From 177319db1b5951efe923398cf9ddbaf857deebff Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?F=C3=A9lix=20Boisselier?= <felix@fboisselier.fr>
Date: Tue, 12 Nov 2024 23:06:50 +0100
Subject: [PATCH] first step of separation of computations vs plotters

---
 README.md                                     |    4 +
 my_graph.png                                  |  Bin 0 -> 1755533 bytes
 shaketune/cli.py                              |  140 ++
 shaketune/graph_creators/__init__.py          |   25 +-
 .../graph_creators/axes_map_graph_creator.py  |  686 +++-----
 .../graph_creators/belts_graph_creator.py     |  780 +++------
 shaketune/graph_creators/graph_creator.py     |   20 +-
 shaketune/graph_creators/plotter.py           | 1476 +++++++++++++++++
 .../graph_creators/shaper_graph_creator.py    |  903 +++-------
 .../graph_creators/static_graph_creator.py    |  240 +--
 .../vibrations_graph_creator.py               | 1257 +++++---------
 shaketune/helpers/common_func.py              |    1 +
 shaketune/shaketune.py                        |    6 +-
 shaketune/shaketune_config.py                 |    3 +
 14 files changed, 2781 insertions(+), 2760 deletions(-)
 create mode 100644 my_graph.png
 create mode 100644 shaketune/cli.py
 create mode 100644 shaketune/graph_creators/plotter.py

diff --git a/README.md b/README.md
index 9cd7ead..a8ee86a 100644
--- a/README.md
+++ b/README.md
@@ -47,6 +47,10 @@ Follow these steps to install Shake&Tune on your printer:
      #    RAM, and should work for everyone. However, if you are using a powerful computer, you may
      #    wish to increase this value to keep more measurements in memory (e.g., 15-20) before writing
      #    the chunk and avoid stressing the filesystem too much.
+     # max_freq: 200
+     #    This setting defines the maximum frequency at which the calculation of the power spectral density
+     #    is cutoff. The default value should be fine for most machines and accelerometer combinations and
+     #    avoid touching it unless you know what you're doing.
      # dpi: 300
      #    Controls the resolution of the generated graphs. The default value of 300 dpi was optimized
      #    and strikes a balance between performance and readability, ensuring that graphs are clear
diff --git a/my_graph.png b/my_graph.png
new file mode 100644
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HcmV?d00001

diff --git a/shaketune/cli.py b/shaketune/cli.py
new file mode 100644
index 0000000..d8d51b0
--- /dev/null
+++ b/shaketune/cli.py
@@ -0,0 +1,140 @@
+import argparse
+import os
+import sys
+from importlib import import_module
+from pathlib import Path
+
+from .graph_creators.graph_creator_factory import GraphCreatorFactory
+from .helpers.accelerometer import MeasurementsManager
+from .shaketune_config import ShakeTuneConfig
+
+
+def add_common_arguments(parser):
+    """Helper function to add common arguments to all subparsers."""
+    parser.add_argument('-o', '--output', required=True, help='Output filename')
+    parser.add_argument('files', nargs='+', help='Input data files (.csv or .stdata)')
+    parser.add_argument('--max_freq', type=float, help='Maximum frequency to graph')
+    parser.add_argument('--dpi', type=int, help='DPI value to use for the graph')
+
+
+def configure_graph_creator(graph_type, args, dummy_config):
+    """Helper function to get and configure a graph creator based on graph type and args."""
+    graph_creator = GraphCreatorFactory.create_graph_creator(graph_type, dummy_config)
+    config_kwargs = {}
+
+    # Dynamically configure the graph creator based on graph type
+    if graph_type == 'axes map':
+        config_kwargs.update({'accel': args.accel, 'segment_length': args.length})
+    elif graph_type == 'static frequency':
+        config_kwargs.update({'accel_per_hz': args.accel_per_hz, 'freq': args.frequency, 'duration': args.duration})
+    elif graph_type == 'belts comparison':
+        config_kwargs.update({'kinematics': args.kinematics, 'accel_per_hz': args.accel_per_hz})
+    elif graph_type == 'input shaper':
+        config_kwargs.update({'scv': args.scv, 'max_smoothing': args.max_smoothing, 'accel_per_hz': args.accel_per_hz})
+    elif graph_type == 'vibrations profile':
+        config_kwargs.update({'kinematics': args.kinematics, 'accel': args.accel})
+
+    graph_creator.configure(**config_kwargs)
+    return graph_creator
+
+
+def load_klipper_module(args):
+    """Helper function to load the shaper_calibrate module from the specified Klipper folder."""
+    if hasattr(args, 'klipper_dir') and args.klipper_dir:
+        kdir = os.path.expanduser(args.klipper_dir)
+        sys.path.append(os.path.join(kdir, 'klippy'))
+        sys.modules['shaper_calibrate'] = import_module('.shaper_calibrate', 'extras')
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Shake&Tune command line interface')
+    subparsers = parser.add_subparsers(dest='graph_type', help='Type of graph to create')
+
+    # Static frequency graph parser
+    static_freq_parser = subparsers.add_parser('static_freq', help='Create static frequency graph')
+    add_common_arguments(static_freq_parser)
+    static_freq_parser.add_argument('--accel_per_hz', type=float, help='Accel per Hz used during the measurement')
+    static_freq_parser.add_argument('--frequency', type=float, help='Maintained frequency of the measurement')
+    static_freq_parser.add_argument('--duration', type=float, help='Duration of the measurement')
+
+    # Axes map detection graph parser
+    axes_map_parser = subparsers.add_parser('axes_map', help='Create axes map detection graph')
+    add_common_arguments(axes_map_parser)
+    axes_map_parser.add_argument('--accel', required=True, type=float, help='Accel value used for the measurement')
+    axes_map_parser.add_argument('--length', required=True, type=float, help='Recorded length for each segment')
+
+    # Belts graph parser
+    belts_parser = subparsers.add_parser('belts', help='Create belts comparison graph')
+    add_common_arguments(belts_parser)
+    belts_parser.add_argument('-k', '--klipper_dir', default='~/klipper', help='Main klipper directory')
+    belts_parser.add_argument('--kinematics', help='Machine kinematics configuration')
+    belts_parser.add_argument('--accel_per_hz', type=float, help='Accel per Hz used during the measurement')
+
+    # Input Shaper graph parser
+    shaper_parser = subparsers.add_parser('input_shaper', help='Create input shaper graph')
+    add_common_arguments(shaper_parser)
+    shaper_parser.add_argument('-k', '--klipper_dir', default='~/klipper', help='Main klipper directory')
+    shaper_parser.add_argument('--scv', type=float, default=5.0, help='Square corner velocity')
+    shaper_parser.add_argument('--max_smoothing', type=float, help='Maximum shaper smoothing to allow')
+    shaper_parser.add_argument('--accel_per_hz', type=float, help='Accel per Hz used during the measurement')
+
+    # Vibrations graph parser
+    vibrations_parser = subparsers.add_parser('vibrations', help='Create vibrations profile graph')
+    add_common_arguments(vibrations_parser)
+    vibrations_parser.add_argument('-k', '--klipper_dir', default='~/klipper', help='Main klipper directory')
+    vibrations_parser.add_argument('--kinematics', required=True, default='cartesian', help='Used kinematics')
+    vibrations_parser.add_argument('--accel', type=int, help='Accel value to be printed on the graph')
+
+    args = parser.parse_args()
+
+    if args.graph_type is None:
+        parser.print_help()
+        exit(1)
+
+    graph_type_map = {
+        'static_freq': 'static frequency',
+        'axes_map': 'axes map',
+        'belts': 'belts comparison',
+        'input_shaper': 'input shaper',
+        'vibrations': 'vibrations profile',
+    }
+    graph_type = graph_type_map[args.graph_type]
+
+    # Load configuration
+    dummy_config = ShakeTuneConfig()
+    if args.dpi is not None:
+        dummy_config.dpi = args.dpi
+    if args.max_freq is not None:
+        if graph_type == 'vibrations profile':
+            dummy_config.max_freq_vibrations = args.max_freq
+        else:
+            dummy_config.max_freq = args.max_freq
+
+    # Load shaper_calibrate module if needed
+    load_klipper_module(args)
+
+    # Create the graph creator and configure it
+    graph_creator = configure_graph_creator(graph_type, args, dummy_config)
+    graph_creator.override_output_target(args.output)
+
+    print(f'Creating {graph_type} graph...')
+
+    # Load measurements
+    measurements_manager = MeasurementsManager(10)
+    args.files = [Path(f) for f in args.files]
+    if args.files[0].suffix == '.csv':
+        measurements_manager.load_from_csvs(args.files)
+    elif args.files[0].suffix == '.stdata':
+        measurements_manager.load_from_stdata(args.files[0])
+    else:
+        raise ValueError('Only .stdata or legacy Klipper raw accelerometer CSV files are supported!')
+
+    # Create graph
+    graph_creator.create_graph(measurements_manager)
+
+    print('...done!')
+
+
+if __name__ == '__main__':
+    os.environ['SHAKETUNE_IN_CLI'] = '1'
+    main()
diff --git a/shaketune/graph_creators/__init__.py b/shaketune/graph_creators/__init__.py
index e8370f8..dd175b2 100644
--- a/shaketune/graph_creators/__init__.py
+++ b/shaketune/graph_creators/__init__.py
@@ -6,11 +6,22 @@
 # File: __init__.py
 # Description: Imports various graph creator classes for the Shake&Tune package.
 
+import os
+import sys
 
-from .axes_map_graph_creator import AxesMapGraphCreator as AxesMapGraphCreator
-from .belts_graph_creator import BeltsGraphCreator as BeltsGraphCreator
-from .graph_creator import GraphCreator as GraphCreator
-from .graph_creator_factory import GraphCreatorFactory as GraphCreatorFactory
-from .shaper_graph_creator import ShaperGraphCreator as ShaperGraphCreator
-from .static_graph_creator import StaticGraphCreator as StaticGraphCreator
-from .vibrations_graph_creator import VibrationsGraphCreator as VibrationsGraphCreator
+
+def get_shaper_calibrate_module():
+    if not os.environ.get('SHAKETUNE_IN_CLI') == '1':
+        from ... import shaper_calibrate
+    else:
+        shaper_calibrate = sys.modules['shaper_calibrate']
+    return shaper_calibrate
+
+
+from .axes_map_graph_creator import AxesMapGraphCreator as AxesMapGraphCreator  # noqa: E402
+from .belts_graph_creator import BeltsGraphCreator as BeltsGraphCreator  # noqa: E402
+from .graph_creator import GraphCreator as GraphCreator  # noqa: E402
+from .graph_creator_factory import GraphCreatorFactory as GraphCreatorFactory  # noqa: E402
+from .shaper_graph_creator import ShaperGraphCreator as ShaperGraphCreator  # noqa: E402
+from .static_graph_creator import StaticGraphCreator as StaticGraphCreator  # noqa: E402
+from .vibrations_graph_creator import VibrationsGraphCreator as VibrationsGraphCreator  # noqa: E402
diff --git a/shaketune/graph_creators/axes_map_graph_creator.py b/shaketune/graph_creators/axes_map_graph_creator.py
index 28641a0..a468217 100644
--- a/shaketune/graph_creators/axes_map_graph_creator.py
+++ b/shaketune/graph_creators/axes_map_graph_creator.py
@@ -7,34 +7,17 @@
 # Description: Implements the axes map detection script for Shake&Tune, including
 #              calibration tools and graph creation for 3D printer vibration analysis.
 
-import optparse
-import os
-from datetime import datetime
 from typing import List, Optional, Tuple
 
-import matplotlib
-import matplotlib.colors
-import matplotlib.font_manager
-import matplotlib.pyplot as plt
-import matplotlib.ticker
 import numpy as np
 import pywt
 from scipy import stats
 
-matplotlib.use('Agg')
-
 from ..helpers.accelerometer import Measurement, MeasurementsManager
 from ..helpers.console_output import ConsoleOutput
 from ..shaketune_config import ShakeTuneConfig
 from .graph_creator import GraphCreator
 
-KLIPPAIN_COLORS = {
-    'purple': '#70088C',
-    'orange': '#FF8D32',
-    'dark_purple': '#150140',
-    'dark_orange': '#F24130',
-    'red_pink': '#F2055C',
-}
 MACHINE_AXES = ['x', 'y', 'z']
 
 
@@ -50,12 +33,14 @@ def configure(self, accel: int, segment_length: float) -> None:
         self._segment_length = segment_length
 
     def create_graph(self, measurements_manager: MeasurementsManager) -> None:
-        fig = axesmap_calibration(
+        computer = AxesMapComputation(
             measurements=measurements_manager.get_measurements(),
             accel=self._accel,
             fixed_length=self._segment_length,
             st_version=self._version,
         )
+        computation = computer.compute()
+        fig = self._plotter.plot_axes_map_detection_graph(computation)
         self._save_figure(fig, measurements_manager)
 
 
@@ -64,445 +49,232 @@ def create_graph(self, measurements_manager: MeasurementsManager) -> None:
 ######################################################################
 
