updated CI smoke tests

.github/workflows/test.yml

@@ -27,7 +27,7 @@ jobs:
       - name: Install build dependencies
         run: |
           sudo apt-get update
-          sudo apt-get install -y build-essential
+          sudo apt-get install -y build-essential gcc-avr avr-libc
       - name: Build klipper dict
         run: |
           pushd klipper
@@ -50,7 +50,7 @@ jobs:
         run: |
           pushd klipper
           mkdir ../dicts
-          cp ../klipper/out/klipper.dict ../dicts/linux_basic.dict
+          cp ../klipper/out/klipper.dict ../dicts/atmega2560.dict
           ../klippy-env/bin/python scripts/test_klippy.py -d ../dicts ../shaketune/ci/smoke-test/klippy-tests/simple.test
   lint:
     runs-on: ubuntu-latest

ci/smoke-test/klipper-smoketest.kconfig

@@ -1,34 +1,4 @@
-CONFIG_LOW_LEVEL_OPTIONS=y
-# CONFIG_MACH_AVR is not set
-# CONFIG_MACH_ATSAM is not set
-# CONFIG_MACH_ATSAMD is not set
-# CONFIG_MACH_LPC176X is not set
-# CONFIG_MACH_STM32 is not set
-# CONFIG_MACH_HC32F460 is not set
-# CONFIG_MACH_RP2040 is not set
-# CONFIG_MACH_PRU is not set
-# CONFIG_MACH_AR100 is not set
-CONFIG_MACH_LINUX=y
-# CONFIG_MACH_SIMU is not set
-CONFIG_BOARD_DIRECTORY="linux"
-CONFIG_CLOCK_FREQ=50000000
-CONFIG_LINUX_SELECT=y
-CONFIG_USB_VENDOR_ID=0x1d50
-CONFIG_USB_DEVICE_ID=0x614e
-CONFIG_USB_SERIAL_NUMBER="12345"
-CONFIG_WANT_GPIO_BITBANGING=y
-CONFIG_WANT_DISPLAYS=y
-CONFIG_WANT_SENSORS=y
-CONFIG_WANT_LIS2DW=y
-CONFIG_WANT_LDC1612=y
-CONFIG_WANT_SOFTWARE_I2C=y
-CONFIG_WANT_SOFTWARE_SPI=y
-CONFIG_NEED_SENSOR_BULK=y
-CONFIG_CANBUS_FREQUENCY=1000000
-CONFIG_INITIAL_PINS=""
-CONFIG_HAVE_GPIO=y
-CONFIG_HAVE_GPIO_ADC=y
-CONFIG_HAVE_GPIO_SPI=y
-CONFIG_HAVE_GPIO_I2C=y
-CONFIG_HAVE_GPIO_HARD_PWM=y
-CONFIG_INLINE_STEPPER_HACK=y
+# Base Kconfig file for atmega2560
+CONFIG_MACH_AVR=y
+CONFIG_MACH_atmega2560=y
+CONFIG_CLOCK_FREQ=16000000

ci/smoke-test/klippy-tests/simple.cfg

@@ -1,9 +1,85 @@
+# Test config with a minimal setup to have kind
+# of a machine ready with an ADXL345 and an MPU9250
+# to have the required the resonance_tester section
+# and allow loading and initializing Shake&Tune into Klipper
+
+[stepper_x]
+step_pin: PF0
+dir_pin: PF1
+enable_pin: !PD7
+microsteps: 16
+rotation_distance: 40
+endstop_pin: ^PE5
+position_endstop: 0
+position_max: 200
+homing_speed: 50
+
+[stepper_y]
+step_pin: PF6
+dir_pin: !PF7
+enable_pin: !PF2
+microsteps: 16
+rotation_distance: 40
+endstop_pin: ^PJ1
+position_endstop: 0
+position_max: 200
+homing_speed: 50
+
+[stepper_z]
+step_pin: PL3
+dir_pin: PL1
+enable_pin: !PK0
+microsteps: 16
+rotation_distance: 8
+endstop_pin: ^PD3
+position_endstop: 0.5
+position_max: 200
+
+[extruder]
+step_pin: PA4
+dir_pin: PA6
+enable_pin: !PA2
+microsteps: 16
+rotation_distance: 33.5
+nozzle_diameter: 0.500
+filament_diameter: 3.500
+heater_pin: PB4
+sensor_type: EPCOS 100K B57560G104F
+sensor_pin: PK5
+control: pid
+pid_Kp: 22.2
+pid_Ki: 1.08
+pid_Kd: 114
+min_temp: 0
+max_temp: 210
+
+[heater_bed]
+heater_pin: PH5
+sensor_type: EPCOS 100K B57560G104F
+sensor_pin: PK6
+control: watermark
+min_temp: 0
+max_temp: 110
+
 [mcu]
-serial: /tmp/klipper_host_mcu
+serial: /dev/ttyACM0
 
 [printer]
-kinematics: none
+kinematics: cartesian
 max_velocity: 300
-max_accel: 300
+max_accel: 3000
+max_z_velocity: 5
+max_z_accel: 100
+
+[adxl345]
+cs_pin: PK7
+axes_map: -x,-y,z
+
+[mpu9250 my_mpu]
+
+[resonance_tester]
+probe_points: 20,20,20
+accel_chip_x: adxl345
+accel_chip_y: mpu9250 my_mpu
 
 [shaketune]

ci/smoke-test/klippy-tests/simple.test

@@ -1,4 +1,4 @@
-DICTIONARY linux_basic.dict
 CONFIG simple.cfg
+DICTIONARY atmega2560.dict
 
 G4 P1000

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')
 
