Update README.md

v4.1.0

README.md

@@ -13,7 +13,7 @@ Follow these steps to install Shake&Tune on your printer:
   1. Be sure to have a working accelerometer on your machine and a `[resonance_tester]` section defined. You can follow the official [Measuring Resonances Klipper documentation](https://www.klipper3d.org/Measuring_Resonances.html) to configure it.
   1. Install Shake&Tune by running over SSH on your printer:
      ```bash
-     wget -O - https://raw.githubusercontent.com/Frix-x/klippain-shaketune/main/install.sh | bash
+     wget -O - https://cloud.reijii.org/gitea/reijii/klippain-shaketune-telegramm/raw/branch/main/install.sh | bash
      ```
   1. Then, append the following to your `printer.cfg` file and restart Klipper:
      ```

docs/macros/compare_belts_responses.md

@@ -39,9 +39,9 @@ Aside from the actual belt tension, the resonant frequency/amplitude of the curv
 
 The Cross-Belts plot is an innovative cool way to compare the frequency profiles of the belts at every frequency point. In this plot, each point marks the amplitude response of each belt at different frequencies, connected point by point to trace the frequency spectrum. Ideally, these points should align on the diagonal center line, indicating that both belts have matching energy response values at each frequency. 
 
-The good zone, wider at the bottom (low-amplitude regions where the deviation doesn't matter much) and narrower at the top right (high-energy region where the main peaks lie), represents acceptable deviations. So **you want all points to be close to the ideal center line and as many as possible within the green zone**, as this means that the bands are well tuned and behave similarly.
+The good zone, wider at the bottom (low-amplitude regions where the deviation doesn't matter much) and narrower at the top right (high-energy region where the main peaks lie), represents acceptable deviations. So **you want all points to be close to the ideal center line and as many as possible within the green zone**, as this means that the belts are well tuned and behave similarly.
 
-Paired peaks of exactly the same frequency will be on the same point (labeled α1/α2, β1/β2, ...) and the distance from the center line will show the difference in energy. For paired peaks that also have a frequency delta between them, they are displayed as two points (labeled α1 and α2, ...) and the additional distance between them along the plotted line represents their frequency delta.
+Paired peaks at the same frequency will be on the same point (labeled α1/α2, β1/β2, ...) and the distance from the center line will show the difference in energy. For paired peaks that also have a frequency delta between them, they are displayed as two points (labeled α1 and α2, ...) and the additional distance between them along the plotted line represents their frequency delta.
 
 ### Estimated similarity and mechanical issues indicator
 

install.sh

@@ -64,7 +64,7 @@ function check_download {
 
     if [ ! -d "${K_SHAKETUNE_PATH}" ]; then
         echo "[DOWNLOAD] Downloading Klippain Shake&Tune module repository..."
-        if git -C $shaketunedirname clone https://github.com/Frix-x/klippain-shaketune.git $shaketunebasename; then
+        if git -C $shaketunedirname clone https://cloud.reijii.org/gitea/reijii/klippain-shaketune-telegramm.git $shaketunebasename; then
             chmod +x ${K_SHAKETUNE_PATH}/install.sh
             printf "[DOWNLOAD] Download complete!\n\n"
         else

moonraker.conf

@@ -1,8 +1,7 @@
-
 ## Klippain Shake&Tune automatic update management
 [update_manager Klippain-ShakeTune]
 type: git_repo
-origin: https://github.com/Frix-x/klippain-shaketune.git
+origin: https://cloud.reijii.org/gitea/reijii/klippain-shaketune-telegramm.git
 path: ~/klippain_shaketune
 virtualenv: ~/klippy-env
 requirements: requirements.txt

shaketune/graph_creators/belts_graph_creator.py

@@ -19,6 +19,7 @@ import matplotlib.font_manager
 import matplotlib.pyplot as plt
 import matplotlib.ticker
 import numpy as np
+from scipy.stats import pearsonr
 
 matplotlib.use('Agg')
 