 
-def wavelet_denoise(data: np.ndarray, wavelet: str = 'db1', level: int = 1) -> Tuple[np.ndarray, np.ndarray]:
-    coeffs = pywt.wavedec(data, wavelet, mode='smooth')
-    threshold = np.median(np.abs(coeffs[-level])) / 0.6745 * np.sqrt(2 * np.log(len(data)))
-    new_coeffs = [pywt.threshold(c, threshold, mode='soft') for c in coeffs]
-    denoised_data = pywt.waverec(new_coeffs, wavelet)
-
-    # Compute noise by subtracting denoised data from original data
-    noise = data - denoised_data[: len(data)]
-    return denoised_data, noise
-
-
-def integrate_trapz(accel: np.ndarray, time: np.ndarray) -> np.ndarray:
-    return np.array([np.trapz(accel[:i], time[:i]) for i in range(2, len(time) + 1)])
-
-
-def process_acceleration_data(
-    time: np.ndarray, accel_x: np.ndarray, accel_y: np.ndarray, accel_z: np.ndarray
-) -> Tuple[float, float, float, np.ndarray, np.ndarray, np.ndarray, float]:
-    # Calculate the constant offset (gravity component)
-    offset_x = np.mean(accel_x)
-    offset_y = np.mean(accel_y)
-    offset_z = np.mean(accel_z)
-
-    # Remove the constant offset from acceleration data
-    accel_x -= offset_x
-    accel_y -= offset_y
-    accel_z -= offset_z
-
-    # Apply wavelet denoising
-    accel_x, noise_x = wavelet_denoise(accel_x)
-    accel_y, noise_y = wavelet_denoise(accel_y)
-    accel_z, noise_z = wavelet_denoise(accel_z)
-
-    # Integrate acceleration to get velocity using trapezoidal rule
-    velocity_x = integrate_trapz(accel_x, time)
-    velocity_y = integrate_trapz(accel_y, time)
-    velocity_z = integrate_trapz(accel_z, time)
-
-    # Correct drift in velocity by resetting to zero at the beginning and end
-    velocity_x -= np.linspace(velocity_x[0], velocity_x[-1], len(velocity_x))
-    velocity_y -= np.linspace(velocity_y[0], velocity_y[-1], len(velocity_y))
-    velocity_z -= np.linspace(velocity_z[0], velocity_z[-1], len(velocity_z))
-
-    # Integrate velocity to get position using trapezoidal rule
-    position_x = integrate_trapz(velocity_x, time[1:])
-    position_y = integrate_trapz(velocity_y, time[1:])
-    position_z = integrate_trapz(velocity_z, time[1:])
-
-    noise_intensity = np.mean([np.std(noise_x), np.std(noise_y), np.std(noise_z)])
-
-    return offset_x, offset_y, offset_z, position_x, position_y, position_z, noise_intensity
-
-
-def scale_positions_to_fixed_length(
-    position_x: np.ndarray, position_y: np.ndarray, position_z: np.ndarray, fixed_length: float
-) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
-    # Calculate the total distance traveled in 3D space
-    total_distance = np.sqrt(np.diff(position_x) ** 2 + np.diff(position_y) ** 2 + np.diff(position_z) ** 2).sum()
-    scale_factor = fixed_length / total_distance
-
-    # Apply the scale factor to the positions
-    position_x *= scale_factor
-    position_y *= scale_factor
-    position_z *= scale_factor
-
-    return position_x, position_y, position_z
-
-
-def find_nearest_perfect_vector(average_direction_vector: np.ndarray) -> Tuple[np.ndarray, float]:
-    # Define the perfect vectors
-    perfect_vectors = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [-1, 0, 0], [0, -1, 0], [0, 0, -1]])
-
-    # Find the nearest perfect vector
-    dot_products = perfect_vectors @ average_direction_vector
-    nearest_vector_idx = np.argmax(dot_products)
-    nearest_vector = perfect_vectors[nearest_vector_idx]
-
-    # Calculate the angle error
-    angle_error = np.arccos(dot_products[nearest_vector_idx]) * 180 / np.pi
-
-    return nearest_vector, angle_error
-
-
-def linear_regression_direction(
-    position_x: np.ndarray, position_y: np.ndarray, position_z: np.ndarray, trim_length: float = 0.25
-) -> np.ndarray:
-    # Trim the start and end of the position data to keep only the center of the segment
-    # as the start and stop positions are not always perfectly aligned and can be a bit noisy
-    t = len(position_x)
-    trim_start = int(t * trim_length)
-    trim_end = int(t * (1 - trim_length))
-    position_x = position_x[trim_start:trim_end]
-    position_y = position_y[trim_start:trim_end]
-    position_z = position_z[trim_start:trim_end]
-
-    # Compute the direction vector using linear regression over the position data
-    time = np.arange(len(position_x))
-    slope_x, intercept_x, _, _, _ = stats.linregress(time, position_x)
-    slope_y, intercept_y, _, _, _ = stats.linregress(time, position_y)
-    slope_z, intercept_z, _, _, _ = stats.linregress(time, position_z)
-    end_position = np.array(
-        [slope_x * time[-1] + intercept_x, slope_y * time[-1] + intercept_y, slope_z * time[-1] + intercept_z]
-    )
-    direction_vector = end_position - np.array([intercept_x, intercept_y, intercept_z])
-    direction_vector = direction_vector / np.linalg.norm(direction_vector)
-    return direction_vector
-
-
-######################################################################
-# Graphing
-######################################################################
-
-
-def plot_compare_frequency(
-    ax: plt.Axes,
-    time_data: List[np.ndarray],
-    accel_data: List[Tuple[np.ndarray, np.ndarray, np.ndarray]],
-    offset: float,
-    noise_level: str,
-) -> None:
-    # Plot acceleration data
-    for i, (time, (accel_x, accel_y, accel_z)) in enumerate(zip(time_data, accel_data)):
-        ax.plot(
-            time,
-            accel_x,
-            label='X' if i == 0 else '',
-            color=KLIPPAIN_COLORS['purple'],
-            linewidth=0.5,
-            zorder=50 if i == 0 else 10,
-        )
-        ax.plot(
-            time,
-            accel_y,
-            label='Y' if i == 0 else '',
-            color=KLIPPAIN_COLORS['orange'],
-            linewidth=0.5,
-            zorder=50 if i == 1 else 10,
-        )
-        ax.plot(
-            time,
-            accel_z,
-            label='Z' if i == 0 else '',
-            color=KLIPPAIN_COLORS['red_pink'],
-            linewidth=0.5,
-            zorder=50 if i == 2 else 10,
-        )
-
-    ax.set_xlabel('Time (s)')
-    ax.set_ylabel('Acceleration (mm/s²)')
-
-    ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
-    ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
-    ax.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))
-    ax.grid(which='major', color='grey')
-    ax.grid(which='minor', color='lightgrey')
-    fontP = matplotlib.font_manager.FontProperties()
-    fontP.set_size('small')
-    ax.set_title(
-        'Acceleration (gravity offset removed)',
-        fontsize=14,
-        color=KLIPPAIN_COLORS['dark_orange'],
-        weight='bold',
-    )
-
-    ax.legend(loc='upper left', prop=fontP)
-
-    # Add the gravity and noise level to the graph legend
-    ax2 = ax.twinx()
-    ax2.yaxis.set_visible(False)
-    ax2.plot([], [], ' ', label=noise_level)
-    ax2.plot([], [], ' ', label=f'Measured gravity: {offset / 1000:0.3f} m/s²')
-    ax2.legend(loc='upper right', prop=fontP)
-
-
-def plot_3d_path(
-    ax: plt.Axes,
-    position_data: List[Tuple[np.ndarray, np.ndarray, np.ndarray]],
-    direction_vectors: List[np.ndarray],
-    angle_errors: List[float],
-) -> None:
-    # Plot the 3D path of the movement
-    for i, ((position_x, position_y, position_z), average_direction_vector, angle_error) in enumerate(
-        zip(position_data, direction_vectors, angle_errors)
+class AxesMapComputation:
+    def __init__(
+        self,
+        measurements: List[Measurement],
+        accel: float,
+        fixed_length: float,
+        st_version: str,
     ):
-        ax.plot(position_x, position_y, position_z, color=KLIPPAIN_COLORS['orange'], linestyle=':', linewidth=2)
-        ax.scatter(position_x[0], position_y[0], position_z[0], color=KLIPPAIN_COLORS['red_pink'], zorder=10)
-        ax.text(
-            position_x[0] + 1,
-            position_y[0],
-            position_z[0],
-            str(i + 1),
-            color='black',
-            fontsize=16,
-            fontweight='bold',
-            zorder=20,
-        )
-
-        # Plot the average direction vector
-        start_position = np.array([position_x[0], position_y[0], position_z[0]])
-        end_position = start_position + average_direction_vector * np.linalg.norm(
-            [position_x[-1] - position_x[0], position_y[-1] - position_y[0], position_z[-1] - position_z[0]]
-        )
-        ax.plot(
-            [start_position[0], end_position[0]],
-            [start_position[1], end_position[1]],
-            [start_position[2], end_position[2]],
-            label=f'{["X", "Y", "Z"][i]} angle: {angle_error:0.2f}°',
-            color=KLIPPAIN_COLORS['purple'],
-            linestyle='-',
-            linewidth=2,
-        )
-
-    ax.set_xlabel('X Position (mm)')
-    ax.set_ylabel('Y Position (mm)')
-    ax.set_zlabel('Z Position (mm)')
-
-    ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
-    ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
-    ax.grid(which='major', color='grey')
-    ax.grid(which='minor', color='lightgrey')
-    fontP = matplotlib.font_manager.FontProperties()
-    fontP.set_size('small')
-    ax.set_title(
-        'Estimated movement in 3D space',
-        fontsize=14,
-        color=KLIPPAIN_COLORS['dark_orange'],
-        weight='bold',
-    )
-
-    ax.legend(loc='upper left', prop=fontP)
-
-
-def format_direction_vector(vectors: List[np.ndarray]) -> str:
-    formatted_vector = []
-    axes_count = {'x': 0, 'y': 0, 'z': 0}
-
-    for vector in vectors:
-        for i in range(len(vector)):
-            if vector[i] > 0:
-                formatted_vector.append(MACHINE_AXES[i])
-                axes_count[MACHINE_AXES[i]] += 1
-                break
-            elif vector[i] < 0:
-                formatted_vector.append(f'-{MACHINE_AXES[i]}')
-                axes_count[MACHINE_AXES[i]] += 1
-                break
-
-    # Check if all axes are present in the axes_map and return an error message if not
-    for _, count in axes_count.items():
-        if count != 1:
-            return 'unable to determine it correctly!'
-
-    return ', '.join(formatted_vector)
-
-
-######################################################################
-# Startup and main routines
-######################################################################
-
-
-def axesmap_calibration(
-    measurements: List[Measurement], fixed_length: float, accel: Optional[float] = None, st_version: str = 'unknown'
-) -> plt.Figure:
-    if len(measurements) != 3:
-        raise ValueError('This tool needs 3 measurements to work with (like axesmap_X, axesmap_Y and axesmap_Z)')
-
-    raw_datas = {}
-    for measurement in measurements:
-        data = np.array(measurement['samples'])
-        if data is not None:
-            _axis = measurement['name'].split('_')[1].lower()
-            raw_datas[_axis] = data
-
-    fig, ((ax1, ax2)) = plt.subplots(
-        1,
-        2,
-        gridspec_kw={
-            'width_ratios': [5, 3],
-            'bottom': 0.080,
-            'top': 0.840,
-            'left': 0.055,
-            'right': 0.960,
-            'hspace': 0.166,
-            'wspace': 0.060,
-        },
-    )
-    fig.set_size_inches(15, 7)
-    ax2.remove()
-    ax2 = fig.add_subplot(122, projection='3d')
-
-    cumulative_start_position = np.array([0, 0, 0])
-    direction_vectors = []
-    angle_errors = []
-    total_noise_intensity = 0.0
-    acceleration_data = []
-    position_data = []
-    gravities = []
-    for _, machine_axis in enumerate(MACHINE_AXES):
-        if machine_axis not in raw_datas:
-            raise ValueError(f'Missing measurement for axis {machine_axis}')
-
-        # Get the accel data according to the current axes_map
-        time = raw_datas[machine_axis][:, 0]
-        accel_x = raw_datas[machine_axis][:, 1]
-        accel_y = raw_datas[machine_axis][:, 2]
-        accel_z = raw_datas[machine_axis][:, 3]
-
-        offset_x, offset_y, offset_z, position_x, position_y, position_z, noise_intensity = process_acceleration_data(
-            time, accel_x, accel_y, accel_z
-        )
-        position_x, position_y, position_z = scale_positions_to_fixed_length(
-            position_x, position_y, position_z, fixed_length
-        )
-        position_x += cumulative_start_position[0]
-        position_y += cumulative_start_position[1]
-        position_z += cumulative_start_position[2]
-
-        gravity = np.linalg.norm(np.array([offset_x, offset_y, offset_z]))
-        average_direction_vector = linear_regression_direction(position_x, position_y, position_z)
-        direction_vector, angle_error = find_nearest_perfect_vector(average_direction_vector)
-        ConsoleOutput.print(
-            f'Machine axis {machine_axis.upper()} -> nearest accelerometer direction vector: {direction_vector} (angle error: {angle_error:.2f}°)'
+        self.measurements = measurements
+        self.accel = accel
+        self.fixed_length = fixed_length
+        self.st_version = st_version
+
+    def compute(self):
+        if len(self.measurements) != 3:
+            raise ValueError('This tool needs 3 measurements to work with (like axesmap_X, axesmap_Y and axesmap_Z)')
+
+        raw_datas = {}
+        for measurement in self.measurements:
+            data = np.array(measurement['samples'])
+            if data is not None:
+                _axis = measurement['name'].split('_')[1].lower()
+                print(_axis)
+                raw_datas[_axis] = data
+
+        cumulative_start_position = np.array([0, 0, 0])
+        direction_vectors = []
+        angle_errors = []
+        total_noise_intensity = 0.0
+        acceleration_data = []
+        position_data = []
+        gravities = []
+        for _, machine_axis in enumerate(MACHINE_AXES):
+            if machine_axis not in raw_datas:
+                raise ValueError(f'Missing measurement for axis {machine_axis}')
+
+            # Get the accel data according to the current axes_map
+            time = raw_datas[machine_axis][:, 0]
+            accel_x = raw_datas[machine_axis][:, 1]
+            accel_y = raw_datas[machine_axis][:, 2]
+            accel_z = raw_datas[machine_axis][:, 3]
+
+            offset_x, offset_y, offset_z, position_x, position_y, position_z, noise_intensity = (
+                self._process_acceleration_data(time, accel_x, accel_y, accel_z)
+            )
+            position_x, position_y, position_z = self._scale_positions_to_fixed_length(
+                position_x, position_y, position_z, self.fixed_length
+            )
+            position_x += cumulative_start_position[0]
+            position_y += cumulative_start_position[1]
+            position_z += cumulative_start_position[2]
+
+            gravity = np.linalg.norm(np.array([offset_x, offset_y, offset_z]))
+            average_direction_vector = self._linear_regression_direction(position_x, position_y, position_z)
+            direction_vector, angle_error = self._find_nearest_perfect_vector(average_direction_vector)
+            ConsoleOutput.print(
+                f'Machine axis {machine_axis.upper()} -> nearest accelerometer direction vector: {direction_vector} (angle error: {angle_error:.2f}°)'
+            )
+            direction_vectors.append(direction_vector)
+            angle_errors.append(angle_error)
+
+            total_noise_intensity += noise_intensity
+
+            acceleration_data.append((time, (accel_x, accel_y, accel_z)))
+            position_data.append((position_x, position_y, position_z))
+            gravities.append(gravity)
+
+            # Update the cumulative start position for the next segment
+            cumulative_start_position = np.array([position_x[-1], position_y[-1], position_z[-1]])
+
+        gravity = np.mean(gravities)
+
+        average_noise_intensity = total_noise_intensity / len(raw_datas)
+        if average_noise_intensity <= 350:
+            average_noise_intensity_text = '-> OK'
+        elif 350 < average_noise_intensity <= 700:
+            average_noise_intensity_text = '-> WARNING: accelerometer noise is a bit high'
+        else:
+            average_noise_intensity_text = '-> ERROR: accelerometer noise is too high!'
+
+        average_noise_intensity_label = (
+            f'Dynamic noise level: {average_noise_intensity:.2f} mm/s² {average_noise_intensity_text}'
         )
-        direction_vectors.append(direction_vector)
-        angle_errors.append(angle_error)
-
-        total_noise_intensity += noise_intensity
-
-        acceleration_data.append((time, (accel_x, accel_y, accel_z)))
-        position_data.append((position_x, position_y, position_z))
-        gravities.append(gravity)
-
-        # Update the cumulative start position for the next segment
-        cumulative_start_position = np.array([position_x[-1], position_y[-1], position_z[-1]])
-
-    gravity = np.mean(gravities)
-
-    average_noise_intensity = total_noise_intensity / len(raw_datas)
-    if average_noise_intensity <= 350:
-        average_noise_intensity_text = '-> OK'
-    elif 350 < average_noise_intensity <= 700:
-        average_noise_intensity_text = '-> WARNING: accelerometer noise is a bit high'
-    else:
-        average_noise_intensity_text = '-> ERROR: accelerometer noise is too high!'
-
-    average_noise_intensity_label = (
-        f'Dynamic noise level: {average_noise_intensity:.2f} mm/s² {average_noise_intensity_text}'
-    )
-    ConsoleOutput.print(average_noise_intensity_label)
-
-    ConsoleOutput.print(f'--> Detected gravity: {gravity / 1000 :.2f} m/s²')
-
-    formatted_direction_vector = format_direction_vector(direction_vectors)
-    ConsoleOutput.print(f'--> Detected axes_map: {formatted_direction_vector}')
-
-    # Plot the differents graphs
-    plot_compare_frequency(
-        ax1,
-        [d[0] for d in acceleration_data],
-        [d[1] for d in acceleration_data],
-        gravity,
-        average_noise_intensity_label,
-    )
-    plot_3d_path(ax2, position_data, direction_vectors, angle_errors)
-
-    # Add title
-    title_line1 = 'AXES MAP CALIBRATION TOOL'
-    fig.text(
-        0.060, 0.947, title_line1, ha='left', va='bottom', fontsize=20, color=KLIPPAIN_COLORS['purple'], weight='bold'
-    )
-    try:
-        filename = measurements[0]['name']
-        dt = datetime.strptime(f"{filename.split('_')[2]} {filename.split('_')[3]}", '%Y%m%d %H%M%S')
-        title_line2 = dt.strftime('%x %X')
-        if accel is not None:
-            title_line2 += f' -- at {accel:0.0f} mm/s²'
-    except Exception:
-        ConsoleOutput.print(
-            f"Warning: Shake&Tune measurements names look to be different than expected ({measurements[0]['name']}, "
-            f"{measurements[1]['name']}, {measurements[2]['name']})"
+        ConsoleOutput.print(average_noise_intensity_label)
+
+        ConsoleOutput.print(f'--> Detected gravity: {gravity / 1000 :.2f} m/s²')
+
+        formatted_direction_vector = self._format_direction_vector(direction_vectors)
+        ConsoleOutput.print(f'--> Detected axes_map: {formatted_direction_vector}')
+
+        # Prepare data for plotting
+        computation_result = {
+            'acceleration_data_0': [d[0] for d in acceleration_data],
+            'acceleration_data_1': [d[1] for d in acceleration_data],
+            'gravity': gravity,
+            'average_noise_intensity_label': average_noise_intensity_label,
+            'position_data': position_data,
+            'direction_vectors': direction_vectors,
+            'angle_errors': angle_errors,
+            'formatted_direction_vector': formatted_direction_vector,
+            'measurements': self.measurements,
+            'st_version': self.st_version,
+        }
+
+        return computation_result
+
+    def _wavelet_denoise(self, data: np.ndarray, wavelet: str = 'db1', level: int = 1) -> Tuple[np.ndarray, np.ndarray]:
+        coeffs = pywt.wavedec(data, wavelet, mode='smooth')
+        threshold = np.median(np.abs(coeffs[-level])) / 0.6745 * np.sqrt(2 * np.log(len(data)))
+        new_coeffs = [pywt.threshold(c, threshold, mode='soft') for c in coeffs]
+        denoised_data = pywt.waverec(new_coeffs, wavelet)
+
+        # Compute noise by subtracting denoised data from original data
+        noise = data - denoised_data[: len(data)]
+        return denoised_data, noise
+
+    def _integrate_trapz(self, accel: np.ndarray, time: np.ndarray) -> np.ndarray:
+        return np.array([np.trapz(accel[:i], time[:i]) for i in range(2, len(time) + 1)])
+
+    def _process_acceleration_data(
+        self, time: np.ndarray, accel_x: np.ndarray, accel_y: np.ndarray, accel_z: np.ndarray
+    ) -> Tuple[float, float, float, np.ndarray, np.ndarray, np.ndarray, float]:
+        # Calculate the constant offset (gravity component)
+        offset_x = np.mean(accel_x)
+        offset_y = np.mean(accel_y)
+        offset_z = np.mean(accel_z)
+
+        # Remove the constant offset from acceleration data
+        accel_x -= offset_x
+        accel_y -= offset_y
+        accel_z -= offset_z
+
+        # Apply wavelet denoising
+        accel_x, noise_x = self._wavelet_denoise(accel_x)
+        accel_y, noise_y = self._wavelet_denoise(accel_y)
+        accel_z, noise_z = self._wavelet_denoise(accel_z)
+
+        # Integrate acceleration to get velocity using trapezoidal rule
+        velocity_x = self._integrate_trapz(accel_x, time)
+        velocity_y = self._integrate_trapz(accel_y, time)
+        velocity_z = self._integrate_trapz(accel_z, time)
+
+        # Correct drift in velocity by resetting to zero at the beginning and end
+        velocity_x -= np.linspace(velocity_x[0], velocity_x[-1], len(velocity_x))
+        velocity_y -= np.linspace(velocity_y[0], velocity_y[-1], len(velocity_y))
+        velocity_z -= np.linspace(velocity_z[0], velocity_z[-1], len(velocity_z))
+
+        # Integrate velocity to get position using trapezoidal rule
+        position_x = self._integrate_trapz(velocity_x, time[1:])
+        position_y = self._integrate_trapz(velocity_y, time[1:])
+        position_z = self._integrate_trapz(velocity_z, time[1:])
+
+        noise_intensity = np.mean([np.std(noise_x), np.std(noise_y), np.std(noise_z)])
+
+        return offset_x, offset_y, offset_z, position_x, position_y, position_z, noise_intensity
+
+    def _scale_positions_to_fixed_length(
+        self, position_x: np.ndarray, position_y: np.ndarray, position_z: np.ndarray, fixed_length: float
+    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+        # Calculate the total distance traveled in 3D space
+        total_distance = np.sqrt(np.diff(position_x) ** 2 + np.diff(position_y) ** 2 + np.diff(position_z) ** 2).sum()
+        scale_factor = fixed_length / total_distance
+
+        # Apply the scale factor to the positions
+        position_x *= scale_factor
+        position_y *= scale_factor
+        position_z *= scale_factor
+
+        return position_x, position_y, position_z
+
+    def _find_nearest_perfect_vector(self, average_direction_vector: np.ndarray) -> Tuple[np.ndarray, float]:
+        # Define the perfect vectors
+        perfect_vectors = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [-1, 0, 0], [0, -1, 0], [0, 0, -1]])
+
+        # Find the nearest perfect vector
+        dot_products = perfect_vectors @ average_direction_vector
+        nearest_vector_idx = np.argmax(dot_products)
+        nearest_vector = perfect_vectors[nearest_vector_idx]
+
+        # Calculate the angle error
+        angle_error = np.arccos(dot_products[nearest_vector_idx]) * 180 / np.pi
+
+        return nearest_vector, angle_error
+
+    def _linear_regression_direction(
+        self, position_x: np.ndarray, position_y: np.ndarray, position_z: np.ndarray, trim_length: float = 0.25
+    ) -> np.ndarray:
+        # Trim the start and end of the position data to keep only the center of the segment
+        # as the start and stop positions are not always perfectly aligned and can be a bit noisy
+        t = len(position_x)
+        trim_start = int(t * trim_length)
+        trim_end = int(t * (1 - trim_length))
+        position_x = position_x[trim_start:trim_end]
+        position_y = position_y[trim_start:trim_end]
+        position_z = position_z[trim_start:trim_end]
+
+        # Compute the direction vector using linear regression over the position data
+        time = np.arange(len(position_x))
+        slope_x, intercept_x, _, _, _ = stats.linregress(time, position_x)
+        slope_y, intercept_y, _, _, _ = stats.linregress(time, position_y)
+        slope_z, intercept_z, _, _, _ = stats.linregress(time, position_z)
+        end_position = np.array(
+            [slope_x * time[-1] + intercept_x, slope_y * time[-1] + intercept_y, slope_z * time[-1] + intercept_z]
         )
-        title_line2 = measurements[0]['name'] + ' ...'
-    fig.text(0.060, 0.939, title_line2, ha='left', va='top', fontsize=16, color=KLIPPAIN_COLORS['dark_purple'])
-
-    title_line3 = f'| Detected axes_map: {formatted_direction_vector}'
-    fig.text(0.50, 0.985, title_line3, ha='left', va='top', fontsize=16, color=KLIPPAIN_COLORS['dark_purple'])
-
-    # 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')
-    ax_logo.imshow(plt.imread(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'klippain.png')))
-    ax_logo.axis('off')
-
-    # Adding Shake&Tune version in the top right corner
-    if st_version != 'unknown':
-        fig.text(0.995, 0.980, st_version, ha='right', va='bottom', fontsize=8, color=KLIPPAIN_COLORS['purple'])
-
-    return fig
-
-
-def main():
-    # Parse command-line arguments
-    usage = '%prog [options] <raw logs>'
-    opts = optparse.OptionParser(usage)
-    opts.add_option('-o', '--output', type='string', dest='output', default=None, help='filename of output graph')
-    opts.add_option(
-        '-a', '--accel', type='string', dest='accel', default=None, help='acceleration value used to do the movements'
-    )
-    opts.add_option(
-        '-l', '--length', type='float', dest='length', default=None, help='recorded length for each segment'
-    )
-    options, args = opts.parse_args()
-    if len(args) < 1:
-        opts.error('No measurements to analyse')
-    if options.accel is None:
-        opts.error('You must specify the acceleration value used when recording the measurements (option -a)')
-    try:
-        accel_value = float(options.accel)
-    except ValueError:
-        opts.error('Invalid acceleration value. It should be a numeric value.')
-    if options.length is None:
-        opts.error('You must specify the length of the measured segments (option -l)')
-    try:
-        length_value = float(options.length)
-    except ValueError:
-        opts.error('Invalid length value. It should be a numeric value.')
-    if options.output is None:
-        opts.error('You must specify an output file.png to use the script (option -o)')
-
-    measurements_manager = MeasurementsManager(10)
-    if args[0].endswith('.csv'):
-        measurements_manager.load_from_csvs(args)
-    elif args[0].endswith('.stdata'):
-        measurements_manager.load_from_stdata(args[0])
-    else:
-        raise ValueError('Only .stdata or legacy Klipper raw accelerometer CSV files are supported!')
-
-    fig = axesmap_calibration(measurements_manager.get_measurements(), length_value, accel_value, 'unknown')
-    fig.savefig(options.output, dpi=150)
-
-
-if __name__ == '__main__':
-    main()
+        direction_vector = end_position - np.array([intercept_x, intercept_y, intercept_z])
+        direction_vector = direction_vector / np.linalg.norm(direction_vector)
+        return direction_vector
+
+    def _format_direction_vector(self, vectors: List[np.ndarray]) -> str:
+        formatted_vector = []
+        axes_count = {'x': 0, 'y': 0, 'z': 0}
+
+        for vector in vectors:
+            for i in range(len(vector)):
+                if vector[i] > 0:
+                    formatted_vector.append(MACHINE_AXES[i])
+                    axes_count[MACHINE_AXES[i]] += 1
+                    break
+                elif vector[i] < 0:
+                    formatted_vector.append(f'-{MACHINE_AXES[i]}')
+                    axes_count[MACHINE_AXES[i]] += 1
+                    break
+
+        # Check if all axes are present in the axes_map and return an error message if not
+        for _, count in axes_count.items():
+            if count != 1:
+                return 'unable to determine it correctly!'
+
+        return ', '.join(formatted_vector)
diff --git a/shaketune/graph_creators/belts_graph_creator.py b/shaketune/graph_creators/belts_graph_creator.py
index b348c08..7dbe267 100644
--- a/shaketune/graph_creators/belts_graph_creator.py
+++ b/shaketune/graph_creators/belts_graph_creator.py
@@ -8,59 +8,18 @@
 #              including computation and graphing functions for 3D printer belt paths analysis.
 
