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rename folders in measurement and post-processing

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This commit is contained in:
Félix Boisselier
2024-05-13 17:22:05 +02:00
parent 375190610c
commit a37ece7ece
16 changed files with 2 additions and 2 deletions
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7
shaketune/graph_creators/__init__.py
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#!/usr/bin/env python3
from .graph_creator import AxesMapFinder as AxesMapFinder
from .graph_creator import BeltsGraphCreator as BeltsGraphCreator
from .graph_creator import GraphCreator as GraphCreator
from .graph_creator import ShaperGraphCreator as ShaperGraphCreator
from .graph_creator import VibrationsGraphCreator as VibrationsGraphCreator

154
shaketune/graph_creators/analyze_axesmap.py
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#!/usr/bin/env python3
######################################
###### AXE_MAP DETECTION SCRIPT ######
######################################
# Written by Frix_x#0161 #
import optparse
import numpy as np
from scipy.signal import butter, filtfilt
from ..helpers.console_output import ConsoleOutput
NUM_POINTS = 500
######################################################################
# Computation
######################################################################
def accel_signal_filter(data, cutoff=2, fs=100, order=5):
nyq = 0.5 * fs
normal_cutoff = cutoff / nyq
b, a = butter(order, normal_cutoff, btype='low', analog=False)
filtered_data = filtfilt(b, a, data)
filtered_data -= np.mean(filtered_data)
return filtered_data
def find_first_spike(data):
min_index, max_index = np.argmin(data), np.argmax(data)
return ('-', min_index) if min_index < max_index else ('', max_index)
def get_movement_vector(data, start_idx, end_idx):
if start_idx < end_idx:
vector = []
for i in range(3):
vector.append(np.mean(data[i][start_idx:end_idx], axis=0))
return vector
else:
return np.zeros(3)
def angle_between(v1, v2):
v1_u = v1 / np.linalg.norm(v1)
v2_u = v2 / np.linalg.norm(v2)
return np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0))
def compute_errors(filtered_data, spikes_sorted, accel_value, num_points):
# Get the movement start points in the correct order from the sorted bag of spikes
movement_starts = [spike[0][1] for spike in spikes_sorted]
# Theoretical unit vectors for X, Y, Z printer axes
printer_axes = {'x': np.array([1, 0, 0]), 'y': np.array([0, 1, 0]), 'z': np.array([0, 0, 1])}
alignment_errors = {}
sensitivity_errors = {}
for i, axis in enumerate(['x', 'y', 'z']):
movement_start = movement_starts[i]
movement_end = movement_start + num_points
movement_vector = get_movement_vector(filtered_data, movement_start, movement_end)
alignment_errors[axis] = angle_between(movement_vector, printer_axes[axis])
measured_accel_magnitude = np.linalg.norm(movement_vector)
if accel_value != 0:
sensitivity_errors[axis] = abs(measured_accel_magnitude - accel_value) / accel_value * 100
else:
sensitivity_errors[axis] = None
return alignment_errors, sensitivity_errors
######################################################################
# Startup and main routines
######################################################################
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=',')
# Power spectral density data or shaper calibration data
raise ValueError(
'File %s does not contain raw accelerometer data and therefore '
'is not supported by this script. Please use the official Klipper '
'calibrate_shaper.py script to process it instead.' % (logname,)
)
def axesmap_calibration(lognames, accel=None):
# Parse the raw data and get them ready for analysis
raw_datas = [parse_log(filename) for filename in lognames]
if len(raw_datas) > 1:
raise ValueError('Analysis of multiple CSV files at once is not possible with this script')
filtered_data = [accel_signal_filter(raw_datas[0][:, i + 1]) for i in range(3)]
spikes = [find_first_spike(filtered_data[i]) for i in range(3)]
spikes_sorted = sorted([(spikes[0], 'x'), (spikes[1], 'y'), (spikes[2], 'z')], key=lambda x: x[0][1])
# Using the previous variables to get the axes_map and errors
axes_map = ','.join([f'{spike[0][0]}{spike[1]}' for spike in spikes_sorted])
# alignment_error, sensitivity_error = compute_errors(filtered_data, spikes_sorted, accel, NUM_POINTS)
results = f'Detected axes_map:\n {axes_map}\n'
# TODO: work on this function that is currently not giving good results...
# results += "Accelerometer angle deviation:\n"
# for axis, angle in alignment_error.items():
# angle_degrees = np.degrees(angle) # Convert radians to degrees
# results += f" {axis.upper()} axis: {angle_degrees:.2f} degrees\n"
# results += "Accelerometer sensitivity error:\n"
# for axis, error in sensitivity_error.items():
# results += f" {axis.upper()} axis: {error:.2f}%\n"
return results
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(
'-a', '--accel', type='string', dest='accel', default=None, help='acceleration value used to do the movements'
)
options, args = opts.parse_args()
if len(args) < 1:
opts.error('No CSV file(s) to analyse')
if options.accel is None:
opts.error('You must specify the acceleration value used when generating the CSV file (option -a)')
try:
accel_value = float(options.accel)
except ValueError:
opts.error('Invalid acceleration value. It should be a numeric value.')
results = axesmap_calibration(args, accel_value)
ConsoleOutput.print(results)
if options.output is not None:
with open(options.output, 'w') as f:
f.write(results)
if __name__ == '__main__':
main()

555
shaketune/graph_creators/graph_belts.py
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#!/usr/bin/env python3
#################################################
######## CoreXY BELTS CALIBRATION SCRIPT ########
#################################################
# Written by Frix_x#0161 #
import optparse
import os
from collections import namedtuple
from datetime import datetime
import matplotlib
import matplotlib.colors
import matplotlib.font_manager
import matplotlib.pyplot as plt
import matplotlib.ticker
import numpy as np
from scipy.interpolate import griddata
matplotlib.use('Agg')
from ..helpers.common_func import (
compute_curve_similarity_factor,
compute_spectrogram,
detect_peaks,
parse_log,
setup_klipper_import,
)
from ..helpers.console_output import ConsoleOutput
ALPHABET = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' # For paired peaks names
PEAKS_DETECTION_THRESHOLD = 0.20
CURVE_SIMILARITY_SIGMOID_K = 0.6
DC_GRAIN_OF_SALT_FACTOR = 0.75
DC_THRESHOLD_METRIC = 1.5e9
DC_MAX_UNPAIRED_PEAKS_ALLOWED = 4
# Define the SignalData namedtuple
SignalData = namedtuple('CalibrationData', ['freqs', 'psd', 'peaks', 'paired_peaks', 'unpaired_peaks'])
KLIPPAIN_COLORS = {
'purple': '#70088C',
'orange': '#FF8D32',
'dark_purple': '#150140',
'dark_orange': '#F24130',
'red_pink': '#F2055C',
}
######################################################################
# Computation of the PSD graph
######################################################################
# This function create pairs of peaks that are close in frequency on two curves (that are known
# to be resonances points and must be similar on both belts on a CoreXY kinematic)
def pair_peaks(peaks1, freqs1, psd1, peaks2, freqs2, psd2):
# Compute a dynamic detection threshold to filter and pair peaks efficiently
# even if the signal is very noisy (this get clipped to a maximum of 10Hz diff)
distances = []
for p1 in peaks1:
for p2 in peaks2:
distances.append(abs(freqs1[p1] - freqs2[p2]))
distances = np.array(distances)
median_distance = np.median(distances)
iqr = np.percentile(distances, 75) - np.percentile(distances, 25)
threshold = median_distance + 1.5 * iqr
threshold = min(threshold, 10)
# Pair the peaks using the dynamic thresold
paired_peaks = []
unpaired_peaks1 = list(peaks1)
unpaired_peaks2 = list(peaks2)
while unpaired_peaks1 and unpaired_peaks2:
min_distance = threshold + 1
pair = None
for p1 in unpaired_peaks1:
for p2 in unpaired_peaks2:
distance = abs(freqs1[p1] - freqs2[p2])
if distance < min_distance:
min_distance = distance
pair = (p1, p2)
if pair is None: # No more pairs below the threshold
break
p1, p2 = pair
paired_peaks.append(((p1, freqs1[p1], psd1[p1]), (p2, freqs2[p2], psd2[p2])))
unpaired_peaks1.remove(p1)
unpaired_peaks2.remove(p2)
return paired_peaks, unpaired_peaks1, unpaired_peaks2
######################################################################
# Computation of the differential spectrogram
######################################################################
# Interpolate source_data (2D) to match target_x and target_y in order to
# get similar time and frequency dimensions for the differential spectrogram
def interpolate_2d(target_x, target_y, source_x, source_y, source_data):
# Create a grid of points in the source and target space
source_points = np.array([(x, y) for y in source_y for x in source_x])
target_points = np.array([(x, y) for y in target_y for x in target_x])
# Flatten the source data to match the flattened source points
source_values = source_data.flatten()
# Interpolate and reshape the interpolated data to match the target grid shape and replace NaN with zeros
interpolated_data = griddata(source_points, source_values, target_points, method='nearest')
interpolated_data = interpolated_data.reshape((len(target_y), len(target_x)))
interpolated_data = np.nan_to_num(interpolated_data)
return interpolated_data
# Main logic function to combine two similar spectrogram - ie. from both belts paths - by substracting signals in order to create
# a new composite spectrogram. This result of a divergent but mostly centered new spectrogram (center will be white) with some colored zones
