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https://gitee.com/sunhf/gprMax.git
已同步 2025-08-07 04:56:51 +08:00
Restructured so this module can be imported as a package as well as used with command line arguments.
这个提交包含在：
Craig Warren
@@ -25,109 +25,147 @@ from gprMax.exceptions import CmdInputError
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from gprMax.utilities import round_value
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from gprMax.waveforms import Waveform
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"""Plot built-in waveforms that can be used with sources."""
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"""Plot built-in waveforms that can be used for sources."""
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# Parse command line arguments
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parser = argparse.ArgumentParser(description='Plot built-in waveforms that can be used for sources.', usage='cd gprMax; python -m tools.plot_builtin_wave type amp freq timewindow dt')
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parser.add_argument('type', help='type of waveform, e.g. gaussian, ricker etc...')
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parser.add_argument('amp', type=float, help='amplitude of waveform')
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parser.add_argument('freq', type=float, help='centre frequency of waveform')
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parser.add_argument('timewindow', help='time window to view waveform')
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parser.add_argument('dt', type=float, help='time step to view waveform')
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parser.add_argument('-fft', action='store_true', default=False, help='plot FFT')
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args = parser.parse_args()
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# Check waveform parameters
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if args.type.lower() not in Waveform.types:
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raise CmdInputError('The waveform must have one of the following types {}'.format(', '.join(Waveform.types)))
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if args.freq <= 0:
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raise CmdInputError('The waveform requires an excitation frequency value of greater than zero')
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w = Waveform()
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w.type = args.type
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w.amp = args.amp
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w.freq = args.freq
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dt = args.dt
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# Check time window
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if '.' in args.timewindow or 'e' in args.timewindow:
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if float(args.timewindow) > 0:
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timewindow = float(args.timewindow)
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iterations = round_value((float(args.timewindow) / dt)) + 1
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else:
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raise CmdInputError('Time window must have a value greater than zero')
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# If number of iterations given
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else:
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timewindow = (int(args.timewindow) - 1) * dt
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iterations = int(args.timewindow)
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time = np.linspace(0, 1, iterations)
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time *= (iterations * dt)
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waveform = np.zeros(len(time))
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timeiter = np.nditer(time, flags=['c_index'])
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while not timeiter.finished:
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waveform[timeiter.index] = w.calculate_value(timeiter[0], dt)
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timeiter.iternext()
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print('Waveform characteristics...')
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print('Type: {}'.format(w.type))
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print('Amplitude: {:g}'.format(w.amp))
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print('Centre frequency: {:g} Hz'.format(w.freq))
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print('Time to centre of pulse: {:g} s'.format(1 / w.freq))
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# Calculate pulse width for gaussian
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if w.type == 'gaussian':
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powerdrop = -3 #dB
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start = np.where((10 * np.log10(waveform / np.amax(waveform))) > powerdrop)[0][0]
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stop = np.where((10 * np.log10(waveform[start:] / np.amax(waveform))) < powerdrop)[0][0] + start
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print('Pulse width at {:d}dB, i.e. FWHM: {:g} s'.format(powerdrop, time[stop] - time[start]))
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print('Time window: {:g} s ({} iterations)'.format(timewindow, iterations))
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print('Time step: {:g} s'.format(dt))
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if args.fft:
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# Calculate magnitude of frequency spectra of waveform
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power = 10 * np.log10(np.abs(np.fft.fft(waveform))**2)
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freqs = np.fft.fftfreq(power.size, d=dt)
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# Shift powers so that frequency with maximum power is at zero decibels
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power -= np.amax(power)
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# Set plotting range to 4 times centre frequency of waveform
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pltrange = np.where(freqs > 4 * w.freq)[0][0]
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pltrange = np.s_[0:pltrange]
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fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, num=w.type, figsize=(20, 10), facecolor='w', edgecolor='w')
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def check_timewindow(timewindow, dt):
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"""Checks and sets time window and number of iterations.
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Args:
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timewindow (float): Time window.
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dt (float): Time discretisation.
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Returns:
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timewindow (float): Time window.
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iterations (int): Number of interations.
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"""
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# Plot waveform
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ax1.plot(time, waveform, 'r', lw=2)
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ax1.set_xlabel('Time [s]')
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ax1.set_ylabel('Amplitude')
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# Time window could be a string, float or int, so convert to string then check
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timewindow = str(timewindow)
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try:
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timewindow = int(timewindow)
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iterations = timewindow
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timewindow = (timewindow - 1) * dt
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# Plot frequency spectra
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markerline, stemlines, baseline = ax2.stem(freqs[pltrange], power[pltrange], '-.')
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plt.setp(baseline, 'linewidth', 0)
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plt.setp(stemlines, 'color', 'r')
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plt.setp(markerline, 'markerfacecolor', 'r', 'markeredgecolor', 'r')
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ax2.plot(freqs[pltrange]/1e9, power[pltrange], 'r', lw=2)
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ax2.set_xlabel('Frequency [Hz]')
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ax2.set_ylabel('Power [dB]')
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except:
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timewindow = float(timewindow)
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if timewindow > 0:
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iterations = round_value((timewindow / dt)) + 1
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else:
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raise CmdInputError('Time window must have a value greater than zero')
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else:
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fig, ax1 = plt.subplots(num=w.type, figsize=(20, 10), facecolor='w', edgecolor='w')
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return timewindow, iterations
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# Plot waveform
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ax1.plot(time, waveform, 'r', lw=2)
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ax1.set_xlabel('Time [s]')
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ax1.set_ylabel('Amplitude')
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[ax.grid() for ax in fig.axes] # Turn on grid
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def plot_waveform(w, timewindow, dt, iterations, fft=False):
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"""Plots waveform and prints useful information about its properties.