 
-import optparse
-import os
-from datetime import datetime
 from typing import List, NamedTuple, Optional, Tuple
 
-import matplotlib
-import matplotlib.colors
-import matplotlib.font_manager
-import matplotlib.pyplot as plt
-import matplotlib.ticker
 import numpy as np
 from scipy.stats import pearsonr
 
-matplotlib.use('Agg')
-
 from ..helpers.accelerometer import Measurement, MeasurementsManager
-from ..helpers.common_func import detect_peaks, setup_klipper_import
+from ..helpers.common_func import detect_peaks
 from ..helpers.console_output import ConsoleOutput
 from ..shaketune_config import ShakeTuneConfig
+from . import get_shaper_calibrate_module
 from .graph_creator import GraphCreator
 
-ALPHABET = (
-    'αβγδεζηθικλμνξοπρστυφχψω'  # For paired peak names (using the Greek alphabet to avoid confusion with belt names)
-)
-
-PEAKS_DETECTION_THRESHOLD = 0.1  # Threshold to detect peaks in the PSD signal (10% of max)
-DC_MAX_PEAKS = 2  # Maximum ideal number of peaks
-DC_MAX_UNPAIRED_PEAKS_ALLOWED = 0  # No unpaired peaks are tolerated
-
-KLIPPAIN_COLORS = {
-    'purple': '#70088C',
-    'orange': '#FF8D32',
-    'dark_purple': '#150140',
-    'dark_orange': '#F24130',
-    'red_pink': '#F2055C',
-}
-
-
-# Define the SignalData type to store the data of a signal (PSD, peaks, etc.)
-class SignalData(NamedTuple):
-    freqs: np.ndarray
-    psd: np.ndarray
-    peaks: np.ndarray
-    paired_peaks: Optional[List[Tuple[Tuple[int, float, float], Tuple[int, float, float]]]] = None
-    unpaired_peaks: Optional[List[int]] = None
-
-
-# Define the PeakPairingResult type to store the result of the peak pairing function
-class PeakPairingResult(NamedTuple):
-    paired_peaks: List[Tuple[Tuple[int, float, float], Tuple[int, float, float]]]
-    unpaired_peaks1: List[int]
-    unpaired_peaks2: List[int]
-
 
 @GraphCreator.register('belts comparison')
 class BeltsGraphCreator(GraphCreator):
@@ -74,550 +33,223 @@ def configure(self, kinematics: Optional[str] = None, accel_per_hz: Optional[flo
         self._accel_per_hz = accel_per_hz
 
     def create_graph(self, measurements_manager: MeasurementsManager) -> None:
-        fig = belts_calibration(
+        computer = BeltsGraphComputation(
             measurements=measurements_manager.get_measurements(),
             kinematics=self._kinematics,
-            klipperdir=str(self._config.klipper_folder),
+            max_freq=self._config.max_freq,
             accel_per_hz=self._accel_per_hz,
             st_version=self._version,
         )
+        computation = computer.compute()
+        fig = self._plotter.plot_belts_graph(computation)
         self._save_figure(fig, measurements_manager)
 
 
-######################################################################
-# Computation of the PSD graph
-######################################################################
-
-
-# This function create pairs of peaks that are close in frequency on two curves (that are known
-# to be resonances points and must be similar on both belts on a CoreXY kinematic)
-def pair_peaks(
-    peaks1: np.ndarray, freqs1: np.ndarray, psd1: np.ndarray, peaks2: np.ndarray, freqs2: np.ndarray, psd2: np.ndarray
-) -> PeakPairingResult:
-    # Compute a dynamic detection threshold to filter and pair peaks efficiently
-    # even if the signal is very noisy (this get clipped to a maximum of 10Hz diff)
-    distances = []
-    for p1 in peaks1:
-        for p2 in peaks2:
-            distances.append(abs(freqs1[p1] - freqs2[p2]))
-    distances = np.array(distances)
-
-    median_distance = np.median(distances)
-    iqr = np.percentile(distances, 75) - np.percentile(distances, 25)
-
-    threshold = median_distance + 1.5 * iqr
-    threshold = min(threshold, 10)
-
-    # Pair the peaks using the dynamic thresold
-    paired_peaks = []
-    unpaired_peaks1 = list(peaks1)
-    unpaired_peaks2 = list(peaks2)
-
-    while unpaired_peaks1 and unpaired_peaks2:
-        min_distance = threshold + 1
-        pair = None
-
-        for p1 in unpaired_peaks1:
-            for p2 in unpaired_peaks2:
-                distance = abs(freqs1[p1] - freqs2[p2])
-                if distance < min_distance:
-                    min_distance = distance
-                    pair = (p1, p2)
-
-        if pair is None:  # No more pairs below the threshold
-            break
-
-        p1, p2 = pair
-        paired_peaks.append(((p1, freqs1[p1], psd1[p1]), (p2, freqs2[p2], psd2[p2])))
-        unpaired_peaks1.remove(p1)
-        unpaired_peaks2.remove(p2)
-
-    return PeakPairingResult(
-        paired_peaks=paired_peaks, unpaired_peaks1=unpaired_peaks1, unpaired_peaks2=unpaired_peaks2
-    )
-
-
-######################################################################
-# Computation of the differential spectrogram
-######################################################################
-
-
-def compute_mhi(similarity_factor: float, signal1: SignalData, signal2: SignalData) -> str:
-    num_unpaired_peaks = len(signal1.unpaired_peaks) + len(signal2.unpaired_peaks)
-    num_paired_peaks = len(signal1.paired_peaks)
-    # Combine unpaired peaks from both signals, tagging each peak with its respective signal
-    combined_unpaired_peaks = [(peak, signal1) for peak in signal1.unpaired_peaks] + [
-        (peak, signal2) for peak in signal2.unpaired_peaks
-    ]
-    psd_highest_max = max(signal1.psd.max(), signal2.psd.max())
-
-    # Start with the similarity factor directly scaled to a percentage
-    mhi = similarity_factor
-
-    # Bonus for ideal number of total peaks (1 or 2)
-    if num_paired_peaks >= DC_MAX_PEAKS:
-        mhi *= DC_MAX_PEAKS / num_paired_peaks  # Reduce MHI if more than ideal number of peaks
-
-    # Penalty from unpaired peaks weighted by their amplitude relative to the maximum PSD amplitude
-    unpaired_peak_penalty = 0
-    if num_unpaired_peaks > DC_MAX_UNPAIRED_PEAKS_ALLOWED:
-        for peak, signal in combined_unpaired_peaks:
-            unpaired_peak_penalty += (signal.psd[peak] / psd_highest_max) * 30
-        mhi -= unpaired_peak_penalty
-
-    # Ensure the result lies between 0 and 100 by clipping the computed value
-    mhi = np.clip(mhi, 0, 100)
-
-    return mhi_lut(mhi)
-
-
-# LUT to transform the MHI into a textual value easy to understand for the users of the script
-def mhi_lut(mhi: float) -> str:
-    ranges = [
-        (70, 100, 'Excellent mechanical health'),
-        (55, 70, 'Good mechanical health'),
-        (45, 55, 'Acceptable mechanical health'),
-        (30, 45, 'Potential signs of a mechanical issue'),
-        (15, 30, 'Likely a mechanical issue'),
-        (0, 15, 'Mechanical issue detected'),
-    ]
-    mhi = np.clip(mhi, 1, 100)
-    return next(
-        (message for lower, upper, message in ranges if lower < mhi <= upper),
-        'Unknown mechanical health',
-    )
-
-
-######################################################################
-# Graphing
-######################################################################
-
-
-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['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())
-
-    # Trace and annotate the peaks on the graph
-    paired_peak_count = 0
-    unpaired_peak_count = 0
-    offsets_table_data = []
-
-    for _, (peak1, peak2) in enumerate(signal1.paired_peaks):
-        label = ALPHABET[paired_peak_count]
-        amplitude_offset = abs(((signal2.psd[peak2[0]] - signal1.psd[peak1[0]]) / psd_highest_max) * 100)
-        frequency_offset = abs(signal2.freqs[peak2[0]] - signal1.freqs[peak1[0]])
-        offsets_table_data.append([f'Peaks {label}', f'{frequency_offset:.1f} Hz', f'{amplitude_offset:.1f} %'])
-
-        ax.plot(signal1.freqs[peak1[0]], signal1.psd[peak1[0]], 'x', color='black')
-        ax.plot(signal2.freqs[peak2[0]], signal2.psd[peak2[0]], 'x', color='black')
-        ax.plot(
-            [signal1.freqs[peak1[0]], signal2.freqs[peak2[0]]],
-            [signal1.psd[peak1[0]], signal2.psd[peak2[0]]],
-            ':',
-            color='gray',
-        )
+class PeakPairingResult(NamedTuple):
+    paired_peaks: List[Tuple[Tuple[int, float, float], Tuple[int, float, float]]]
+    unpaired_peaks1: List[int]
+    unpaired_peaks2: List[int]
 
