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fully moved to Pathlib and global code improvements

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Félix Boisselier
2024-04-19 17:19:56 +02:00
parent 6184233b03
commit 94e110736a
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src/graph_creators/graph_shaper.py Normal file
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#!/usr/bin/env python3
#################################################
######## INPUT SHAPER CALIBRATION SCRIPT ########
#################################################
# Derived from the calibrate_shaper.py official Klipper script
# Copyright (C) 2020 Dmitry Butyugin <dmbutyugin@google.com>
# Copyright (C) 2020 Kevin O'Connor <kevin@koconnor.net>
# Highly modified and improved by Frix_x#0161 #
import optparse
import os
from datetime import datetime
import matplotlib
import matplotlib.font_manager
import matplotlib.pyplot as plt
import matplotlib.ticker
import numpy as np
matplotlib.use('Agg')
from helpers.common_func import (
compute_mechanical_parameters,
compute_spectrogram,
detect_peaks,
parse_log,
setup_klipper_import,
)
from helpers.locale_utils import print_with_c_locale, set_locale
PEAKS_DETECTION_THRESHOLD = 0.05
PEAKS_EFFECT_THRESHOLD = 0.12
SPECTROGRAM_LOW_PERCENTILE_FILTER = 5
MAX_SMOOTHING = 0.1
KLIPPAIN_COLORS = {
'purple': '#70088C',
'orange': '#FF8D32',
'dark_purple': '#150140',
'dark_orange': '#F24130',
'red_pink': '#F2055C',
}
######################################################################
# Computation
######################################################################
# Find the best shaper parameters using Klipper's official algorithm selection with
# a proper precomputed damping ratio (zeta) and using the configured printer SQV value
def calibrate_shaper(datas, max_smoothing, scv, max_freq):
helper = shaper_calibrate.ShaperCalibrate(printer=None)
calibration_data = helper.process_accelerometer_data(datas)
calibration_data.normalize_to_frequencies()
fr, zeta, _, _ = compute_mechanical_parameters(calibration_data.psd_sum, calibration_data.freq_bins)
# If the damping ratio computation fail, we use Klipper default value instead
if zeta is None:
zeta = 0.1
compat = False
try:
shaper, all_shapers = helper.find_best_shaper(
calibration_data,
shapers=None,
damping_ratio=zeta,
scv=scv,
shaper_freqs=None,
max_smoothing=max_smoothing,
test_damping_ratios=None,
max_freq=max_freq,
logger=print_with_c_locale,
)
except TypeError:
print_with_c_locale(
'[WARNING] You seem to be using an older version of Klipper that is not compatible with all the latest Shake&Tune features!'
)
print_with_c_locale(
'Shake&Tune now runs in compatibility mode: be aware that the results may be slightly off, since the real damping ratio cannot be used to create the filter recommendations'
)
compat = True
shaper, all_shapers = helper.find_best_shaper(calibration_data, max_smoothing, print_with_c_locale)
print_with_c_locale(
'\n-> Recommended shaper is %s @ %.1f Hz (when using a square corner velocity of %.1f and a damping ratio of %.3f)'
% (shaper.name.upper(), shaper.freq, scv, zeta)
)
return shaper.name, all_shapers, calibration_data, fr, zeta, compat
######################################################################
# Graphing
######################################################################
def plot_freq_response(