# highlighting differences in the belts paths. The summative spectrogram is used for the MHI calculation.
def compute_combined_spectrogram(data1, data2):
pdata1, bins1, t1 = compute_spectrogram(data1)
pdata2, bins2, t2 = compute_spectrogram(data2)
# Interpolate the spectrograms
pdata2_interpolated = interpolate_2d(bins1, t1, bins2, t2, pdata2)
# Combine them in two form: a summed diff for the MHI computation and a diverging diff for the spectrogram colors
combined_sum = np.abs(pdata1 - pdata2_interpolated)
combined_divergent = pdata1 - pdata2_interpolated
return combined_sum, combined_divergent, bins1, t1
# Compute a composite and highly subjective value indicating the "mechanical health of the printer (0 to 100%)" that represent the
# likelihood of mechanical issues on the printer. It is based on the differential spectrogram sum of gradient, salted with a bit
# of the estimated similarity cross-correlation from compute_curve_similarity_factor() and with a bit of the number of unpaired peaks.
# This result in a percentage value quantifying the machine behavior around the main resonances that give an hint if only touching belt tension
# will give good graphs or if there is a chance of mechanical issues in the background (above 50% should be considered as probably problematic)
def compute_mhi(combined_data, similarity_coefficient, num_unpaired_peaks):
# filtered_data = combined_data[combined_data > 100]
filtered_data = np.abs(combined_data)
# First compute a "total variability metric" based on the sum of the gradient that sum the magnitude of will emphasize regions of the
# spectrogram where there are rapid changes in magnitude (like the edges of resonance peaks).
total_variability_metric = np.sum(np.abs(np.gradient(filtered_data)))
# Scale the metric to a percentage using the threshold (found empirically on a large number of user data shared to me)
base_percentage = (np.log1p(total_variability_metric) / np.log1p(DC_THRESHOLD_METRIC)) * 100
# Adjust the percentage based on the similarity_coefficient to add a grain of salt
adjusted_percentage = base_percentage * (1 - DC_GRAIN_OF_SALT_FACTOR * (similarity_coefficient / 100))
# Adjust the percentage again based on the number of unpaired peaks to add a second grain of salt
peak_confidence = num_unpaired_peaks / DC_MAX_UNPAIRED_PEAKS_ALLOWED
final_percentage = (1 - peak_confidence) * adjusted_percentage + peak_confidence * 100
# Ensure the result lies between 0 and 100 by clipping the computed value
final_percentage = np.clip(final_percentage, 0, 100)
return final_percentage, mhi_lut(final_percentage)
# LUT to transform the MHI into a textual value easy to understand for the users of the script
def mhi_lut(mhi):
ranges = [
(0, 30, 'Excellent mechanical health'),
(30, 45, 'Good mechanical health'),
(45, 55, 'Acceptable mechanical health'),
(55, 70, 'Potential signs of a mechanical issue'),
(70, 85, 'Likely a mechanical issue'),
(85, 100, 'Mechanical issue detected'),
]
for lower, upper, message in ranges:
if lower < mhi <= upper:
return message
return 'Error computing MHI value'
######################################################################
# Graphing
######################################################################
def plot_compare_frequency(ax, lognames, signal1, signal2, similarity_factor, max_freq):
# Get the belt name for the legend to avoid putting the full file name
signal1_belt = (lognames[0].split('/')[-1]).split('_')[-1][0]
signal2_belt = (lognames[1].split('/')[-1]).split('_')[-1][0]
if signal1_belt == 'A' and signal2_belt == 'B':
signal1_belt += ' (axis 1,-1)'
signal2_belt += ' (axis 1, 1)'
elif signal1_belt == 'B' and signal2_belt == 'A':
signal1_belt += ' (axis 1, 1)'
signal2_belt += ' (axis 1,-1)'
else:
ConsoleOutput.print(
"Warning: belts doesn't seem to have the correct name A and B (extracted from the filename.csv)"
)
# Plot the two belts PSD signals
ax.plot(signal1.freqs, signal1.psd, label='Belt ' + signal1_belt, color=KLIPPAIN_COLORS['purple'])
ax.plot(signal2.freqs, signal2.psd, label='Belt ' + signal2_belt, color=KLIPPAIN_COLORS['orange'])
# Trace the "relax region" (also used as a threshold to filter and detect the peaks)
psd_lowest_max = min(signal1.psd.max(), signal2.psd.max())
peaks_warning_threshold = PEAKS_DETECTION_THRESHOLD * psd_lowest_max
ax.axhline(y=peaks_warning_threshold, color='black', linestyle='--', linewidth=0.5)
ax.fill_between(signal1.freqs, 0, peaks_warning_threshold, color='green', alpha=0.15, label='Relax Region')
# Trace and annotate the peaks on the graph
paired_peak_count = 0
unpaired_peak_count = 0
offsets_table_data = []
for _, (peak1, peak2) in enumerate(signal1.paired_peaks):
label = ALPHABET[paired_peak_count]
amplitude_offset = abs(
((signal2.psd[peak2[0]] - signal1.psd[peak1[0]]) / max(signal1.psd[peak1[0]], signal2.psd[peak2[0]])) * 100
)
frequency_offset = abs(signal2.freqs[peak2[0]] - signal1.freqs[peak1[0]])
offsets_table_data.append([f'Peaks {label}', f'{frequency_offset:.1f} Hz', f'{amplitude_offset:.1f} %'])
ax.plot(signal1.freqs[peak1[0]], signal1.psd[peak1[0]], 'x', color='black')
ax.plot(signal2.freqs[peak2[0]], signal2.psd[peak2[0]], 'x', color='black')
ax.plot(
[signal1.freqs[peak1[0]], signal2.freqs[peak2[0]]],
[signal1.psd[peak1[0]], signal2.psd[peak2[0]]],
':',
color='gray',
)
ax.annotate(
label + '1',
(signal1.freqs[peak1[0]], signal1.psd[peak1[0]]),
textcoords='offset points',
xytext=(8, 5),
ha='left',
fontsize=13,
color='black',
)
ax.annotate(
label + '2',
(signal2.freqs[peak2[0]], signal2.psd[peak2[0]]),
textcoords='offset points',
xytext=(8, 5),
ha='left',
fontsize=13,
color='black',
)
paired_peak_count += 1
for peak in signal1.unpaired_peaks:
ax.plot(signal1.freqs[peak], signal1.psd[peak], 'x', color='black')
ax.annotate(
str(unpaired_peak_count + 1),
(signal1.freqs[peak], signal1.psd[peak]),
textcoords='offset points',
xytext=(8, 5),
ha='left',
fontsize=13,
color='red',
weight='bold',
)
unpaired_peak_count += 1
for peak in signal2.unpaired_peaks:
ax.plot(signal2.freqs[peak], signal2.psd[peak], 'x', color='black')
ax.annotate(
str(unpaired_peak_count + 1),
(signal2.freqs[peak], signal2.psd[peak]),
textcoords='offset points',
xytext=(8, 5),
ha='left',
fontsize=13,
color='red',
weight='bold',
)
unpaired_peak_count += 1
# Add estimated similarity to the graph
ax2 = ax.twinx() # To split the legends in two box
ax2.yaxis.set_visible(False)
ax2.plot([], [], ' ', label=f'Estimated similarity: {similarity_factor:.1f}%')
ax2.plot([], [], ' ', label=f'Number of unpaired peaks: {unpaired_peak_count}')
# Setting axis parameters, grid and graph title
ax.set_xlabel('Frequency (Hz)')
ax.set_xlim([0, max_freq])
ax.set_ylabel('Power spectral density')
psd_highest_max = max(signal1.psd.max(), signal2.psd.max())
ax.set_ylim([0, psd_highest_max + psd_highest_max * 0.05])
ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
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')
fontP = matplotlib.font_manager.FontProperties()
fontP.set_size('small')
ax.set_title(
'Belts Frequency Profiles (estimated similarity: {:.1f}%)'.format(similarity_factor),
fontsize=14,
color=KLIPPAIN_COLORS['dark_orange'],
weight='bold',
)
# Print the table of offsets ontop of the graph below the original legend (upper right)
if len(offsets_table_data) > 0:
columns = [
'',
'Frequency delta',
'Amplitude delta',
]
offset_table = ax.table(
cellText=offsets_table_data,
colLabels=columns,
bbox=[0.66, 0.75, 0.33, 0.15],
loc='upper right',
cellLoc='center',
)
offset_table.auto_set_font_size(False)
offset_table.set_fontsize(8)
offset_table.auto_set_column_width([0, 1, 2])
offset_table.set_zorder(100)
cells = [key for key in offset_table.get_celld().keys()]
for cell in cells:
offset_table[cell].set_facecolor('white')
offset_table[cell].set_alpha(0.6)
ax.legend(loc='upper left', prop=fontP)
ax2.legend(loc='upper right', prop=fontP)
return
def plot_difference_spectrogram(ax, signal1, signal2, t, bins, combined_divergent, textual_mhi, max_freq):
ax.set_title('Differential Spectrogram', fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
ax.plot([], [], ' ', label=f'{textual_mhi} (experimental)')
# Draw the differential spectrogram with a specific custom norm to get orange or purple values where there is signal or white near zeros
