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Updated the scripts and improved vibration graphs

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Full revamped documentation
This commit is contained in:
Félix Boisselier
2023-10-19 23:45:11 +02:00
parent 10cf8d3566
commit 33ce0ada85
53 changed files with 445 additions and 128 deletions
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40
K-ShakeTune/scripts/graph_shaper.py
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@@ -8,9 +8,10 @@
# Copyright (C) 2020 Kevin O'Connor <kevin@koconnor.net>
#
# Written by Frix_x#0161 #
# @version: 1.1
# @version: 2.0
# CHANGELOG:
# v2.0: updated the script to align it to the new K-Shake&Tune module
# v1.1: - improved the damping ratio computation with linear approximation for more precision
# - reworked the top graph to add more information to it with colored zones,
# automated peak detection, etc...
@@ -40,8 +41,6 @@ except locale.Error:
locale.setlocale(locale.LC_TIME, 'C')
MAX_TITLE_LENGTH = 65
PEAKS_DETECTION_THRESHOLD = 0.05
PEAKS_EFFECT_THRESHOLD = 0.12
SPECTROGRAM_LOW_PERCENTILE_FILTER = 5
@@ -122,7 +121,9 @@ def compute_spectrogram(data):
def detect_peaks(psd, freqs, window_size=5, vicinity=3):
# Smooth the curve using a moving average to avoid catching peaks everywhere in noisy signals
kernel = np.ones(window_size) / window_size
smoothed_psd = np.convolve(psd, kernel, mode='same')
smoothed_psd = np.convolve(psd, kernel, mode='valid')
mean_pad = [np.mean(psd[:window_size])] * (window_size // 2)
smoothed_psd = np.concatenate((mean_pad, smoothed_psd))
# Find peaks on the smoothed curve
smoothed_peaks = np.where((smoothed_psd[:-2] < smoothed_psd[1:-1]) & (smoothed_psd[1:-1] > smoothed_psd[2:]))[0] + 1
@@ -178,7 +179,7 @@ def plot_freq_response_with_damping(ax, calibration_data, shapers, selected_shap
ax.grid(which='minor', color='lightgrey')
ax2 = ax.twinx()
ax2.set_ylabel('Shaper vibration reduction (ratio)')
ax2.yaxis.set_visible(False)
best_shaper_vals = None
no_vibr_shaper = None
@@ -211,7 +212,7 @@ def plot_freq_response_with_damping(ax, calibration_data, shapers, selected_shap
peaks_warning_threshold = PEAKS_DETECTION_THRESHOLD * psd.max()
peaks_effect_threshold = PEAKS_EFFECT_THRESHOLD * psd.max()
ax.plot(freqs[peaks], psd[peaks], "x", color='black', label='Detected peaks', markersize=8)
ax.plot(freqs[peaks], psd[peaks], "x", color='black', markersize=8)
for idx, peak in enumerate(peaks):
if psd[peak] > peaks_effect_threshold:
fontcolor = 'red'
@@ -221,7 +222,7 @@ def plot_freq_response_with_damping(ax, calibration_data, shapers, selected_shap
fontweight = 'normal'
ax.annotate(f"{idx+1}", (freqs[peak], psd[peak]),
textcoords="offset points", xytext=(8, 5),
ha='left', fontsize=14, color=fontcolor, weight=fontweight)
ha='left', fontsize=13, color=fontcolor, weight=fontweight)
ax.axhline(y=peaks_warning_threshold, color='black', linestyle='--', linewidth=0.5)
ax.axhline(y=peaks_effect_threshold, color='black', linestyle='--', linewidth=0.5)
ax.fill_between(freqs, 0, peaks_warning_threshold, color='green', alpha=0.15, label='Relax Region')
@@ -260,7 +261,7 @@ def plot_spectrogram(ax, data, peaks, max_freq):
for idx, peak in enumerate(peaks):
ax.axvline(peak, color='cyan', linestyle='dotted', linewidth=0.75)
ax.annotate(f"Peak {idx+1}", (peak, t[-1]*0.9),
textcoords="data", color='cyan', rotation=90, fontsize=12,
textcoords="data", color='cyan', rotation=90, fontsize=10,
verticalalignment='top', horizontalalignment='right')
ax.set_xlim([0., max_freq])
@@ -309,19 +310,26 @@ def shaper_calibration(lognames, klipperdir="~/klipper", max_smoothing=None, max
ax1 = fig.add_subplot(gs[0])
ax2 = fig.add_subplot(gs[1])
# Add title
filename_parts = (lognames[0].split('/')[-1]).split('_')
dt = datetime.strptime(f"{filename_parts[3]} {filename_parts[4].split('.')[0]}", "%Y%m%d %H%M%S")
title_line1 = "INPUT SHAPER CALIBRATION TOOL"
title_line2 = filename_parts[2].upper() + ' axis - ' + dt.strftime('%x %X')
fig.text(0.88, 0.965, title_line1, ha='right', va='bottom', fontsize=20, color=KLIPPAIN_COLORS['purple'], weight='bold')
fig.text(0.88, 0.957, title_line2, ha='right', va='top', fontsize=16, color=KLIPPAIN_COLORS['dark_purple'])
title_line2 = dt.strftime('%x %X') + ' -- ' + filename_parts[2].upper() + ' axis'
fig.text(0.12, 0.965, title_line1, ha='left', va='bottom', fontsize=20, color=KLIPPAIN_COLORS['purple'], weight='bold')
fig.text(0.12, 0.957, title_line2, ha='left', va='top', fontsize=16, color=KLIPPAIN_COLORS['dark_purple'])
# Plot the graphs
peaks = plot_freq_response_with_damping(ax1, calibration_data, shapers, selected_shaper, fr, zeta, max_freq)
plot_spectrogram(ax2, datas[0], peaks, max_freq)
fig.set_size_inches(10, 13)
fig.set_size_inches(8.3, 11.6)
fig.tight_layout()
fig.subplots_adjust(top=0.90)
fig.subplots_adjust(top=0.89)
# Adding a small Klippain logo to the top left corner of the figure
ax_logo = fig.add_axes([0.001, 0.899, 0.1, 0.1], anchor='NW', zorder=-1)
ax_logo.imshow(matplotlib.pyplot.imread(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'klippain.png')))
ax_logo.axis('off')
return fig
@@ -347,12 +355,6 @@ def main():
opts.error("Too small max_smoothing specified (must be at least 0.05)")
fig = shaper_calibration(args, options.klipperdir, options.max_smoothing, options.max_freq)
# Adding a small Klippain logo to the top left corner of the figure
ax_logo = fig.add_axes([0.899, 0.899, 0.1, 0.1], anchor='NE', zorder=-1)
ax_logo.imshow(matplotlib.pyplot.imread(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'klippain.png')))
ax_logo.axis('off')
fig.savefig(options.output)