diff options
Diffstat (limited to 'scripts/graph_accelerometer.py')
| -rwxr-xr-x | scripts/graph_accelerometer.py | 224 |
1 files changed, 145 insertions, 79 deletions
diff --git a/scripts/graph_accelerometer.py b/scripts/graph_accelerometer.py index 84b31311..7286c041 100755 --- a/scripts/graph_accelerometer.py +++ b/scripts/graph_accelerometer.py @@ -8,59 +8,73 @@ import importlib, optparse, os, sys from textwrap import wrap import numpy as np, matplotlib -sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), - '..', 'klippy')) -shaper_calibrate = importlib.import_module('.shaper_calibrate', 'extras') -MAX_TITLE_LENGTH=65 +sys.path.append( + os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", "klippy") +) +shaper_calibrate = importlib.import_module(".shaper_calibrate", "extras") + +MAX_TITLE_LENGTH = 65 + def parse_log(logname, opts): with open(logname) as f: for header in f: - if header.startswith('#'): + if header.startswith("#"): continue - if header.startswith('freq,psd_x,psd_y,psd_z,psd_xyz'): + if header.startswith("freq,psd_x,psd_y,psd_z,psd_xyz"): # Processed power spectral density file break # Raw accelerometer data - return np.loadtxt(logname, comments='#', delimiter=',') + return np.loadtxt(logname, comments="#", delimiter=",") # Parse power spectral density data - data = np.loadtxt(logname, skiprows=1, comments='#', delimiter=',') + data = np.loadtxt(logname, skiprows=1, comments="#", delimiter=",") calibration_data = shaper_calibrate.CalibrationData( - freq_bins=data[:,0], psd_sum=data[:,4], - psd_x=data[:,1], psd_y=data[:,2], psd_z=data[:,3]) + freq_bins=data[:, 0], + psd_sum=data[:, 4], + psd_x=data[:, 1], + psd_y=data[:, 2], + psd_z=data[:, 3], + ) calibration_data.set_numpy(np) return calibration_data + ###################################################################### # Raw accelerometer graphing ###################################################################### + def plot_accel(datas, lognames): fig, axes = matplotlib.pyplot.subplots(nrows=3, sharex=True) - axes[0].set_title("\n".join(wrap( - "Accelerometer data (%s)" % (', '.join(lognames)), MAX_TITLE_LENGTH))) - axis_names = ['x', 'y', 'z'] + axes[0].set_title( + "\n".join( + wrap("Accelerometer data (%s)" % (", ".join(lognames)), MAX_TITLE_LENGTH) + ) + ) + axis_names = ["x", "y", "z"] for data, logname in zip(datas, lognames): if isinstance(data, shaper_calibrate.CalibrationData): - raise error("Cannot plot raw accelerometer data using the processed" - " resonances, raw_data input is required") + raise error( + "Cannot plot raw accelerometer data using the processed" + " resonances, raw_data input is required" + ) first_time = data[0, 0] - times = data[:,0] - first_time + times = data[:, 0] - first_time for i in range(len(axis_names)): - avg = data[:,i+1].mean() - adata = data[:,i+1] - data[:,i+1].mean() + avg = data[:, i + 1].mean() + adata = data[:, i + 1] - data[:, i + 1].mean() ax = axes[i] - label = '\n'.join(wrap(logname, 60)) + ' (%+.3f mm/s^2)' % (-avg,) + label = "\n".join(wrap(logname, 60)) + " (%+.3f mm/s^2)" % (-avg,) ax.plot(times, adata, alpha=0.8, label=label) - axes[-1].set_xlabel('Time (s)') + axes[-1].set_xlabel("Time (s)") fontP = matplotlib.font_manager.FontProperties() - fontP.set_size('x-small') + fontP.set_size("x-small") for i in range(len(axis_names)): ax = axes[i] ax.grid(True) - ax.legend(loc='best', prop=fontP) - ax.set_ylabel('%s accel' % (axis_names[i],)) + ax.legend(loc="best", prop=fontP) + ax.set_ylabel("%s accel" % (axis_names[i],)) fig.tight_layout() return fig @@ -69,6 +83,7 @@ def plot_accel(datas, lognames): # Frequency graphing ###################################################################### + # Calculate estimated "power spectral density" def calc_freq_response(data, max_freq): if isinstance(data, shaper_calibrate.CalibrationData): @@ -76,29 +91,41 @@ def calc_freq_response(data, max_freq): helper = shaper_calibrate.ShaperCalibrate(printer=None) return helper.process_accelerometer_data(data) + def calc_specgram(data, axis): if isinstance(data, shaper_calibrate.CalibrationData): - raise error("Cannot calculate