-        ax.annotate(
-            label + '1',
-            (signal1.freqs[peak1[0]], signal1.psd[peak1[0]]),
-            textcoords='offset points',
-            xytext=(8, 5),
-            ha='left',
-            fontsize=13,
-            color='black',
-        )
-        ax.annotate(
-            label + '2',
-            (signal2.freqs[peak2[0]], signal2.psd[peak2[0]]),
-            textcoords='offset points',
-            xytext=(8, 5),
-            ha='left',
-            fontsize=13,
-            color='black',
-        )
-        paired_peak_count += 1
-
-    for peak in signal1.unpaired_peaks:
-        ax.plot(signal1.freqs[peak], signal1.psd[peak], 'x', color='black')
-        ax.annotate(
-            str(unpaired_peak_count + 1),
-            (signal1.freqs[peak], signal1.psd[peak]),
-            textcoords='offset points',
-            xytext=(8, 5),
-            ha='left',
-            fontsize=13,
-            color='red',
-            weight='bold',
-        )
-        unpaired_peak_count += 1
-
-    for peak in signal2.unpaired_peaks:
-        ax.plot(signal2.freqs[peak], signal2.psd[peak], 'x', color='black')
-        ax.annotate(
-            str(unpaired_peak_count + 1),
-            (signal2.freqs[peak], signal2.psd[peak]),
-            textcoords='offset points',
-            xytext=(8, 5),
-            ha='left',
-            fontsize=13,
-            color='red',
-            weight='bold',
-        )
-        unpaired_peak_count += 1
-
-    # Add estimated similarity to the graph
-    ax2 = ax.twinx()  # To split the legends in two box
-    ax2.yaxis.set_visible(False)
-    ax2.plot([], [], ' ', label=f'Number of unpaired peaks: {unpaired_peak_count}')
-
-    # Setting axis parameters, grid and graph title
-    ax.set_xlabel('Frequency (Hz)')
-    ax.set_xlim([0, max_freq])
-    ax.set_ylabel('Power spectral density')
-    ax.set_ylim([0, psd_highest_max * 1.1])
-
-    ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
-    ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
-    ax.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))
-    ax.grid(which='major', color='grey')
-    ax.grid(which='minor', color='lightgrey')
-    fontP = matplotlib.font_manager.FontProperties()
-    fontP.set_size('small')
-    ax.set_title(
-        'Belts frequency profiles',
-        fontsize=14,
-        color=KLIPPAIN_COLORS['dark_orange'],
-        weight='bold',
-    )
-
-    # Print the table of offsets ontop of the graph below the original legend (upper right)
-    if len(offsets_table_data) > 0:
-        columns = [
-            '',
-            'Frequency delta',
-            'Amplitude delta',
-        ]
-        offset_table = ax.table(
-            cellText=offsets_table_data,
-            colLabels=columns,
-            bbox=[0.66, 0.79, 0.33, 0.15],
-            loc='upper right',
-            cellLoc='center',
+
+class SignalData(NamedTuple):
+    freqs: np.ndarray
+    psd: np.ndarray
+    peaks: np.ndarray
+    paired_peaks: Optional[List[Tuple[Tuple[int, float, float], Tuple[int, float, float]]]] = None
+    unpaired_peaks: Optional[List[int]] = None
+
+
+class BeltsGraphComputation:
+    PEAKS_DETECTION_THRESHOLD = 0.1  # Threshold to detect peaks in the PSD signal (10% of max)
+    DC_MAX_PEAKS = 2  # Maximum ideal number of peaks
+    DC_MAX_UNPAIRED_PEAKS_ALLOWED = 0  # No unpaired peaks are tolerated
+
+    def __init__(
+        self,
+        measurements: List[Measurement],
+        kinematics: Optional[str],
+        max_freq: float,
+        accel_per_hz: Optional[float],
+        st_version: str,
+    ):
+        self.measurements = measurements
+        self.kinematics = kinematics
+        self.max_freq = max_freq
+        self.accel_per_hz = accel_per_hz
+        self.st_version = st_version
+
+    def compute(self):
+        if len(self.measurements) != 2:
+            raise ValueError('This tool needs 2 measurements to work with (one for each belt)!')
+
+        datas = [np.array(m['samples']) for m in self.measurements if m['samples'] is not None]
+
+        # Get belt names for labels
+        belt_info = {'A': ' (axis 1,-1)', 'B': ' (axis 1, 1)'}
+        signal1_belt = self.measurements[0]['name'].split('_')[1]
+        signal2_belt = self.measurements[1]['name'].split('_')[1]
+        signal1_belt += belt_info.get(signal1_belt, '')
+        signal2_belt += belt_info.get(signal2_belt, '')
+
+        # Compute calibration data
+        common_freqs = np.linspace(0, self.max_freq, 500)
+        signal1 = self._compute_signal_data(datas[0], common_freqs, self.max_freq)
+        signal2 = self._compute_signal_data(datas[1], common_freqs, self.max_freq)
+        del datas
+
+        # Pair peaks
+        pairing_result = self._pair_peaks(
+            signal1.peaks, signal1.freqs, signal1.psd, signal2.peaks, signal2.freqs, signal2.psd
         )
-        offset_table.auto_set_font_size(False)
-        offset_table.set_fontsize(8)
-        offset_table.auto_set_column_width([0, 1, 2])
-        offset_table.set_zorder(100)
-        cells = [key for key in offset_table.get_celld().keys()]
-        for cell in cells:
-            offset_table[cell].set_facecolor('white')
-            offset_table[cell].set_alpha(0.6)
-
-    ax.legend(loc='upper left', prop=fontP)
-    ax2.legend(loc='upper right', prop=fontP)
-
-    return
-
-
-# Compute quantile-quantile plot to compare the two belts
-def plot_versus_belts(
-    ax: plt.Axes,
-    common_freqs: np.ndarray,
-    signal1: SignalData,
-    signal2: SignalData,
-    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(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)
-    ax.fill_between(ideal_line, ideal_line, green_boundary, color='green', alpha=0.15, label='Good zone')
-    ax.plot(
-        ideal_line,
-        ideal_line,
-        '--',
-        label='Ideal line',
-        color='red',
-        linewidth=2,
-    )
-
-    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
-
-    for _, (peak1, peak2) in enumerate(signal1.paired_peaks):
-        label = ALPHABET[paired_peak_count]
-        freq1 = signal1.freqs[peak1[0]]
-        freq2 = signal2.freqs[peak2[0]]
-
-        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',
-                (signal1.psd[peak1[0]], signal2.psd[peak2[0]]),
-                textcoords='offset points',
-                xytext=(-7, 7),
-                fontsize=13,
-                color='black',
-            )
-        else:
-            ax.plot(
-                signal1.psd[peak2[0]], signal2.psd[peak2[0]], marker='o', color=KLIPPAIN_COLORS['purple'], markersize=7
-            )
-            ax.plot(
-                signal1.psd[peak1[0]], signal2.psd[peak1[0]], marker='o', color=KLIPPAIN_COLORS['orange'], markersize=7
-            )
-            ax.annotate(
-                f'{label}1',
-                (signal1.psd[peak1[0]], signal2.psd[peak1[0]]),
-                textcoords='offset points',
-                xytext=(0, 7),
-                fontsize=13,
-                color='black',
-            )
-            ax.annotate(
-                f'{label}2',
-                (signal1.psd[peak2[0]], signal2.psd[peak2[0]]),
-                textcoords='offset points',
-                xytext=(0, 7),
-                fontsize=13,
-                color='black',
-            )
-        paired_peak_count += 1
-
-    for _, peak_index in enumerate(signal1.unpaired_peaks):
-        ax.plot(
-            signal1.psd[peak_index], signal2.psd[peak_index], marker='o', color=KLIPPAIN_COLORS['orange'], markersize=7
+        signal1 = signal1._replace(
+            paired_peaks=pairing_result.paired_peaks, unpaired_peaks=pairing_result.unpaired_peaks1
         )
-        ax.annotate(
-            str(unpaired_peak_count + 1),
-            (signal1.psd[peak_index], signal2.psd[peak_index]),
-            textcoords='offset points',
-            fontsize=13,
-            weight='bold',
-            color=KLIPPAIN_COLORS['red_pink'],
-            xytext=(0, 7),
+        signal2 = signal2._replace(
+            paired_peaks=pairing_result.paired_peaks, unpaired_peaks=pairing_result.unpaired_peaks2
         )
-        unpaired_peak_count += 1
 
-    for _, peak_index in enumerate(signal2.unpaired_peaks):
-        ax.plot(
-            signal1.psd[peak_index], signal2.psd[peak_index], marker='o', color=KLIPPAIN_COLORS['purple'], markersize=7
+        # Compute similarity factor and MHI if needed (for symmetric kinematics)
+        similarity_factor = None
+        mhi = None
+        if self.kinematics in {'limited_corexy', 'corexy', 'limited_corexz', 'corexz'}:
+            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 = self._compute_mhi(similarity_factor, signal1, signal2)
+            ConsoleOutput.print(f'Mechanical health: {mhi}')
+
+        # Prepare data for plotting
+        computation_result = {
+            'signal1': signal1,
+            'signal2': signal2,
+            'similarity_factor': similarity_factor,
+            'mhi': mhi,
+            'signal1_belt': signal1_belt,
+            'signal2_belt': signal2_belt,
+            'kinematics': self.kinematics,
+            'accel_per_hz': self.accel_per_hz,
+            'st_version': self.st_version,
+            'measurements': self.measurements,
+            'max_freq': self.max_freq,
+        }
+
+        return computation_result
+
+    def _compute_signal_data(self, data: np.ndarray, common_freqs: np.ndarray, max_freq: float):
+        helper = get_shaper_calibrate_module().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]
+
+        # 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)
+
+        _, peaks, _ = detect_peaks(
+            interp_psd,
+            common_freqs,
+            self.PEAKS_DETECTION_THRESHOLD * interp_psd.max(),
+            window_size=20,
+            vicinity=15,
         )
-        ax.annotate(
-            str(unpaired_peak_count + 1),
-            (signal1.psd[peak_index], signal2.psd[peak_index]),
-            textcoords='offset points',
-            fontsize=13,
-            weight='bold',
-            color=KLIPPAIN_COLORS['red_pink'],
-            xytext=(0, 7),
+
+        return SignalData(freqs=common_freqs, psd=interp_psd, peaks=peaks)
+
+    # This function create pairs of peaks that are close in frequency on two curves (that are known
+    # to be resonances points and must be similar on both belts on a CoreXY kinematic)
+    def _pair_peaks(
+        self,
+        peaks1: np.ndarray,
+        freqs1: np.ndarray,
+        psd1: np.ndarray,
+        peaks2: np.ndarray,
+        freqs2: np.ndarray,
+        psd2: np.ndarray,
+    ) -> PeakPairingResult:
+        # Compute a dynamic detection threshold to filter and pair peaks efficiently
+        # even if the signal is very noisy (this get clipped to a maximum of 10Hz diff)
+        distances = []
+        for p1 in peaks1:
+            for p2 in peaks2:
+                distances.append(abs(freqs1[p1] - freqs2[p2]))
+        distances = np.array(distances)
+
+        median_distance = np.median(distances)
+        iqr = np.percentile(distances, 75) - np.percentile(distances, 25)
+
+        threshold = median_distance + 1.5 * iqr
+        threshold = min(threshold, 10)
+
+        # Pair the peaks using the dynamic thresold
+        paired_peaks = []
+        unpaired_peaks1 = list(peaks1)
+        unpaired_peaks2 = list(peaks2)
+
+        while unpaired_peaks1 and unpaired_peaks2:
+            min_distance = threshold + 1
+            pair = None
+
+            for p1 in unpaired_peaks1:
+                for p2 in unpaired_peaks2:
+                    distance = abs(freqs1[p1] - freqs2[p2])
+                    if distance < min_distance:
+                        min_distance = distance
+                        pair = (p1, p2)
+
+            if pair is None:  # No more pairs below the threshold
+                break
+
+            p1, p2 = pair
+            paired_peaks.append(((p1, freqs1[p1], psd1[p1]), (p2, freqs2[p2], psd2[p2])))
+            unpaired_peaks1.remove(p1)
+            unpaired_peaks2.remove(p2)
+
+        return PeakPairingResult(
+            paired_peaks=paired_peaks, unpaired_peaks1=unpaired_peaks1, unpaired_peaks2=unpaired_peaks2
         )
-        unpaired_peak_count += 1
-
-    ax.set_xlabel(f'Belt {signal1_belt}')
-    ax.set_ylabel(f'Belt {signal2_belt}')
-    ax.set_xlim([0, max_psd * 1.1])
-    ax.set_ylim([0, max_psd * 1.1])
-
-    ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
-    ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
-    ax.ticklabel_format(style='scientific', scilimits=(0, 0))
-    ax.grid(which='major', color='grey')
-    ax.grid(which='minor', color='lightgrey')
-
-    fontP = matplotlib.font_manager.FontProperties()
-    fontP.set_size('medium')
-    ax.legend(loc='upper left', prop=fontP)
-
-    return
-
-
-######################################################################
-# Custom tools
-######################################################################
-
-
-# Original Klipper function to get the PSD data of a raw accelerometer signal
-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]
-
-    # 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)
-
-    _, 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)
-
-
-######################################################################
-# Startup and main routines
-######################################################################
-
-
-def belts_calibration(
-    measurements: List[Measurement],
-    kinematics: Optional[str],
-    klipperdir: str = '~/klipper',
-    max_freq: float = 200.0,
-    accel_per_hz: Optional[float] = None,
-    st_version: str = 'unknown',
-) -> plt.Figure:
-    if len(measurements) != 2:
-        raise ValueError('This tool needs 2 measurements to work with (one for each belt)!')
-
-    global shaper_calibrate
-    shaper_calibrate = setup_klipper_import(klipperdir)
-
-    datas = [np.array(m['samples']) for m in measurements if m['samples'] is not None]
-
-    # Get the belts name for the legend to avoid putting the full file name
-    belt_info = {'A': ' (axis 1,-1)', 'B': ' (axis 1, 1)'}
-    signal1_belt = measurements[0]['name'].split('_')[1]
-    signal2_belt = measurements[1]['name'].split('_')[1]
-    signal1_belt += belt_info.get(signal1_belt, '')
-    signal2_belt += belt_info.get(signal2_belt, '')
-
-    # Compute calibration data for the two datasets with automatic peaks detection
-    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
-    pairing_result = pair_peaks(signal1.peaks, signal1.freqs, signal1.psd, signal2.peaks, signal2.freqs, signal2.psd)
-    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)
-
-    # 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, signal1, signal2)
-    ConsoleOutput.print(f'[experimental] Mechanical health: {mhi}')
-
-    fig, ((ax1, ax3)) = plt.subplots(
-        1,
-        2,
-        gridspec_kw={
-            'width_ratios': [5, 3],
-            'bottom': 0.080,
-            'top': 0.840,
-            'left': 0.050,
-            'right': 0.985,
-            'hspace': 0.166,
-            'wspace': 0.138,
-        },
-    )
-    fig.set_size_inches(15, 7)
-
-    # Add title
-    title_line1 = 'RELATIVE BELTS CALIBRATION TOOL'
-    fig.text(
-        0.060, 0.947, title_line1, ha='left', va='bottom', fontsize=20, color=KLIPPAIN_COLORS['purple'], weight='bold'
-    )
-    try:
-        filename = measurements[0]['name']
-        dt = datetime.strptime(f"{filename.split('_')[2]} {filename.split('_')[3]}", '%Y%m%d %H%M%S')
-        title_line2 = dt.strftime('%x %X')
-        if kinematics is not None:
-            title_line2 += ' -- ' + kinematics.upper() + ' kinematics'
-    except Exception:
-        ConsoleOutput.print(
-            f'Warning: Unable to parse the date from the measurements names '
-            f"({measurements[0]['name']}, {measurements[1]['name']})"
+
+    def _compute_mhi(self, similarity_factor: float, signal1, signal2) -> str:
+        num_unpaired_peaks = len(signal1.unpaired_peaks) + len(signal2.unpaired_peaks)
+        num_paired_peaks = len(signal1.paired_peaks)
+        # Combine unpaired peaks from both signals, tagging each peak with its respective signal
+        combined_unpaired_peaks = [(peak, signal1) for peak in signal1.unpaired_peaks] + [
+            (peak, signal2) for peak in signal2.unpaired_peaks
+        ]
+        psd_highest_max = max(signal1.psd.max(), signal2.psd.max())
+
+        # Start with the similarity factor directly scaled to a percentage
+        mhi = similarity_factor
+
+        # Bonus for ideal number of total peaks (1 or 2)
+        if num_paired_peaks >= self.DC_MAX_PEAKS:
+            mhi *= self.DC_MAX_PEAKS / num_paired_peaks  # Reduce MHI if more than ideal number of peaks
+
+        # Penalty from unpaired peaks weighted by their amplitude relative to the maximum PSD amplitude
+        unpaired_peak_penalty = 0
+        if num_unpaired_peaks > self.DC_MAX_UNPAIRED_PEAKS_ALLOWED:
+            for peak, signal in combined_unpaired_peaks:
+                unpaired_peak_penalty += (signal.psd[peak] / psd_highest_max) * 30
+            mhi -= unpaired_peak_penalty
+
+        # Ensure the result lies between 0 and 100 by clipping the computed value
+        mhi = np.clip(mhi, 0, 100)
+
+        return self._mhi_lut(mhi)
+
+    # LUT to transform the MHI into a textual value easy to understand for the users of the script
+    def _mhi_lut(self, mhi: float) -> str:
+        ranges = [
+            (70, 100, 'Excellent mechanical health'),
+            (55, 70, 'Good mechanical health'),
+            (45, 55, 'Acceptable mechanical health'),
+            (30, 45, 'Potential signs of a mechanical issue'),
+            (15, 30, 'Likely a mechanical issue'),
+            (0, 15, 'Mechanical issue detected'),
+        ]
+        mhi = np.clip(mhi, 1, 100)
+        return next(
+            (message for lower, upper, message in ranges if lower < mhi <= upper),
+            'Unknown mechanical health',
         )
-        title_line2 = measurements[0]['name'] + ' / ' + measurements[1]['name']
-    fig.text(0.060, 0.939, title_line2, ha='left', va='top', fontsize=16, color=KLIPPAIN_COLORS['dark_purple'])
-
-    # We add the estimated similarity and the MHI value to the title only if the kinematics is CoreXY
-    # as it make no sense to compute these values for other kinematics that doesn't have paired belts
-    if kinematics in {'limited_corexy', 'corexy', 'limited_corexz', 'corexz'}:
-        title_line3 = f'| Estimated similarity: {similarity_factor:.1f}%'
-        title_line4 = f'| {mhi} (experimental)'
-        fig.text(0.55, 0.985, title_line3, ha='left', va='top', fontsize=14, color=KLIPPAIN_COLORS['dark_purple'])
-        fig.text(0.55, 0.950, title_line4, ha='left', va='top', fontsize=14, color=KLIPPAIN_COLORS['dark_purple'])
-
-    # Add the accel_per_hz value to the title
-    title_line5 = f'| Accel per Hz used: {accel_per_hz} mm/s²/Hz'
-    fig.text(0.551, 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, 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')
-    ax_logo.imshow(plt.imread(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'klippain.png')))
-    ax_logo.axis('off')
-
-    # Adding Shake&Tune version in the top right corner
-    if st_version != 'unknown':
-        fig.text(0.995, 0.980, st_version, ha='right', va='bottom', fontsize=8, color=KLIPPAIN_COLORS['purple'])
-
-    return fig
-
-
-def main():
-    # Parse command-line arguments
-    usage = '%prog [options] <raw logs>'
-    opts = optparse.OptionParser(usage)
-    opts.add_option('-o', '--output', type='string', dest='output', default=None, help='filename of output graph')
-    opts.add_option('-f', '--max_freq', type='float', default=200.0, help='maximum frequency to graph')
-    opts.add_option('--accel_per_hz', type='float', default=None, help='accel_per_hz used during the measurement')
-    opts.add_option(
-        '-k', '--klipper_dir', type='string', dest='klipperdir', default='~/klipper', help='main klipper directory'
-    )
-    opts.add_option(
-        '-m',
-        '--kinematics',
-        type='string',
-        dest='kinematics',
-        help='machine kinematics configuration',
-    )
-    options, args = opts.parse_args()
-    if len(args) < 1:
-        opts.error('Incorrect number of arguments')
-    if options.output is None:
-        opts.error('You must specify an output file.png to use the script (option -o)')
-
-    measurements_manager = MeasurementsManager(10)
-    if args[0].endswith('.csv'):
-        measurements_manager.load_from_csvs(args)
-    elif args[0].endswith('.stdata'):
-        measurements_manager.load_from_stdata(args[0])
-    else:
-        raise ValueError('Only .stdata or legacy Klipper raw accelerometer CSV files are supported!')
-
-    fig = belts_calibration(
-        measurements_manager.get_measurements(),
-        options.kinematics,
-        options.klipperdir,
-        options.max_freq,
-        options.accel_per_hz,
-        'unknown',
-    )
-    fig.savefig(options.output, dpi=150)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/shaketune/graph_creators/graph_creator.py b/shaketune/graph_creators/graph_creator.py
index 0106828..7ce1464 100644
--- a/shaketune/graph_creators/graph_creator.py
+++ b/shaketune/graph_creators/graph_creator.py
@@ -11,6 +11,7 @@
 