@@ -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')

shaketune/graph_creators/shaper_graph_creator.py

@@ -22,13 +22,14 @@
 import optparse
 import os
 from datetime import datetime
-from typing import List, Optional
+from typing import Dict, List, Optional
 
 import matplotlib
 import matplotlib.font_manager
 import matplotlib.pyplot as plt
 import matplotlib.ticker
 import numpy as np
+from scipy.interpolate import interp1d
 
 matplotlib.use('Agg')
 
@@ -47,7 +48,9 @@ PEAKS_DETECTION_THRESHOLD = 0.05
 PEAKS_EFFECT_THRESHOLD = 0.12
 SPECTROGRAM_LOW_PERCENTILE_FILTER = 5
 MAX_VIBRATIONS = 5.0
-
+MAX_VIBRATIONS_PLOTTED = 80.0
+MAX_VIBRATIONS_PLOTTED_ZOOM = 1.25  # 1.25x max vibs values from the standard filters selection
+SMOOTHING_TESTS = 10  # Number of smoothing values to test (it will significantly increase the computation time)
 KLIPPAIN_COLORS = {
     'purple': '#70088C',
     'orange': '#FF8D32',
@@ -112,15 +115,13 @@ def calibrate_shaper(datas: List[np.ndarray], max_smoothing: Optional[float], sc
     calibration_data = helper.process_accelerometer_data(datas)
     calibration_data.normalize_to_frequencies()
 
+    # We compute the damping ratio using the Klipper's default value if it fails
     fr, zeta, _, _ = compute_mechanical_parameters(calibration_data.psd_sum, calibration_data.freq_bins)
-
-    # If the damping ratio computation fail, we use Klipper default value instead
-    if zeta is None:
-        zeta = 0.1
+    zeta = zeta if zeta is not None else 0.1
 
     compat = False
     try:
-        shaper, all_shapers = helper.find_best_shaper(
+        k_shaper_choice, all_shapers = helper.find_best_shaper(
             calibration_data,
             shapers=None,
             damping_ratio=zeta,
@@ -129,23 +130,79 @@ def calibrate_shaper(datas: List[np.ndarray], max_smoothing: Optional[float], sc
             max_smoothing=max_smoothing,
             test_damping_ratios=None,
             max_freq=max_freq,
-            logger=ConsoleOutput.print,
+            logger=None,
+        )
+        ConsoleOutput.print(
+            (
+                f'Detected a square corner velocity of {scv:.1f} and a damping ratio of {zeta:.3f}. '
+                'These values will be used to compute the input shaper filter recommendations'
+            )
         )
     except TypeError:
         ConsoleOutput.print(
-            '[WARNING] You seem to be using an older version of Klipper that is not compatible with all the latest Shake&Tune features!'
-        )
-        ConsoleOutput.print(
-            'Shake&Tune now runs in compatibility mode: be aware that the results may be slightly off, since the real damping ratio cannot be used to create the filter recommendations'
+            (
+                '[WARNING] You seem to be using an older version of Klipper that is not compatible with all the latest '
+                'Shake&Tune features!\nShake&Tune now runs in compatibility mode: be aware that the results may be '
+                'slightly off, since the real damping ratio cannot be used to craft accurate filter recommendations'
+            )
         )
         compat = True
-        shaper, all_shapers = helper.find_best_shaper(calibration_data, max_smoothing, ConsoleOutput.print)
+        k_shaper_choice, all_shapers = helper.find_best_shaper(calibration_data, max_smoothing, None)
 
-    ConsoleOutput.print(
-        f'\n-> Recommended shaper is {shaper.name.upper()} @ {shaper.freq:.1f} Hz (when using a square corner velocity of {scv:.1f} and a damping ratio of {zeta:.3f})'
+    # If max_smoothing is not None, we run the same computation but without a smoothing value
+    # to get the max smoothing values from the filters and create the testing list
+    all_shapers_nosmoothing = None
+    if max_smoothing is not None:
+        if compat:
+            _, all_shapers_nosmoothing = helper.find_best_shaper(calibration_data, None, None)
+        else:
+            _, all_shapers_nosmoothing = helper.find_best_shaper(
+                calibration_data,
+                shapers=None,
+                damping_ratio=zeta,
+                scv=scv,
+                shaper_freqs=None,
+                max_smoothing=None,
+                test_damping_ratios=None,
+                max_freq=max_freq,
+                logger=None,
+            )
+
+    # Then we iterate over the all_shaperts_nosmoothing list to get the max of the smoothing values
+    max_smoothing = 0.0
+    if all_shapers_nosmoothing is not None:
+        for shaper in all_shapers_nosmoothing:
+            if shaper.smoothing > max_smoothing:
+                max_smoothing = shaper.smoothing
+    else:
+        for shaper in all_shapers:
+            if shaper.smoothing > max_smoothing:
+                max_smoothing = shaper.smoothing
+
+    # Then we create a list of smoothing values to test (no need to test the max smoothing value as it was already tested)
+    smoothing_test_list = np.linspace(0.001, max_smoothing, SMOOTHING_TESTS)[:-1]
+    additional_all_shapers = {}
+    for smoothing in smoothing_test_list:
+        if compat:
+            _, all_shapers_bis = helper.find_best_shaper(calibration_data, smoothing, None)
+        else:
+            _, all_shapers_bis = helper.find_best_shaper(
+                calibration_data,
+                shapers=None,
+                damping_ratio=zeta,
+                scv=scv,
+                shaper_freqs=None,
+                max_smoothing=smoothing,
+                test_damping_ratios=None,
+                max_freq=max_freq,
+                logger=None,
+            )
+        additional_all_shapers[f'sm_{smoothing}'] = all_shapers_bis
+    additional_all_shapers['max_smoothing'] = (
+        all_shapers_nosmoothing if all_shapers_nosmoothing is not None else all_shapers
     )
 
