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updated vibration documentation and graph accordingly

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to change the wording of the bad speed indicator to vibration metric
that is more generic and easy to understand
This commit is contained in:
Félix Boisselier
2024-04-10 18:52:03 +02:00
parent bde8577d0e
commit 7652f0d8e7
2 changed files with 29 additions and 23 deletions
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48
K-ShakeTune/scripts/graph_vibrations.py
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@@ -137,8 +137,14 @@ def compute_angle_powers(spectrogram_data):
def compute_speed_powers(spectrogram_data, smoothing_window=15): def compute_speed_powers(spectrogram_data, smoothing_window=15):
min_values = np.amin(spectrogram_data, axis=0) min_values = np.amin(spectrogram_data, axis=0)
max_values = np.amax(spectrogram_data, axis=0) max_values = np.amax(spectrogram_data, axis=0)
avg_values = np.mean(spectrogram_data, axis=0) var_values = np.var(spectrogram_data, axis=0)
composite_variance = max_values * np.var(spectrogram_data, axis=0)
# rescale the variance to the same range as max_values to plot it on the same graph
var_values = var_values / var_values.max() * max_values.max()
# Create a vibration metric that is the product of the max values and the variance to quantify the best
# speeds that have at the same time a low global energy level that is also consistent at every angles
vibration_metric = max_values * var_values
# utility function to pad and smooth the data avoiding edge effects # utility function to pad and smooth the data avoiding edge effects
conv_filter = np.ones(smoothing_window) / smoothing_window conv_filter = np.ones(smoothing_window) / smoothing_window
@@ -149,7 +155,7 @@ def compute_speed_powers(spectrogram_data, smoothing_window=15):
return smoothed_data return smoothed_data
# Stack the arrays and apply padding and smoothing in batch # Stack the arrays and apply padding and smoothing in batch
data_arrays = np.stack([min_values, max_values, avg_values, composite_variance]) data_arrays = np.stack([min_values, max_values, var_values, vibration_metric])
smoothed_arrays = np.array([pad_and_smooth(data) for data in data_arrays]) smoothed_arrays = np.array([pad_and_smooth(data) for data in data_arrays])
return smoothed_arrays return smoothed_arrays
@@ -217,36 +223,36 @@ def plot_angle_profile_polar(ax, angles, angles_powers, low_energy_zones, symmet
return return
def plot_global_speed_profile(ax, all_speeds, sp_min_energy, sp_max_energy, sp_avg_energy, sp_composite_variance, num_peaks, peaks, low_energy_zones): def plot_global_speed_profile(ax, all_speeds, sp_min_energy, sp_max_energy, sp_variance_energy, vibration_metric, num_peaks, peaks, low_energy_zones):
ax.set_title("Global speed energy profile", fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold') ax.set_title("Global speed energy profile", fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
ax.set_xlabel('Speed (mm/s)') ax.set_xlabel('Speed (mm/s)')
ax.set_ylabel('Energy') ax.set_ylabel('Energy')
ax2 = ax.twinx() ax2 = ax.twinx()
ax2.yaxis.set_visible(False) ax2.yaxis.set_visible(False)
ax.plot(all_speeds, sp_avg_energy, label='Average energy', color=KLIPPAIN_COLORS['dark_orange'], zorder=5) ax.plot(all_speeds, sp_min_energy, label='Minimum', color=KLIPPAIN_COLORS['dark_purple'], zorder=5)
ax.plot(all_speeds, sp_min_energy, label='Minimum energy', color=KLIPPAIN_COLORS['dark_purple'], zorder=5) ax.plot(all_speeds, sp_max_energy, label='Maximum', color=KLIPPAIN_COLORS['purple'], zorder=5)
ax.plot(all_speeds, sp_max_energy, label='Maximum energy', color=KLIPPAIN_COLORS['purple'], zorder=5) ax.plot(all_speeds, sp_variance_energy, label='Variance', color=KLIPPAIN_COLORS['orange'], zorder=5, linestyle='--')