# imgshow 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.
colors = [
KLIPPAIN_COLORS['dark_orange'],
KLIPPAIN_COLORS['orange'],
'white',
KLIPPAIN_COLORS['purple'],
KLIPPAIN_COLORS['dark_purple'],
]
cm = matplotlib.colors.LinearSegmentedColormap.from_list(
'klippain_divergent', list(zip([0, 0.25, 0.5, 0.75, 1], colors))
)
norm = matplotlib.colors.TwoSlopeNorm(vmin=np.min(combined_divergent), vcenter=0, vmax=np.max(combined_divergent))
ax.imshow(
combined_divergent.T,
cmap=cm,
norm=norm,
aspect='auto',
extent=[t[0], t[-1], bins[0], bins[-1]],
interpolation='bilinear',
origin='lower',
)
ax.set_xlabel('Frequency (hz)')
ax.set_xlim([0.0, max_freq])
ax.set_ylabel('Time (s)')
ax.set_ylim([0, bins[-1]])
fontP = matplotlib.font_manager.FontProperties()
fontP.set_size('medium')
ax.legend(loc='best', prop=fontP)
# Plot vertical lines for unpaired peaks
unpaired_peak_count = 0
for _, peak in enumerate(signal1.unpaired_peaks):
ax.axvline(signal1.freqs[peak], color=KLIPPAIN_COLORS['red_pink'], linestyle='dotted', linewidth=1.5)
ax.annotate(
f'Peak {unpaired_peak_count + 1}',
(signal1.freqs[peak], t[-1] * 0.05),
textcoords='data',
color=KLIPPAIN_COLORS['red_pink'],
rotation=90,
fontsize=10,
verticalalignment='bottom',
horizontalalignment='right',
)
unpaired_peak_count += 1
for _, peak in enumerate(signal2.unpaired_peaks):
ax.axvline(signal2.freqs[peak], color=KLIPPAIN_COLORS['red_pink'], linestyle='dotted', linewidth=1.5)
ax.annotate(
f'Peak {unpaired_peak_count + 1}',
(signal2.freqs[peak], t[-1] * 0.05),
textcoords='data',
color=KLIPPAIN_COLORS['red_pink'],
rotation=90,
fontsize=10,
verticalalignment='bottom',
horizontalalignment='right',
)
unpaired_peak_count += 1
# Plot vertical lines and zones for paired peaks
for idx, (peak1, peak2) in enumerate(signal1.paired_peaks):
label = ALPHABET[idx]
x_min = min(peak1[1], peak2[1])
x_max = max(peak1[1], peak2[1])
ax.axvline(x_min, color=KLIPPAIN_COLORS['dark_purple'], linestyle='dotted', linewidth=1.5)
ax.axvline(x_max, color=KLIPPAIN_COLORS['dark_purple'], linestyle='dotted', linewidth=1.5)
ax.fill_between([x_min, x_max], 0, np.max(combined_divergent), color=KLIPPAIN_COLORS['dark_purple'], alpha=0.3)
ax.annotate(
f'Peaks {label}',
(x_min, t[-1] * 0.05),
textcoords='data',
color=KLIPPAIN_COLORS['dark_purple'],
rotation=90,
fontsize=10,
verticalalignment='bottom',
horizontalalignment='right',
)
return
######################################################################
# Custom tools
######################################################################
# Original Klipper function to get the PSD data of a raw accelerometer signal
def compute_signal_data(data, max_freq):
helper = shaper_calibrate.ShaperCalibrate(printer=None)
calibration_data = helper.process_accelerometer_data(data)
freqs = calibration_data.freq_bins[calibration_data.freq_bins <= max_freq]
psd = calibration_data.get_psd('all')[calibration_data.freq_bins <= max_freq]
_, peaks, _ = detect_peaks(psd, freqs, PEAKS_DETECTION_THRESHOLD * psd.max())
return SignalData(freqs=freqs, psd=psd, peaks=peaks, paired_peaks=None, unpaired_peaks=None)
######################################################################
# Startup and main routines
######################################################################
def belts_calibration(lognames, klipperdir='~/klipper', max_freq=200.0, st_version=None):
global shaper_calibrate
shaper_calibrate = setup_klipper_import(klipperdir)
# Parse data from the log files while ignoring CSV in the wrong format
datas = [data for data in (parse_log(fn) for fn in lognames) if data is not None]
if len(datas) > 2:
raise ValueError('Incorrect number of .csv files used (this function needs exactly two files to compare them)!')
# Compute calibration data for the two datasets with automatic peaks detection
signal1 = compute_signal_data(datas[0], max_freq)
signal2 = compute_signal_data(datas[1], max_freq)
combined_sum, combined_divergent, bins, t = compute_combined_spectrogram(datas[0], datas[1])
del datas
# Pair the peaks across the two datasets
paired_peaks, unpaired_peaks1, unpaired_peaks2 = pair_peaks(
signal1.peaks, signal1.freqs, signal1.psd, signal2.peaks, signal2.freqs, signal2.psd
)
signal1 = signal1._replace(paired_peaks=paired_peaks, unpaired_peaks=unpaired_peaks1)
signal2 = signal2._replace(paired_peaks=paired_peaks, unpaired_peaks=unpaired_peaks2)
# Compute the similarity (using cross-correlation of the PSD signals)
similarity_factor = compute_curve_similarity_factor(
signal1.freqs, signal1.psd, signal2.freqs, signal2.psd, CURVE_SIMILARITY_SIGMOID_K
)
ConsoleOutput.print(f'Belts estimated similarity: {similarity_factor:.1f}%')
# Compute the MHI value from the differential spectrogram sum of gradient, salted with the similarity factor and the number of
# unpaired peaks from the belts frequency profile. Be careful, this value is highly opinionated and is pretty experimental!