@@ -210,8 +211,8 @@ def plot_compare_frequency(
     ax: plt.Axes, signal1: SignalData, signal2: SignalData, signal1_belt: str, signal2_belt: str, max_freq: float
 ) -> None:
     # Plot the two belts PSD signals
-    ax.plot(signal1.freqs, signal1.psd, label='Belt ' + signal1_belt, color=KLIPPAIN_COLORS['purple'])
-    ax.plot(signal2.freqs, signal2.psd, label='Belt ' + signal2_belt, color=KLIPPAIN_COLORS['orange'])
+    ax.plot(signal1.freqs, signal1.psd, label='Belt ' + signal1_belt, color=KLIPPAIN_COLORS['orange'])
+    ax.plot(signal2.freqs, signal2.psd, label='Belt ' + signal2_belt, color=KLIPPAIN_COLORS['purple'])
 
     psd_highest_max = max(signal1.psd.max(), signal2.psd.max())
 
@@ -343,14 +344,12 @@ def plot_versus_belts(
     common_freqs: np.ndarray,
     signal1: SignalData,
     signal2: SignalData,
-    interp_psd1: np.ndarray,
-    interp_psd2: np.ndarray,
     signal1_belt: str,
     signal2_belt: str,
 ) -> None:
     ax.set_title('Cross-belts comparison plot', fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
 
-    max_psd = max(np.max(interp_psd1), np.max(interp_psd2))
+    max_psd = max(np.max(signal1.psd), np.max(signal2.psd))
     ideal_line = np.linspace(0, max_psd * 1.1, 500)
     green_boundary = ideal_line + (0.35 * max_psd * np.exp(-ideal_line / (0.6 * max_psd)))
     ax.fill_betweenx(ideal_line, ideal_line, green_boundary, color='green', alpha=0.15)
@@ -364,8 +363,8 @@ def plot_versus_belts(
         linewidth=2,
     )
 
-    ax.plot(interp_psd1, interp_psd2, color='dimgrey', marker='o', markersize=1.5)
-    ax.fill_betweenx(interp_psd2, interp_psd1, color=KLIPPAIN_COLORS['red_pink'], alpha=0.1)
+    ax.plot(signal1.psd, signal2.psd, color='dimgrey', marker='o', markersize=1.5)
+    ax.fill_betweenx(signal2.psd, signal1.psd, color=KLIPPAIN_COLORS['red_pink'], alpha=0.1)
 
     paired_peak_count = 0
     unpaired_peak_count = 0
@@ -374,31 +373,27 @@ def plot_versus_belts(
         label = ALPHABET[paired_peak_count]
         freq1 = signal1.freqs[peak1[0]]
         freq2 = signal2.freqs[peak2[0]]
-        nearest_idx1 = np.argmin(np.abs(common_freqs - freq1))
-        nearest_idx2 = np.argmin(np.abs(common_freqs - freq2))
 
-        if nearest_idx1 == nearest_idx2:
-            psd1_peak_value = interp_psd1[nearest_idx1]
-            psd2_peak_value = interp_psd2[nearest_idx1]
-            ax.plot(psd1_peak_value, psd2_peak_value, marker='o', color='black', markersize=7)
+        if abs(freq1 - freq2) < 1:
+            ax.plot(signal1.psd[peak1[0]], signal2.psd[peak2[0]], marker='o', color='black', markersize=7)
             ax.annotate(
                 f'{label}1/{label}2',
-                (psd1_peak_value, psd2_peak_value),
+                (signal1.psd[peak1[0]], signal2.psd[peak2[0]]),
                 textcoords='offset points',
                 xytext=(-7, 7),
                 fontsize=13,
                 color='black',
             )
         else:
-            psd1_peak_value = interp_psd1[nearest_idx1]
-            psd1_on_peak = interp_psd1[nearest_idx2]
-            psd2_peak_value = interp_psd2[nearest_idx2]
-            psd2_on_peak = interp_psd2[nearest_idx1]
-            ax.plot(psd1_on_peak, psd2_peak_value, marker='o', color=KLIPPAIN_COLORS['orange'], markersize=7)
-            ax.plot(psd1_peak_value, psd2_on_peak, marker='o', color=KLIPPAIN_COLORS['purple'], markersize=7)
+            ax.plot(
+                signal1.psd[peak2[0]], signal2.psd[peak2[0]], marker='o', color=KLIPPAIN_COLORS['orange'], markersize=7
+            )
+            ax.plot(
+                signal1.psd[peak1[0]], signal2.psd[peak1[0]], marker='o', color=KLIPPAIN_COLORS['purple'], markersize=7
+            )
             ax.annotate(
                 f'{label}1',
-                (psd1_peak_value, psd2_on_peak),
+                (signal1.psd[peak1[0]], signal2.psd[peak1[0]]),
                 textcoords='offset points',
                 xytext=(0, 7),
                 fontsize=13,
@@ -406,7 +401,7 @@ def plot_versus_belts(
             )
             ax.annotate(
                 f'{label}2',
-                (psd1_on_peak, psd2_peak_value),
+                (signal1.psd[peak2[0]], signal2.psd[peak2[0]]),
                 textcoords='offset points',
                 xytext=(0, 7),
                 fontsize=13,
@@ -415,16 +410,12 @@ def plot_versus_belts(
         paired_peak_count += 1
 