ax2.plot(all_speeds, sp_composite_variance, label=f'Bad speed indicator ({num_peaks} peaks)', color=KLIPPAIN_COLORS['orange'], zorder=5) ax2.plot(all_speeds, vibration_metric, label=f'Vibration metric ({num_peaks} bad peaks)', color=KLIPPAIN_COLORS['red_pink'], zorder=5)
ax.set_xlim([all_speeds.min(), all_speeds.max()]) ax.set_xlim([all_speeds.min(), all_speeds.max()])
ax.set_ylim([0, sp_max_energy.max() * 1.1]) ax.set_ylim([0, sp_max_energy.max() * 1.15])
y2min = -(sp_composite_variance.max() * 0.025) y2min = -(vibration_metric.max() * 0.025)
y2max = sp_composite_variance.max() * 1.1 y2max = vibration_metric.max() * 1.07
ax2.set_ylim([y2min, y2max]) ax2.set_ylim([y2min, y2max])
if peaks is not None: if peaks is not None:
ax2.plot(all_speeds[peaks], sp_composite_variance[peaks], "x", color='black', markersize=8, zorder=10) ax2.plot(all_speeds[peaks], vibration_metric[peaks], "x", color='black', markersize=8, zorder=10)
for idx, peak in enumerate(peaks): for idx, peak in enumerate(peaks):
ax2.annotate(f"{idx+1}", (all_speeds[peak], sp_composite_variance[peak]), ax2.annotate(f"{idx+1}", (all_speeds[peak], vibration_metric[peak]),
textcoords="offset points", xytext=(5, 5), fontweight='bold', textcoords="offset points", xytext=(5, 5), fontweight='bold',
ha='left', fontsize=13, color=KLIPPAIN_COLORS['red_pink'], zorder=10) ha='left', fontsize=13, color=KLIPPAIN_COLORS['red_pink'], zorder=10)
for idx, (start, end, _) in enumerate(low_energy_zones): for idx, (start, end, _) in enumerate(low_energy_zones):
ax2.axvline(all_speeds[start], color=KLIPPAIN_COLORS['red_pink'], linestyle='dotted', linewidth=1.5, zorder=8) # ax2.axvline(all_speeds[start], color=KLIPPAIN_COLORS['red_pink'], linestyle='dotted', linewidth=1.5, zorder=8)
ax2.axvline(all_speeds[end], color=KLIPPAIN_COLORS['red_pink'], linestyle='dotted', linewidth=1.5, zorder=8) # ax2.axvline(all_speeds[end], color=KLIPPAIN_COLORS['red_pink'], linestyle='dotted', linewidth=1.5, zorder=8)
ax2.fill_between(all_speeds[start:end], y2min, sp_composite_variance[start:end], color='green', alpha=0.2, label=f'Zone {idx+1}: {all_speeds[start]:.1f} to {all_speeds[end]:.1f} mm/s') ax2.fill_between(all_speeds[start:end], y2min, vibration_metric[start:end], color='green', alpha=0.2, label=f'Zone {idx+1}: {all_speeds[start]:.1f} to {all_speeds[end]:.1f} mm/s')
ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator()) ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator()) ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
@@ -458,7 +464,7 @@ def vibrations_profile(lognames, klipperdir="~/klipper", kinematics="cartesian",
# Precompute the variables used in plot functions # Precompute the variables used in plot functions
all_angles, all_speeds, spectrogram_data = compute_dir_speed_spectrogram(measured_speeds, psds_sum, kinematics, main_angles) all_angles, all_speeds, spectrogram_data = compute_dir_speed_spectrogram(measured_speeds, psds_sum, kinematics, main_angles)
all_angles_energy = compute_angle_powers(spectrogram_data) all_angles_energy = compute_angle_powers(spectrogram_data)
sp_min_energy, sp_max_energy, sp_avg_energy, sp_composite_variance = compute_speed_powers(spectrogram_data) sp_min_energy, sp_max_energy, sp_variance_energy, vibration_metric = compute_speed_powers(spectrogram_data)
motor_profiles, global_motor_profile = compute_motor_profiles(target_freqs, psds, all_angles_energy, main_angles) motor_profiles, global_motor_profile = compute_motor_profiles(target_freqs, psds, all_angles_energy, main_angles)
# symmetry_factor = compute_symmetry_analysis(all_angles, all_angles_energy) # symmetry_factor = compute_symmetry_analysis(all_angles, all_angles_energy)
@@ -468,14 +474,14 @@ def vibrations_profile(lognames, klipperdir="~/klipper", kinematics="cartesian",
