def mpl_plot(filename, outputs=Rx.defaultoutputs, fft=False): """Plots electric and magnetic fields and currents from all receiver points in the given output file. Each receiver point is plotted in a new figure window. Args: filename (string): Filename (including path) of output file. outputs (list): List of field/current components to plot. fft (boolean): Plot FFT switch. Returns: plt (object): matplotlib plot object. """ # Open output file and read some attributes f = h5py.File(filename, 'r') nrx = f.attrs['nrx'] dt = f.attrs['dt'] iterations = f.attrs['Iterations'] time = np.linspace(0, (iterations - 1) * dt, num=iterations) # Check there are any receivers if nrx == 0: raise CmdInputError('No receivers found in {}'.format(filename)) # Check for single output component when doing a FFT if fft: if not len(outputs) == 1: raise CmdInputError('A single output must be specified when using the -fft option') # New plot for each receiver for rx in range(1, nrx + 1): path = '/rxs/rx' + str(rx) + '/' availableoutputs = list(f[path].keys()) # If only a single output is required, create one subplot if len(outputs) == 1: # Check for polarity of output and if requested output is in file if outputs[0][-1] == '-': polarity = -1 outputtext = '-' + outputs[0][0:-1] output = outputs[0][0:-1] else: polarity = 1 outputtext = outputs[0] output = outputs[0] if output not in availableoutputs: raise CmdInputError('{} output requested to plot, but the available output for receiver 1 is {}'.format(output, ', '.join(availableoutputs))) outputdata = f[path + output][:] * polarity # Plotting if FFT required if fft: # FFT freqs, power = fft_power(outputdata, dt) freqmaxpower = np.where(np.isclose(power, 0))[0][0] # Set plotting range to -60dB from maximum power or 4 times # frequency at maximum power try: pltrange = np.where(power[freqmaxpower:] < -60)[0][0] + freqmaxpower + 1 except: pltrange = freqmaxpower * 4 pltrange = np.s_[0:pltrange] # Plot time history of output component fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, num='rx' + str(rx), figsize=(20, 10), facecolor='w', edgecolor='w') line1 = ax1.plot(time, outputdata, 'r', lw=2, label=outputtext) ax1.set_xlabel('Time [s]') ax1.set_ylabel(outputtext + ' field strength [V/m]') ax1.set_xlim([0, np.amax(time)]) ax1.grid(which='both', axis='both', linestyle='-.') # Plot frequency spectra markerline, stemlines, baseline = ax2.stem(freqs[pltrange], power[pltrange], '-.') plt.setp(baseline, 'linewidth', 0) plt.setp(stemlines, 'color', 'r') plt.setp(markerline, 'markerfacecolor', 'r', 'markeredgecolor', 'r') line2 = ax2.plot(freqs[pltrange], power[pltrange], 'r', lw=2) ax2.set_xlabel('Frequency [Hz]') ax2.set_ylabel('Power [dB]') ax2.grid(which='both', axis='both', linestyle='-.') # Change colours and labels for magnetic field components or currents if 'H' in outputs[0]: plt.setp(line1, color='g') plt.setp(line2, color='g') plt.setp(ax1, ylabel=outputtext + ' field strength [A/m]') plt.setp(stemlines, 'color', 'g') plt.setp(markerline, 'markerfacecolor', 'g', 'markeredgecolor', 'g') elif 'I' in outputs[0]: plt.setp(line1, color='b') plt.setp(line2, color='b') plt.setp(ax1, ylabel=outputtext + ' current [A]') plt.setp(stemlines, 'color', 'b') plt.setp(markerline, 'markerfacecolor', 'b', 'markeredgecolor', 'b') plt.show() # Plotting if no FFT required else: fig, ax = plt.subplots(subplot_kw=dict(xlabel='Time [s]', ylabel=outputtext + ' field strength [V/m]'), num='rx' + str(rx), figsize=(20, 10), facecolor='w', edgecolor='w') line = ax.plot(time, outputdata, 'r', lw=2, label=outputtext) ax.set_xlim([0, np.amax(time)]) # ax.set_ylim([-15, 20]) ax.grid(which='both', axis='both', linestyle='-.') if 'H' in output: plt.setp(line, color='g') plt.setp(ax, ylabel=outputtext + ', field strength [A/m]') elif 'I' in output: plt.setp(line, color='b') plt.setp(ax, ylabel=outputtext + ', current [A]') # If multiple outputs required, create all nine subplots and populate only the specified ones else: fig, ax = plt.subplots(subplot_kw=dict(xlabel='Time [s]'), num='rx' + str(rx), figsize=(20, 10), facecolor='w', edgecolor='w') if len(outputs) == 9: gs = gridspec.GridSpec(3, 3, hspace=0.3, wspace=0.3) else: gs = gridspec.GridSpec(3, 2, hspace=0.3, wspace=0.3) for output in outputs: # Check for polarity of output and if requested output is in file if output[-1] == 'm': polarity = -1 