ax, calibration_data, shapers, performance_shaper, peaks, peaks_freqs, peaks_threshold, fr, zeta, max_freq
):
freqs = calibration_data.freqs
psd = calibration_data.psd_sum
px = calibration_data.psd_x
py = calibration_data.psd_y
pz = calibration_data.psd_z
fontP = matplotlib.font_manager.FontProperties()
fontP.set_size('x-small')
ax.set_xlabel('Frequency (Hz)')
ax.set_xlim([0, max_freq])
ax.set_ylabel('Power spectral density')
ax.set_ylim([0, psd.max() + psd.max() * 0.05])
ax.plot(freqs, psd, label='X+Y+Z', color='purple', zorder=5)
ax.plot(freqs, px, label='X', color='red')
ax.plot(freqs, py, label='Y', color='green')
ax.plot(freqs, pz, label='Z', color='blue')
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.yaxis.set_visible(False)
lowvib_shaper_vibrs = float('inf')
lowvib_shaper = None
lowvib_shaper_freq = None
lowvib_shaper_accel = 0
# Draw the shappers curves and add their specific parameters in the legend
# This adds also a way to find the best shaper with a low level of vibrations (with a resonable level of smoothing)
for shaper in shapers:
shaper_max_accel = round(shaper.max_accel / 100.0) * 100.0
label = '%s (%.1f Hz, vibr=%.1f%%, sm~=%.2f, accel<=%.f)' % (
shaper.name.upper(),
shaper.freq,
shaper.vibrs * 100.0,
shaper.smoothing,
shaper_max_accel,
)
ax2.plot(freqs, shaper.vals, label=label, linestyle='dotted')
# Get the performance shaper
if shaper.name == performance_shaper:
performance_shaper_freq = shaper.freq
performance_shaper_vibr = shaper.vibrs * 100.0
performance_shaper_vals = shaper.vals
# Get the low vibration shaper
if (
shaper.vibrs * 100 < lowvib_shaper_vibrs
or (shaper.vibrs * 100 == lowvib_shaper_vibrs and shaper_max_accel > lowvib_shaper_accel)
) and shaper.smoothing < MAX_SMOOTHING:
lowvib_shaper_accel = shaper_max_accel
lowvib_shaper = shaper.name
lowvib_shaper_freq = shaper.freq
lowvib_shaper_vibrs = shaper.vibrs * 100
lowvib_shaper_vals = shaper.vals
# User recommendations are added to the legend: one is Klipper's original suggestion that is usually good for performances
# and the other one is the custom "low vibration" recommendation that looks for a suitable shaper that doesn't have excessive
# smoothing and that have a lower vibration level. If both recommendation are the same shaper, or if no suitable "low
# vibration" shaper is found, then only a single line as the "best shaper" recommendation is added to the legend
if (
lowvib_shaper is not None
and lowvib_shaper != performance_shaper
and lowvib_shaper_vibrs <= performance_shaper_vibr
):
ax2.plot(
[],
[],
' ',
label='Recommended performance shaper: %s @ %.1f Hz'
% (performance_shaper.upper(), performance_shaper_freq),
)
ax.plot(
freqs, psd * performance_shaper_vals, label='With %s applied' % (performance_shaper.upper()), color='cyan'
)
ax2.plot(
[],
[],
' ',
label='Recommended low vibrations shaper: %s @ %.1f Hz' % (lowvib_shaper.upper(), lowvib_shaper_freq),
)
ax.plot(freqs, psd * lowvib_shaper_vals, label='With %s applied' % (lowvib_shaper.upper()), color='lime')
else:
ax2.plot(
[],
[],
' ',
label='Recommended best shaper: %s @ %.1f Hz' % (performance_shaper.upper(), performance_shaper_freq),
)
ax.plot(
freqs, psd * performance_shaper_vals, label='With %s applied' % (performance_shaper.upper()), color='cyan'
)
# And the estimated damping ratio is finally added at the end of the legend
ax2.plot([], [], ' ', label='Estimated damping ratio (ζ): %.3f' % (zeta))