mhi, textual_mhi = compute_mhi(
combined_sum, similarity_factor, len(signal1.unpaired_peaks) + len(signal2.unpaired_peaks)
)
ConsoleOutput.print(f'[experimental] Mechanical Health Indicator: {textual_mhi.lower()} ({mhi:.1f}%)')
# 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 title
title_line1 = 'RELATIVE BELTS CALIBRATION TOOL'
fig.text(
0.12, 0.965, title_line1, ha='left', va='bottom', fontsize=20, color=KLIPPAIN_COLORS['purple'], weight='bold'
)
try:
filename = lognames[0].split('/')[-1]
dt = datetime.strptime(f"{filename.split('_')[1]} {filename.split('_')[2]}", '%Y%m%d %H%M%S')
title_line2 = dt.strftime('%x %X')
except Exception:
ConsoleOutput.print(f'Warning: CSV filenames look to be different than expected: {lognames}')
title_line2 = lognames[0].split('/')[-1] + ' / ' + lognames[1].split('/')[-1]
fig.text(0.12, 0.957, title_line2, ha='left', va='top', fontsize=16, color=KLIPPAIN_COLORS['dark_purple'])
# Plot the graphs
plot_compare_frequency(ax1, lognames, signal1, signal2, similarity_factor, max_freq)
plot_difference_spectrogram(ax2, signal1, signal2, t, bins, combined_divergent, textual_mhi, 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] <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=200.0, help='maximum frequency to graph')
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)')
fig = belts_calibration(args, options.klipperdir, options.max_freq)
fig.savefig(options.output, dpi=150)
if __name__ == '__main__':
main()

273
shaketune/graph_creators/graph_creator.py
View File

@@ -1,273 +0,0 @@
#!/usr/bin/env python3
import abc
import shutil
import tarfile
from datetime import datetime
from pathlib import Path
from typing import Callable, Optional
from matplotlib.figure import Figure
from ..helpers import filemanager as fm
from ..helpers.console_output import ConsoleOutput
from ..helpers.motorlogparser import MotorLogParser
from ..shaketune_config import ShakeTuneConfig
from .analyze_axesmap import axesmap_calibration
from .graph_belts import belts_calibration
from .graph_shaper import shaper_calibration
from .graph_vibrations import vibrations_profile
class GraphCreator(abc.ABC):
def __init__(self, config: ShakeTuneConfig):
self._config = config
self._graph_date = datetime.now().strftime('%Y%m%d_%H%M%S')
self._version = ShakeTuneConfig.get_git_version()
self._type = None
self._folder = None
def _setup_folder(self, graph_type: str) -> None:
self._type = graph_type
self._folder = self._config.get_results_folder(graph_type)
def _move_and_prepare_files(
self,
glob_pattern: str,
min_files_required: Optional[int] = None,
custom_name_func: Optional[Callable[[Path], str]] = None,
) -> list[Path]:
tmp_path = Path('/tmp')
globbed_files = list(tmp_path.glob(glob_pattern))
# If min_files_required is not set, use the number of globbed files as the minimum
min_files_required = min_files_required or len(globbed_files)
if not globbed_files:
raise FileNotFoundError(f'no CSV files found in the /tmp folder to create the {self._type} graphs!')
if len(globbed_files) < min_files_required:
raise FileNotFoundError(f'{min_files_required} CSV files are needed to create the {self._type} graphs!')
lognames = []
for filename in sorted(globbed_files, key=lambda f: f.stat().st_mtime, reverse=True)[:min_files_required]:
fm.wait_file_ready(filename)
custom_name = custom_name_func(filename) if custom_name_func else filename.name
new_file = self._folder / f'{self._type}_{self._graph_date}_{custom_name}.csv'
# shutil.move() is needed to move the file across filesystems (mainly for BTT CB1 Pi default OS image)
shutil.move(filename, new_file)
lognames.append(new_file)
return lognames
def _save_figure_and_cleanup(self, fig: Figure, lognames: list[Path], axis_label: Optional[str] = None) -> None:
axis_suffix = f'_{axis_label}' if axis_label else ''
png_filename = self._folder / f'{self._type}_{self._graph_date}{axis_suffix}.png'
fig.savefig(png_filename, dpi=self._config.dpi)
if self._config.keep_csv:
self._archive_files(lognames)
else:
self._remove_files(lognames)
def _archive_files(self, _: list[Path]) -> None:
return
def _remove_files(self, lognames: list[Path]) -> None:
for csv in lognames:
csv.unlink(missing_ok=True)
def get_type(self) -> str:
return self._type
@abc.abstractmethod
def create_graph(self) -> None:
pass
@abc.abstractmethod
def clean_old_files(self, keep_results: int) -> None:
pass
class BeltsGraphCreator(GraphCreator):
def __init__(self, config: ShakeTuneConfig):
super().__init__(config)
self._setup_folder('belts')
def create_graph(self) -> None:
lognames = self._move_and_prepare_files(
glob_pattern='shaketune-belt_*.csv',
min_files_required=2,
custom_name_func=lambda f: f.stem.split('_')[1].upper(),
)
fig = belts_calibration(
lognames=[str(path) for path in lognames],
klipperdir=str(self._config.klipper_folder),
st_version=self._version,
)
self._save_figure_and_cleanup(fig, lognames)
def clean_old_files(self, keep_results: int = 3) -> None:
# Get all PNG files in the directory as a list of Path objects
files = sorted(self._folder.glob('*.png'), key=lambda f: f.stat().st_mtime, reverse=True)
if len(files) <= keep_results:
return # No need to delete any files
# Delete the older files
for old_file in files[keep_results:]:
file_date = '_'.join(old_file.stem.split('_')[1:3])
for suffix in ['A', 'B']:
csv_file = self._folder / f'belts_{file_date}_{suffix}.csv'
csv_file.unlink(missing_ok=True)
old_file.unlink()
class ShaperGraphCreator(GraphCreator):
def __init__(self, config: ShakeTuneConfig):
super().__init__(config)
self._max_smoothing = None
self._scv = None
self._setup_folder('shaper')
def configure(self, scv: float, max_smoothing: float = None) -> None:
self._scv = scv
self._max_smoothing = max_smoothing
def create_graph(self) -> None:
if not self._scv:
raise ValueError('scv must be set to create the input shaper graph!')
lognames = self._move_and_prepare_files(
glob_pattern='raw_data*.csv',
min_files_required=1,
custom_name_func=lambda f: f.stem.split('_')[3].upper(),
)
fig = shaper_calibration(
lognames=[str(path) for path in lognames],
klipperdir=str(self._config.klipper_folder),
max_smoothing=self._max_smoothing,
scv=self._scv,
st_version=self._version,
)
self._save_figure_and_cleanup(fig, lognames, lognames[0].stem.split('_')[-1])
def clean_old_files(self, keep_results: int = 3) -> None:
# Get all PNG files in the directory as a list of Path objects
files = sorted(self._folder.glob('*.png'), key=lambda f: f.stat().st_mtime, reverse=True)
if len(files) <= 2 * keep_results:
return # No need to delete any files
# Delete the older files
for old_file in files[2 * keep_results :]:
csv_file = old_file.with_suffix('.csv')
csv_file.unlink(missing_ok=True)
old_file.unlink()
class VibrationsGraphCreator(GraphCreator):
def __init__(self, config: ShakeTuneConfig):
super().__init__(config)
self._kinematics = None
self._accel = None
self._chip_name = None
self._motors = None
self._setup_folder('vibrations')
def configure(self, kinematics: str, accel: float, chip_name: str, metadata: str) -> None:
self._kinematics = kinematics
self._accel = accel
self._chip_name = chip_name
parser = MotorLogParser(self._config.klipper_log_folder / 'klippy.log', metadata)
self._motors = parser.get_motors()
def _archive_files(self, lognames: list[Path]) -> None:
tar_path = self._folder / f'{self._type}_{self._graph_date}.tar.gz'
with tarfile.open(tar_path, 'w:gz') as tar:
for csv_file in lognames:
tar.add(csv_file, arcname=csv_file.name, recursive=False)
def create_graph(self) -> None:
if not self._accel or not self._chip_name or not self._kinematics:
raise ValueError('accel, chip_name and kinematics must be set to create the vibrations profile graph!')
lognames = self._move_and_prepare_files(
glob_pattern=f'{self._chip_name}-*.csv',
min_files_required=None,
custom_name_func=lambda f: f.name.replace(self._chip_name, self._type),
)
fig = vibrations_profile(
lognames=[str(path) for path in lognames],
klipperdir=str(self._config.klipper_folder),
kinematics=self._kinematics,
accel=self._accel,
st_version=self._version,
motors=self._motors,
)
self._save_figure_and_cleanup(fig, lognames)
def clean_old_files(self, keep_results: int = 3) -> None:
# Get all PNG files in the directory as a list of Path objects
files = sorted(self._folder.glob('*.png'), key=lambda f: f.stat().st_mtime, reverse=True)
if len(files) <= keep_results:
return # No need to delete any files
# Delete the older files
for old_file in files[keep_results:]:
old_file.unlink()
tar_file = old_file.with_suffix('.tar.gz')
tar_file.unlink(missing_ok=True)
class AxesMapFinder(GraphCreator):
def __init__(self, config: ShakeTuneConfig):
super().__init__(config)
self._graph_date = datetime.now().strftime('%Y%m%d_%H%M%S')
self._type = 'axesmap'
self._folder = config.get_results_folder()
self._accel = None
self._chip_name = None
def configure(self, accel: int, chip_name: str) -> None:
self._accel = accel
self._chip_name = chip_name
def find_axesmap(self) -> None:
tmp_folder = Path('/tmp')
globbed_files = list(tmp_folder.glob('shaketune-axemap_*.csv'))
if not globbed_files:
raise FileNotFoundError('no CSV files found in the /tmp folder to find the axes map!')