outputtext = '-' + output[0:-1] output = output[0:-1] else: polarity = 1 outputtext = output # Check if requested output is in file if output not in availableoutputs: raise CmdInputError('Output(s) requested to plot: {}, but available output(s) for receiver {} in the file: {}'.format(', '.join(outputs), rx, ', '.join(availableoutputs))) outputdata = f[path + output][:] * polarity if output == 'Ex': ax = plt.subplot(gs[0, 0]) ax.plot(time, outputdata, 'r', lw=2, label=outputtext) ax.set_ylabel(outputtext + ', field strength [V/m]') # ax.set_ylim([-15, 20]) elif output == 'Ey': ax = plt.subplot(gs[1, 0]) ax.plot(time, outputdata, 'r', lw=2, label=outputtext) ax.set_ylabel(outputtext + ', field strength [V/m]') # ax.set_ylim([-15, 20]) elif output == 'Ez': ax = plt.subplot(gs[2, 0]) ax.plot(time, outputdata, 'r', lw=2, label=outputtext) ax.set_ylabel(outputtext + ', field strength [V/m]') # ax.set_ylim([-15, 20]) elif output == 'Hx': ax = plt.subplot(gs[0, 1]) ax.plot(time, outputdata, 'g', lw=2, label=outputtext) ax.set_ylabel(outputtext + ', field strength [A/m]') # ax.set_ylim([-0.03, 0.03]) elif output == 'Hy': ax = plt.subplot(gs[1, 1]) ax.plot(time, outputdata, 'g', lw=2, label=outputtext) ax.set_ylabel(outputtext + ', field strength [A/m]') # ax.set_ylim([-0.03, 0.03]) elif output == 'Hz': ax = plt.subplot(gs[2, 1]) ax.plot(time, outputdata, 'g', lw=2, label=outputtext) ax.set_ylabel(outputtext + ', field strength [A/m]') # ax.set_ylim([-0.03, 0.03]) elif output == 'Ix': ax = plt.subplot(gs[0, 2]) ax.plot(time, outputdata, 'b', lw=2, label=outputtext) ax.set_ylabel(outputtext + ', current [A]') elif output == 'Iy': ax = plt.subplot(gs[1, 2]) ax.plot(time, outputdata, 'b', lw=2, label=outputtext) ax.set_ylabel(outputtext + ', current [A]') elif output == 'Iz': ax = plt.subplot(gs[2, 2]) ax.plot(time, outputdata, 'b', lw=2, label=outputtext) ax.set_ylabel(outputtext + ', current [A]') for ax in fig.axes: ax.set_xlim([0, np.amax(time)]) ax.grid(which='both', axis='both', linestyle='-.') # Save a PDF/PNG of the figure # fig.savefig(os.path.splitext(os.path.abspath(filename))[0] + '_rx' + str(rx) + '.pdf', dpi=None, format='pdf', bbox_inches='tight', pad_inches=0.1) # fig.savefig(os.path.splitext(os.path.abspath(filename))[0] + '_rx' + str(rx) + '.png', dpi=150, format='png', bbox_inches='tight', pad_inches=0.1) return plt
def dispersion_analysis(G): """ Analysis of numerical dispersion (Taflove et al, 2005, p112) - worse case of maximum frequency and minimum wavelength Args: G (class): Grid class instance - holds essential parameters describing the model. Returns: results (dict): Results from dispersion analysis """ # Physical phase velocity error (percentage); grid sampling density; # material with maximum permittivity; maximum significant frequency; error message results = { 'deltavp': False, 'N': False, 'material': False, 'maxfreq': [], 'error': '' } # Find maximum significant frequency if G.waveforms: for waveform in G.waveforms: if waveform.type == 'sine' or waveform.type == 'contsine': results['maxfreq'].append(4 * waveform.freq) elif waveform.type == 'impulse': results['error'] = 'impulse waveform used.' else: # User-defined waveform if waveform.type == 'user': iterations = G.iterations # Built-in waveform else: # Time to analyse waveform - 4*pulse_width as using entire # time window can result in demanding FFT waveform.calculate_coefficients() iterations = round_value(4 * waveform.chi / G.dt) if iterations > G.iterations: iterations = G.iterations waveformvalues = np.zeros(G.iterations) for iteration in range(G.iterations): waveformvalues[iteration] = waveform.calculate_value( iteration * G.dt, G.dt) # Ensure source waveform is not being overly truncated before attempting any FFT if np.abs(waveformvalues[-1]) < np.abs( np.amax(waveformvalues)) / 100: # FFT freqs, power = fft_power(waveformvalues, G.dt) # Get frequency for max power freqmaxpower = np.where(np.isclose(power, 0))[0][0] # Set maximum frequency to a threshold drop from maximum power, ignoring DC value try: freqthres = np.where( power[freqmaxpower:] < -G.highestfreqthres )[0][0] + freqmaxpower results['maxfreq'].append(freqs[freqthres]) except ValueError: results[ 'error'] = 'unable to calculate maximum power from waveform, most likely due to undersampling.' # Ignore case where someone is using a waveform with zero amplitude, i.e. on