mirror of https://github.com/Desuuuu/klipper.git
175 lines
6.9 KiB
Python
Executable File
175 lines
6.9 KiB
Python
Executable File
#!/usr/bin/env python3
|
|
# 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 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):
|
|
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 = shaper_calibrate.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 = shaper_calibrate.ShaperCalibrate(printer=None)
|
|
if isinstance(datas[0], shaper_calibrate.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()
|