 
 import abc
+import os
 from datetime import datetime
 from typing import Optional
 
@@ -18,6 +19,7 @@
 
 from ..helpers.accelerometer import MeasurementsManager
 from ..shaketune_config import ShakeTuneConfig
+from .plotter import Plotter
 
 
 class GraphCreator(abc.ABC):
@@ -38,25 +40,33 @@ def __init__(self, config: ShakeTuneConfig):
         self._version = ShakeTuneConfig.get_git_version()
         self._type = self.__class__.graph_type
         self._folder = self._config.get_results_folder(self._type)
+        self._plotter = Plotter()
+        self._custom_filepath = None
 
     def _save_figure(
         self, fig: Figure, measurements_manager: MeasurementsManager, axis_label: Optional[str] = None
     ) -> None:
-        axis_suffix = f'_{axis_label}' if axis_label else ''
-        filename = self._folder / f"{self._type.replace(' ', '')}_{self._graph_date}{axis_suffix}"
-        fig.savefig(f'{filename}.png', dpi=self._config.dpi)
+        if os.environ.get('SHAKETUNE_IN_CLI') == '1' and self._custom_filepath is not None:
+            fig.savefig(f'{self._custom_filepath}', dpi=self._config.dpi)
+        else:
+            axis_suffix = f'_{axis_label}' if axis_label else ''
+            filename = self._folder / f"{self._type.replace(' ', '')}_{self._graph_date}{axis_suffix}"
+            fig.savefig(f'{filename}.png', dpi=self._config.dpi)
 
-        if self._config.keep_raw_data:
+        if self._config.keep_raw_data and not os.environ.get('SHAKETUNE_IN_CLI') == '1':
             measurements_manager.save_stdata(f'{filename}.stdata')
 
     def get_type(self) -> str:
         return self._type
 
+    def override_output_target(self, filepath: str) -> None:
+        self._custom_filepath = filepath
+
     @abc.abstractmethod
     def create_graph(self, measurements_manager: MeasurementsManager) -> None:
         pass
 