# Draw the detected peaks and name them
# This also draw the detection threshold and warning threshold (aka "effect zone")
ax.plot(peaks_freqs, psd[peaks], 'x', color='black', markersize=8)
for idx, peak in enumerate(peaks):
if psd[peak] > peaks_threshold[1]:
fontcolor = 'red'
fontweight = 'bold'
else:
fontcolor = 'black'
fontweight = 'normal'
ax.annotate(
f'{idx+1}',
(freqs[peak], psd[peak]),
textcoords='offset points',
xytext=(8, 5),
ha='left',
fontsize=13,
color=fontcolor,
weight=fontweight,
)
ax.axhline(y=peaks_threshold[0], color='black', linestyle='--', linewidth=0.5)
ax.axhline(y=peaks_threshold[1], color='black', linestyle='--', linewidth=0.5)
ax.fill_between(freqs, 0, peaks_threshold[0], color='green', alpha=0.15, label='Relax Region')
ax.fill_between(freqs, peaks_threshold[0], peaks_threshold[1], color='orange', alpha=0.2, label='Warning Region')
# Add the main resonant frequency and damping ratio of the axis to the graph title
ax.set_title(
'Axis Frequency Profile (ω0=%.1fHz, ζ=%.3f)' % (fr, zeta),
fontsize=14,
color=KLIPPAIN_COLORS['dark_orange'],
weight='bold',
)
ax.legend(loc='upper left', prop=fontP)
ax2.legend(loc='upper right', prop=fontP)
return
# Plot a time-frequency spectrogram to see how the system respond over time during the
# resonnance test. This can highlight hidden spots from the standard PSD graph from other harmonics
def plot_spectrogram(ax, t, bins, pdata, peaks, max_freq):
ax.set_title('Time-Frequency Spectrogram', fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
# We need to normalize the data to get a proper signal on the spectrogram
# However, while using "LogNorm" provide too much background noise, using
# "Normalize" make only the resonnance appearing and hide interesting elements
# So we need to filter out the lower part of the data (ie. find the proper vmin for LogNorm)
vmin_value = np.percentile(pdata, SPECTROGRAM_LOW_PERCENTILE_FILTER)
# Draw the spectrogram using imgshow that is better suited here than pcolormesh since its result is already rasterized and
# we doesn't need to keep vector graphics when saving to a final .png file. Using it also allow to
# save ~150-200MB of RAM during the "fig.savefig" operation.
cm = 'inferno'
norm = matplotlib.colors.LogNorm(vmin=vmin_value)
ax.imshow(
pdata.T,
norm=norm,
cmap=cm,
aspect='auto',
extent=[t[0], t[-1], bins[0], bins[-1]],
origin='lower',
interpolation='antialiased',
)
ax.set_xlim([0.0, max_freq])
ax.set_ylabel('Time (s)')
ax.set_xlabel('Frequency (Hz)')
# Add peaks lines in the spectrogram to get hint from peaks found in the first graph
if peaks is not None:
for idx, peak in enumerate(peaks):
ax.axvline(peak, color='cyan', linestyle='dotted', linewidth=1)
ax.annotate(
f'Peak {idx+1}',
(peak, bins[-1] * 0.9),
textcoords='data',
color='cyan',
rotation=90,
fontsize=10,
verticalalignment='top',
horizontalalignment='right',
)
return
######################################################################
# Startup and main routines
######################################################################
def shaper_calibration(lognames, klipperdir='~/klipper', max_smoothing=None, scv=5.0, max_freq=200.0, st_version=None):
set_locale()
global shaper_calibrate
shaper_calibrate = setup_klipper_import(klipperdir)
# Parse data
datas = [parse_log(fn) for fn in lognames]
if len(datas) > 1:
print_with_c_locale('Warning: incorrect number of .csv files detected. Only the first one will be used!')
