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modified shaper recomendation to account for changes when injecting damping ratio

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This commit is contained in:
Félix Boisselier
2024-05-24 22:57:18 +02:00
parent b6ec4d0229
commit 1d3d22ef38
1 changed files with 51 additions and 40 deletions
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91
shaketune/post_processing/graph_shaper.py
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@@ -32,7 +32,7 @@ from ..helpers.console_output import ConsoleOutput
PEAKS_DETECTION_THRESHOLD = 0.05 PEAKS_DETECTION_THRESHOLD = 0.05
PEAKS_EFFECT_THRESHOLD = 0.12 PEAKS_EFFECT_THRESHOLD = 0.12
SPECTROGRAM_LOW_PERCENTILE_FILTER = 5 SPECTROGRAM_LOW_PERCENTILE_FILTER = 5
MAX_SMOOTHING = 0.1 MAX_VIBRATIONS = 5.0
KLIPPAIN_COLORS = { KLIPPAIN_COLORS = {
'purple': '#70088C', 'purple': '#70088C',
@@ -98,7 +98,7 @@ def calibrate_shaper(datas, max_smoothing, scv, max_freq):
def plot_freq_response( def plot_freq_response(
ax, calibration_data, shapers, performance_shaper, peaks, peaks_freqs, peaks_threshold, fr, zeta, max_freq ax, calibration_data, shapers, klipper_shaper_choice, peaks, peaks_freqs, peaks_threshold, fr, zeta, max_freq
): ):
freqs = calibration_data.freqs freqs = calibration_data.freqs
psd = calibration_data.psd_sum psd = calibration_data.psd_sum
@@ -128,13 +128,7 @@ def plot_freq_response(
ax2 = ax.twinx() ax2 = ax.twinx()
ax2.yaxis.set_visible(False) 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 # 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: for shaper in shapers:
shaper_max_accel = round(shaper.max_accel / 100.0) * 100.0 shaper_max_accel = round(shaper.max_accel / 100.0) * 100.0
label = '%s (%.1f Hz, vibr=%.1f%%, sm~=%.2f, accel<=%.f)' % ( label = '%s (%.1f Hz, vibr=%.1f%%, sm~=%.2f, accel<=%.f)' % (
@@ -146,58 +140,75 @@ def plot_freq_response(
) )
ax2.plot(freqs, shaper.vals, label=label, linestyle='dotted') ax2.plot(freqs, shaper.vals, label=label, linestyle='dotted')
# Get the performance shaper # Get the Klipper recommended shaper (usually it's a good low vibration compromise)
if shaper.name == performance_shaper: if shaper.name == klipper_shaper_choice:
performance_shaper_freq = shaper.freq klipper_shaper_freq = shaper.freq
performance_shaper_vibr = shaper.vibrs * 100.0 klipper_shaper_vals = shaper.vals
performance_shaper_vals = shaper.vals klipper_shaper_accel = shaper_max_accel
# Get the low vibration shaper # This adds also the two selected shapers with good performances or low level of vibrations
perf_shaper_vibrs = float('inf')
perf_shaper = None
perf_shaper_vals = None
perf_shaper_freq = None
perf_shaper_accel = 0
for shaper in shapers:
