2, label='refresp')
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# ax.plot(modelresp,'b', lw=2, label='modelresp')
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# ax.grid()
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# plt.show()
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@@ -142,10 +142,10 @@ def xcorr(filename, args):
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xcorr = np.nan_to_num(xcorr)
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# Plot cross-correlation for checking
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# fig, ax = plt.subplots(subplot_kw=dict(xlabel='Iterations', ylabel='Voltage [V]'), figsize=(20, 10), facecolor='w', edgecolor='w')
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# ax.plot(xcorr,'r', lw=2, label='xcorr')
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# ax.grid()
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# plt.show()
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# fig, ax = plt.subplots(subplot_kw=dict(xlabel='Iterations', ylabel='Voltage [V]'), figsize=(20, 10), facecolor='w', edgecolor='w')
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# ax.plot(xcorr,'r', lw=2, label='xcorr')
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# ax.grid()
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# plt.show()
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xcorrmax = np.amax(xcorr)
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@@ -237,15 +237,15 @@ def compactness(filename, args):
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# Amplitude ratio of the 1st to 3rd peak - hopefully be a measure of a compact envelope
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compactness = np.abs(outputdata[peaks[0]]) / np.abs(outputdata[peaks[2]])
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# # Percentage of maximum value to measure compactness of signal
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# durationthreshold = 2
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# # Check if there is a peak/trough smaller than threshold
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# durationthresholdexist = np.where(np.abs(outputdata[peaks]) < (peak * (durationthreshold / 100)))[0]
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# if durationthresholdexist.size == 0:
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# compactness = time[peaks[-1]]
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# else:
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# time2threshold = time[peaks[durationthresholdexist[0]]]
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# compactness = time2threshold - time[min(peaks)]
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# Percentage of maximum value to measure compactness of signal
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# durationthreshold = 2
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# Check if there is a peak/trough smaller than threshold
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# durationthresholdexist = np.where(np.abs(outputdata[peaks]) < (peak * (durationthreshold / 100)))[0]
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# if durationthresholdexist.size == 0:
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# compactness = time[peaks[-1]]
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# else:
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# time2threshold = time[peaks[durationthresholdexist[0]]]
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# compactness = time2threshold - time[min(peaks)]
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# Check in case no outputs where found
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if not outputsused:
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