a receiver elif waveform.amp == 0: pass # If waveform is truncated don't do any further analysis else: results[ 'error'] = 'waveform does not fit within specified time window and is therefore being truncated.' else: results['error'] = 'no waveform detected.' if results['maxfreq']: results['maxfreq'] = max(results['maxfreq']) # Find minimum wavelength (material with maximum permittivity) maxer = 0 matmaxer = '' for x in G.materials: if x.se != float('inf'): er = x.er # If there are dispersive materials calculate the complex relative permittivity # at maximum frequency and take the real part if x.poles > 0: er = x.calculate_er(results['maxfreq']) er = er.real if er > maxer: maxer = er matmaxer = x.ID results['material'] = next(x for x in G.materials if x.ID == matmaxer) # Minimum velocity minvelocity = c / np.sqrt(maxer) # Minimum wavelength minwavelength = minvelocity / results['maxfreq'] # Maximum spatial step if '3D' in G.mode: delta = max(G.dx, G.dy, G.dz) elif '2D' in G.mode: if G.nx == 1: delta = max(G.dy, G.dz) elif G.ny == 1: delta = max(G.dx, G.dz) elif G.nz == 1: delta = max(G.dx, G.dy) # Courant stability factor S = (c * G.dt) / delta # Grid sampling density results['N'] = minwavelength / delta # Check grid sampling will result in physical wave propagation if int(np.floor(results['N'])) >= G.mingridsampling: # Numerical phase velocity vp = np.pi / (results['N'] * np.arcsin((1 / S) * np.sin( (np.pi * S) / results['N']))) # Physical phase velocity error (percentage) results['deltavp'] = (((vp * c) - c) / c) * 100 # Store rounded down value of grid sampling density results['N'] = int(np.floor(results['N'])) return results
def mpl_plot(w, timewindow, dt, iterations, fft=False): """Plots waveform and prints useful information about its properties. Args: w (class): Waveform class instance. timewindow (float): Time window. dt (float): Time discretisation. iterations (int): Number of iterations. fft (boolean): Plot FFT switch. Returns: plt (object): matplotlib plot object. """ time = np.linspace(0, 1, iterations) time *= (iterations * dt) waveform = np.zeros(len(time)) timeiter = np.nditer(time, flags=['c_index']) while not timeiter.finished: waveform[timeiter.index] = w.calculate_value(timeiter[0], dt) timeiter.iternext() print('Waveform characteristics...') print('Type: {}'.format(w.type)) print('Maximum (absolute) amplitude: {:g}'.format(np.max( np.abs(waveform)))) if w.freq and not w.type == 'gaussian': print('Centre frequency: {:g} Hz'.format(w.freq)) if w.type == 'gaussian' or w.type == 'gaussiandot' or w.type == 'gaussiandotnorm' or w.type == 'gaussianprime' or w.type == 'gaussiandoubleprime': delay = 1 / w.freq print('Time to centre of pulse: {:g} s'.format(delay)) elif w.type == 'gaussiandotdot' or w.type == 'gaussiandotdotnorm' or w.type == 'ricker': delay = np.sqrt(2) / w.freq print('Time to centre of pulse: {:g} s'.format(delay)) print('Time window: {:g} s ({} iterations)'.format(timewindow, iterations)) print('Time step: {:g} s'.format(dt)) if fft: # FFT freqs, power = fft_power(waveform, dt) # Set plotting range to 4 times frequency at max power of waveform or # 4 times the centre frequency freqmaxpower = np.where(np.isclose(power, 0))[0][0] if freqs[freqmaxpower] > w.freq: pltrange = np.where(freqs > 4 * freqs[freqmaxpower])[0][0] else: pltrange = np.where(freqs > 4 * w.freq)[0][0] pltrange = np.s_[0:pltrange] fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, num=w.type, figsize=(20, 10), facecolor='w', edgecolor='w') # Plot waveform ax1.plot(time, waveform, 'r', lw=2) ax1.set_xlabel('Time [s]') ax1.set_ylabel('Amplitude') # Plot frequency spectra markerline, stemlines, baseline = ax2.stem(freqs[pltrange], power[pltrange], '-.') plt.setp(baseline, 'linewidth', 0) plt.setp(stemlines, 'color', 'r') plt.setp(markerline, 'markerfacecolor', 'r', 'markeredgecolor', 'r') ax2.plot(freqs[pltrange], power[pltrange], 'r', lw=2) ax2.set_xlabel('Frequency [Hz]') ax2.set_ylabel('Power [dB]') else: fig, ax1 = plt.subplots(num=w.type, figsize=(20, 10), facecolor='w', edgecolor='w') # Plot waveform ax1.plot(time, waveform, 'r', lw=2) ax1.set_xlabel('Time [s]') ax1.set_ylabel('Amplitude') [ax.grid() for ax in fig.axes] # Turn on grid # Save a PDF/PNG of the figure # 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) # 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) return plt