shaper_max_accel = round(shaper.max_accel / 100.0) * 100.0
# Get the a new performance filter that have higher accel than Klipper filter reco but still under 5% vibration
# as this looks to be reasonable when injecting the SCV and damping ratio in the computation...
if ( if (
shaper.vibrs * 100 < lowvib_shaper_vibrs shaper_max_accel > klipper_shaper_accel
or (shaper.vibrs * 100 == lowvib_shaper_vibrs and shaper_max_accel > lowvib_shaper_accel) and shaper.vibrs * 100 < MAX_VIBRATIONS
) and shaper.smoothing < MAX_SMOOTHING: and (
lowvib_shaper_accel = shaper_max_accel shaper.vibrs * 100 < perf_shaper_vibrs
lowvib_shaper = shaper.name or (shaper.vibrs * 100 == perf_shaper_vibrs and shaper_max_accel > perf_shaper_accel)
lowvib_shaper_freq = shaper.freq )
lowvib_shaper_vibrs = shaper.vibrs * 100 ):
lowvib_shaper_vals = shaper.vals perf_shaper_accel = shaper_max_accel
perf_shaper = shaper.name
perf_shaper_freq = shaper.freq
perf_shaper_vibrs = shaper.vibrs * 100
perf_shaper_vals = shaper.vals
# User recommendations are added to the legend: one is Klipper's original suggestion that is usually good for performances # User recommendations are added to the legend: one is Klipper's original suggestion that is usually good for low vibrations
# and the other one is the custom "low vibration" recommendation that looks for a suitable shaper that doesn't have excessive # and the other one is the custom "performance" 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 # vibrations level but have higher accelerations. If both recommendation are the same shaper, or if no suitable "performance"
# vibration" shaper is found, then only a single line as the "best shaper" recommendation is added to the legend # shaper is found, then only a single line as the "best shaper" recommendation is added to the legend
if ( if perf_shaper is not None and perf_shaper != klipper_shaper_choice and perf_shaper_accel >= klipper_shaper_accel:
lowvib_shaper is not None
and lowvib_shaper != performance_shaper
and lowvib_shaper_vibrs <= performance_shaper_vibr
):
ax2.plot( ax2.plot(
[], [],
[], [],
' ', ' ',
label='Recommended performance shaper: %s @ %.1f Hz' label='Recommended performance shaper: %s @ %.1f Hz' % (perf_shaper.upper(), perf_shaper_freq),
% (performance_shaper.upper(), performance_shaper_freq),
) )
ax.plot( ax.plot(
freqs, psd * performance_shaper_vals, label='With %s applied' % (performance_shaper.upper()), color='cyan' freqs,
psd * perf_shaper_vals,
label='With %s applied' % (perf_shaper.upper()),
color='cyan',
) )
ax2.plot( ax2.plot(
[], [],
[], [],
' ', ' ',
label='Recommended low vibrations shaper: %s @ %.1f Hz' % (lowvib_shaper.upper(), lowvib_shaper_freq), label='Recommended low vibrations shaper: %s @ %.1f Hz'
% (klipper_shaper_choice.upper(), klipper_shaper_freq),
)
ax.plot(
freqs, psd * klipper_shaper_vals, label='With %s applied' % (klipper_shaper_choice.upper()), color='lime'
) )
ax.plot(freqs, psd * lowvib_shaper_vals, label='With %s applied' % (lowvib_shaper.upper()), color='lime')
else: else:
ax2.plot( ax2.plot(
[], [],
[], [],
' ', ' ',
label='Recommended best shaper: %s @ %.1f Hz' % (performance_shaper.upper(), performance_shaper_freq), label='Recommended best shaper: %s @ %.1f Hz' % (klipper_shaper_choice.upper(), klipper_shaper_freq),
) )
ax.plot( ax.plot(
freqs, psd * performance_shaper_vals, label='With %s applied' % (performance_shaper.upper()), color='cyan' freqs,
psd * klipper_shaper_vals,
label='With %s applied' % (klipper_shaper_choice.upper()),
color='cyan',
) )
# And the estimated damping ratio is finally added at the end of the legend # And the estimated damping ratio is finally added at the end of the legend
@@ -312,7 +323,7 @@ def shaper_calibration(
ConsoleOutput.print('Warning: incorrect number of .csv files detected. Only the first one will be used!') ConsoleOutput.print('Warning: incorrect number of .csv files detected. Only the first one will be used!')
# Compute shapers, PSD outputs and spectrogram # Compute shapers, PSD outputs and spectrogram
performance_shaper, shapers, calibration_data, fr, zeta, compat = calibrate_shaper( klipper_shaper_choice, shapers, calibration_data, fr, zeta, compat = calibrate_shaper(
datas[0], max_smoothing, scv, max_freq datas[0], max_smoothing, scv, max_freq
) )
pdata, bins, t = compute_spectrogram(datas[0]) pdata, bins, t = compute_spectrogram(datas[0])
@@ -387,7 +398,7 @@ def shaper_calibration(
# Plot the graphs # Plot the graphs
plot_freq_response( plot_freq_response(
ax1, calibration_data, shapers, performance_shaper, peaks, peaks_freqs, peaks_threshold, fr, zeta, max_freq ax1, calibration_data, shapers, klipper_shaper_choice, peaks, peaks_freqs, peaks_threshold, fr, zeta, max_freq
) )
plot_spectrogram(ax2, t, bins, pdata, peaks_freqs, max_freq) plot_spectrogram(ax2, t, bins, pdata, peaks_freqs, max_freq)