-    def clean_old_files(self, keep_results: int = 3) -> None:
+    def clean_old_files(self, keep_results: int = 10) -> None:
         files = sorted(self._folder.glob('*.png'), key=lambda f: f.stat().st_mtime, reverse=True)
         if len(files) <= keep_results:
             return  # No need to delete any files
diff --git a/shaketune/graph_creators/plotter.py b/shaketune/graph_creators/plotter.py
new file mode 100644
index 0000000..1671fda
--- /dev/null
+++ b/shaketune/graph_creators/plotter.py
@@ -0,0 +1,1476 @@
+# Shake&Tune: 3D printer analysis tools
+#
+# Copyright (C) 2022 - 2024 Félix Boisselier <felix@fboisselier.fr> (Frix_x on Discord)
+# Licensed under the GNU General Public License v3.0 (GPL-3.0)
+#
+# File: plotter.py
+# Description: Contains the Plotter class to handle the plotting logic in order to
+#              create the .png graphs in Shake&Tune with all the infos using matplotlib.
+
+
+import os
+from datetime import datetime
+
+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')  # Use 'Agg' backend for non-GUI environments
+
+
+class Plotter:
+    KLIPPAIN_COLORS = {
+        'purple': '#70088C',
+        'orange': '#FF8D32',
+        'dark_purple': '#150140',
+        'dark_orange': '#F24130',
+        'red_pink': '#F2055C',
+    }
+
+    # static_frequency tool
+    SPECTROGRAM_LOW_PERCENTILE_FILTER = 5
+
+    # belts tool
+    ALPHABET = 'αβγδεζηθικλμνξοπρστυφχψω'  # For paired peak names (using the Greek alphabet to avoid confusion with belt names)
+
+    # input shaper tool
+    SPECTROGRAM_LOW_PERCENTILE_FILTER = 5
+    MAX_VIBRATIONS_PLOTTED = 80.0
+    MAX_VIBRATIONS_PLOTTED_ZOOM = 1.25  # 1.25x max vibs values from the standard filters selection
+
+    def plot_axes_map_detection_graph(self, data):
+        time_data = data['acceleration_data_0']
+        accel_data = data['acceleration_data_1']
+        gravity = data['gravity']
+        average_noise_intensity_label = data['average_noise_intensity_label']
+        position_data = data['position_data']
+        direction_vectors = data['direction_vectors']
+        angle_errors = data['angle_errors']
+        formatted_direction_vector = data['formatted_direction_vector']
+        measurements = data['measurements']
+        st_version = data['st_version']
+
+        fig, ((ax_1, ax_3)) = plt.subplots(
+            1,
+            2,
+            gridspec_kw={
+                'width_ratios': [5, 3],
+                'bottom': 0.080,
+                'top': 0.840,
+                'left': 0.055,
+                'right': 0.960,
+                'hspace': 0.166,
+                'wspace': 0.060,
+            },
+        )
+        fig.set_size_inches(15, 7)
+        ax_3.remove()
+        ax_3 = fig.add_subplot(122, projection='3d')
+
+        # ------------------------------------------------------------------------------------------------------------------------------------------------
+        # Plot the differents graphs
+        for i, (time, (accel_x, accel_y, accel_z)) in enumerate(zip(time_data, accel_data)):
+            ax_1.plot(
+                time,
+                accel_x,
+                label='X' if i == 0 else '',
+                color=self.KLIPPAIN_COLORS['purple'],
+                linewidth=0.5,
+                zorder=50 if i == 0 else 10,
+            )
+            ax_1.plot(
+                time,
+                accel_y,
+                label='Y' if i == 0 else '',
+                color=self.KLIPPAIN_COLORS['orange'],
+                linewidth=0.5,
+                zorder=50 if i == 1 else 10,
+            )
+            ax_1.plot(
+                time,
+                accel_z,
+                label='Z' if i == 0 else '',
+                color=self.KLIPPAIN_COLORS['red_pink'],
+                linewidth=0.5,
+                zorder=50 if i == 2 else 10,
+            )
+
+        ax_1.set_xlabel('Time (s)')
+        ax_1.set_ylabel('Acceleration (mm/s²)')
+
+        ax_1.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
+        ax_1.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
+        ax_1.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))
+        ax_1.grid(which='major', color='grey')
+        ax_1.grid(which='minor', color='lightgrey')
+        fontP = matplotlib.font_manager.FontProperties()
+        fontP.set_size('small')
+        ax_1.set_title(
+            'Acceleration (gravity offset removed)',
+            fontsize=14,
+            color=self.KLIPPAIN_COLORS['dark_orange'],
+            weight='bold',
+        )
+
+        ax_1.legend(loc='upper left', prop=fontP)
+
+        # Add the gravity and noise level to the graph legend
+        ax_1_2 = ax_1.twinx()
+        ax_1_2.yaxis.set_visible(False)
+        ax_1_2.plot([], [], ' ', label=average_noise_intensity_label)
+        ax_1_2.plot([], [], ' ', label=f'Measured gravity: {gravity / 1000:0.3f} m/s²')
+        ax_1_2.legend(loc='upper right', prop=fontP)
+
+        # ------------------------------------------------------------------------------------------------------------------------------------------------
+        # Plot the 3D path of the movement
+        for i, ((position_x, position_y, position_z), average_direction_vector, angle_error) in enumerate(
+            zip(position_data, direction_vectors, angle_errors)
+        ):
+            ax_3.plot(
+                position_x, position_y, position_z, color=self.KLIPPAIN_COLORS['orange'], linestyle=':', linewidth=2
+            )
+            ax_3.scatter(position_x[0], position_y[0], position_z[0], color=self.KLIPPAIN_COLORS['red_pink'], zorder=10)
+            ax_3.text(
+                position_x[0] + 1,
+                position_y[0],
+                position_z[0],
+                str(i + 1),
+                color='black',
+                fontsize=16,
+                fontweight='bold',
+                zorder=20,
+            )
+
+            # Plot the average direction vector
+            start_position = np.array([position_x[0], position_y[0], position_z[0]])
+            end_position = start_position + average_direction_vector * np.linalg.norm(
+                [position_x[-1] - position_x[0], position_y[-1] - position_y[0], position_z[-1] - position_z[0]]
+            )
+            ax_3.plot(
+                [start_position[0], end_position[0]],
+                [start_position[1], end_position[1]],
+                [start_position[2], end_position[2]],
+                label=f'{["X", "Y", "Z"][i]} angle: {angle_error:0.2f}°',
+                color=self.KLIPPAIN_COLORS['purple'],
+                linestyle='-',
+                linewidth=2,
+            )
+
+        ax_3.set_xlabel('X Position (mm)')
+        ax_3.set_ylabel('Y Position (mm)')
+        ax_3.set_zlabel('Z Position (mm)')
+
+        ax_3.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
+        ax_3.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
+        ax_3.grid(which='major', color='grey')
+        ax_3.grid(which='minor', color='lightgrey')
+        fontP = matplotlib.font_manager.FontProperties()
+        fontP.set_size('small')
+        ax_3.set_title(
+            'Estimated movement in 3D space',
+            fontsize=14,
+            color=self.KLIPPAIN_COLORS['dark_orange'],
+            weight='bold',
+        )
+
+        ax_3.legend(loc='upper left', prop=fontP)
+        # ------------------------------------------------------------------------------------------------------------------------------------------------
+
+        # Add title
+        title_line1 = 'AXES MAP CALIBRATION TOOL'
+        fig.text(
+            0.060,
+            0.947,
+            title_line1,
+            ha='left',
+            va='bottom',
+            fontsize=20,
+            color=self.KLIPPAIN_COLORS['purple'],
+            weight='bold',
+        )
+        try:
+            filename = measurements[0]['name']
+            dt = datetime.strptime(f"{filename.split('_')[2]} {filename.split('_')[3]}", '%Y%m%d %H%M%S')
+            title_line2 = dt.strftime('%x %X')
+            if self.accel is not None:
+                title_line2 += f' -- at {self.accel:0.0f} mm/s²'
+        except Exception:
+            title_line2 = measurements[0]['name'] + ' ...'
+        fig.text(0.060, 0.939, title_line2, ha='left', va='top', fontsize=16, color=self.KLIPPAIN_COLORS['dark_purple'])
+
+        title_line3 = f'| Detected axes_map: {formatted_direction_vector}'
+        fig.text(0.50, 0.985, title_line3, ha='left', va='top', fontsize=16, color=self.KLIPPAIN_COLORS['dark_purple'])
+
+        # Adding logo
+        current_dir = os.path.dirname(__file__)
+        image_path = os.path.join(current_dir, 'klippain.png')
+        ax_logo = fig.add_axes([0.001, 0.894, 0.105, 0.105], anchor='NW')
+        ax_logo.imshow(plt.imread(image_path))
+        ax_logo.axis('off')
+
+        if st_version != 'unknown':
+            fig.text(
+                0.995, 0.980, st_version, ha='right', va='bottom', fontsize=8, color=self.KLIPPAIN_COLORS['purple']
+            )
+
+        return fig
+
+    def plot_static_frequency_graph(self, data):
+        # Extract data from computation_result
+        freq = data['freq']
+        duration = data['duration']
+        accel_per_hz = data['accel_per_hz']
+        st_version = data['st_version']
+        measurements = data['measurements']
+        t = data['t']
+        bins = data['bins']
+        pdata = data['pdata']
+        max_freq = data['max_freq']
+
+        fig, ((ax_1, ax_3)) = plt.subplots(
+            1,
+            2,
+            gridspec_kw={
+                'width_ratios': [5, 3],
+                'bottom': 0.080,
+                'top': 0.840,
+                'left': 0.050,
+                'right': 0.985,
+                'hspace': 0.166,
+                'wspace': 0.138,
+            },
+        )
+        fig.set_size_inches(15, 7)
+
+        title_line1 = 'STATIC FREQUENCY HELPER TOOL'
+        fig.text(
+            0.060,
+            0.947,
+            title_line1,
+            ha='left',
+            va='bottom',
+            fontsize=20,
+            color=self.KLIPPAIN_COLORS['purple'],
+            weight='bold',
+        )
+        try:
+            filename_parts = measurements[0]['name'].split('_')
+            dt = datetime.strptime(f'{filename_parts[2]} {filename_parts[3]}', '%Y%m%d %H%M%S')
+            title_line2 = dt.strftime('%x %X') + ' -- ' + filename_parts[1].upper() + ' axis'
+            title_line3 = f'| Maintained frequency: {freq}Hz for {duration}s'
+            title_line4 = f'| Accel per Hz used: {accel_per_hz} mm/s²/Hz' if accel_per_hz is not None else ''
+        except Exception:
+            title_line2 = measurements[0]['name']
+            title_line3 = ''
+            title_line4 = ''
+        fig.text(0.060, 0.939, title_line2, ha='left', va='top', fontsize=16, color=self.KLIPPAIN_COLORS['dark_purple'])
+        fig.text(0.55, 0.985, title_line3, ha='left', va='top', fontsize=14, color=self.KLIPPAIN_COLORS['dark_purple'])
+        fig.text(0.55, 0.950, title_line4, ha='left', va='top', fontsize=11, color=self.KLIPPAIN_COLORS['dark_purple'])
+
+        ax_1.set_title(
+            'Time-Frequency Spectrogram', fontsize=14, color=self.KLIPPAIN_COLORS['dark_orange'], weight='bold'
+        )
+
+        vmin_value = np.percentile(pdata, self.SPECTROGRAM_LOW_PERCENTILE_FILTER)
+
+        cm = 'inferno'
+        norm = matplotlib.colors.LogNorm(vmin=vmin_value)
+        ax_1.imshow(
+            pdata.T,
+            norm=norm,
+            cmap=cm,
+            aspect='auto',
+            extent=[t[0], t[-1], bins[0], bins[-1]],
+            origin='lower',
+            interpolation='antialiased',
+        )
+
+        ax_1.set_xlim([0.0, max_freq])
+        ax_1.set_ylabel('Time (s)')
+        ax_1.set_xlabel('Frequency (Hz)')
+
+        # Integrate the energy over the frequency bins for each time step and plot this vertically
+        ax_3.plot(np.trapz(pdata, t, axis=0), bins, color=self.KLIPPAIN_COLORS['orange'])
+        ax_3.set_title('Vibrations', fontsize=14, color=self.KLIPPAIN_COLORS['dark_orange'], weight='bold')
+        ax_3.set_xlabel('Cumulative Energy')
+        ax_3.set_ylabel('Time (s)')
+        ax_3.set_ylim([bins[0], bins[-1]])
+
+        ax_3.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
+        ax_3.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
+        ax_3.ticklabel_format(axis='x', style='scientific', scilimits=(0, 0))
+        ax_3.grid(which='major', color='grey')
+        ax_3.grid(which='minor', color='lightgrey')
+
+        # Adding logo
+        current_dir = os.path.dirname(__file__)
+        image_path = os.path.join(current_dir, 'klippain.png')
+        ax_logo = fig.add_axes([0.001, 0.894, 0.105, 0.105], anchor='NW')
+        ax_logo.imshow(plt.imread(image_path))
+        ax_logo.axis('off')
+
+        if st_version != 'unknown':
+            fig.text(
+                0.995, 0.980, st_version, ha='right', va='bottom', fontsize=8, color=self.KLIPPAIN_COLORS['purple']
+            )
+
+        return fig
+
+    def plot_belts_graph(self, data):
+        # Extract data from computation_result
+        signal1 = data['signal1']
+        signal2 = data['signal2']
+        similarity_factor = data['similarity_factor']
+        mhi = data['mhi']
+        signal1_belt = data['signal1_belt']
+        signal2_belt = data['signal2_belt']
+        kinematics = data['kinematics']
+        accel_per_hz = data['accel_per_hz']
+        st_version = data['st_version']
+        measurements = data['measurements']
+        max_freq = data['max_freq']
+
+        # Begin plotting
+        fig, ((ax_1, ax_3)) = plt.subplots(
+            1,
+            2,
+            gridspec_kw={
+                'width_ratios': [5, 3],
+                'bottom': 0.080,
+                'top': 0.840,
+                'left': 0.050,
+                'right': 0.985,
+                'hspace': 0.166,
+                'wspace': 0.138,
+            },
+        )
+        fig.set_size_inches(15, 7)
+
+        # Add title
+        title_line1 = 'RELATIVE BELTS CALIBRATION TOOL'
+        fig.text(
+            0.060,
+            0.947,
+            title_line1,
+            ha='left',
+            va='bottom',
+            fontsize=20,
+            color=self.KLIPPAIN_COLORS['purple'],
+            weight='bold',
+        )
+        try:
+            filename = measurements[0]['name']
+            dt = datetime.strptime(f"{filename.split('_')[2]} {filename.split('_')[3]}", '%Y%m%d %H%M%S')
+            title_line2 = dt.strftime('%x %X')
+            if kinematics is not None:
+                title_line2 += ' -- ' + kinematics.upper() + ' kinematics'
+        except Exception:
+            title_line2 = measurements[0]['name'] + ' / ' + measurements[1]['name']
+        fig.text(0.060, 0.939, title_line2, ha='left', va='top', fontsize=16, color=self.KLIPPAIN_COLORS['dark_purple'])
+
+        # Add similarity and MHI if kinematics is CoreXY
+        if kinematics in {'limited_corexy', 'corexy', 'limited_corexz', 'corexz'}:
+            title_line3 = f'| Estimated similarity: {similarity_factor:.1f}%'
+            title_line4 = f'| {mhi} (experimental)'
+            fig.text(
+                0.55, 0.985, title_line3, ha='left', va='top', fontsize=14, color=self.KLIPPAIN_COLORS['dark_purple']
+            )
+            fig.text(
+                0.55, 0.950, title_line4, ha='left', va='top', fontsize=14, color=self.KLIPPAIN_COLORS['dark_purple']
+            )
+
+        # Add accel_per_hz to the title
+        title_line5 = f'| Accel per Hz used: {accel_per_hz} mm/s²/Hz'
+        fig.text(0.551, 0.915, title_line5, ha='left', va='top', fontsize=10, color=self.KLIPPAIN_COLORS['dark_purple'])
+
+        # ------------------------------------------------------------------------------------------------------------------------------------------------
+        # Plot the two belts PSD signals
+        ax_1.plot(signal1.freqs, signal1.psd, label='Belt ' + signal1_belt, color=self.KLIPPAIN_COLORS['orange'])
+        ax_1.plot(signal2.freqs, signal2.psd, label='Belt ' + signal2_belt, color=self.KLIPPAIN_COLORS['purple'])
+
+        psd_highest_max = max(signal1.psd.max(), signal2.psd.max())
+
+        # Trace and annotate the peaks on the graph
+        paired_peak_count = 0
+        unpaired_peak_count = 0
+        offsets_table_data = []
+
+        for _, (peak1, peak2) in enumerate(signal1.paired_peaks):
+            label = self.ALPHABET[paired_peak_count]
+            amplitude_offset = abs(((signal2.psd[peak2[0]] - signal1.psd[peak1[0]]) / psd_highest_max) * 100)
+            frequency_offset = abs(signal2.freqs[peak2[0]] - signal1.freqs[peak1[0]])
+            offsets_table_data.append([f'Peaks {label}', f'{frequency_offset:.1f} Hz', f'{amplitude_offset:.1f} %'])
+
+            ax_1.plot(signal1.freqs[peak1[0]], signal1.psd[peak1[0]], 'x', color='black')
+            ax_1.plot(signal2.freqs[peak2[0]], signal2.psd[peak2[0]], 'x', color='black')
+            ax_1.plot(
+                [signal1.freqs[peak1[0]], signal2.freqs[peak2[0]]],
+                [signal1.psd[peak1[0]], signal2.psd[peak2[0]]],
+                ':',
+                color='gray',
+            )
+
+            ax_1.annotate(
+                label + '1',
+                (signal1.freqs[peak1[0]], signal1.psd[peak1[0]]),
+                textcoords='offset points',
+                xytext=(8, 5),
+                ha='left',
+                fontsize=13,
+                color='black',
+            )
+            ax_1.annotate(
+                label + '2',
+                (signal2.freqs[peak2[0]], signal2.psd[peak2[0]]),
+                textcoords='offset points',
+                xytext=(8, 5),
+                ha='left',
+                fontsize=13,
+                color='black',
+            )
+            paired_peak_count += 1
+
+        for peak in signal1.unpaired_peaks:
+            ax_1.plot(signal1.freqs[peak], signal1.psd[peak], 'x', color='black')
+            ax_1.annotate(
+                str(unpaired_peak_count + 1),
+                (signal1.freqs[peak], signal1.psd[peak]),
+                textcoords='offset points',
+                xytext=(8, 5),
+                ha='left',
+                fontsize=13,
+                color='red',
+                weight='bold',
+            )
+            unpaired_peak_count += 1
+
+        for peak in signal2.unpaired_peaks:
+            ax_1.plot(signal2.freqs[peak], signal2.psd[peak], 'x', color='black')
+            ax_1.annotate(
+                str(unpaired_peak_count + 1),
+                (signal2.freqs[peak], signal2.psd[peak]),
+                textcoords='offset points',
+                xytext=(8, 5),
+                ha='left',
+                fontsize=13,
+                color='red',
+                weight='bold',
+            )
+            unpaired_peak_count += 1
+
+        # Add estimated similarity to the graph
+        ax_1_2 = ax_1.twinx()  # To split the legends in two box
+        ax_1_2.yaxis.set_visible(False)
+        ax_1_2.plot([], [], ' ', label=f'Number of unpaired peaks: {unpaired_peak_count}')
+
+        # Setting axis parameters, grid and graph title
+        ax_1.set_xlabel('Frequency (Hz)')
+        ax_1.set_xlim([0, max_freq])
+        ax_1.set_ylabel('Power spectral density')
+        ax_1.set_ylim([0, psd_highest_max * 1.1])
+
+        ax_1.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
+        ax_1.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
+        ax_1.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))