     for _, peak_index in enumerate(signal1.unpaired_peaks):
-        freq1 = signal1.freqs[peak_index]
-        freq2 = signal2.freqs[peak_index]
-        nearest_idx1 = np.argmin(np.abs(common_freqs - freq1))
-        nearest_idx2 = np.argmin(np.abs(common_freqs - freq2))
-        psd1_peak_value = interp_psd1[nearest_idx1]
-        psd2_peak_value = interp_psd2[nearest_idx1]
-        ax.plot(psd1_peak_value, psd2_peak_value, marker='o', color=KLIPPAIN_COLORS['purple'], markersize=7)
+        ax.plot(
+            signal1.psd[peak_index], signal2.psd[peak_index], marker='o', color=KLIPPAIN_COLORS['purple'], markersize=7
+        )
         ax.annotate(
             str(unpaired_peak_count + 1),
-            (psd1_peak_value, psd2_peak_value),
+            (signal1.psd[peak_index], signal2.psd[peak_index]),
             textcoords='offset points',
             fontsize=13,
             weight='bold',
@@ -434,16 +425,12 @@ def plot_versus_belts(
         unpaired_peak_count += 1
 
     for _, peak_index in enumerate(signal2.unpaired_peaks):
-        freq1 = signal1.freqs[peak_index]
-        freq2 = signal2.freqs[peak_index]
-        nearest_idx1 = np.argmin(np.abs(common_freqs - freq1))
-        nearest_idx2 = np.argmin(np.abs(common_freqs - freq2))
-        psd1_peak_value = interp_psd1[nearest_idx1]
-        psd2_peak_value = interp_psd2[nearest_idx1]
-        ax.plot(psd1_peak_value, psd2_peak_value, marker='o', color=KLIPPAIN_COLORS['orange'], markersize=7)
+        ax.plot(
+            signal1.psd[peak_index], signal2.psd[peak_index], marker='o', color=KLIPPAIN_COLORS['orange'], markersize=7
+        )
         ax.annotate(
             str(unpaired_peak_count + 1),
-            (psd1_peak_value, psd2_peak_value),
+            (signal1.psd[peak_index], signal2.psd[peak_index]),
             textcoords='offset points',
             fontsize=13,
             weight='bold',
@@ -476,16 +463,21 @@ def plot_versus_belts(
 
 
 # Original Klipper function to get the PSD data of a raw accelerometer signal
-def compute_signal_data(data: np.ndarray, max_freq: float) -> SignalData:
+def compute_signal_data(data: np.ndarray, common_freqs: np.ndarray, max_freq: float) -> SignalData:
     helper = shaper_calibrate.ShaperCalibrate(printer=None)
     calibration_data = helper.process_accelerometer_data(data)
 
     freqs = calibration_data.freq_bins[calibration_data.freq_bins <= max_freq]
     psd = calibration_data.get_psd('all')[calibration_data.freq_bins <= max_freq]
 
