reijii/klippain-shaketune-telegramm
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

fixed last commit about changes in recommendations

Browse Source
This commit is contained in:
Félix Boisselier
2024-05-26 22:23:49 +02:00
parent 1d3d22ef38
commit c892e1a03d
1 changed files with 17 additions and 25 deletions
Download Patch File Download Diff File Expand all files Collapse all files

42
shaketune/post_processing/graph_shaper.py
View File

@@ -129,6 +129,10 @@ def plot_freq_response(
ax2.yaxis.set_visible(False) ax2.yaxis.set_visible(False)
# Draw the shappers curves and add their specific parameters in the legend # Draw the shappers curves and add their specific parameters in the legend
perf_shaper_choice = None
perf_shaper_vals = None
perf_shaper_freq = None
perf_shaper_accel = 0
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)' % (
@@ -146,45 +150,33 @@ def plot_freq_response(
klipper_shaper_vals = shaper.vals klipper_shaper_vals = shaper.vals
klipper_shaper_accel = shaper_max_accel klipper_shaper_accel = shaper_max_accel
# This adds also the two selected shapers with good performances or low level of vibrations # Find the shaper with the highest accel but with vibrs under MAX_VIBRATIONS as it's
perf_shaper_vibrs = float('inf') # a good performance compromise when injecting the SCV and damping ratio in the computation
perf_shaper = None if perf_shaper_accel < shaper_max_accel and shaper.vibrs * 100 < MAX_VIBRATIONS:
perf_shaper_vals = None perf_shaper_choice = shaper.name
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 (
shaper_max_accel > klipper_shaper_accel
and shaper.vibrs * 100 < MAX_VIBRATIONS
and (
shaper.vibrs * 100 < perf_shaper_vibrs
or (shaper.vibrs * 100 == perf_shaper_vibrs and shaper_max_accel > perf_shaper_accel)
)
):
perf_shaper_accel = shaper_max_accel perf_shaper_accel = shaper_max_accel
perf_shaper = shaper.name
perf_shaper_freq = shaper.freq perf_shaper_freq = shaper.freq
perf_shaper_vibrs = shaper.vibrs * 100
perf_shaper_vals = shaper.vals perf_shaper_vals = shaper.vals
# User recommendations are added to the legend: one is Klipper's original suggestion that is usually good for low vibrations # 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 "performance" 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
# vibrations level but have higher accelerations. If both recommendation are the same shaper, or if no suitable "performance" # vibrations level but have higher accelerations. If both recommendations are the same shaper, or if no suitable "performance"
# 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 perf_shaper is not None and perf_shaper != klipper_shaper_choice and perf_shaper_accel >= klipper_shaper_accel: if (
perf_shaper_choice is not None
and perf_shaper_choice != klipper_shaper_choice
and perf_shaper_accel >= klipper_shaper_accel
):
ax2.plot( ax2.plot(
[], [],
[], [],
' ', ' ',
label='Recommended performance shaper: %s @ %.1f Hz' % (perf_shaper.upper(), perf_shaper_freq), label='Recommended performance shaper: %s @ %.1f Hz' % (perf_shaper_choice.upper(), perf_shaper_freq),
) )
ax.plot( ax.plot(
freqs, freqs,
psd * perf_shaper_vals, psd * perf_shaper_vals,
label='With %s applied' % (perf_shaper.upper()), label='With %s applied' % (perf_shaper_choice.upper()),
color='cyan', color='cyan',
) )
ax2.plot( ax2.plot(