-    return shaper.name, all_shapers, calibration_data, fr, zeta, compat
+    return k_shaper_choice.name, all_shapers, additional_all_shapers, calibration_data, fr, zeta, max_smoothing, compat
 
 
 ######################################################################
@@ -164,7 +221,7 @@ def plot_freq_response(
     fr: float,
     zeta: float,
     max_freq: float,
-) -> None:
+) -> Dict[str, List[Dict[str, str]]]:
     freqs = calibration_data.freqs
     psd = calibration_data.psd_sum
     px = calibration_data.psd_x
@@ -193,27 +250,40 @@ def plot_freq_response(
     ax2 = ax.twinx()
     ax2.yaxis.set_visible(False)
 
+    shaper_table_data = {
+        'shapers': [],
+        'recommendations': [],
+        'damping_ratio': zeta,
+    }
+
     # 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:
-        shaper_max_accel = round(shaper.max_accel / 100.0) * 100.0
-        label = f'{shaper.name.upper()} ({shaper.freq:.1f} Hz, vibr={shaper.vibrs * 100.0:.1f}%, sm~={shaper.smoothing:.2f}, accel<={shaper_max_accel:.0f})'
-        ax2.plot(freqs, shaper.vals, label=label, linestyle='dotted')
+        ax2.plot(freqs, shaper.vals, label=shaper.name.upper(), linestyle='dotted')
+
+        shaper_info = {
+            'type': shaper.name.upper(),
+            'frequency': shaper.freq,
+            'vibrations': shaper.vibrs,
+            'smoothing': shaper.smoothing,
+            'max_accel': shaper.max_accel,
+        }
+        shaper_table_data['shapers'].append(shaper_info)
 
         # Get the Klipper recommended shaper (usually it's a good low vibration compromise)
         if shaper.name == klipper_shaper_choice:
             klipper_shaper_freq = shaper.freq
             klipper_shaper_vals = shaper.vals
-            klipper_shaper_accel = shaper_max_accel
+            klipper_shaper_accel = shaper.max_accel
 
         # Find the shaper with the highest accel but with vibrs under MAX_VIBRATIONS as it's
         # a good performance compromise when injecting the SCV and damping ratio in the computation
-        if perf_shaper_accel < shaper_max_accel and shaper.vibrs * 100 < MAX_VIBRATIONS:
+        if perf_shaper_accel < shaper.max_accel and shaper.vibrs * 100 < MAX_VIBRATIONS:
             perf_shaper_choice = shaper.name
-            perf_shaper_accel = shaper_max_accel
+            perf_shaper_accel = shaper.max_accel
             perf_shaper_freq = shaper.freq
             perf_shaper_vals = shaper.vals
 
@@ -226,32 +296,30 @@ def plot_freq_response(
         and perf_shaper_choice != klipper_shaper_choice
         and perf_shaper_accel >= klipper_shaper_accel
     ):
-        ax2.plot(
-            [],
-            [],
-            ' ',
-            label=f'Recommended performance shaper: {perf_shaper_choice.upper()} @ {perf_shaper_freq:.1f} Hz',
+        perf_shaper_string = f'Recommended for performance: {perf_shaper_choice.upper()} @ {perf_shaper_freq:.1f} Hz'
+        lowvibr_shaper_string = (
+            f'Recommended for low vibrations: {klipper_shaper_choice.upper()} @ {klipper_shaper_freq:.1f} Hz'
         )
+        shaper_table_data['recommendations'].append(perf_shaper_string)
+        shaper_table_data['recommendations'].append(lowvibr_shaper_string)
+        ConsoleOutput.print(f'{perf_shaper_string} (with a damping ratio of {zeta:.3f})')
+        ConsoleOutput.print(f'{lowvibr_shaper_string} (with a damping ratio of {zeta:.3f})')
         ax.plot(
             freqs,
             psd * perf_shaper_vals,
             label=f'With {perf_shaper_choice.upper()} applied',
             color='cyan',
         )
-        ax2.plot(
-            [],
-            [],
-            ' ',
-            label=f'Recommended low vibrations shaper: {klipper_shaper_choice.upper()} @ {klipper_shaper_freq:.1f} Hz',
+        ax.plot(
+            freqs,
+            psd * klipper_shaper_vals,
+            label=f'With {klipper_shaper_choice.upper()} applied',
+            color='lime',
         )
-        ax.plot(freqs, psd * klipper_shaper_vals, label=f'With {klipper_shaper_choice.upper()} applied', color='lime')
     else:
-        ax2.plot(
-            [],
-            [],
-            ' ',
-            label=f'Recommended performance shaper: {klipper_shaper_choice.upper()} @ {klipper_shaper_freq:.1f} Hz',
-        )
+        shaper_string = f'Recommended best shaper: {klipper_shaper_choice.upper()} @ {klipper_shaper_freq:.1f} Hz'
+        shaper_table_data['recommendations'].append(shaper_string)
+        ConsoleOutput.print(f'{shaper_string} (with a damping ratio of {zeta:.3f})')
         ax.plot(
             freqs,
             psd * klipper_shaper_vals,
@@ -259,9 +327,6 @@ def plot_freq_response(
             color='cyan',
         )
 