# Find the CSV files with the latest timestamp and process it
logname = sorted(globbed_files, key=lambda f: f.stat().st_mtime, reverse=True)[0]
results = axesmap_calibration(
lognames=[str(logname)],
accel=self._accel,
)
ConsoleOutput.print(results)
result_filename = self._folder / f'{self._type}_{self._graph_date}.txt'
with result_filename.open('w') as f:
f.write(results)
# While the AxesMapFinder doesn't directly create a graph, we need to implement this
# method to allow using it seemlessly like all the other GraphCreator objects
def create_graph(self) -> None:
self.find_axesmap()
def clean_old_files(self, keep_results: int) -> None:
tmp_folder = Path('/tmp')
globbed_files = list(tmp_folder.glob('shaketune-axemap_*.csv'))
for csv_file in globbed_files:
csv_file.unlink()

420
shaketune/graph_creators/graph_shaper.py
View File

@@ -1,420 +0,0 @@
#!/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.console_output import ConsoleOutput
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=ConsoleOutput.print,
)
except TypeError:
ConsoleOutput.print(
'[WARNING] You seem to be using an older version of Klipper that is not compatible with all the latest Shake&Tune features!'
)
ConsoleOutput.print(
'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, ConsoleOutput.print)
ConsoleOutput.print(
'\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):
global shaper_calibrate
shaper_calibrate = setup_klipper_import(klipperdir)
# Parse data from the log files while ignoring CSV in the wrong format
datas = [data for data in (parse_log(fn) for fn in lognames) if data is not None]
if len(datas) > 1:
ConsoleOutput.print('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])
ConsoleOutput.print(
'\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:
ConsoleOutput.print('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()

841
shaketune/graph_creators/graph_vibrations.py
View File

@@ -1,841 +0,0 @@
#!/usr/bin/env python3
##################################################
#### DIRECTIONAL VIBRATIONS PLOTTING SCRIPT ######
##################################################
# Written by Frix_x#0161 #
import math
import optparse
import os
import re
from collections import defaultdict
from datetime import datetime
import matplotlib
import matplotlib.font_manager
import matplotlib.gridspec
import matplotlib.pyplot as plt
import matplotlib.ticker
import numpy as np
matplotlib.use('Agg')
from ..helpers.common_func import (
compute_mechanical_parameters,
detect_peaks,
identify_low_energy_zones,
parse_log,
setup_klipper_import,
)
from ..helpers.console_output import ConsoleOutput
PEAKS_DETECTION_THRESHOLD = 0.05
PEAKS_RELATIVE_HEIGHT_THRESHOLD = 0.04
CURVE_SIMILARITY_SIGMOID_K = 0.5
SPEEDS_VALLEY_DETECTION_THRESHOLD = 0.7 # Lower is more sensitive
SPEEDS_AROUND_PEAK_DELETION = 3 # to delete +-3mm/s around a peak
ANGLES_VALLEY_DETECTION_THRESHOLD = 1.1 # Lower is more sensitive
KLIPPAIN_COLORS = {
'purple': '#70088C',
'orange': '#FF8D32',
'dark_purple': '#150140',
'dark_orange': '#F24130',
'red_pink': '#F2055C',
}
######################################################################
# Computation
######################################################################
# Call to the official Klipper input shaper object to do the PSD computation
def calc_freq_response(data):
helper = shaper_calibrate.ShaperCalibrate(printer=None)
return helper.process_accelerometer_data(data)
# Calculate motor frequency profiles based on the measured Power Spectral Density (PSD) measurements for the machine kinematics
# main angles and then create a global motor profile as a weighted average (from their own vibrations) of all calculated profiles
def compute_motor_profiles(freqs, psds, all_angles_energy, measured_angles=None, energy_amplification_factor=2):
if measured_angles is None:
measured_angles = [0, 90]
motor_profiles = {}
weighted_sum_profiles = np.zeros_like(freqs)
total_weight = 0
conv_filter = np.ones(20) / 20
# Creating the PSD motor profiles for each angles
for angle in measured_angles:
# Calculate the sum of PSDs for the current angle and then convolve
sum_curve = np.sum(np.array([psds[angle][speed] for speed in psds[angle]]), axis=0)
motor_profiles[angle] = np.convolve(sum_curve / len(psds[angle]), conv_filter, mode='same')
# Calculate weights
angle_energy = (
all_angles_energy[angle] ** energy_amplification_factor
) # First weighting factor is based on the total vibrations of the machine at the specified angle
curve_area = (
np.trapz(motor_profiles[angle], freqs) ** energy_amplification_factor
) # Additional weighting factor is based on the area under the current motor profile at this specified angle
total_angle_weight = angle_energy * curve_area
# Update weighted sum profiles to get the global motor profile
weighted_sum_profiles += motor_profiles[angle] * total_angle_weight
total_weight += total_angle_weight
# Creating a global average motor profile that is the weighted average of all the PSD motor profiles
global_motor_profile = weighted_sum_profiles / total_weight if total_weight != 0 else weighted_sum_profiles
return motor_profiles, global_motor_profile
# Since it was discovered that there is no non-linear mixing in the stepper "steps" vibrations, instead of measuring
# the effects of each speeds at each angles, this function simplify it by using only the main motors axes (X/Y for Cartesian
# printers and A/B for CoreXY) measurements and project each points on the [0,360] degrees range using trigonometry
# to "sum" the vibration impact of each axis at every points of the generated spectrogram. The result is very similar at the end.
def compute_dir_speed_spectrogram(measured_speeds, data, kinematics='cartesian', measured_angles=None):
if measured_angles is None:
measured_angles = [0, 90]
# We want to project the motor vibrations measured on their own axes on the [0, 360] range
spectrum_angles = np.linspace(0, 360, 720) # One point every 0.5 degrees
spectrum_speeds = np.linspace(min(measured_speeds), max(measured_speeds), len(measured_speeds) * 6)
spectrum_vibrations = np.zeros((len(spectrum_angles), len(spectrum_speeds)))
def get_interpolated_vibrations(data, speed, speeds):
idx = np.clip(np.searchsorted(speeds, speed, side='left'), 1, len(speeds) - 1)
lower_speed = speeds[idx - 1]
upper_speed = speeds[idx]
lower_vibrations = data.get(lower_speed, 0)
upper_vibrations = data.get(upper_speed, 0)
return lower_vibrations + (speed - lower_speed) * (upper_vibrations - lower_vibrations) / (
upper_speed - lower_speed
)
# Precompute trigonometric values and constant before the loop
angle_radians = np.deg2rad(spectrum_angles)
cos_vals = np.cos(angle_radians)
sin_vals = np.sin(angle_radians)
sqrt_2_inv = 1 / math.sqrt(2)
# Compute the spectrum vibrations for each angle and speed combination
for target_angle_idx, (cos_val, sin_val) in enumerate(zip(cos_vals, sin_vals)):
for target_speed_idx, target_speed in enumerate(spectrum_speeds):
if kinematics == 'cartesian':
speed_1 = np.abs(target_speed * cos_val)
speed_2 = np.abs(target_speed * sin_val)
elif kinematics == 'corexy':
speed_1 = np.abs(target_speed * (cos_val + sin_val) * sqrt_2_inv)
speed_2 = np.abs(target_speed * (cos_val - sin_val) * sqrt_2_inv)
vibrations_1 = get_interpolated_vibrations(data[measured_angles[0]], speed_1, measured_speeds)
vibrations_2 = get_interpolated_vibrations(data[measured_angles[1]], speed_2, measured_speeds)
spectrum_vibrations[target_angle_idx, target_speed_idx] = vibrations_1 + vibrations_2
return spectrum_angles, spectrum_speeds, spectrum_vibrations
def compute_angle_powers(spectrogram_data):
angles_powers = np.trapz(spectrogram_data, axis=1)
# Since we want to plot it on a continuous polar plot later on, we need to append parts of
# the array to start and end of it to smooth transitions when doing the convolution
# and get the same value at modulo 360. Then we return the array without the extras
extended_angles_powers = np.concatenate([angles_powers[-9:], angles_powers, angles_powers[:9]])
