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Merge pull request #4 from Frix-x/develop

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v1.1.1
This commit is contained in:
Félix Boisselier
2023-11-01 20:09:50 +01:00
committed by GitHub
parent 5366ad0581 c156459420
commit 83f517758a
1 changed files with 39 additions and 33 deletions
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72
K-ShakeTune/scripts/graph_shaper.py
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@@ -164,7 +164,7 @@ def detect_peaks(psd, freqs, window_size=5, vicinity=3):
# Graphing # Graphing
###################################################################### ######################################################################
def plot_freq_response_with_damping(ax, calibration_data, shapers, selected_shaper, fr, zeta, max_freq): def plot_freq_response_with_damping(ax, calibration_data, shapers, performance_shaper, fr, zeta, max_freq):
freqs = calibration_data.freq_bins freqs = calibration_data.freq_bins
psd = calibration_data.psd_sum[freqs <= max_freq] psd = calibration_data.psd_sum[freqs <= max_freq]
px = calibration_data.psd_x[freqs <= max_freq] px = calibration_data.psd_x[freqs <= max_freq]
@@ -194,32 +194,50 @@ def plot_freq_response_with_damping(ax, calibration_data, shapers, selected_shap
ax2 = ax.twinx() ax2 = ax.twinx()
ax2.yaxis.set_visible(False) ax2.yaxis.set_visible(False)
best_shaper_vals = None lowvib_shaper_vibrs = float('inf')
lowest_vibration = float('inf') lowvib_shaper = None
lowest_vibration_shaper = None lowvib_shaper_freq = None
lowest_vibration_shaper_freq = None lowvib_shaper_accel = 0
lowest_vibration_shaper_accel = 0
# Draw the shappers curves and add their specific parameters in the legend # Draw the shappers curves and add their specific parameters in the legend
# This adds also a way to find the best shaper with 0% of vibrations (to be printed in the legend later) # 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: for shaper in shapers:
shaper_max_accel = round(shaper.max_accel / 100.) * 100. shaper_max_accel = round(shaper.max_accel / 100.) * 100.
label = "%s (%.1f Hz, vibr=%.1f%%, sm~=%.2f, accel<=%.f)" % ( label = "%s (%.1f Hz, vibr=%.1f%%, sm~=%.2f, accel<=%.f)" % (
shaper.name.upper(), shaper.freq, shaper.name.upper(), shaper.freq,
shaper.vibrs * 100., shaper.smoothing, shaper.vibrs * 100., shaper.smoothing,
shaper_max_accel) shaper_max_accel)
linestyle = 'dotted' ax2.plot(freqs, shaper.vals, label=label, linestyle='dotted')
if shaper.name == selected_shaper:
linestyle = 'dashdot' # Get the performance shaper
selected_shaper_freq = shaper.freq if shaper.name == performance_shaper:
best_shaper_vals = shaper.vals performance_shaper_freq = shaper.freq
if (shaper.vibrs * 100 < lowest_vibration or (shaper.vibrs * 100 == lowest_vibration and shaper_max_accel > lowest_vibration_shaper_accel)) and shaper.smoothing < MAX_SMOOTHING: performance_shaper_vibr = shaper.vibrs * 100.
lowest_vibration = shaper.vibrs * 100 performance_shaper_vals = shaper.vals
lowest_vibration_shaper_accel = shaper_max_accel
lowest_vibration_shaper = shaper.name # Get the low vibration shaper
lowest_vibration_shaper_freq = shaper.freq 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:
ax2.plot(freqs, shaper.vals, label=label, linestyle=linestyle) lowvib_shaper_accel = shaper_max_accel
ax.plot(freqs, psd * best_shaper_vals, label='With %s applied' % (selected_shaper.upper()), color='cyan') 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 != 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 # Draw the detected peaks and name them
# This also draw the detection threshold and warning threshold (aka "effect zone") # This also draw the detection threshold and warning threshold (aka "effect zone")
@@ -243,18 +261,6 @@ def plot_freq_response_with_damping(ax, calibration_data, shapers, selected_shap
ax.fill_between(freqs, 0, peaks_warning_threshold, color='green', alpha=0.15, label='Relax Region') ax.fill_between(freqs, 0, peaks_warning_threshold, color='green', alpha=0.15, label='Relax Region')
ax.fill_between(freqs, peaks_warning_threshold, peaks_effect_threshold, color='orange', alpha=0.2, label='Warning Region') ax.fill_between(freqs, peaks_warning_threshold, peaks_effect_threshold, color='orange', alpha=0.2, label='Warning Region')
# 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 (<0.1) 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 lowest_vibration_shaper != selected_shaper and lowest_vibration_shaper != None:
ax2.plot([], [], ' ', label="Recommended performance shaper: %s @ %.1f Hz" % (selected_shaper.upper(), selected_shaper_freq))
ax2.plot([], [], ' ', label="Recommended low vibrations shaper: %s @ %.1f Hz" % (lowest_vibration_shaper.upper(), lowest_vibration_shaper_freq))
else:
ax2.plot([], [], ' ', label="Recommended best shaper: %s @ %.1f Hz" % (selected_shaper.upper(), selected_shaper_freq))
# And the estimated damping ratio is finally added at the end of the legend
ax2.plot([], [], ' ', label="Estimated damping ratio (ζ): %.3f" % (zeta))
# Add the main resonant frequency and damping ratio of the axis to the graph title # 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.set_title("Axis Frequency Profile (ω0=%.1fHz, ζ=%.3f)" % (fr, zeta), fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
@@ -326,7 +332,7 @@ def shaper_calibration(lognames, klipperdir="~/klipper", max_smoothing=None, max
datas = [parse_log(fn) for fn in lognames] datas = [parse_log(fn) for fn in lognames]
# Calibrate shaper and generate outputs # Calibrate shaper and generate outputs
selected_shaper, shapers, calibration_data, fr, zeta = calibrate_shaper_with_damping(datas, max_smoothing) performance_shaper, shapers, calibration_data, fr, zeta = calibrate_shaper_with_damping(datas, max_smoothing)
fig = matplotlib.pyplot.figure() fig = matplotlib.pyplot.figure()
gs = matplotlib.gridspec.GridSpec(2, 1, height_ratios=[4, 3]) gs = matplotlib.gridspec.GridSpec(2, 1, height_ratios=[4, 3])
@@ -346,7 +352,7 @@ def shaper_calibration(lognames, klipperdir="~/klipper", max_smoothing=None, max
fig.text(0.12, 0.957, title_line2, ha='left', va='top', fontsize=16, color=KLIPPAIN_COLORS['dark_purple']) fig.text(0.12, 0.957, title_line2, ha='left', va='top', fontsize=16, color=KLIPPAIN_COLORS['dark_purple'])
# Plot the graphs # Plot the graphs
peaks = plot_freq_response_with_damping(ax1, calibration_data, shapers, selected_shaper, fr, zeta, max_freq) peaks = plot_freq_response_with_damping(ax1, calibration_data, shapers, performance_shaper, fr, zeta, max_freq)
plot_spectrogram(ax2, datas[0], peaks, max_freq) plot_spectrogram(ax2, datas[0], peaks, max_freq)
fig.set_size_inches(8.3, 11.6) fig.set_size_inches(8.3, 11.6)