-    # And the estimated damping ratio is finally added at the end of the legend
-    ax2.plot([], [], ' ', label=f'Estimated damping ratio (ζ): {zeta:.3f}')
-
     # Draw the detected peaks and name them
     # This also draw the detection threshold and warning threshold (aka "effect zone")
     ax.plot(peaks_freqs, psd[peaks], 'x', color='black', markersize=8)
@@ -297,7 +362,7 @@ def plot_freq_response(
     ax.legend(loc='upper left', prop=fontP)
     ax2.legend(loc='upper right', prop=fontP)
 
-    return
+    return shaper_table_data
 
 
 # Plot a time-frequency spectrogram to see how the system respond over time during the
@@ -350,6 +415,170 @@ def plot_spectrogram(
     return
 
 
+def plot_smoothing_vs_accel(
+    ax: plt.Axes,
+    shaper_table_data: Dict[str, List[Dict[str, str]]],
+    additional_shapers: Dict[str, List[Dict[str, str]]],
+) -> None:
+    fontP = matplotlib.font_manager.FontProperties()
+    fontP.set_size('x-small')
+
+    ax.xaxis.set_minor_locator(matplotlib.ticker.MultipleLocator(1000))
+    ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
+    ax.grid(which='major', color='grey')
+    ax.grid(which='minor', color='lightgrey')
+
+    shaper_data = {}
+
+    # Extract data from additional_shapers first
+    for _, shapers in additional_shapers.items():
+        for shaper in shapers:
+            shaper_type = shaper.name.upper()
+            if shaper_type not in shaper_data:
+                shaper_data[shaper_type] = []
+            shaper_data[shaper_type].append(
+                {
+                    'max_accel': shaper.max_accel,
+                    'vibrs': shaper.vibrs * 100.0,
+                }
+            )
+
+    # Extract data from shaper_table_data and insert into shaper_data
+    max_shaper_vibrations = 0
+    for shaper in shaper_table_data['shapers']:
+        shaper_type = shaper['type']
+        if shaper_type not in shaper_data:
+            shaper_data[shaper_type] = []
+        max_shaper_vibrations = max(max_shaper_vibrations, float(shaper['vibrations']) * 100.0)
+        shaper_data[shaper_type].append(
+            {
+                'max_accel': float(shaper['max_accel']),
+                'vibrs': float(shaper['vibrations']) * 100.0,
+            }
+        )
+
+    # Calculate the maximum `max_accel` for points below the thresholds to get a good plot with
+    # continuous lines and a zoom on the graph to show details at low vibrations
+    min_accel_limit = 99999
+    max_accel_limit = 0
+    max_accel_limit_zoom = 0
+    for data in shaper_data.values():
+        min_accel_limit = min(min_accel_limit, min(d['max_accel'] for d in data))
+        max_accel_limit = max(
+            max_accel_limit, max(d['max_accel'] for d in data if d['vibrs'] <= MAX_VIBRATIONS_PLOTTED)
+        )
+        max_accel_limit_zoom = max(
+            max_accel_limit_zoom,
+            max(d['max_accel'] for d in data if d['vibrs'] <= max_shaper_vibrations * MAX_VIBRATIONS_PLOTTED_ZOOM),
+        )
+
+    # Add a zoom axes on the graph to show details at low vibrations
+    zoomed_window = np.clip(max_shaper_vibrations * MAX_VIBRATIONS_PLOTTED_ZOOM, 0, 20)
+    axins = ax.inset_axes(
+        [0.575, 0.125, 0.40, 0.45],
+        xlim=(min_accel_limit * 0.95, max_accel_limit_zoom * 1.1),
+        ylim=(-0.5, zoomed_window),
+    )
+    ax.indicate_inset_zoom(axins, edgecolor=KLIPPAIN_COLORS['purple'], linewidth=3)
+    axins.xaxis.set_minor_locator(matplotlib.ticker.MultipleLocator(500))
+    axins.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
+    axins.grid(which='major', color='grey')
+    axins.grid(which='minor', color='lightgrey')
+
+    # Draw the green zone on both axes to highlight the low vibrations zone
+    number_of_interpolated_points = 100
+    x_fill = np.linspace(min_accel_limit * 0.95, max_accel_limit * 1.1, number_of_interpolated_points)
+    y_fill = np.full_like(x_fill, 5.0)
+    ax.axhline(y=5.0, color='black', linestyle='--', linewidth=0.5)
+    ax.fill_between(x_fill, -0.5, y_fill, color='green', alpha=0.15)
+    if zoomed_window > 5.0:
+        axins.axhline(y=5.0, color='black', linestyle='--', linewidth=0.5)
+        axins.fill_between(x_fill, -0.5, y_fill, color='green', alpha=0.15)
+
+    # Plot each shaper remaining vibrations response over acceleration
+    max_vibrations = 0
+    for _, (shaper_type, data) in enumerate(shaper_data.items()):
+        max_accel_values = np.array([d['max_accel'] for d in data])
+        vibrs_values = np.array([d['vibrs'] for d in data])
+
+        # remove duplicate values in max_accel_values and delete the corresponding vibrs_values
+        # and interpolate the curves to get them smoother with more datapoints
+        unique_max_accel_values, unique_indices = np.unique(max_accel_values, return_index=True)
+        max_accel_values = unique_max_accel_values
+        vibrs_values = vibrs_values[unique_indices]
+        interp_func = interp1d(max_accel_values, vibrs_values, kind='cubic')
+        max_accel_fine = np.linspace(max_accel_values.min(), max_accel_values.max(), number_of_interpolated_points)
+        vibrs_fine = interp_func(max_accel_fine)
+
+        ax.plot(max_accel_fine, vibrs_fine, label=f'{shaper_type}', zorder=10)
+        axins.plot(max_accel_fine, vibrs_fine, label=f'{shaper_type}', zorder=15)
+        max_vibrations = max(max_vibrations, max(vibrs_fine))
+
+    ax.set_xlabel('Max Acceleration')
+    ax.set_ylabel('Remaining Vibrations (%)')
+    ax.set_xlim([min_accel_limit * 0.95, max_accel_limit * 1.1])
+    ax.set_ylim([-0.5, np.clip(max_vibrations * 1.05, 50, MAX_VIBRATIONS_PLOTTED)])
+    ax.set_title(
+        'Filters performances over acceleration',
+        fontsize=14,
+        color=KLIPPAIN_COLORS['dark_orange'],
+        weight='bold',
+    )
+    ax.legend(loc='best', prop=fontP)
+
+
+def print_shaper_table(fig: plt.Figure, shaper_table_data: Dict[str, List[Dict[str, str]]]) -> None:
+    columns = ['Type', 'Frequency', 'Vibrations', 'Smoothing', 'Max Accel']
+    table_data = []
+
+    for shaper_info in shaper_table_data['shapers']:
+        row = [
+            f'{shaper_info["type"].upper()}',
+            f'{shaper_info["frequency"]:.1f} Hz',
+            f'{shaper_info["vibrations"] * 100:.1f} %',
+            f'{shaper_info["smoothing"]:.3f}',
+            f'{round(shaper_info["max_accel"] / 10) * 10:.0f}',
+        ]
+        table_data.append(row)
+    table = plt.table(cellText=table_data, colLabels=columns, bbox=[1.130, -0.4, 0.803, 0.25], cellLoc='center')
+    table.auto_set_font_size(False)
+    table.set_fontsize(10)
+    table.auto_set_column_width([0, 1, 2, 3, 4])
+    table.set_zorder(100)
+
+    # Add the recommendations and damping ratio using fig.text
+    fig.text(
+        0.585,
+        0.235,
+        f'Estimated damping ratio (ζ): {shaper_table_data["damping_ratio"]:.3f}',
+        fontsize=14,
+        color=KLIPPAIN_COLORS['purple'],
+    )
+    if len(shaper_table_data['recommendations']) == 1:
+        fig.text(
+            0.585,
+            0.200,
+            shaper_table_data['recommendations'][0],
+            fontsize=14,
+            color=KLIPPAIN_COLORS['red_pink'],
+        )
+    elif len(shaper_table_data['recommendations']) == 2:
+        fig.text(
+            0.585,
+            0.200,
+            shaper_table_data['recommendations'][0],
+            fontsize=14,
+            color=KLIPPAIN_COLORS['red_pink'],
+        )
+        fig.text(
+            0.585,
+            0.175,
+            shaper_table_data['recommendations'][1],
+            fontsize=14,
+            color=KLIPPAIN_COLORS['red_pink'],
+        )
+
+
 ######################################################################
 # Startup and main routines
 ######################################################################
@@ -375,8 +604,8 @@ def shaper_calibration(
         ConsoleOutput.print('Warning: incorrect number of .csv files detected. Only the first one will be used!')
 