convolved_extended = np.convolve(extended_angles_powers, np.ones(15) / 15, mode='same')
return convolved_extended[9:-9]
def compute_speed_powers(spectrogram_data, smoothing_window=15):
min_values = np.amin(spectrogram_data, axis=0)
max_values = np.amax(spectrogram_data, axis=0)
var_values = np.var(spectrogram_data, axis=0)
# rescale the variance to the same range as max_values to plot it on the same graph
var_values = var_values / var_values.max() * max_values.max()
# Create a vibration metric that is the product of the max values and the variance to quantify the best
# speeds that have at the same time a low global energy level that is also consistent at every angles
vibration_metric = max_values * var_values
# utility function to pad and smooth the data avoiding edge effects
conv_filter = np.ones(smoothing_window) / smoothing_window
window = int(smoothing_window / 2)
def pad_and_smooth(data):
data_padded = np.pad(data, (window,), mode='edge')
smoothed_data = np.convolve(data_padded, conv_filter, mode='valid')
return smoothed_data
# Stack the arrays and apply padding and smoothing in batch
data_arrays = np.stack([min_values, max_values, var_values, vibration_metric])
smoothed_arrays = np.array([pad_and_smooth(data) for data in data_arrays])
return smoothed_arrays
# Function that filter and split the good_speed ranges. The goal is to remove some zones around
# additional detected small peaks in order to suppress them if there is a peak, even if it's low,
# that's probably due to a crossing in the motor resonance pattern that still need to be removed
def filter_and_split_ranges(all_speeds, good_speeds, peak_speed_indices, deletion_range):
# Process each range to filter out and split based on peak indices
filtered_good_speeds = []
for start, end, energy in good_speeds:
start_speed, end_speed = all_speeds[start], all_speeds[end]
# Identify peaks that intersect with the current speed range
intersecting_peaks_indices = [
idx for speed, idx in peak_speed_indices.items() if start_speed <= speed <= end_speed
]
if not intersecting_peaks_indices:
filtered_good_speeds.append((start, end, energy))
else:
intersecting_peaks_indices.sort()
current_start = start
for peak_index in intersecting_peaks_indices:
before_peak_end = max(current_start, peak_index - deletion_range)
if current_start < before_peak_end:
filtered_good_speeds.append((current_start, before_peak_end, energy))
current_start = peak_index + deletion_range + 1
if current_start < end:
filtered_good_speeds.append((current_start, end, energy))
# Sorting by start point once and then merge overlapping ranges
sorted_ranges = sorted(filtered_good_speeds, key=lambda x: x[0])
merged_ranges = [sorted_ranges[0]]
for current in sorted_ranges[1:]:
last_merged_start, last_merged_end, last_merged_energy = merged_ranges[-1]
if current[0] <= last_merged_end:
new_end = max(last_merged_end, current[1])
new_energy = min(last_merged_energy, current[2])
merged_ranges[-1] = (last_merged_start, new_end, new_energy)
else:
merged_ranges.append(current)
return merged_ranges
# This function allow the computation of a symmetry score that reflect the spectrogram apparent symmetry between
# measured axes on both the shape of the signal and the energy level consistency across both side of the signal
def compute_symmetry_analysis(all_angles, spectrogram_data, measured_angles=None):
if measured_angles is None:
measured_angles = [0, 90]
total_spectrogram_angles = len(all_angles)
half_spectrogram_angles = total_spectrogram_angles // 2
# Extend the spectrogram by adding half to the beginning (in order to not get an out of bounds error later)
extended_spectrogram = np.concatenate((spectrogram_data[-half_spectrogram_angles:], spectrogram_data), axis=0)
# Calculate the split index directly within the slicing
midpoint_angle = np.mean(measured_angles)
split_index = int(midpoint_angle * (total_spectrogram_angles / 360) + half_spectrogram_angles)
half_segment_length = half_spectrogram_angles // 2
# Slice out the two segments of the spectrogram and flatten them for comparison
segment_1_flattened = extended_spectrogram[split_index - half_segment_length : split_index].flatten()
segment_2_flattened = extended_spectrogram[split_index : split_index + half_segment_length].flatten()
# Compute the correlation coefficient between the two segments of spectrogram
correlation = np.corrcoef(segment_1_flattened, segment_2_flattened)[0, 1]
percentage_correlation_biased = (100 * np.power(correlation, 0.75)) + 10
return np.clip(0, 100, percentage_correlation_biased)
######################################################################
# Graphing
######################################################################
def plot_angle_profile_polar(ax, angles, angles_powers, low_energy_zones, symmetry_factor):
angles_radians = np.deg2rad(angles)
ax.set_title('Polar angle energy profile', fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
ax.set_theta_zero_location('E')
ax.set_theta_direction(1)
ax.plot(angles_radians, angles_powers, color=KLIPPAIN_COLORS['purple'], zorder=5)
ax.fill(angles_radians, angles_powers, color=KLIPPAIN_COLORS['purple'], alpha=0.3)
ax.set_xlim([0, np.deg2rad(360)])
ymax = angles_powers.max() * 1.05
ax.set_ylim([0, ymax])
ax.set_thetagrids([theta * 15 for theta in range(360 // 15)])
ax.text(
0,
0,
f'Symmetry: {symmetry_factor:.1f}%',
ha='center',
va='center',
color=KLIPPAIN_COLORS['red_pink'],
fontsize=12,
fontweight='bold',
zorder=6,
)
for _, (start, end, _) in enumerate(low_energy_zones):
ax.axvline(
angles_radians[start],
angles_powers[start] / ymax,
color=KLIPPAIN_COLORS['red_pink'],
linestyle='dotted',
linewidth=1.5,
)
ax.axvline(
angles_radians[end],
angles_powers[end] / ymax,
color=KLIPPAIN_COLORS['red_pink'],
linestyle='dotted',
linewidth=1.5,
)
ax.fill_between(
angles_radians[start:end], angles_powers[start:end], angles_powers.max() * 1.05, color='green', alpha=0.2
)
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')
# Polar plot doesn't follow the gridspec margin, so we adjust it manually here
pos = ax.get_position()
new_pos = [pos.x0 - 0.01, pos.y0 - 0.01, pos.width, pos.height]
ax.set_position(new_pos)
return
def plot_global_speed_profile(
ax,
all_speeds,
sp_min_energy,
sp_max_energy,
sp_variance_energy,
vibration_metric,
num_peaks,
peaks,
low_energy_zones,
):
ax.set_title('Global speed energy profile', fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
ax.set_xlabel('Speed (mm/s)')
ax.set_ylabel('Energy')
ax2 = ax.twinx()
ax2.yaxis.set_visible(False)
ax.plot(all_speeds, sp_min_energy, label='Minimum', color=KLIPPAIN_COLORS['dark_purple'], zorder=5)
ax.plot(all_speeds, sp_max_energy, label='Maximum', color=KLIPPAIN_COLORS['purple'], zorder=5)
ax.plot(all_speeds, sp_variance_energy, label='Variance', color=KLIPPAIN_COLORS['orange'], zorder=5, linestyle='--')
ax2.plot(
all_speeds,
vibration_metric,
label=f'Vibration metric ({num_peaks} bad peaks)',
color=KLIPPAIN_COLORS['red_pink'],
zorder=5,
)
ax.set_xlim([all_speeds.min(), all_speeds.max()])
ax.set_ylim([0, sp_max_energy.max() * 1.15])
y2min = -(vibration_metric.max() * 0.025)
y2max = vibration_metric.max() * 1.07
ax2.set_ylim([y2min, y2max])
if peaks is not None and len(peaks) > 0:
ax2.plot(all_speeds[peaks], vibration_metric[peaks], 'x', color='black', markersize=8, zorder=10)
for idx, peak in enumerate(peaks):
ax2.annotate(
f'{idx+1}',
(all_speeds[peak], vibration_metric[peak]),
textcoords='offset points',
xytext=(5, 5),
fontweight='bold',
ha='left',
fontsize=13,
color=KLIPPAIN_COLORS['red_pink'],
zorder=10,
)
for idx, (start, end, _) in enumerate(low_energy_zones):
# ax2.axvline(all_speeds[start], color=KLIPPAIN_COLORS['red_pink'], linestyle='dotted', linewidth=1.5, zorder=8)
# ax2.axvline(all_speeds[end], color=KLIPPAIN_COLORS['red_pink'], linestyle='dotted', linewidth=1.5, zorder=8)
ax2.fill_between(