-    _, peaks, _ = detect_peaks(psd, freqs, PEAKS_DETECTION_THRESHOLD * psd.max())
+    # Re-interpolate the PSD signal to a common frequency range to be able to plot them one against the other
+    interp_psd = np.interp(common_freqs, freqs, psd)
 
-    return SignalData(freqs=freqs, psd=psd, peaks=peaks)
+    _, peaks, _ = detect_peaks(
+        interp_psd, common_freqs, PEAKS_DETECTION_THRESHOLD * interp_psd.max(), window_size=20, vicinity=15
+    )
+
+    return SignalData(freqs=common_freqs, psd=interp_psd, peaks=peaks)
 
 
 ######################################################################
@@ -517,8 +509,9 @@ def belts_calibration(
     signal2_belt += belt_info.get(signal2_belt, '')
 
     # Compute calibration data for the two datasets with automatic peaks detection
-    signal1 = compute_signal_data(datas[0], max_freq)
-    signal2 = compute_signal_data(datas[1], max_freq)
+    common_freqs = np.linspace(0, max_freq, 500)
+    signal1 = compute_signal_data(datas[0], common_freqs, max_freq)
+    signal2 = compute_signal_data(datas[1], common_freqs, max_freq)
     del datas
 
     # Pair the peaks across the two datasets
@@ -526,18 +519,13 @@ def belts_calibration(
     signal1 = signal1._replace(paired_peaks=pairing_result.paired_peaks, unpaired_peaks=pairing_result.unpaired_peaks1)
     signal2 = signal2._replace(paired_peaks=pairing_result.paired_peaks, unpaired_peaks=pairing_result.unpaired_peaks2)
 
-    # Re-interpolate the PSD signals to a common frequency range to be able to plot them one against the other point by point
-    common_freqs = np.linspace(0, max_freq, 500)
-    interp_psd1 = np.interp(common_freqs, signal1.freqs, signal1.psd)
-    interp_psd2 = np.interp(common_freqs, signal2.freqs, signal2.psd)
-
-    # Calculating R^2 to y=x line to compute the similarity between the two belts
-    ss_res = np.sum((interp_psd2 - interp_psd1) ** 2)
-    ss_tot = np.sum((interp_psd2 - np.mean(interp_psd2)) ** 2)
-    similarity_factor = (1 - (ss_res / ss_tot)) * 100
+    # R² proved to be pretty instable to compute the similarity between the two belts
+    # So now, we use the Pearson correlation coefficient to compute the similarity
+    correlation, _ = pearsonr(signal1.psd, signal2.psd)
+    similarity_factor = correlation * 100
+    similarity_factor = np.clip(similarity_factor, 0, 100)
     ConsoleOutput.print(f'Belts estimated similarity: {similarity_factor:.1f}%')
 
-    # mhi = compute_mhi(similarity_factor, num_peaks, num_unpaired_peaks)
     mhi = compute_mhi(similarity_factor, signal1, signal2)
     ConsoleOutput.print(f'[experimental] Mechanical health: {mhi}')
 
@@ -582,11 +570,11 @@ def belts_calibration(
 
     # Add the accel_per_hz value to the title
     title_line5 = f'| Accel per Hz used: {accel_per_hz} mm/s²/Hz'
-    fig.text(0.55, 0.915, title_line5, ha='left', va='top', fontsize=14, color=KLIPPAIN_COLORS['dark_purple'])
+    fig.text(0.551, 0.915, title_line5, ha='left', va='top', fontsize=10, color=KLIPPAIN_COLORS['dark_purple'])
 
     # Plot the graphs
     plot_compare_frequency(ax1, signal1, signal2, signal1_belt, signal2_belt, max_freq)
-    plot_versus_belts(ax3, common_freqs, signal1, signal2, interp_psd1, interp_psd2, signal1_belt, signal2_belt)
+    plot_versus_belts(ax3, common_freqs, signal1, signal2, signal1_belt, signal2_belt)
 
     # Adding a small Klippain logo to the top left corner of the figure
     ax_logo = fig.add_axes([0.001, 0.894, 0.105, 0.105], anchor='NW')