     # Compute shapers, PSD outputs and spectrogram
-    klipper_shaper_choice, shapers, calibration_data, fr, zeta, compat = calibrate_shaper(
-        datas[0], max_smoothing, scv, max_freq
+    klipper_shaper_choice, shapers, additional_shapers, calibration_data, fr, zeta, max_smoothing_computed, compat = (
+        calibrate_shaper(datas[0], max_smoothing, scv, max_freq)
     )
     pdata, bins, t = compute_spectrogram(datas[0])
     del datas
@@ -400,29 +629,31 @@ def shaper_calibration(
     peak_freqs_formated = ['{:.1f}'.format(f) for f in peaks_freqs]
     num_peaks_above_effect_threshold = np.sum(calibration_data.psd_sum[peaks] > peaks_threshold[1])
     ConsoleOutput.print(
-        f"\nPeaks detected on the graph: {num_peaks} @ {', '.join(map(str, peak_freqs_formated))} Hz ({num_peaks_above_effect_threshold} above effect threshold)"
+        f"Peaks detected on the graph: {num_peaks} @ {', '.join(map(str, peak_freqs_formated))} Hz ({num_peaks_above_effect_threshold} above effect threshold)"
     )
 
     # Create graph layout
-    fig, (ax1, ax2) = plt.subplots(
+    fig, ((ax1, ax3), (ax2, ax4)) = plt.subplots(
+        2,
         2,
-        1,
         gridspec_kw={
             'height_ratios': [4, 3],
+            'width_ratios': [5, 4],
             'bottom': 0.050,
             'top': 0.890,
-            'left': 0.085,
+            'left': 0.048,
             'right': 0.966,
             'hspace': 0.169,
-            'wspace': 0.200,
+            'wspace': 0.150,
         },
     )
-    fig.set_size_inches(8.3, 11.6)
+    ax4.remove()
+    fig.set_size_inches(15, 11.6)
 
