260 lines
9.8 KiB
Python
Executable File
260 lines
9.8 KiB
Python
Executable File
#!/usr/bin/env python3
|
|
# Generate adxl345 accelerometer graphs
|
|
#
|
|
# Copyright (C) 2020 Kevin O'Connor <kevin@koconnor.net>
|
|
# Copyright (C) 2020 Dmitry Butyugin <dmbutyugin@google.com>
|
|
#
|
|
# This file may be distributed under the terms of the GNU GPLv3 license.
|
|
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
|
|
|
|
def parse_log(logname, opts):
|
|
with open(logname) as f:
|
|
for header in f:
|
|
if not header.startswith('#'):
|
|
break
|
|
if not header.startswith('freq,psd_x,psd_y,psd_z,psd_xyz'):
|
|
# Raw accelerometer data
|
|
return np.loadtxt(logname, comments='#', delimiter=',')
|
|
# Power spectral density data or shaper calibration data
|
|
opts.error("File %s does not contain raw accelerometer data and therefore "
|
|
"is not supported by graph_accelerometer.py script. Please use "
|
|
"calibrate_shaper.py script to process it instead." % (logname,))
|
|
|
|
######################################################################
|
|
# Raw accelerometer graphing
|
|
######################################################################
|
|
|
|
def plot_accel(data, logname):
|
|
first_time = data[0, 0]
|
|
times = data[:,0] - first_time
|
|
fig, axes = matplotlib.pyplot.subplots(nrows=3, sharex=True)
|
|
axes[0].set_title("\n".join(wrap("Accelerometer data (%s)" % (logname,),
|
|
MAX_TITLE_LENGTH)))
|
|
axis_names = ['x', 'y', 'z']
|
|
for i in range(len(axis_names)):
|
|
avg = data[:,i+1].mean()
|
|
adata = data[:,i+1] - data[:,i+1].mean()
|
|
ax = axes[i]
|
|
ax.plot(times, adata, alpha=0.8)
|
|
ax.grid(True)
|
|
ax.set_ylabel('%s accel (%+.3f)\n(mm/s^2)' % (axis_names[i], -avg))
|
|
axes[-1].set_xlabel('Time (%+.3f)\n(s)' % (-first_time,))
|
|
fig.tight_layout()
|
|
return fig
|
|
|
|
|
|
######################################################################
|
|
# Frequency graphing
|
|
######################################################################
|
|
|
|
# Calculate estimated "power spectral density"
|
|
def calc_freq_response(data, max_freq):
|
|
helper = shaper_calibrate.ShaperCalibrate(printer=None)
|
|
return helper.process_accelerometer_data(data)
|
|
|
|
def calc_specgram(data, axis):
|
|
N = data.shape[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.)
|
|
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)
|
|
|
|
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 += _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:]:
|
|
calibration_data.add_data(calc_freq_response(data, max_freq))
|
|
freqs = calibration_data.freq_bins
|
|
psd = calibration_data.psd_sum[freqs <= max_freq]
|
|
px = calibration_data.psd_x[freqs <= max_freq]
|
|
py = calibration_data.psd_y[freqs <= max_freq]
|
|
pz = calibration_data.psd_z[freqs <= 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.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))
|
|
|
|
fontP = matplotlib.font_manager.FontProperties()
|
|
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')
|
|
|
|
for data, logname in zip(datas, lognames):
|
|
calibration_data = calc_freq_response(data, max_freq)
|
|
freqs = calibration_data.freq_bins
|
|
psd = calibration_data.get_psd(axis)[freqs <= max_freq]
|
|
freqs = freqs[freqs <= max_freq]
|
|
ax.plot(freqs, psd, label="\n".join(wrap(logname, 60)), 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')
|
|
fontP = matplotlib.font_manager.FontProperties()
|
|
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.pcolormesh(t, bins, pdata, norm=matplotlib.colors.LogNorm())
|
|
ax.set_ylim([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])
|
|
for data in datas[1:]:
|
|
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:
|
|
csvfile.write("freq\\t")
|
|
for ts in time:
|
|
csvfile.write(",%.6f" % (ts,))
|
|
csvfile.write("\n")
|
|
for i in range(M):
|
|
csvfile.write("%.1f" % (freq_bins[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'
|
|
|
|
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')
|
|
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')")
|
|
options, args = opts.parse_args()
|
|
if len(args) < 1:
|
|
opts.error("Incorrect number of arguments")
|
|
|
|
# Parse data
|
|
datas = [parse_log(fn, opts) for fn in args]
|
|
|
|
setup_matplotlib(options.output)
|
|
|
|
if is_csv_output(options.output):
|
|
if options.raw:
|
|
opts.error("raw mode is not supported with csv output")
|
|
if options.compare:
|
|
opts.error("comparison mode is not supported with csv output")
|
|
if options.specgram:
|
|
if len(args) > 1:
|
|
opts.error("Only 1 input is supported in specgram mode")
|
|
pdata, bins, t = calc_specgram(datas[0], options.axis)
|
|
write_specgram(pdata, bins, t, options.output)
|
|
else:
|
|
write_frequency_response(datas, options.output)
|
|
return
|
|
|
|
# Draw graph
|
|
if options.raw:
|
|
if len(args) > 1:
|
|
opts.error("Only 1 input is supported in raw mode")
|
|
fig = plot_accel(datas[0], args[0])
|
|
elif options.specgram:
|
|
if len(args) > 1:
|
|
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)
|
|
else:
|
|
fig = plot_frequency(datas, args, options.max_freq)
|
|
|
|
# Show graph
|
|
if options.output is None:
|
|
matplotlib.pyplot.show()
|
|
else:
|
|
fig.set_size_inches(8, 6)
|
|
fig.savefig(options.output)
|
|
|
|
if __name__ == '__main__':
|
|
main()
|