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Merge pull request #4 from Frix-x/develop
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v1.1.1
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@Frix-x
Frix-x committed Nov 1, 2023
2 parents 5366ad0 + c156459 commit 83f5177
Showing 1 changed file with 39 additions and 33 deletions.
72 changes: 39 additions & 33 deletions K-ShakeTune/scripts/graph_shaper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -164,7 +164,7 @@ def detect_peaks(psd, freqs, window_size=5, vicinity=3):
# 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
psd = calibration_data.psd_sum[freqs <= max_freq]
px = calibration_data.psd_x[freqs <= max_freq]
Expand Down Expand Up @@ -194,32 +194,50 @@ def plot_freq_response_with_damping(ax, calibration_data, shapers, selected_shap
ax2 = ax.twinx()
ax2.yaxis.set_visible(False)

best_shaper_vals = None
lowest_vibration = float('inf')
lowest_vibration_shaper = None
lowest_vibration_shaper_freq = None
lowest_vibration_shaper_accel = 0
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 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:
shaper_max_accel = round(shaper.max_accel / 100.) * 100.
label = "%s (%.1f Hz, vibr=%.1f%%, sm~=%.2f, accel<=%.f)" % (
shaper.name.upper(), shaper.freq,
shaper.vibrs * 100., shaper.smoothing,
shaper_max_accel)
linestyle = 'dotted'
if shaper.name == selected_shaper:
linestyle = 'dashdot'
selected_shaper_freq = shaper.freq
best_shaper_vals = shaper.vals
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:
lowest_vibration = shaper.vibrs * 100
lowest_vibration_shaper_accel = shaper_max_accel
lowest_vibration_shaper = shaper.name
lowest_vibration_shaper_freq = shaper.freq
ax2.plot(freqs, shaper.vals, label=label, linestyle=linestyle)
ax.plot(freqs, psd * best_shaper_vals, label='With %s applied' % (selected_shaper.upper()), color='cyan')
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.
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 != 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")
Expand All @@ -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, 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
ax.set_title("Axis Frequency Profile (ω0=%.1fHz, ζ=%.3f)" % (fr, zeta), fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
Expand Down Expand Up @@ -326,7 +332,7 @@ def shaper_calibration(lognames, klipperdir="~/klipper", max_smoothing=None, max
datas = [parse_log(fn) for fn in lognames]

# 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()
gs = matplotlib.gridspec.GridSpec(2, 1, height_ratios=[4, 3])
Expand All @@ -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'])

# 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)

fig.set_size_inches(8.3, 11.6)
Expand Down

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