     # Add a title with some test info
     title_line1 = 'INPUT SHAPER CALIBRATION TOOL'
     fig.text(
-        0.12, 0.965, title_line1, ha='left', va='bottom', fontsize=20, color=KLIPPAIN_COLORS['purple'], weight='bold'
+        0.065, 0.965, title_line1, ha='left', va='bottom', fontsize=20, color=KLIPPAIN_COLORS['purple'], weight='bold'
     )
     try:
         filename_parts = (lognames[0].split('/')[-1]).split('_')
@@ -433,8 +664,11 @@ def shaper_calibration(
             title_line4 = '| and SCV are not used for filter recommendations!'
             title_line5 = f'| Accel per Hz used: {accel_per_hz} mm/s²/Hz' if accel_per_hz is not None else ''
         else:
+            max_smoothing_string = (
+                f'maximum ({max_smoothing_computed:0.3f})' if max_smoothing is None else f'{max_smoothing:0.3f}'
+            )
             title_line3 = f'| Square corner velocity: {scv} mm/s'
-            title_line4 = f'| Max allowed smoothing: {max_smoothing}'
+            title_line4 = f'| Allowed smoothing: {max_smoothing_string}'
             title_line5 = f'| Accel per Hz used: {accel_per_hz} mm/s²/Hz' if accel_per_hz is not None else ''
     except Exception:
         ConsoleOutput.print(f'Warning: CSV filename look to be different than expected ({lognames[0]})')
@@ -442,19 +676,22 @@ def shaper_calibration(
         title_line3 = ''
         title_line4 = ''
         title_line5 = ''
-    fig.text(0.12, 0.957, title_line2, ha='left', va='top', fontsize=16, color=KLIPPAIN_COLORS['dark_purple'])
-    fig.text(0.58, 0.963, title_line3, ha='left', va='top', fontsize=10, color=KLIPPAIN_COLORS['dark_purple'])
-    fig.text(0.58, 0.948, title_line4, ha='left', va='top', fontsize=10, color=KLIPPAIN_COLORS['dark_purple'])
-    fig.text(0.58, 0.933, title_line5, ha='left', va='top', fontsize=10, color=KLIPPAIN_COLORS['dark_purple'])
+    fig.text(0.065, 0.957, title_line2, ha='left', va='top', fontsize=16, color=KLIPPAIN_COLORS['dark_purple'])
+    fig.text(0.50, 0.990, title_line3, ha='left', va='top', fontsize=14, color=KLIPPAIN_COLORS['dark_purple'])
+    fig.text(0.50, 0.968, title_line4, ha='left', va='top', fontsize=14, color=KLIPPAIN_COLORS['dark_purple'])
+    fig.text(0.501, 0.945, title_line5, ha='left', va='top', fontsize=10, color=KLIPPAIN_COLORS['dark_purple'])
 
     # Plot the graphs
-    plot_freq_response(
+    shaper_table_data = plot_freq_response(
         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_smoothing_vs_accel(ax3, shaper_table_data, additional_shapers)
+
+    print_shaper_table(fig, shaper_table_data)
 
     # Adding a small Klippain logo to the top left corner of the figure
-    ax_logo = fig.add_axes([0.001, 0.8995, 0.1, 0.1], anchor='NW')
+    ax_logo = fig.add_axes([0.001, 0.924, 0.075, 0.075], anchor='NW')
     ax_logo.imshow(plt.imread(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'klippain.png')))
     ax_logo.axis('off')
 

shaketune/shaketune.py

@@ -8,6 +8,7 @@
 #              loading of the plugin, and the registration of the tuning commands
 
 
+import importlib
 import os
 from pathlib import Path
 
@@ -29,156 +30,176 @@ from .helpers.console_output import ConsoleOutput
 from .shaketune_config import ShakeTuneConfig
 from .shaketune_process import ShakeTuneProcess
 
-IN_DANGER = False
+DEFAULT_FOLDER = '~/printer_data/config/ShakeTune_results'
+DEFAULT_NUMBER_OF_RESULTS = 3
+DEFAULT_KEEP_RAW_CSV = False
+DEFAULT_DPI = 150
+DEFAULT_TIMEOUT = 300
+DEFAULT_SHOW_MACROS = True
+ST_COMMANDS = {
+    'EXCITATE_AXIS_AT_FREQ': (
+        'Maintain a specified excitation frequency for a period '
+        'of time to diagnose and locate a source of vibrations'
+    ),
+    'AXES_MAP_CALIBRATION': (
+        'Perform a set of movements to measure the orientation of the accelerometer '
+        'and help you set the best axes_map configuration for your printer'
+    ),
+    'COMPARE_BELTS_RESPONSES': (
+        'Perform a custom half-axis test to analyze and compare the '
+        'frequency profiles of individual belts on CoreXY or CoreXZ printers'
+    ),
+    'AXES_SHAPER_CALIBRATION': 'Perform standard axis input shaper tests on one or both XY axes to select the best input shaper filter',
+    'CREATE_VIBRATIONS_PROFILE': (
+        'Run a series of motions to find speed/angle ranges where the printer could be '
+        'exposed to VFAs to optimize your slicer speed profiles and TMC driver parameters'
+    ),
+}
 
 
 class ShakeTune:
     def __init__(self, config) -> None:
-        self._pconfig = config
+        self._config = config
         self._printer = config.get_printer()
+        self._printer.register_event_handler('klippy:connect', self._on_klippy_connect)
+
+        # Check if Shake&Tune is running in DangerKlipper
+        self.IN_DANGER = importlib.util.find_spec('extras.danger_options') is not None
+
+        # Register the console print output callback to the corresponding Klipper function
         gcode = self._printer.lookup_object('gcode')
-
-        res_tester = self._printer.lookup_object('resonance_tester', None)
-        if res_tester is None:
-            config.error('No [resonance_tester] config section found in printer.cfg! Please add one to use Shake&Tune.')
-
-        self.timeout = config.getfloat('timeout', 300, above=0.0)
-        result_folder = config.get('result_folder', default='~/printer_data/config/ShakeTune_results')
-        result_folder_path = Path(result_folder).expanduser() if result_folder else None
-        keep_n_results = config.getint('number_of_results_to_keep', default=3, minval=0)
-        keep_csv = config.getboolean('keep_raw_csv', default=False)
-        show_macros = config.getboolean('show_macros_in_webui', default=True)
-        dpi = config.getint('dpi', default=150, minval=100, maxval=500)
-
-        self._config = ShakeTuneConfig(result_folder_path, keep_n_results, keep_csv, dpi)
         ConsoleOutput.register_output_callback(gcode.respond_info)
 