# Compute shapers, PSD outputs and spectrogram
performance_shaper, shapers, calibration_data, fr, zeta, compat = calibrate_shaper(
datas[0], max_smoothing, scv, max_freq
)
pdata, bins, t = compute_spectrogram(datas[0])
del datas
# Select only the relevant part of the PSD data
freqs = calibration_data.freq_bins
calibration_data.psd_sum = calibration_data.psd_sum[freqs <= max_freq]
calibration_data.psd_x = calibration_data.psd_x[freqs <= max_freq]
calibration_data.psd_y = calibration_data.psd_y[freqs <= max_freq]
calibration_data.psd_z = calibration_data.psd_z[freqs <= max_freq]
calibration_data.freqs = freqs[freqs <= max_freq]
# Peak detection algorithm
peaks_threshold = [
PEAKS_DETECTION_THRESHOLD * calibration_data.psd_sum.max(),
PEAKS_EFFECT_THRESHOLD * calibration_data.psd_sum.max(),
]
num_peaks, peaks, peaks_freqs = detect_peaks(calibration_data.psd_sum, calibration_data.freqs, peaks_threshold[0])
# Print the peaks info in the console
peak_freqs_formated = ['{:.1f}'.format(f) for f in peaks_freqs]
num_peaks_above_effect_threshold = np.sum(calibration_data.psd_sum[peaks] > peaks_threshold[1])
print_with_c_locale(
'\nPeaks detected on the graph: %d @ %s Hz (%d above effect threshold)'
% (num_peaks, ', '.join(map(str, peak_freqs_formated)), num_peaks_above_effect_threshold)
)
# Create graph layout
fig, (ax1, ax2) = plt.subplots(
2,
1,
gridspec_kw={
'height_ratios': [4, 3],
'bottom': 0.050,
'top': 0.890,
'left': 0.085,
'right': 0.966,
'hspace': 0.169,
'wspace': 0.200,
},
)
fig.set_size_inches(8.3, 11.6)
# Add a title with some test info
title_line1 = 'INPUT SHAPER CALIBRATION TOOL'
fig.text(
0.12, 0.965, title_line1, ha='left', va='bottom', fontsize=20, color=KLIPPAIN_COLORS['purple'], weight='bold'
)
try:
filename_parts = (lognames[0].split('/')[-1]).split('_')
dt = datetime.strptime(f'{filename_parts[1]} {filename_parts[2]}', '%Y%m%d %H%M%S')
title_line2 = dt.strftime('%x %X') + ' -- ' + filename_parts[3].upper().split('.')[0] + ' axis'
if compat:
title_line3 = '| Compatibility mode with older Klipper,'
title_line4 = '| and no custom S&T parameters are used!'
else:
title_line3 = '| Square corner velocity: ' + str(scv) + 'mm/s'
title_line4 = '| Max allowed smoothing: ' + str(max_smoothing)
except Exception:
print_with_c_locale('Warning: CSV filename look to be different than expected (%s)' % (lognames[0]))
title_line2 = lognames[0].split('/')[-1]
title_line3 = ''
title_line4 = ''
fig.text(0.12, 0.957, title_line2, ha='left', va='top', fontsize=16, color=KLIPPAIN_COLORS['dark_purple'])
fig.text(0.58, 0.960, title_line3, ha='left', va='top', fontsize=10, color=KLIPPAIN_COLORS['dark_purple'])
fig.text(0.58, 0.946, title_line4, ha='left', va='top', fontsize=10, color=KLIPPAIN_COLORS['dark_purple'])
# Plot the graphs
plot_freq_response(
ax1, calibration_data, shapers, performance_shaper, peaks, peaks_freqs, peaks_threshold, fr, zeta, max_freq
)
plot_spectrogram(ax2, t, bins, pdata, peaks_freqs, max_freq)
# Adding a small Klippain logo to the top left corner of the figure
ax_logo = fig.add_axes([0.001, 0.8995, 0.1, 0.1], anchor='NW')
ax_logo.imshow(plt.imread(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'klippain.png')))
ax_logo.axis('off')
# Adding Shake&Tune version in the top right corner
if st_version != 'unknown':
fig.text(0.995, 0.985, st_version, ha='right', va='bottom', fontsize=8, color=KLIPPAIN_COLORS['purple'])
return fig
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('-f', '--max_freq', type='float', default=200.0, help='maximum frequency to graph')
opts.add_option('-s', '--max_smoothing', type='float', default=None, help='maximum shaper smoothing to allow')
opts.add_option(
'--scv', '--square_corner_velocity', type='float', dest='scv', default=5.0, help='square corner velocity'
)
opts.add_option(
'-k', '--klipper_dir', type='string', dest='klipperdir', default='~/klipper', help='main klipper directory'
)
options, args = opts.parse_args()
if len(args) < 1:
opts.error('Incorrect number of arguments')
if options.output is None:
opts.error('You must specify an output file.png to use the script (option -o)')
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)')
fig = shaper_calibration(args, options.klipperdir, options.max_smoothing, options.scv, options.max_freq)
fig.savefig(options.output, dpi=150)
if __name__ == '__main__':
main()