+        ax_1.grid(which='major', color='grey')
+        ax_1.grid(which='minor', color='lightgrey')
+        fontP = matplotlib.font_manager.FontProperties()
+        fontP.set_size('small')
+        ax_1.set_title(
+            'Belts frequency profiles',
+            fontsize=14,
+            color=self.KLIPPAIN_COLORS['dark_orange'],
+            weight='bold',
+        )
+
+        # Print the table of offsets ontop of the graph below the original legend (upper right)
+        if len(offsets_table_data) > 0:
+            columns = [
+                '',
+                'Frequency delta',
+                'Amplitude delta',
+            ]
+            offset_table = ax_1.table(
+                cellText=offsets_table_data,
+                colLabels=columns,
+                bbox=[0.66, 0.79, 0.33, 0.15],
+                loc='upper right',
+                cellLoc='center',
+            )
+            offset_table.auto_set_font_size(False)
+            offset_table.set_fontsize(8)
+            offset_table.auto_set_column_width([0, 1, 2])
+            offset_table.set_zorder(100)
+            cells = [key for key in offset_table.get_celld().keys()]
+            for cell in cells:
+                offset_table[cell].set_facecolor('white')
+                offset_table[cell].set_alpha(0.6)
+
+        ax_1.legend(loc='upper left', prop=fontP)
+        ax_1_2.legend(loc='upper right', prop=fontP)
+
+        # ------------------------------------------------------------------------------------------------------------------------------------------------
+        ax_3.set_title(
+            'Cross-belts comparison plot', fontsize=14, color=self.KLIPPAIN_COLORS['dark_orange'], weight='bold'
+        )
+
+        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_3.fill_betweenx(ideal_line, ideal_line, green_boundary, color='green', alpha=0.15)
+        ax_3.fill_between(ideal_line, ideal_line, green_boundary, color='green', alpha=0.15, label='Good zone')
+        ax_3.plot(
+            ideal_line,
+            ideal_line,
+            '--',
+            label='Ideal line',
+            color='red',
+            linewidth=2,
+        )
+
+        ax_3.plot(signal1.psd, signal2.psd, color='dimgrey', marker='o', markersize=1.5)
+        ax_3.fill_betweenx(signal2.psd, signal1.psd, color=self.KLIPPAIN_COLORS['red_pink'], alpha=0.1)
+
+        paired_peak_count = 0
+        unpaired_peak_count = 0
+
+        for _, (peak1, peak2) in enumerate(signal1.paired_peaks):
+            label = self.ALPHABET[paired_peak_count]
+            freq1 = signal1.freqs[peak1[0]]
+            freq2 = signal2.freqs[peak2[0]]
+
+            if abs(freq1 - freq2) < 1:
+                ax_3.plot(signal1.psd[peak1[0]], signal2.psd[peak2[0]], marker='o', color='black', markersize=7)
+                ax_3.annotate(
+                    f'{label}1/{label}2',
+                    (signal1.psd[peak1[0]], signal2.psd[peak2[0]]),
+                    textcoords='offset points',
+                    xytext=(-7, 7),
+                    fontsize=13,
+                    color='black',
+                )
+            else:
+                ax_3.plot(
+                    signal1.psd[peak2[0]],
+                    signal2.psd[peak2[0]],
+                    marker='o',
+                    color=self.KLIPPAIN_COLORS['purple'],
+                    markersize=7,
+                )
+                ax_3.plot(
+                    signal1.psd[peak1[0]],
+                    signal2.psd[peak1[0]],
+                    marker='o',
+                    color=self.KLIPPAIN_COLORS['orange'],
+                    markersize=7,
+                )
+                ax_3.annotate(
+                    f'{label}1',
+                    (signal1.psd[peak1[0]], signal2.psd[peak1[0]]),
+                    textcoords='offset points',
+                    xytext=(0, 7),
+                    fontsize=13,
+                    color='black',
+                )
+                ax_3.annotate(
+                    f'{label}2',
+                    (signal1.psd[peak2[0]], signal2.psd[peak2[0]]),
+                    textcoords='offset points',
+                    xytext=(0, 7),
+                    fontsize=13,
+                    color='black',
+                )
+            paired_peak_count += 1
+
+        for _, peak_index in enumerate(signal1.unpaired_peaks):
+            ax_3.plot(
+                signal1.psd[peak_index],
+                signal2.psd[peak_index],
+                marker='o',
+                color=self.KLIPPAIN_COLORS['orange'],
+                markersize=7,
+            )
+            ax_3.annotate(
+                str(unpaired_peak_count + 1),
+                (signal1.psd[peak_index], signal2.psd[peak_index]),
+                textcoords='offset points',
+                fontsize=13,
+                weight='bold',
+                color=self.KLIPPAIN_COLORS['red_pink'],
+                xytext=(0, 7),
+            )
+            unpaired_peak_count += 1
+
+        for _, peak_index in enumerate(signal2.unpaired_peaks):
+            ax_3.plot(
+                signal1.psd[peak_index],
+                signal2.psd[peak_index],
+                marker='o',
+                color=self.KLIPPAIN_COLORS['purple'],
+                markersize=7,
+            )
+            ax_3.annotate(
+                str(unpaired_peak_count + 1),
+                (signal1.psd[peak_index], signal2.psd[peak_index]),
+                textcoords='offset points',
+                fontsize=13,
+                weight='bold',
+                color=self.KLIPPAIN_COLORS['red_pink'],
+                xytext=(0, 7),
+            )
+            unpaired_peak_count += 1
+
+        ax_3.set_xlabel(f'Belt {signal1_belt}')
+        ax_3.set_ylabel(f'Belt {signal2_belt}')
+        ax_3.set_xlim([0, max_psd * 1.1])
+        ax_3.set_ylim([0, max_psd * 1.1])
+
+        ax_3.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
+        ax_3.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
+        ax_3.ticklabel_format(style='scientific', scilimits=(0, 0))
+        ax_3.grid(which='major', color='grey')
+        ax_3.grid(which='minor', color='lightgrey')
+
+        fontP = matplotlib.font_manager.FontProperties()
+        fontP.set_size('medium')
+        ax_3.legend(loc='upper left', prop=fontP)
+
+        # ------------------------------------------------------------------------------------------------------------------------------------------------
+        # Adding logo
+        current_dir = os.path.dirname(__file__)
+        image_path = os.path.join(current_dir, 'klippain.png')
+        ax_logo = fig.add_axes([0.001, 0.894, 0.105, 0.105], anchor='NW')
+        ax_logo.imshow(plt.imread(image_path))
+        ax_logo.axis('off')
+
+        # Adding version
+        if st_version != 'unknown':
+            fig.text(
+                0.995, 0.980, st_version, ha='right', va='bottom', fontsize=8, color=self.KLIPPAIN_COLORS['purple']
+            )
+
+        return fig
+
+    def plot_input_shaper_graph(self, data):
+        measurements = data['measurements']
+        compat = data['compat']
+        max_smoothing_computed = data['max_smoothing_computed']
+        max_freq = data['max_freq']
+        calibration_data = data['calibration_data']
+        shapers = data['shapers']
+        shaper_table_data = data['shaper_table_data']
+        shaper_choices = data['shaper_choices']
+        peaks = data['peaks']
+        peaks_freqs = data['peaks_freqs']
+        peaks_threshold = data['peaks_threshold']
+        fr = data['fr']
+        zeta = data['zeta']
+        t = data['t']
+        bins = data['bins']
+        pdata = data['pdata']
+        additional_shapers = data['additional_shapers']
+        st_version = data['st_version']
+
+        fig, ((ax_1, ax_3), (ax_2, ax_4)) = plt.subplots(
+            2,
+            2,
+            gridspec_kw={
+                'height_ratios': [4, 3],
+                'width_ratios': [5, 4],
+                'bottom': 0.050,
+                'top': 0.890,
+                'left': 0.048,
+                'right': 0.966,
+                'hspace': 0.169,
+                'wspace': 0.150,
+            },
+        )
+        ax_4.remove()
+        fig.set_size_inches(15, 11.6)
+
+        # Add a title with some test info
+        title_line1 = 'INPUT SHAPER CALIBRATION TOOL'
+        fig.text(
+            0.065,
+            0.965,
+            title_line1,
+            ha='left',
+            va='bottom',
+            fontsize=20,
+            color=self.KLIPPAIN_COLORS['purple'],
+            weight='bold',
+        )
+        try:
+            filename_parts = measurements[0]['name'].split('_')
+            dt = datetime.strptime(f'{filename_parts[2]} {filename_parts[3]}', '%Y%m%d %H%M%S')
+            title_line2 = dt.strftime('%x %X') + ' -- ' + filename_parts[1].upper() + ' axis'
+            if compat:
+                title_line3 = '| Older Klipper version detected, damping ratio'
+                title_line4 = '| and SCV are not used for filter recommendations!'
+                title_line5 = (
+                    f'| Accel per Hz used: {self.accel_per_hz} mm/s²/Hz' if self.accel_per_hz is not None else ''
+                )
+            else:
+                max_smoothing_string = (
+                    f'maximum ({max_smoothing_computed:0.3f})'
+                    if self.max_smoothing is None
+                    else f'{self.max_smoothing:0.3f}'
+                )
+                title_line3 = f'| Square corner velocity: {self.scv} mm/s'
+                title_line4 = f'| Allowed smoothing: {max_smoothing_string}'
+                title_line5 = (
+                    f'| Accel per Hz used: {self.accel_per_hz} mm/s²/Hz' if self.accel_per_hz is not None else ''
+                )
+        except Exception:
+            title_line2 = measurements[0]['name']
+            title_line3 = ''
+            title_line4 = ''
+            title_line5 = ''
+        fig.text(0.065, 0.957, title_line2, ha='left', va='top', fontsize=16, color=self.KLIPPAIN_COLORS['dark_purple'])
+        fig.text(0.50, 0.990, title_line3, ha='left', va='top', fontsize=14, color=self.KLIPPAIN_COLORS['dark_purple'])
+        fig.text(0.50, 0.968, title_line4, ha='left', va='top', fontsize=14, color=self.KLIPPAIN_COLORS['dark_purple'])
+        fig.text(0.501, 0.945, title_line5, ha='left', va='top', fontsize=10, color=self.KLIPPAIN_COLORS['dark_purple'])
+
+        # ------------------------------------------------------------------------------------------------------------------------------------------------
+        freqs = calibration_data.freqs
+        psd = calibration_data.psd_sum
+        px = calibration_data.psd_x
+        py = calibration_data.psd_y
+        pz = calibration_data.psd_z
+
+        fontP = matplotlib.font_manager.FontProperties()
+        fontP.set_size('x-small')
+
+        ax_1.set_xlabel('Frequency (Hz)')
+        ax_1.set_xlim([0, max_freq])
+        ax_1.set_ylabel('Power spectral density')
+        ax_1.set_ylim([0, psd.max() + psd.max() * 0.05])
+
+        ax_1.plot(freqs, psd, label='X+Y+Z', color='purple', zorder=5)
+        ax_1.plot(freqs, px, label='X', color='red')
+        ax_1.plot(freqs, py, label='Y', color='green')
+        ax_1.plot(freqs, pz, label='Z', color='blue')
+
+        ax_1.xaxis.set_minor_locator(matplotlib.ticker.MultipleLocator(5))
+        ax_1.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
+        ax_1.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))
+        ax_1.grid(which='major', color='grey')
+        ax_1.grid(which='minor', color='lightgrey')
+
+        ax_1_2 = ax_1.twinx()
+        ax_1_2.yaxis.set_visible(False)
+
+        for shaper in shapers:
+            ax_1_2.plot(freqs, shaper.vals, label=shaper.name.upper(), linestyle='dotted')
+
+        # Draw the shappers curves and add their specific parameters in the legend
+        for shaper in shaper_table_data['shapers']:
+            if shaper['type'] == shaper_choices[0]:
+                ax_1.plot(freqs, psd * shaper['vals'], label=f'With {shaper_choices[0]} applied', color='cyan')
+            if len(shaper_choices) == 2 and shaper['type'] == shaper_choices[1]:
+                ax_1.plot(freqs, psd * shaper['vals'], label=f'With {shaper_choices[1]} applied', color='lime')
+
+        # Draw detected peaks and their respective labels
+        ax_1.plot(peaks_freqs, psd[peaks], 'x', color='black', markersize=8)
+        for idx, peak in enumerate(peaks):
+            if psd[peak] > peaks_threshold[1]:
+                fontcolor = 'red'
+                fontweight = 'bold'
+            else:
+                fontcolor = 'black'
+                fontweight = 'normal'
+            ax_1.annotate(
+                f'{idx+1}',
+                (freqs[peak], psd[peak]),
+                textcoords='offset points',
+                xytext=(8, 5),
+                ha='left',
+                fontsize=13,
+                color=fontcolor,
+                weight=fontweight,
+            )
+        ax_1.axhline(y=peaks_threshold[0], color='black', linestyle='--', linewidth=0.5)
+        ax_1.axhline(y=peaks_threshold[1], color='black', linestyle='--', linewidth=0.5)
+        ax_1.fill_between(freqs, 0, peaks_threshold[0], color='green', alpha=0.15, label='Relax Region')
+        ax_1.fill_between(
+            freqs, peaks_threshold[0], peaks_threshold[1], color='orange', alpha=0.2, label='Warning Region'
+        )
+
+        # Add the main resonant frequency and damping ratio of the axis to the graph title
+        ax_1.set_title(
+            f'Axis Frequency Profile (ω0={fr:.1f}Hz, ζ={zeta:.3f})',
+            fontsize=14,
+            color=self.KLIPPAIN_COLORS['dark_orange'],
+            weight='bold',
+        )
+        ax_1.legend(loc='upper left', prop=fontP)
+        ax_1_2.legend(loc='upper right', prop=fontP)
+
+        # ------------------------------------------------------------------------------------------------------------------
+        # Plot a time-frequency spectrogram to see how the system respond over time during the
+        # resonnance test. This can highlight hidden spots from the standard PSD graph from other harmonics
+        ax_2.set_title(
+            'Time-Frequency Spectrogram', fontsize=14, color=self.KLIPPAIN_COLORS['dark_orange'], weight='bold'
+        )
+
+        # We need to normalize the data to get a proper signal on the spectrogram
+        # However, while using "LogNorm" provide too much background noise, using
+        # "Normalize" make only the resonnance appearing and hide interesting elements
+        # So we need to filter out the lower part of the data (ie. find the proper vmin for LogNorm)
+        vmin_value = np.percentile(pdata, self.SPECTROGRAM_LOW_PERCENTILE_FILTER)
+        cm = 'inferno'
+        norm = matplotlib.colors.LogNorm(vmin=vmin_value)
+
+        # Draw the spectrogram using imgshow that is better suited here than pcolormesh since its result is already rasterized and
+        # we doesn't need to keep vector graphics when saving to a final .png file. Using it also allow to
+        # save ~150-200MB of RAM during the "fig.savefig" operation.
+        ax_2.imshow(
+            pdata.T,
+            norm=norm,
+            cmap=cm,
+            aspect='auto',
+            extent=[t[0], t[-1], bins[0], bins[-1]],
+            origin='lower',
+            interpolation='antialiased',
+        )
+
+        ax_2.set_xlim([0.0, max_freq])
+        ax_2.set_ylabel('Time (s)')
+        ax_2.set_xlabel('Frequency (Hz)')
+
+        # Add peaks lines in the spectrogram to get hint from peaks found in the first graph
+        for idx, peak in enumerate(peaks):
+            ax_2.axvline(peak, color='cyan', linestyle='dotted', linewidth=1)
+            ax_2.annotate(
+                f'Peak {idx+1}',
+                (peak, bins[-1] * 0.9),
+                textcoords='data',
+                color='cyan',
+                rotation=90,
+                fontsize=10,
+                verticalalignment='top',
+                horizontalalignment='right',
+            )
+
+        # ------------------------------------------------------------------------------------------------------------------
+        ax_3.set_title(
+            'Filters performances over acceleration',
+            fontsize=14,
+            color=self.KLIPPAIN_COLORS['dark_orange'],
+            weight='bold',
+        )
+        ax_3.set_xlabel('Max Acceleration')
+        ax_3.set_ylabel('Remaining Vibrations (%)')
+
+        fontP = matplotlib.font_manager.FontProperties()
+        fontP.set_size('x-small')
+
+        ax_3.xaxis.set_minor_locator(matplotlib.ticker.MultipleLocator(1000))
+        ax_3.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
+        ax_3.grid(which='major', color='grey')
+        ax_3.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'] <= self.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 * self.MAX_VIBRATIONS_PLOTTED_ZOOM
+                ),
+            )
+
+        # Add a zoom axes on the graph to show details at low vibrations
+        zoomed_window = np.clip(max_shaper_vibrations * self.MAX_VIBRATIONS_PLOTTED_ZOOM, 0, 20)
+        axins = ax_3.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_3.indicate_inset_zoom(axins, edgecolor=self.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_3.axhline(y=5.0, color='black', linestyle='--', linewidth=0.5)
+        ax_3.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_3.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_3.set_xlim([min_accel_limit * 0.95, max_accel_limit * 1.1])
+        ax_3.set_ylim([-0.5, np.clip(max_vibrations * 1.05, 50, self.MAX_VIBRATIONS_PLOTTED)])
+        ax_3.legend(loc='best', prop=fontP)
+
+        # ------------------------------------------------------------------------------------------------------------------------------------------------
+        # Print the table of offsets ontop of the graph below the original legend (upper right)
+        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=self.KLIPPAIN_COLORS['purple'],
+        )
+        if len(shaper_table_data['recommendations']) == 1:
+            fig.text(
+                0.585,
+                0.200,
+                shaper_table_data['recommendations'][0],
+                fontsize=14,
+                color=self.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=self.KLIPPAIN_COLORS['red_pink'],
+            )
+            fig.text(
+                0.585,
+                0.175,
+                shaper_table_data['recommendations'][1],
+                fontsize=14,
+                color=self.KLIPPAIN_COLORS['red_pink'],
+            )
+        # ------------------------------------------------------------------------------------------------------------------------------------------------
+
+        # Adding a small Klippain logo to the top left corner of the figure
+        current_dir = os.path.dirname(__file__)
+        image_path = os.path.join(current_dir, 'klippain.png')
+        ax_logo = fig.add_axes([0.001, 0.924, 0.075, 0.075], anchor='NW')
+        ax_logo.imshow(plt.imread(image_path))
+        ax_logo.axis('off')
+
+        # Adding Shake&Tune version in the top right corner
+        if st_version != 'unknown':
+            fig.text(
+                0.995, 0.985, st_version, ha='right', va='bottom', fontsize=8, color=self.KLIPPAIN_COLORS['purple']
+            )
+
+        return fig
+
+    def plot_vibrations_graph(self, data):