-        # Register Shake&Tune's measurement commands
-        measurement_commands = [
-            (
-                'EXCITATE_AXIS_AT_FREQ',
-                self.cmd_EXCITATE_AXIS_AT_FREQ,
-                (
-                    'Maintain a specified excitation frequency for a period '
-                    'of time to diagnose and locate a source of vibrations'
-                ),
-            ),
-            (
-                'AXES_MAP_CALIBRATION',
-                self.cmd_AXES_MAP_CALIBRATION,
-                (
-                    'Perform a set of movements to measure the orientation of the accelerometer '
-                    'and help you set the best axes_map configuration for your printer'
-                ),
-            ),
-            (
-                'COMPARE_BELTS_RESPONSES',
-                self.cmd_COMPARE_BELTS_RESPONSES,
-                (
-                    'Perform a custom half-axis test to analyze and compare the '
-                    'frequency profiles of individual belts on CoreXY or CoreXZ printers'
-                ),
-            ),
-            (
-                'AXES_SHAPER_CALIBRATION',
-                self.cmd_AXES_SHAPER_CALIBRATION,
-                'Perform standard axis input shaper tests on one or both XY axes to select the best input shaper filter',
-            ),
-            (
-                'CREATE_VIBRATIONS_PROFILE',
-                self.cmd_CREATE_VIBRATIONS_PROFILE,
-                (
-                    'Run a series of motions to find speed/angle ranges where the printer could be '
-                    'exposed to VFAs to optimize your slicer speed profiles and TMC driver parameters'
-                ),
-            ),
-        ]
-        command_descriptions = {name: desc for name, _, desc in measurement_commands}
-        for name, command, description in measurement_commands:
-            gcode.register_command(f'_{name}' if show_macros else name, command, desc=description)
+        self._initialize_config(config)
+        self._register_commands()
 
-        # Load the dummy macros with their description in order to show them in the web interfaces
-        if show_macros:
-            pconfig = self._printer.lookup_object('configfile')
+    # Initialize the ShakeTune object and its configuration
+    def _initialize_config(self, config) -> None:
+        result_folder = config.get('result_folder', default=DEFAULT_FOLDER)
+        result_folder_path = Path(result_folder).expanduser() if result_folder else None
+        keep_n_results = config.getint('number_of_results_to_keep', default=DEFAULT_NUMBER_OF_RESULTS, minval=0)
+        keep_csv = config.getboolean('keep_raw_csv', default=DEFAULT_KEEP_RAW_CSV)
+        dpi = config.getint('dpi', default=DEFAULT_DPI, minval=100, maxval=500)
+        self._st_config = ShakeTuneConfig(result_folder_path, keep_n_results, keep_csv, dpi)
+
+        self.timeout = config.getfloat('timeout', 300, above=0.0)
+        self._show_macros = config.getboolean('show_macros_in_webui', default=True)
+
+    # Create the Klipper commands to allow the user to run Shake&Tune's tools
+    def _register_commands(self) -> None:
+        gcode = self._printer.lookup_object('gcode')
+        measurement_commands = [
+            ('EXCITATE_AXIS_AT_FREQ', self.cmd_EXCITATE_AXIS_AT_FREQ, ST_COMMANDS['EXCITATE_AXIS_AT_FREQ']),
+            ('AXES_MAP_CALIBRATION', self.cmd_AXES_MAP_CALIBRATION, ST_COMMANDS['AXES_MAP_CALIBRATION']),
+            ('COMPARE_BELTS_RESPONSES', self.cmd_COMPARE_BELTS_RESPONSES, ST_COMMANDS['COMPARE_BELTS_RESPONSES']),
+            ('AXES_SHAPER_CALIBRATION', self.cmd_AXES_SHAPER_CALIBRATION, ST_COMMANDS['AXES_SHAPER_CALIBRATION']),
+            ('CREATE_VIBRATIONS_PROFILE', self.cmd_CREATE_VIBRATIONS_PROFILE, ST_COMMANDS['CREATE_VIBRATIONS_PROFILE']),
+        ]
+
+        # Register Shake&Tune's measurement commands using the official Klipper API (gcode.register_command)
+        # Doing this makes the commands available in Klipper but they are not shown in the web interfaces
+        # and are only available by typing the full name in the console (like all the other Klipper commands)
+        for name, command, description in measurement_commands:
+            gcode.register_command(f'_{name}' if self._show_macros else name, command, desc=description)
+
+        # Then, a hack to inject the macros into Klipper's config system in order to show them in the web
+        # interfaces. This is not a good way to do it, but it's the only way to do it for now to get
+        # a good user experience while using Shake&Tune (it's indeed easier to just click a macro button)
+        if self._show_macros:
+            configfile = self._printer.lookup_object('configfile')
             dirname = os.path.dirname(os.path.realpath(__file__))
             filename = os.path.join(dirname, 'dummy_macros.cfg')
             try:
-                dummy_macros_cfg = pconfig.read_config(filename)
+                dummy_macros_cfg = configfile.read_config(filename)
             except Exception as err:
-                raise config.error(f'Cannot load Shake&Tune dummy macro {filename}') from err
+                raise self._config.error(f'Cannot load Shake&Tune dummy macro {filename}') from err
 
             for gcode_macro in dummy_macros_cfg.get_prefix_sections('gcode_macro '):
                 gcode_macro_name = gcode_macro.get_name()
 