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Args:
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w (class): Waveform class instance.
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timewindow (float): Time window.
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dt (float): Time discretisation.
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iterations (int): Number of iterations.
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fft (boolean): Plot FFT of waveform.
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"""
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time = np.linspace(0, 1, iterations)
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time *= (iterations * dt)
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waveform = np.zeros(len(time))
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timeiter = np.nditer(time, flags=['c_index'])
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while not timeiter.finished:
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waveform[timeiter.index] = w.calculate_value(timeiter[0], dt)
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timeiter.iternext()
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print('Waveform characteristics...')
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print('Type: {}'.format(w.type))
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print('Amplitude: {:g}'.format(w.amp))
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print('Centre frequency: {:g} Hz'.format(w.freq))
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print('Time to centre of pulse: {:g} s'.format(1 / w.freq))
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# Calculate pulse width for gaussian
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if w.type == 'gaussian':
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powerdrop = -3 #dB
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start = np.where((10 * np.log10(waveform / np.amax(waveform))) > powerdrop)[0][0]
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stop = np.where((10 * np.log10(waveform[start:] / np.amax(waveform))) < powerdrop)[0][0] + start
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print('Pulse width at {:d}dB, i.e. FWHM: {:g} s'.format(powerdrop, time[stop] - time[start]))
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print('Time window: {:g} s ({} iterations)'.format(timewindow, iterations))
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print('Time step: {:g} s'.format(dt))
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if fft:
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# Calculate magnitude of frequency spectra of waveform
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power = 10 * np.log10(np.abs(np.fft.fft(waveform))**2)
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freqs = np.fft.fftfreq(power.size, d=dt)
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# Shift powers so that frequency with maximum power is at zero decibels
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power -= np.amax(power)
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# Set plotting range to 4 times centre frequency of waveform
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pltrange = np.where(freqs > 4 * w.freq)[0][0]
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pltrange = np.s_[0:pltrange]
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fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, num=w.type, figsize=(20, 10), facecolor='w', edgecolor='w')
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# Plot waveform
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ax1.plot(time, waveform, 'r', lw=2)
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ax1.set_xlabel('Time [s]')
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ax1.set_ylabel('Amplitude')
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# Plot frequency spectra
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markerline, stemlines, baseline = ax2.stem(freqs[pltrange], power[pltrange], '-.')
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plt.setp(baseline, 'linewidth', 0)
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plt.setp(stemlines, 'color', 'r')
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plt.setp(markerline, 'markerfacecolor', 'r', 'markeredgecolor', 'r')
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ax2.plot(freqs[pltrange]/1e9, power[pltrange], 'r', lw=2)
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ax2.set_xlabel('Frequency [Hz]')
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ax2.set_ylabel('Power [dB]')
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else:
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fig, ax1 = plt.subplots(num=w.type, figsize=(20, 10), facecolor='w', edgecolor='w')
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# Plot waveform
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ax1.plot(time, waveform, 'r', lw=2)
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ax1.set_xlabel('Time [s]')
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ax1.set_ylabel('Amplitude')
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[ax.grid() for ax in fig.axes] # Turn on grid
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# Save a PDF/PNG of the figure
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#fig.savefig(os.path.dirname(os.path.abspath(__file__)) + os.sep + w.type + '.pdf', dpi=None, format='pdf', bbox_inches='tight', pad_inches=0.1)
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#fig.savefig(os.path.dirname(os.path.abspath(__file__)) + os.sep + w.type + '.png', dpi=150, format='png', bbox_inches='tight', pad_inches=0.1)
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plt.show()
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if __name__ == "__main__":
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# Parse command line arguments
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parser = argparse.ArgumentParser(description='Plot built-in waveforms that can be used for sources.', usage='cd gprMax; python -m tools.plot_builtin_wave type amp freq timewindow dt')
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parser.add_argument('type', help='type of waveform', choices=Waveform.types)
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parser.add_argument('amp', type=float, help='amplitude of waveform')
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parser.add_argument('freq', type=float, help='centre frequency of waveform')
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parser.add_argument('timewindow', help='time window to view waveform')
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parser.add_argument('dt', type=float, help='time step to view waveform')
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parser.add_argument('-fft', action='store_true', default=False, help='plot FFT of waveform')
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args = parser.parse_args()
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# Check waveform parameters
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if args.type.lower() not in Waveform.types:
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raise CmdInputError('The waveform must have one of the following types {}'.format(', '.join(Waveform.types)))
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if args.freq <= 0:
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raise CmdInputError('The waveform requires an excitation frequency value of greater than zero')
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# Create waveform instance
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w = Waveform()
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w.type = args.type
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w.amp = args.amp
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w.freq = args.freq
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timewindow, iterations = check_timewindow(args.timewindow, args.dt)
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plot_waveform(w, timewindow, args.dt, iterations, args.fft)
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# Save a PDF/PNG of the figure
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#fig.savefig(os.path.dirname(os.path.abspath(__file__)) + os.sep + w.type + '.pdf', dpi=None, format='pdf', bbox_inches='tight', pad_inches=0.1)
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#fig.savefig(os.path.dirname(os.path.abspath(__file__)) + os.sep + w.type + '.png', dpi=150, format='png', bbox_inches='tight', pad_inches=0.1)
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plt.show()
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