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#!/usr/bin/env python2
# Shaper auto-calibration script
#
# Copyright (C) 2020 Dmitry Butyugin <dmbutyugin@google.com>
# Copyright (C) 2020 Kevin O'Connor <kevin@koconnor.net>
#
# This file may be distributed under the terms of the GNU GPLv3 license.
from __future__ import print_function
import 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', 'extras'))
from shaper_calibrate import CalibrationData, ShaperCalibrate
MAX_TITLE_LENGTH=65
def parse_log(logname):
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=',')
# Parse power spectral density data
data = np.loadtxt(logname, skiprows=1, comments='#', delimiter=',')
calibration_data = CalibrationData(
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)
# If input shapers are present in the CSV file, the frequency
# response is already normalized to input frequencies
if 'mzv' not in header:
calibration_data.normalize_to_frequencies()
return calibration_data
######################################################################
# Shaper calibration
######################################################################
# Find the best shaper parameters
def calibrate_shaper(datas, csv_output, max_smoothing):
helper = ShaperCalibrate(printer=None)
if isinstance(datas[0], CalibrationData):
calibration_data = datas[0]
for data in datas[1:]:
calibration_data.add_data(data)
else:
# Process accelerometer data
calibration_data = helper.process_accelerometer_data(datas[0])
for data in datas[1:]:
calibration_data.add_data(helper.process_accelerometer_data(data))
calibration_data.normalize_to_frequencies()
shaper, all_shapers = helper.find_best_shaper(
calibration_data, max_smoothing, print)
print("Recommended shaper is %s @ %.1f Hz" % (shaper.name, shaper.freq))
if csv_output is not None:
helper.save_calibration_data(
csv_output, calibration_data, all_shapers)
return shaper.name, all_shapers, calibration_data
######################################################################
# Plot frequency response and suggested input shapers
######################################################################
def plot_freq_response(lognames, calibration_data, shapers,
selected_shaper, 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]
fontP = matplotlib.font_manager.FontProperties()
fontP.set_size('x-small')
fig, ax = matplotlib.pyplot.subplots()
ax.set_xlabel('Frequency, Hz')
ax.set_xlim([0, max_freq])
ax.set_ylabel('Power spectral density')
ax.plot(freqs, psd, label='X+Y+Z', color='purple')
ax.plot(freqs, px, label='X', color='red')
ax.plot(freqs, py, label='Y', color='green')
ax.plot(freqs, pz, label='Z', color='blue')
title = "Frequency response and shapers (%s)" % (', '.join(lognames))
ax.set_title("\n".join(wrap(title, MAX_TITLE_LENGTH)))
ax.xaxis.set_minor_locator(matplotlib.ticker.MultipleLocator(5))
ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
ax.ticklabel_format(axis='y', style='scientific', scilimits=(0,0))
ax.grid(which='major', color='grey')
ax.grid(which='minor', color='lightgrey')
ax2 = ax.twinx()
ax2.set_ylabel('Shaper vibration reduction (ratio)')
best_shaper_vals = None
for shaper in shapers:
label = "%s (%.1f Hz, vibr=%.1f%%, sm~=%.2f, accel<=%.f)" % (
shaper.name.upper(), shaper.freq,
shaper.vibrs * 100., shaper.smoothing,
round(shaper.max_accel / 100.) * 100.)
linestyle = 'dotted'
if shaper.name == selected_shaper:
linestyle = 'dashdot'
best_shaper_vals = shaper.vals
ax2.plot(freqs, shaper.vals, label=label, linestyle=linestyle)
ax.plot(freqs, psd * best_shaper_vals,
label='After\nshaper', color='cyan')
# A hack to add a human-readable shaper recommendation to legend
ax2.plot([], [], ' ',
label="Recommended shaper: %s" % (selected_shaper.upper()))
ax.legend(loc='upper left', prop=fontP)
ax2.legend(loc='upper right', prop=fontP)
fig.tight_layout()
return fig
######################################################################
# Startup
######################################################################
def setup_matplotlib(output_to_file):
global matplotlib
if output_to_file:
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] <logs>"
opts = optparse.OptionParser(usage)
opts.add_option("-o", "--output", type="string", dest="output",
default=None, help="filename of output graph")
opts.add_option("-c", "--csv", type="string", dest="csv",
default=None, help="filename of output csv file")
opts.add_option("-f", "--max_freq", type="float", default=200.,
help="maximum frequency to graph")
opts.add_option("-s", "--max_smoothing", type="float", default=None,
help="maximum shaper smoothing to allow")
options, args = opts.parse_args()
if len(args) < 1:
opts.error("Incorrect number of arguments")
if options.max_smoothing is not None and options.max_smoothing < 0.05:
opts.error("Too small max_smoothing specified (must be at least 0.05)")
# Parse data
datas = [parse_log(fn) for fn in args]
# Calibrate shaper and generate outputs
selected_shaper, shapers, calibration_data = calibrate_shaper(
datas, options.csv, options.max_smoothing)
if not options.csv or options.output:
# Draw graph
setup_matplotlib(options.output is not None)
fig = plot_freq_response(args, calibration_data, shapers,
selected_shaper, 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()
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