# Analyze low variance ranges of vibration energy across all angles for each speed to identify clean speeds # Analyze low variance ranges of vibration energy across all angles for each speed to identify clean speeds
# and highlight them. Also find the peaks to identify speeds to avoid due to high resonances # and highlight them. Also find the peaks to identify speeds to avoid due to high resonances
num_peaks, vibration_peaks, peaks_speeds = detect_peaks( num_peaks, vibration_peaks, peaks_speeds = detect_peaks(
sp_composite_variance, all_speeds, vibration_metric, all_speeds,
PEAKS_DETECTION_THRESHOLD * sp_composite_variance.max(), PEAKS_DETECTION_THRESHOLD * vibration_metric.max(),
PEAKS_RELATIVE_HEIGHT_THRESHOLD, 10, 10 PEAKS_RELATIVE_HEIGHT_THRESHOLD, 10, 10
) )
formated_peaks_speeds = ["{:.1f}".format(pspeed) for pspeed in peaks_speeds] formated_peaks_speeds = ["{:.1f}".format(pspeed) for pspeed in peaks_speeds]
print_with_c_locale("Vibrations peaks detected: %d @ %s mm/s (avoid setting a speed near these values in your slicer print profile)" % (num_peaks, ", ".join(map(str, formated_peaks_speeds)))) print_with_c_locale("Vibrations peaks detected: %d @ %s mm/s (avoid setting a speed near these values in your slicer print profile)" % (num_peaks, ", ".join(map(str, formated_peaks_speeds))))
good_speeds = identify_low_energy_zones(sp_composite_variance, SPEEDS_VALLEY_DETECTION_THRESHOLD) good_speeds = identify_low_energy_zones(vibration_metric, SPEEDS_VALLEY_DETECTION_THRESHOLD)
if good_speeds is not None: if good_speeds is not None:
deletion_range = int(SPEEDS_AROUND_PEAK_DELETION / (all_speeds[1] - all_speeds[0])) deletion_range = int(SPEEDS_AROUND_PEAK_DELETION / (all_speeds[1] - all_speeds[0]))
peak_speed_indices = {pspeed: np.where(all_speeds == pspeed)[0][0] for pspeed in set(peaks_speeds)} peak_speed_indices = {pspeed: np.where(all_speeds == pspeed)[0][0] for pspeed in set(peaks_speeds)}
@@ -548,7 +554,7 @@ def vibrations_profile(lognames, klipperdir="~/klipper", kinematics="cartesian",
plot_angle_profile_polar(ax1, all_angles, all_angles_energy, good_angles, symmetry_factor) plot_angle_profile_polar(ax1, all_angles, all_angles_energy, good_angles, symmetry_factor)
plot_vibration_spectrogram_polar(ax4, all_angles, all_speeds, spectrogram_data) plot_vibration_spectrogram_polar(ax4, all_angles, all_speeds, spectrogram_data)
plot_global_speed_profile(ax2, all_speeds, sp_min_energy, sp_max_energy, sp_avg_energy, sp_composite_variance, num_peaks, vibration_peaks, good_speeds) plot_global_speed_profile(ax2, all_speeds, sp_min_energy, sp_max_energy, sp_variance_energy, vibration_metric, num_peaks, vibration_peaks, good_speeds)
plot_angular_speed_profiles(ax3, all_speeds, all_angles, spectrogram_data, kinematics) plot_angular_speed_profiles(ax3, all_speeds, all_angles, spectrogram_data, kinematics)
plot_vibration_spectrogram(ax5, all_angles, all_speeds, spectrogram_data, vibration_peaks) plot_vibration_spectrogram(ax5, all_angles, all_speeds, spectrogram_data, vibration_peaks)

4
docs/macros/vibrations_profile.md
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@@ -36,8 +36,8 @@ The `CREATE_VIBRATIONS_PROFILE` macro results are constituted of a set of 6 plot
|:-----|-------------| |:-----|-------------|
|![](../images/vibrations_graphs/global_speed_energy_profile.png)|This plot shows the relationship between toolhead speed (mm/s) and vibrational energy, providing a global view of how speed impacts vibration across all movements. By using speeds from the green zones, your printer will run more smoothly and you will minimize vibrations and related fine artifacts in prints| |![](../images/vibrations_graphs/global_speed_energy_profile.png)|This plot shows the relationship between toolhead speed (mm/s) and vibrational energy, providing a global view of how speed impacts vibration across all movements. By using speeds from the green zones, your printer will run more smoothly and you will minimize vibrations and related fine artifacts in prints|