all_speeds[start:end],
y2min,
vibration_metric[start:end],
color='green',
alpha=0.2,
label=f'Zone {idx+1}: {all_speeds[start]:.1f} to {all_speeds[end]:.1f} mm/s',
)
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('small')
ax.legend(loc='upper left', prop=fontP)
ax2.legend(loc='upper right', prop=fontP)
return
def plot_angular_speed_profiles(ax, speeds, angles, spectrogram_data, kinematics='cartesian'):
ax.set_title('Angular speed energy profiles', fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
ax.set_xlabel('Speed (mm/s)')
ax.set_ylabel('Energy')
# Define mappings for labels and colors to simplify plotting commands
angle_settings = {
0: ('X (0 deg)', 'purple', 10),
90: ('Y (90 deg)', 'dark_purple', 5),
45: ('A (45 deg)' if kinematics == 'corexy' else '45 deg', 'orange', 10),
135: ('B (135 deg)' if kinematics == 'corexy' else '135 deg', 'dark_orange', 5),
}
# Plot each angle using settings from the dictionary
for angle, (label, color, zorder) in angle_settings.items():
idx = np.searchsorted(angles, angle, side='left')
ax.plot(speeds, spectrogram_data[idx], label=label, color=KLIPPAIN_COLORS[color], zorder=zorder)
ax.set_xlim([speeds.min(), speeds.max()])
max_value = max(spectrogram_data[angle].max() for angle in [0, 45, 90, 135])
ax.set_ylim([0, max_value * 1.1])
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('small')
ax.legend(loc='upper right', prop=fontP)
return
def plot_motor_profiles(ax, freqs, main_angles, motor_profiles, global_motor_profile, max_freq):
ax.set_title('Motor frequency profile', fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
ax.set_ylabel('Energy')
ax.set_xlabel('Frequency (Hz)')
ax2 = ax.twinx()
ax2.yaxis.set_visible(False)
# Global weighted average motor profile
ax.plot(freqs, global_motor_profile, label='Combined', color=KLIPPAIN_COLORS['purple'], zorder=5)
max_value = global_motor_profile.max()
# Mapping of angles to axis names
angle_settings = {0: 'X', 90: 'Y', 45: 'A', 135: 'B'}
# And then plot the motor profiles at each measured angles
for angle in main_angles:
profile_max = motor_profiles[angle].max()
if profile_max > max_value:
max_value = profile_max
label = f'{angle_settings[angle]} ({angle} deg)' if angle in angle_settings else f'{angle} deg'
ax.plot(freqs, motor_profiles[angle], linestyle='--', label=label, zorder=2)
ax.set_xlim([0, max_freq])
ax.set_ylim([0, max_value * 1.1])
ax.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))
# Then add the motor resonance peak to the graph and print some infos about it
motor_fr, motor_zeta, motor_res_idx, lowfreq_max = compute_mechanical_parameters(global_motor_profile, freqs, 30)
if lowfreq_max:
ConsoleOutput.print(
'[WARNING] There are a lot of low frequency vibrations that can alter the readings. This is probably due to the test being performed at too high an acceleration!'
)
ConsoleOutput.print(
'Try lowering the ACCEL value and/or increasing the SIZE value before restarting the macro to ensure that only constant speeds are being recorded and that the dynamic behavior of the machine is not affecting the measurements'
)
if motor_zeta is not None:
ConsoleOutput.print(
'Motors have a main resonant frequency at %.1fHz with an estimated damping ratio of %.3f'
% (motor_fr, motor_zeta)
)
else:
ConsoleOutput.print(
'Motors have a main resonant frequency at %.1fHz but it was impossible to estimate a damping ratio.'
% (motor_fr)
)
ax.plot(freqs[motor_res_idx], global_motor_profile[motor_res_idx], 'x', color='black', markersize=10)
ax.annotate(
'R',
(freqs[motor_res_idx], global_motor_profile[motor_res_idx]),
textcoords='offset points',
xytext=(15, 5),
ha='right',
fontsize=14,
color=KLIPPAIN_COLORS['red_pink'],
weight='bold',
)
ax2.plot([], [], ' ', label='Motor resonant frequency (ω0): %.1fHz' % (motor_fr))
if motor_zeta is not None:
ax2.plot([], [], ' ', label='Motor damping ratio (ζ): %.3f' % (motor_zeta))
else:
ax2.plot([], [], ' ', label='No damping ratio computed')
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('small')
ax.legend(loc='upper left', prop=fontP)
ax2.legend(loc='upper right', prop=fontP)
return
def plot_vibration_spectrogram_polar(ax, angles, speeds, spectrogram_data):
angles_radians = np.radians(angles)
# Assuming speeds defines the radial distance from the center, we need to create a meshgrid
# for both angles and speeds to map the spectrogram data onto a polar plot correctly
radius, theta = np.meshgrid(speeds, angles_radians)
ax.set_title(
'Polar vibrations heatmap', fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold', va='bottom'
)
ax.set_theta_zero_location('E')
ax.set_theta_direction(1)
ax.pcolormesh(theta, radius, spectrogram_data, norm=matplotlib.colors.LogNorm(), cmap='inferno', shading='auto')
ax.set_thetagrids([theta * 15 for theta in range(360 // 15)])
ax.tick_params(axis='y', which='both', colors='white', labelsize='medium')
ax.set_ylim([0, max(speeds)])
# Polar plot doesn't follow the gridspec margin, so we adjust it manually here
pos = ax.get_position()
new_pos = [pos.x0 - 0.01, pos.y0 - 0.01, pos.width, pos.height]
ax.set_position(new_pos)
return
def plot_vibration_spectrogram(ax, angles, speeds, spectrogram_data, peaks):
ax.set_title('Vibrations heatmap', fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
ax.set_xlabel('Speed (mm/s)')
ax.set_ylabel('Angle (deg)')
ax.imshow(
spectrogram_data,
norm=matplotlib.colors.LogNorm(),
cmap='inferno',
aspect='auto',
extent=[speeds[0], speeds[-1], angles[0], angles[-1]],
origin='lower',
interpolation='antialiased',
)
# Add peaks lines in the spectrogram to get hint from peaks found in the first graph
if peaks is not None and len(peaks) > 0:
for idx, peak in enumerate(peaks):
ax.axvline(speeds[peak], color='cyan', linewidth=0.75)
ax.annotate(
f'Peak {idx+1}',
(speeds[peak], angles[-1] * 0.9),
textcoords='data',
color='cyan',
rotation=90,
fontsize=10,
verticalalignment='top',
horizontalalignment='right',
)
return
def plot_motor_config_txt(fig, motors, differences):
motor_details = [(motors[0], 'X motor'), (motors[1], 'Y motor')]
distance = 0.12
if motors[0].get_property('autotune_enabled'):
distance = 0.24
config_blocks = [
f"| {lbl}: {mot.get_property('motor').upper()} on {mot.get_property('tmc').upper()} @ {mot.get_property('voltage')}V {mot.get_property('run_current')}A"
for mot, lbl in motor_details
]
config_blocks.append('| TMC Autotune enabled')
else:
config_blocks = [
f"| {lbl}: {mot.get_property('tmc').upper()} @ {mot.get_property('run_current')}A"
for mot, lbl in motor_details
]
config_blocks.append('| TMC Autotune not detected')
for idx, block in enumerate(config_blocks):
fig.text(
0.40, 0.990 - 0.015 * idx, block, ha='left', va='top', fontsize=10, color=KLIPPAIN_COLORS['dark_purple']
)
tmc_registers = motors[0].get_registers()
idx = -1
for idx, (register, settings) in enumerate(tmc_registers.items()):
settings_str = ' '.join(f'{k}={v}' for k, v in settings.items())
tmc_block = f'| {register.upper()}: {settings_str}'
fig.text(
0.40 + distance,
0.990 - 0.015 * idx,
tmc_block,
ha='left',
va='top',
fontsize=10,
color=KLIPPAIN_COLORS['dark_purple'],
)
if differences is not None:
differences_text = f'| Y motor diff: {differences}'
fig.text(
0.40 + distance,
0.990 - 0.015 * (idx + 1),
differences_text,
ha='left',
va='top',
fontsize=10,
color=KLIPPAIN_COLORS['dark_purple'],
)
######################################################################
# Startup and main routines
######################################################################
def extract_angle_and_speed(logname):
try:
match = re.search(r'an(\d+)_\d+sp(\d+)_\d+', os.path.basename(logname))
if match:
angle = match.group(1)
speed = match.group(2)
else:
raise ValueError(f'File {logname} does not match expected format. Clean your /tmp folder and start again!')
except AttributeError as err:
raise ValueError(
f'File {logname} does not match expected format. Clean your /tmp folder and start again!'