the spectrogram using the processed" - " resonances, raw_data input is required") + raise error( + "Cannot calculate the spectrogram using the processed" + " resonances, raw_data input is required" + ) N = data.shape[0] - Fs = N / (data[-1,0] - data[0,0]) + Fs = N / (data[-1, 0] - data[0, 0]) # Round up to a power of 2 for faster FFT - M = 1 << int(.5 * Fs - 1).bit_length() - window = np.kaiser(M, 6.) + M = 1 << int(0.5 * Fs - 1).bit_length() + window = np.kaiser(M, 6.0) + def _specgram(x): return matplotlib.mlab.specgram( - x, Fs=Fs, NFFT=M, noverlap=M//2, window=window, - mode='psd', detrend='mean', scale_by_freq=False) + x, + Fs=Fs, + NFFT=M, + noverlap=M // 2, + window=window, + mode="psd", + detrend="mean", + scale_by_freq=False, + ) - d = {'x': data[:,1], 'y': data[:,2], 'z': data[:,3]} - if axis != 'all': + d = {"x": data[:, 1], "y": data[:, 2], "z": data[:, 3]} + if axis != "all": pdata, bins, t = _specgram(d[axis]) else: - pdata, bins, t = _specgram(d['x']) - for ax in 'yz': + pdata, bins, t = _specgram(d["x"]) + for ax in "yz": pdata += _specgram(d[ax])[0] return pdata, bins, t + def plot_frequency(datas, lognames, max_freq): calibration_data = calc_freq_response(datas[0], max_freq) for data in datas[1:]: @@ -111,33 +138,37 @@ def plot_frequency(datas, lognames, max_freq): freqs = freqs[freqs <= max_freq] fig, ax = matplotlib.pyplot.subplots() - ax.set_title("\n".join(wrap( - "Frequency response (%s)" % (', '.join(lognames)), MAX_TITLE_LENGTH))) - ax.set_xlabel('Frequency (Hz)') - ax.set_ylabel('Power spectral density') + ax.set_title( + "\n".join( + wrap("Frequency response (%s)" % (", ".join(lognames)), MAX_TITLE_LENGTH) + ) + ) + ax.set_xlabel("Frequency (Hz)") + ax.set_ylabel("Power spectral density") - ax.plot(freqs, psd, label='X+Y+Z', alpha=0.6) - ax.plot(freqs, px, label='X', alpha=0.6) - ax.plot(freqs, py, label='Y', alpha=0.6) - ax.plot(freqs, pz, label='Z', alpha=0.6) + ax.plot(freqs, psd, label="X+Y+Z", alpha=0.6) + ax.plot(freqs, px, label="X", alpha=0.6) + ax.plot(freqs, py, label="Y", alpha=0.6) + ax.plot(freqs, pz, label="Z", alpha=0.6) ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator()) ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator()) - ax.grid(which='major', color='grey') - ax.grid(which='minor', color='lightgrey') - ax.ticklabel_format(axis='y', style='scientific', scilimits=(0,0)) + ax.grid(which="major", color="grey") + ax.grid(which="minor", color="lightgrey") + ax.ticklabel_format(axis="y", style="scientific", scilimits=(0, 0)) fontP = matplotlib.font_manager.FontProperties() - fontP.set_size('x-small') - ax.legend(loc='best', prop=fontP) + fontP.set_size("x-small") + ax.legend(loc="best", prop=fontP) fig.tight_layout() return fig + def plot_compare_frequency(datas, lognames, max_freq, axis): fig, ax = matplotlib.pyplot.subplots() - ax.set_title('Frequency responses comparison') - ax.set_xlabel('Frequency (Hz)') - ax.set_ylabel('Power spectral density') + ax.set_title("Frequency responses comparison") + ax.set_xlabel("Frequency (Hz)") + ax.set_ylabel("Power spectral density") for data, logname in zip(datas, lognames): calibration_data = calc_freq_response(data, max_freq) @@ -148,32 +179,36 @@ def plot_compare_frequency(datas, lognames, max_freq, axis): ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator()) ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator()) - ax.grid(which='major', color='grey') - ax.grid(which='minor', color='lightgrey') + ax.grid(which="major", color="grey") + ax.grid(which="minor", color="lightgrey") fontP = matplotlib.font_manager.FontProperties() - fontP.set_size('x-small') - ax.legend(loc='best', prop=fontP) + fontP.set_size("x-small") + ax.legend(loc="best", prop=fontP) fig.tight_layout() return fig + # Plot data in a "spectrogram colormap" def plot_specgram(data, logname, max_freq, axis): pdata, bins, t = calc_specgram(data, axis) fig, ax = matplotlib.pyplot.subplots() - ax.set_title("\n".join(wrap("Spectrogram %s (%s)" % (axis, logname), - MAX_TITLE_LENGTH))) + ax.set_title( + "\n".join(wrap("Spectrogram %s (%s)" % (axis, logname), MAX_TITLE_LENGTH)) + ) ax.pcolormesh(t, bins, pdata, norm=matplotlib.colors.LogNorm()) - ax.set_ylim([0., max_freq]) - ax.set_ylabel('frequency (hz)') - ax.set_xlabel('Time (s)') + ax.set_ylim([0.0, max_freq]) + ax.set_ylabel("frequency (hz)") + ax.set_xlabel("Time (s)") fig.tight_layout() return fig + ###################################################################### # CSV output ###################################################################### + def write_frequency_response(datas, output): helper = shaper_calibrate.ShaperCalibrate(printer=None) calibration_data = helper.process_accelerometer_data(datas[0]) @@ -181,6 +216,7 @@ def write_frequency_response(datas, output): calibration_data.add_data(helper.process_accelerometer_data(data)) helper.save_calibration_data(output, calibration_data) + def write_specgram(psd, freq_bins, time, output): M = freq_bins.shape[0] with open(output, "w") as csvfile: @@ -190,46 +226,76 @@ def write_specgram(psd, freq_bins, time, output): csvfile.write("\n") for i in range(M): csvfile.write("%.1f" % (freq_bins[i],)) - for value in psd[i,:]: + for value in psd[i, :]: csvfile.write(",%.6e" % (value,)) csvfile.write("\n") + ###################################################################### # Startup ###################################################################### + def is_csv_output(output): - return output and os.path.splitext(output)[1].lower() == '.csv' + return output and os.path.splitext(output)[1].lower() == ".csv" + def setup_matplotlib(output): global matplotlib if is_csv_output(output): # Only mlab may be necessary with CSV output import matplotlib.mlab + return if output: - matplotlib.rcParams.update({'figure.autolayout': True}) - matplotlib.use('Agg') + matplotlib.rcParams.update({"figure.autolayout": True}) + matplotlib.use("Agg") import matplotlib.pyplot, matplotlib.dates, matplotlib.font_manager import matplotlib.ticker + def main(): # Parse command-line arguments usage = "%prog [options] <raw logs>" opts = optparse.OptionParser(usage) - opts.add_option("-o", "--output", type="string", dest="output", - default=None, help="filename of output graph") - opts.add_option("-f", "--max_freq", type="float", default=200., - help="maximum frequency to graph") - opts.add_option("-r", "--raw", action="store_true", - help="graph raw accelerometer data") - opts.add_option("-c", "--compare", action="store_true", - help="graph comparison of power spectral density " - "between different accelerometer data files") - opts.add_option("-s", "--specgram", action="store_true", - help="graph spectrogram of accelerometer data") - opts.add_option("-a", type="string", dest="axis", default="all", - help="axis to graph (one of 'all', 'x', 'y', or 'z')") + opts.add_option( + "-o", + "--output", + type="string", + dest="output", + default=None, + help="filename of output graph", + ) + opts.add_option( + "-f", + "--max_freq", + type="float", + default=200.0, + help="maximum frequency to graph", + ) + opts.add_option( + "-r", "--raw", action="store_true", help="graph raw accelerometer data" + ) + opts.add_option( + "-c", + "--compare", + action="store_true", + help="graph comparison of power spectral density " + "between different accelerometer data files", + ) + opts.add_option( + "-s", + "--specgram", + action="store_true", + help="graph spectrogram of accelerometer data", + ) + opts.add_option( + "-a", + type="string", + dest="axis", + default="all", + help="axis to graph (one of 'all', 'x', 'y', or 'z')", + ) options, args = opts.parse_args() if len(args) < 1: opts.error("Incorrect number of arguments") @@ -261,8 +327,7 @@ def main(): opts.error("Only 1 input is supported in specgram mode") fig = plot_specgram(datas[0], args[0], options.max_freq, options.axis) elif options.compare: - fig = plot_compare_frequency(datas, args, options.max_freq, - options.axis) + fig = plot_compare_frequency(datas, args, options.max_freq, options.axis) else: fig = plot_frequency(datas, args, options.max_freq) @@ -273,5 +338,6 @@ def main(): fig.set_size_inches(8, 6) fig.savefig(options.output) -if __name__ == '__main__': + +if __name__ == "__main__": main() |