This graph is the most important one of this tool. You want to use it to adapt your slicer profile, especially by looking at the "bad speed indicator" curve, that will helps you find which speeds can be problematic for your printer. Here's the magic behind it, broken down into two key parts: This graph is the most important one of this tool. You want to use it to adapt your slicer profile, especially by looking at the "vibration metric" curve, that will helps you find which speeds can be problematic for your printer. Here's the magic behind it, broken down into two key parts:
1. **Spectrum Variance**: This is like the mood ring of your printer, showing how the vibes (a.k.a vibrations) change when printing from different angles. If the "bad speed indicator" is low, it means your printer is keeping its cool, staying consistent no matter the angle. But if it spikes, it's a sign that some angles are making your printer jitter more than a caffeinated squirrel. *Imagine it like this: You're looking for a chill party vibe where the music's good at every angle, not one where you turn a corner and suddenly it's too loud or too soft.* 1. **Spectrum Variance**: This is like the mood ring of your printer, showing how the vibes (a.k.a vibrations) change when printing from different angles. If the "vibration metric" is low, it means your printer is keeping its cool, staying consistent no matter the angle. But if it spikes, it's a sign that some angles are making your printer jitter more than a caffeinated squirrel. *Imagine it like this: You're looking for a chill party vibe where the music's good at every angle, not one where you turn a corner and suddenly it's too loud or too soft.*
2. **Spectrum Max**: This one's about the max volume of the party, or how loud the strongest vibration is across all angles at any speed. We're aiming to avoid the speeds that crank up the volume too high, causing a resonance rave in the motors. *Think of it this way: You don't want the base so high that it feels like your heart's going to beat out of your chest. We're looking for a nice background level where everyone can chat and have a good time.* 2. **Spectrum Max**: This one's about the max volume of the party, or how loud the strongest vibration is across all angles at any speed. We're aiming to avoid the speeds that crank up the volume too high, causing a resonance rave in the motors. *Think of it this way: You don't want the base so high that it feels like your heart's going to beat out of your chest. We're looking for a nice background level where everyone can chat and have a good time.*
And why do we care so much about finding these speeds? Because during a print, the toolhead will move in all directions depending on the geometry, and we want a speed that's like a good friend, reliable no matter what the situation. Fortunately, since the motors in our printers share their vibes without non-linear mixing and just add up (think of it as each doing its own dance without bumping into each other), we can find those happy green zones on the graph: these are the speeds that keep the vibe cool and the energy just right, making them perfect for all your print jobs. And why do we care so much about finding these speeds? Because during a print, the toolhead will move in all directions depending on the geometry, and we want a speed that's like a good friend, reliable no matter what the situation. Fortunately, since the motors in our printers share their vibes without non-linear mixing and just add up (think of it as each doing its own dance without bumping into each other), we can find those happy green zones on the graph: these are the speeds that keep the vibe cool and the energy just right, making them perfect for all your print jobs.