-                # Replace the dummy description by the one here (to avoid code duplication and define it in only one place)
+                # Replace the dummy description by the one from ST_COMMANDS (to avoid code duplication and define it in only one place)
                 command = gcode_macro_name.split(' ', 1)[1]
-                description = command_descriptions.get(command, 'Shake&Tune macro')
+                description = ST_COMMANDS.get(command, 'Shake&Tune macro')
                 gcode_macro.fileconfig.set(gcode_macro_name, 'description', description)
 
                 # Add the section to the Klipper configuration object with all its options
-                if not config.fileconfig.has_section(gcode_macro_name.lower()):
-                    config.fileconfig.add_section(gcode_macro_name.lower())
+                if not self._config.fileconfig.has_section(gcode_macro_name.lower()):
+                    self._config.fileconfig.add_section(gcode_macro_name.lower())
                 for option in gcode_macro.fileconfig.options(gcode_macro_name):
                     value = gcode_macro.fileconfig.get(gcode_macro_name, option)
-                    config.fileconfig.set(gcode_macro_name.lower(), option, value)
-
+                    self._config.fileconfig.set(gcode_macro_name.lower(), option, value)
                     # Small trick to ensure the new injected sections are considered valid by Klipper config system
-                    config.access_tracking[(gcode_macro_name.lower(), option.lower())] = 1
+                    self._config.access_tracking[(gcode_macro_name.lower(), option.lower())] = 1
 
                 # Finally, load the section within the printer objects
-                self._printer.load_object(config, gcode_macro_name.lower())
+                self._printer.load_object(self._config, gcode_macro_name.lower())
+
+    def _on_klippy_connect(self) -> None:
+        # Check if the resonance_tester object is available in the printer
+        # configuration as it is required for Shake&Tune to work properly
+        res_tester = self._printer.lookup_object('resonance_tester', None)
+        if res_tester is None:
+            raise self._config.error(
+                'No [resonance_tester] config section found in printer.cfg! Please add one to use Shake&Tune!'
+            )
+
+    # ------------------------------------------------------------------------------------------
+    # ------------------------------------------------------------------------------------------
+    # Following are all the Shake&Tune commands that are registered to the Klipper console
+    # ------------------------------------------------------------------------------------------
+    # ------------------------------------------------------------------------------------------
 
     def cmd_EXCITATE_AXIS_AT_FREQ(self, gcmd) -> None:
         ConsoleOutput.print(f'Shake&Tune version: {ShakeTuneConfig.get_git_version()}')
-        static_freq_graph_creator = StaticGraphCreator(self._config)
+        static_freq_graph_creator = StaticGraphCreator(self._st_config)
         st_process = ShakeTuneProcess(
-            self._config,
+            self._st_config,
             self._printer.get_reactor(),
             static_freq_graph_creator,
             self.timeout,
         )
-        excitate_axis_at_freq(gcmd, self._pconfig, st_process)
+        excitate_axis_at_freq(gcmd, self._config, st_process)
 
     def cmd_AXES_MAP_CALIBRATION(self, gcmd) -> None:
         ConsoleOutput.print(f'Shake&Tune version: {ShakeTuneConfig.get_git_version()}')
-        axes_map_graph_creator = AxesMapGraphCreator(self._config)
+        axes_map_graph_creator = AxesMapGraphCreator(self._st_config)
         st_process = ShakeTuneProcess(
-            self._config,
+            self._st_config,
             self._printer.get_reactor(),
             axes_map_graph_creator,
             self.timeout,
         )
-        axes_map_calibration(gcmd, self._pconfig, st_process)
+        axes_map_calibration(gcmd, self._config, st_process)
 
     def cmd_COMPARE_BELTS_RESPONSES(self, gcmd) -> None:
         ConsoleOutput.print(f'Shake&Tune version: {ShakeTuneConfig.get_git_version()}')
-        belt_graph_creator = BeltsGraphCreator(self._config)
+        belt_graph_creator = BeltsGraphCreator(self._st_config)
         st_process = ShakeTuneProcess(
-            self._config,
+            self._st_config,
             self._printer.get_reactor(),
             belt_graph_creator,
             self.timeout,
         )
-        compare_belts_responses(gcmd, self._pconfig, st_process)
+        compare_belts_responses(gcmd, self._config, st_process)
 
     def cmd_AXES_SHAPER_CALIBRATION(self, gcmd) -> None:
         ConsoleOutput.print(f'Shake&Tune version: {ShakeTuneConfig.get_git_version()}')
-        shaper_graph_creator = ShaperGraphCreator(self._config)
+        shaper_graph_creator = ShaperGraphCreator(self._st_config)
         st_process = ShakeTuneProcess(
-            self._config,
+            self._st_config,
             self._printer.get_reactor(),
             shaper_graph_creator,
             self.timeout,
         )
-        axes_shaper_calibration(gcmd, self._pconfig, st_process)
+        axes_shaper_calibration(gcmd, self._config, st_process)
 
     def cmd_CREATE_VIBRATIONS_PROFILE(self, gcmd) -> None:
         ConsoleOutput.print(f'Shake&Tune version: {ShakeTuneConfig.get_git_version()}')
-        vibration_profile_creator = VibrationsGraphCreator(self._config)
+        vibration_profile_creator = VibrationsGraphCreator(self._st_config)
         st_process = ShakeTuneProcess(
-            self._config,
+            self._st_config,
             self._printer.get_reactor(),
             vibration_profile_creator,
             self.timeout,
         )
-        create_vibrations_profile(gcmd, self._pconfig, st_process)
+        create_vibrations_profile(gcmd, self._config, st_process)