) from err
return float(angle), float(speed)
def vibrations_profile(
lognames, klipperdir='~/klipper', kinematics='cartesian', accel=None, max_freq=1000.0, st_version=None, motors=None
):
global shaper_calibrate
shaper_calibrate = setup_klipper_import(klipperdir)
if kinematics == 'cartesian':
main_angles = [0, 90]
elif kinematics == 'corexy':
main_angles = [45, 135]
else:
raise ValueError('Only Cartesian and CoreXY kinematics are supported by this tool at the moment!')
psds = defaultdict(lambda: defaultdict(list))
psds_sum = defaultdict(lambda: defaultdict(list))
target_freqs_initialized = False
for logname in lognames:
data = parse_log(logname)
if data is None:
continue # File is not in the expected format, skip it
angle, speed = extract_angle_and_speed(logname)
freq_response = calc_freq_response(data)
first_freqs = freq_response.freq_bins
psd_sum = freq_response.psd_sum
if not target_freqs_initialized:
target_freqs = first_freqs[first_freqs <= max_freq]
target_freqs_initialized = True
psd_sum = psd_sum[first_freqs <= max_freq]
first_freqs = first_freqs[first_freqs <= max_freq]
# Store the interpolated PSD and integral values
psds[angle][speed] = np.interp(target_freqs, first_freqs, psd_sum)
psds_sum[angle][speed] = np.trapz(psd_sum, first_freqs)
measured_angles = sorted(psds_sum.keys())
measured_speeds = sorted({speed for angle_speeds in psds_sum.values() for speed in angle_speeds.keys()})
for main_angle in main_angles:
if main_angle not in measured_angles:
raise ValueError('Measurements not taken at the correct angles for the specified kinematics!')
# Precompute the variables used in plot functions
all_angles, all_speeds, spectrogram_data = compute_dir_speed_spectrogram(
measured_speeds, psds_sum, kinematics, main_angles
)
all_angles_energy = compute_angle_powers(spectrogram_data)
sp_min_energy, sp_max_energy, sp_variance_energy, vibration_metric = compute_speed_powers(spectrogram_data)
motor_profiles, global_motor_profile = compute_motor_profiles(target_freqs, psds, all_angles_energy, main_angles)
# symmetry_factor = compute_symmetry_analysis(all_angles, all_angles_energy)
symmetry_factor = compute_symmetry_analysis(all_angles, spectrogram_data, main_angles)
ConsoleOutput.print(f'Machine estimated vibration symmetry: {symmetry_factor:.1f}%')
# Analyze low variance ranges of vibration energy across all angles for each speed to identify clean speeds
# and highlight them. Also find the peaks to identify speeds to avoid due to high resonances
num_peaks, vibration_peaks, peaks_speeds = detect_peaks(
vibration_metric,
all_speeds,
PEAKS_DETECTION_THRESHOLD * vibration_metric.max(),
PEAKS_RELATIVE_HEIGHT_THRESHOLD,
10,
10,
)
formated_peaks_speeds = ['{:.1f}'.format(pspeed) for pspeed in peaks_speeds]
ConsoleOutput.print(
'Vibrations peaks detected: %d @ %s mm/s (avoid setting a speed near these values in your slicer print profile)'
% (num_peaks, ', '.join(map(str, formated_peaks_speeds)))
)
good_speeds = identify_low_energy_zones(vibration_metric, SPEEDS_VALLEY_DETECTION_THRESHOLD)
if good_speeds is not None:
deletion_range = int(SPEEDS_AROUND_PEAK_DELETION / (all_speeds[1] - all_speeds[0]))
peak_speed_indices = {pspeed: np.where(all_speeds == pspeed)[0][0] for pspeed in set(peaks_speeds)}
# Filter and split ranges based on peak indices, avoiding overlaps
good_speeds = filter_and_split_ranges(all_speeds, good_speeds, peak_speed_indices, deletion_range)
# Add some logging about the good speeds found
ConsoleOutput.print(f'Lowest vibrations speeds ({len(good_speeds)} ranges sorted from best to worse):')
for idx, (start, end, _) in enumerate(good_speeds):
ConsoleOutput.print(f'{idx+1}: {all_speeds[start]:.1f} to {all_speeds[end]:.1f} mm/s')
# Angle low energy valleys identification (good angles ranges) and print them to the console
good_angles = identify_low_energy_zones(all_angles_energy, ANGLES_VALLEY_DETECTION_THRESHOLD)
if good_angles is not None:
ConsoleOutput.print(f'Lowest vibrations angles ({len(good_angles)} ranges sorted from best to worse):')
for idx, (start, end, energy) in enumerate(good_angles):
ConsoleOutput.print(
f'{idx+1}: {all_angles[start]:.1f}° to {all_angles[end]:.1f}° (mean vibrations energy: {energy:.2f}% of max)'
)
# Create graph layout
fig, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = plt.subplots(
2,
3,
gridspec_kw={
'height_ratios': [1, 1],
'width_ratios': [4, 8, 6],
'bottom': 0.050,
'top': 0.890,
'left': 0.040,
'right': 0.985,
'hspace': 0.166,
'wspace': 0.138,
},
)
# Transform ax3 and ax4 to polar plots
ax1.remove()
ax1 = fig.add_subplot(2, 3, 1, projection='polar')
ax4.remove()
ax4 = fig.add_subplot(2, 3, 4, projection='polar')
# Set the global .png figure size
fig.set_size_inches(20, 11.5)
# Add title
title_line1 = 'MACHINE VIBRATIONS ANALYSIS TOOL'
fig.text(
0.060, 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].split('-')[0]}", '%Y%m%d %H%M%S')
title_line2 = dt.strftime('%x %X')
if accel is not None:
title_line2 += ' at ' + str(accel) + ' mm/s² -- ' + kinematics.upper() + ' kinematics'
except Exception:
ConsoleOutput.print('Warning: CSV filenames appear to be different than expected (%s)' % (lognames[0]))
title_line2 = lognames[0].split('/')[-1]
fig.text(0.060, 0.957, title_line2, ha='left', va='top', fontsize=16, color=KLIPPAIN_COLORS['dark_purple'])
# Add the motors infos to the top of the graph
if motors is not None and len(motors) == 2:
differences = motors[0].compare_to(motors[1])
plot_motor_config_txt(fig, motors, differences)
if differences is not None and kinematics == 'corexy':
ConsoleOutput.print(f'Warning: motors have different TMC configurations!\n{differences}')
# Plot the graphs
plot_angle_profile_polar(ax1, all_angles, all_angles_energy, good_angles, symmetry_factor)
plot_vibration_spectrogram_polar(ax4, all_angles, all_speeds, spectrogram_data)
plot_global_speed_profile(
ax2,
all_speeds,
sp_min_energy,
sp_max_energy,
sp_variance_energy,
vibration_metric,
num_peaks,
vibration_peaks,
good_speeds,
)
plot_angular_speed_profiles(ax3, all_speeds, all_angles, spectrogram_data, kinematics)
plot_vibration_spectrogram(ax5, all_angles, all_speeds, spectrogram_data, vibration_peaks)
plot_motor_profiles(ax6, target_freqs, main_angles, motor_profiles, global_motor_profile, max_freq)
# Adding a small Klippain logo to the top left corner of the figure
ax_logo = fig.add_axes([0.001, 0.924, 0.075, 0.075], 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] <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(
'-c', '--accel', type='int', dest='accel', default=None, help='accel value to be printed on the graph'
)
opts.add_option('-f', '--max_freq', type='float', default=1000.0, help='maximum frequency to graph')
opts.add_option(
'-k', '--klipper_dir', type='string', dest='klipperdir', default='~/klipper', help='main klipper directory'
)
opts.add_option(
'-m',
'--kinematics',
type='string',
dest='kinematics',
default='cartesian',
help='machine kinematics configuration',
)
options, args = opts.parse_args()
if len(args) < 1:
opts.error('No CSV file(s) to analyse')
if options.output is None:
opts.error('You must specify an output file.png to use the script (option -o)')
if options.kinematics not in ['cartesian', 'corexy']:
opts.error('Only cartesian and corexy kinematics are supported by this tool at the moment!')
fig = vibrations_profile(args, options.klipperdir, options.kinematics, options.accel, options.max_freq)
fig.savefig(options.output, dpi=150)
if __name__ == '__main__':
main()

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