+        measurements = data['measurements']
+        all_speeds = data['all_speeds']
+        all_angles = data['all_angles']
+        all_angles_energy = data['all_angles_energy']
+        good_speeds = data['good_speeds']
+        good_angles = data['good_angles']
+        kinematics = data['kinematics']
+        accel = data['accel']
+        motors = data['motors']
+        motors_config_differences = data['motors_config_differences']
+        symmetry_factor = data['symmetry_factor']
+        spectrogram_data = data['spectrogram_data']
+        sp_min_energy = data['sp_min_energy']
+        sp_max_energy = data['sp_max_energy']
+        sp_variance_energy = data['sp_variance_energy']
+        vibration_metric = data['vibration_metric']
+        num_peaks = data['num_peaks']
+        vibration_peaks = data['vibration_peaks']
+        target_freqs = data['target_freqs']
+        main_angles = data['main_angles']
+        global_motor_profile = data['global_motor_profile']
+        motor_profiles = data['motor_profiles']
+        max_freq = data['max_freq']
+        motor_fr = data['motor_fr']
+        motor_zeta = data['motor_zeta']
+        motor_res_idx = data['motor_res_idx']
+        st_version = data['st_version']
+
+        fig, ((ax_1, ax_2, ax_3), (ax_4, ax_5, ax_6)) = plt.subplots(
+            2,
+            3,
+            gridspec_kw={
+                'height_ratios': [1, 1],
+                'width_ratios': [4, 8, 6],
+                'bottom': 0.050,
+                'top': 0.890,
+                'left': 0.040,
+                'right': 0.985,
+                'hspace': 0.166,
+                'wspace': 0.138,
+            },
+        )
+
+        # Transform ax_3 and ax_4 to polar plots
+        ax_1.remove()
+        ax_1 = fig.add_subplot(2, 3, 1, projection='polar')
+        ax_4.remove()
+        ax_4 = fig.add_subplot(2, 3, 4, projection='polar')
+
+        # Set the global .png figure size
+        fig.set_size_inches(20, 11.5)
+
+        # Add title
+        title_line1 = 'MACHINE VIBRATIONS ANALYSIS TOOL'
+        fig.text(
+            0.060,
+            0.965,
+            title_line1,
+            ha='left',
+            va='bottom',
+            fontsize=20,
+            color=self.KLIPPAIN_COLORS['purple'],
+            weight='bold',
+        )
+        try:
+            filename_parts = measurements[0]['name'].split('_')
+            dt = datetime.strptime(f"{filename_parts[4]} {filename_parts[5].split('-')[0]}", '%Y%m%d %H%M%S')
+            title_line2 = dt.strftime('%x %X')
+            if accel is not None:
+                title_line2 += ' at ' + str(accel) + ' mm/s² -- ' + kinematics.upper() + ' kinematics'
+        except Exception:
+            title_line2 = measurements[0]['name']
+        fig.text(0.060, 0.957, title_line2, ha='left', va='top', fontsize=16, color=self.KLIPPAIN_COLORS['dark_purple'])
+
+        # ------------------------------------------------------------------------------------------------------------------------------------------------
+        # Plot the motors infos to the top of the graph if they are detected / specified (not mandatory for CLI mode)
+        if motors is not None and len(motors) == 2:
+            motor_details = [(motors[0], 'X motor'), (motors[1], 'Y motor')]
+
+            distance = 0.12
+            if motors[0].get_config('autotune_enabled'):
+                distance = 0.27
+                config_blocks = [
+                    f"| {lbl}: {mot.get_config('motor').upper()} on {mot.get_config('tmc').upper()} @ {mot.get_config('voltage'):0.1f}V {mot.get_config('run_current'):0.2f}A - {mot.get_config('microsteps')}usteps"
+                    for mot, lbl in motor_details
+                ]
+                config_blocks.append(
+                    f'| TMC Autotune enabled (PWM freq target: X={int(motors[0].get_config("pwm_freq_target")/1000)}kHz / Y={int(motors[1].get_config("pwm_freq_target")/1000)}kHz)'
+                )
+            else:
+                config_blocks = [
+                    f"| {lbl}: {mot.get_config('tmc').upper()} @ {mot.get_config('run_current'):0.2f}A - {mot.get_config('microsteps')}usteps"
+                    for mot, lbl in motor_details
+                ]
+                config_blocks.append('| TMC Autotune not detected')
+
+            for idx, block in enumerate(config_blocks):
+                fig.text(
+                    0.41,
+                    0.990 - 0.015 * idx,
+                    block,
+                    ha='left',
+                    va='top',
+                    fontsize=10,
+                    color=self.KLIPPAIN_COLORS['dark_purple'],
+                )
+
+            tmc_registers = motors[0].get_registers()
+            idx = -1
+            for idx, (register, settings) in enumerate(tmc_registers.items()):
+                settings_str = ' '.join(f'{k}={v}' for k, v in settings.items())
+                tmc_block = f'| {register.upper()}: {settings_str}'
+                fig.text(
+                    0.41 + distance,
+                    0.990 - 0.015 * idx,
+                    tmc_block,
+                    ha='left',
+                    va='top',
+                    fontsize=10,
+                    color=self.KLIPPAIN_COLORS['dark_purple'],
+                )
+
+            if motors_config_differences is not None:
+                differences_text = f'| Y motor diff: {motors_config_differences}'
+                fig.text(
+                    0.41 + distance,
+                    0.990 - 0.015 * (idx + 1),
+                    differences_text,
+                    ha='left',
+                    va='top',
+                    fontsize=10,
+                    color=self.KLIPPAIN_COLORS['dark_purple'],
+                )
+
+        # ------------------------------------------------------------------------------------------------------------------------------------------------
+        # Plot the angle profile in polar coordinates
+        angles_radians = np.deg2rad(all_angles)
+
+        ax_1.set_title(
+            'Polar angle energy profile', fontsize=14, color=self.KLIPPAIN_COLORS['dark_orange'], weight='bold'
+        )
+        ax_1.set_theta_zero_location('E')
+        ax_1.set_theta_direction(1)
+
+        ax_1.plot(angles_radians, all_angles_energy, color=self.KLIPPAIN_COLORS['purple'], zorder=5)
+        ax_1.fill(angles_radians, all_angles_energy, color=self.KLIPPAIN_COLORS['purple'], alpha=0.3)
+        ax_1.set_xlim([0, np.deg2rad(360)])
+        ymax = all_angles_energy.max() * 1.05
+        ax_1.set_ylim([0, ymax])
+        ax_1.set_thetagrids([theta * 15 for theta in range(360 // 15)])
+
+        ax_1.text(
+            0,
+            0,
+            f'Symmetry: {symmetry_factor:.1f}%',
+            ha='center',
+            va='center',
+            color=self.KLIPPAIN_COLORS['red_pink'],
+            fontsize=12,
+            fontweight='bold',
+            zorder=6,
+        )
+
+        for _, (start, end, _) in enumerate(good_angles):
+            ax_1.axvline(
+                angles_radians[start],
+                all_angles_energy[start] / ymax,
+                color=self.KLIPPAIN_COLORS['red_pink'],
+                linestyle='dotted',
+                linewidth=1.5,
+            )
+            ax_1.axvline(
+                angles_radians[end],
+                all_angles_energy[end] / ymax,
+                color=self.KLIPPAIN_COLORS['red_pink'],
+                linestyle='dotted',
+                linewidth=1.5,
+            )
+            ax_1.fill_between(
+                angles_radians[start:end],
+                all_angles_energy[start:end],
+                all_angles_energy.max() * 1.05,
+                color='green',
+                alpha=0.2,
+            )
+
+        ax_1.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
+        ax_1.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
+        ax_1.grid(which='major', color='grey')
+        ax_1.grid(which='minor', color='lightgrey')
+
+        # Polar plot doesn't follow the gridspec margin, so we adjust it manually here
+        pos = ax_1.get_position()
+        new_pos = [pos.x0 - 0.01, pos.y0 - 0.01, pos.width, pos.height]
+        ax_1.set_position(new_pos)
+
+        # ----------------------------------------------------------------------------------------------------------------------------
+        # Plot the vibration spectrogram in polar coordinates
+
+        # Assuming speeds defines the radial distance from the center, we need to create a meshgrid
+        # for both angles and speeds to map the spectrogram data onto a polar plot correctly
+        radius, theta = np.meshgrid(all_speeds, angles_radians)
+
+        ax_4.set_title(
+            'Polar vibrations heatmap',
+            fontsize=14,
+            color=self.KLIPPAIN_COLORS['dark_orange'],
+            weight='bold',
+            va='bottom',
+        )
+        ax_4.set_theta_zero_location('E')
+        ax_4.set_theta_direction(1)
+
+        ax_4.pcolormesh(
+            theta, radius, spectrogram_data, norm=matplotlib.colors.LogNorm(), cmap='inferno', shading='auto'
+        )
+        ax_4.set_thetagrids([theta * 15 for theta in range(360 // 15)])
+        ax_4.tick_params(axis='y', which='both', colors='white', labelsize='medium')
+        ax_4.set_ylim([0, max(all_speeds)])
+
+        # Polar plot doesn't follow the gridspec margin, so we adjust it manually here
+        pos = ax_4.get_position()
+        new_pos = [pos.x0 - 0.01, pos.y0 - 0.01, pos.width, pos.height]
+        ax_4.set_position(new_pos)
+
+        # ----------------------------------------------------------------------------------------------------------------------------
+        # Plot global speed profile
+        ax_2.set_title(
+            'Global speed energy profile', fontsize=14, color=self.KLIPPAIN_COLORS['dark_orange'], weight='bold'
+        )
+        ax_2.set_xlabel('Speed (mm/s)')
+        ax_2.set_ylabel('Energy')
+        ax_2_2 = ax_2.twinx()
+        ax_2_2.yaxis.set_visible(False)
+
+        ax_2.plot(all_speeds, sp_min_energy, label='Minimum', color=self.KLIPPAIN_COLORS['dark_purple'], zorder=5)
+        ax_2.plot(all_speeds, sp_max_energy, label='Maximum', color=self.KLIPPAIN_COLORS['purple'], zorder=5)
+        ax_2.plot(
+            all_speeds,
+            sp_variance_energy,
+            label='Variance',
+            color=self.KLIPPAIN_COLORS['orange'],
+            zorder=5,
+            linestyle='--',
+        )
+        ax_2_2.plot(
+            all_speeds,
+            vibration_metric,
+            label=f'Vibration metric ({num_peaks} bad peaks)',
+            color=self.KLIPPAIN_COLORS['red_pink'],
+            zorder=5,
+        )
+
+        ax_2.set_xlim([all_speeds.min(), all_speeds.max()])
+        ax_2.set_ylim([0, sp_max_energy.max() * 1.15])
+
+        y2min = -(vibration_metric.max() * 0.025)
+        y2max = vibration_metric.max() * 1.07
+        ax_2_2.set_ylim([y2min, y2max])
+
+        if vibration_peaks is not None and len(vibration_peaks) > 0:
+            ax_2_2.plot(
+                all_speeds[vibration_peaks],
+                vibration_metric[vibration_peaks],
+                'x',
+                color='black',
+                markersize=8,
+                zorder=10,
+            )
+            for idx, peak in enumerate(vibration_peaks):
+                ax_2_2.annotate(
+                    f'{idx+1}',
+                    (all_speeds[peak], vibration_metric[peak]),
+                    textcoords='offset points',
+                    xytext=(5, 5),
+                    fontweight='bold',
+                    ha='left',
+                    fontsize=13,
+                    color=self.KLIPPAIN_COLORS['red_pink'],
+                    zorder=10,
+                )
+
+        for idx, (start, end, _) in enumerate(good_speeds):
+            # ax_2_2.axvline(all_speeds[start], color=self.KLIPPAIN_COLORS['red_pink'], linestyle='dotted', linewidth=1.5, zorder=8)
+            # ax_2_2.axvline(all_speeds[end], color=self.KLIPPAIN_COLORS['red_pink'], linestyle='dotted', linewidth=1.5, zorder=8)
+            ax_2_2.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_2.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
+        ax_2.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
+        ax_2.grid(which='major', color='grey')
+        ax_2.grid(which='minor', color='lightgrey')
+
+        fontP = matplotlib.font_manager.FontProperties()
+        fontP.set_size('small')
+        ax_2.legend(loc='upper left', prop=fontP)
+        ax_2_2.legend(loc='upper right', prop=fontP)
+
+        # ------------------------------------------------------------------------------------------------------------------------------------------------
+        # Plot the angular speed profile
+        ax_3.set_title(
+            'Angular speed energy profiles', fontsize=14, color=self.KLIPPAIN_COLORS['dark_orange'], weight='bold'
+        )
+        ax_3.set_xlabel('Speed (mm/s)')
+        ax_3.set_ylabel('Energy')
+
+        # Define mappings for labels and colors to simplify plotting commands
+        angle_settings = {
+            0: ('X (0 deg)', 'purple', 10),
+            90: ('Y (90 deg)', 'dark_purple', 5),
+            45: ('A (45 deg)' if kinematics in {'corexy', 'limited_corexy'} else '45 deg', 'orange', 10),
+            135: ('B (135 deg)' if kinematics in {'corexy', 'limited_corexy'} else '135 deg', 'dark_orange', 5),
+        }
+
+        # Plot each angle using settings from the dictionary
+        for angle, (label, color, zorder) in angle_settings.items():
+            idx = np.searchsorted(all_angles, angle, side='left')
+            ax_3.plot(all_speeds, spectrogram_data[idx], label=label, color=self.KLIPPAIN_COLORS[color], zorder=zorder)
+
+        ax_3.set_xlim([all_speeds.min(), all_speeds.max()])
+        max_value = max(spectrogram_data[angle].max() for angle in {0, 45, 90, 135})
+        ax_3.set_ylim([0, max_value * 1.1])
+
+        ax_3.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
+        ax_3.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
+        ax_3.grid(which='major', color='grey')
+        ax_3.grid(which='minor', color='lightgrey')
+
+        fontP = matplotlib.font_manager.FontProperties()
+        fontP.set_size('small')
+        ax_3.legend(loc='upper right', prop=fontP)
+
+        # ------------------------------------------------------------------------------------------------------------------------------------------------
+        # Plot the vibration spectrogram
+        ax_5.set_title('Vibrations heatmap', fontsize=14, color=self.KLIPPAIN_COLORS['dark_orange'], weight='bold')
+        ax_5.set_xlabel('Speed (mm/s)')
+        ax_5.set_ylabel('Angle (deg)')
+
+        ax_5.imshow(
+            spectrogram_data,
+            norm=matplotlib.colors.LogNorm(),
+            cmap='inferno',
+            aspect='auto',
+            extent=[all_speeds[0], all_speeds[-1], all_angles[0], all_angles[-1]],
+            origin='lower',
+            interpolation='antialiased',
+        )
+
+        # Add peaks lines in the spectrogram to get hint from peaks found in the first graph
+        if vibration_peaks is not None and len(vibration_peaks) > 0:
+            for idx, peak in enumerate(vibration_peaks):
+                ax_5.axvline(all_speeds[peak], color='cyan', linewidth=0.75)
+                ax_5.annotate(
+                    f'Peak {idx+1}',
+                    (all_speeds[peak], all_angles[-1] * 0.9),
+                    textcoords='data',
+                    color='cyan',
+                    rotation=90,
+                    fontsize=10,
+                    verticalalignment='top',
+                    horizontalalignment='right',
+                )
+
+        # ------------------------------------------------------------------------------------------------------------------------------------------------
+        # Plot the motor profiles
+        ax_6.set_title('Motor frequency profile', fontsize=14, color=self.KLIPPAIN_COLORS['dark_orange'], weight='bold')
+        ax_6.set_ylabel('Energy')
+        ax_6.set_xlabel('Frequency (Hz)')
+
+        ax_6_2 = ax_6.twinx()
+        ax_6_2.yaxis.set_visible(False)
+
+        # Global weighted average motor profile
+        ax_6.plot(target_freqs, global_motor_profile, label='Combined', color=self.KLIPPAIN_COLORS['purple'], zorder=5)
+        max_value = global_motor_profile.max()
+
+        # Mapping of angles to axis names
+        angle_settings = {0: 'X', 90: 'Y', 45: 'A', 135: 'B'}
+
+        # And then plot the motor profiles at each measured angles
+        for angle in main_angles:
+            profile_max = motor_profiles[angle].max()
+            if profile_max > max_value:
+                max_value = profile_max
+            label = f'{angle_settings[angle]} ({angle} deg)' if angle in angle_settings else f'{angle} deg'
+            ax_6.plot(target_freqs, motor_profiles[angle], linestyle='--', label=label, zorder=2)
+
+        ax_6.set_xlim([0, max_freq])
+        ax_6.set_ylim([0, max_value * 1.1])
+        ax_6.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))
+
+        # Then add the motor resonance peak to the graph
+        ax_6.plot(target_freqs[motor_res_idx], global_motor_profile[motor_res_idx], 'x', color='black', markersize=10)
+        ax_6.annotate(
+            'R',
+            (target_freqs[motor_res_idx], global_motor_profile[motor_res_idx]),
+            textcoords='offset points',
+            xytext=(15, 5),
+            ha='right',
+            fontsize=14,
+            color=self.KLIPPAIN_COLORS['red_pink'],
+            weight='bold',
+        )
+
+        ax_6_2.plot([], [], ' ', label=f'Motor resonant frequency (ω0): {motor_fr:.1f}Hz')
+        if motor_zeta is not None:
+            ax_6_2.plot([], [], ' ', label=f'Motor damping ratio (ζ): {motor_zeta:.3f}')
+        else:
+            ax_6_2.plot([], [], ' ', label='No damping ratio computed')
+
+        ax_6.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
+        ax_6.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
+        ax_6.grid(which='major', color='grey')
+        ax_6.grid(which='minor', color='lightgrey')
+
+        fontP = matplotlib.font_manager.FontProperties()
+        fontP.set_size('small')
+        ax_6.legend(loc='upper left', prop=fontP)
+        ax_6_2.legend(loc='upper right', prop=fontP)
+
+        # Adding a small Klippain logo to the top left corner of the figure
+        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')
+
+        # Adding Shake&Tune version in the top right corner
+        if st_version != 'unknown':
+            fig.text(
+                0.995, 0.985, st_version, ha='right', va='bottom', fontsize=8, color=self.KLIPPAIN_COLORS['purple']
+            )
+
+        return fig
diff --git a/shaketune/graph_creators/shaper_graph_creator.py b/shaketune/graph_creators/shaper_graph_creator.py
index 37ee27d..a8a80c9 100644
--- a/shaketune/graph_creators/shaper_graph_creator.py
+++ b/shaketune/graph_creators/shaper_graph_creator.py
@@ -11,53 +11,25 @@
 #              including computation and graphing functions for 3D printer vibration analysis.
 
 
-#################################################
-######## INPUT SHAPER CALIBRATION SCRIPT ########
-#################################################
-# Derived from the calibrate_shaper.py official Klipper script
-# Copyright (C) 2020  Dmitry Butyugin <dmbutyugin@google.com>
-# Copyright (C) 2020  Kevin O'Connor <kevin@koconnor.net>
-# Highly modified and improved by Frix_x#0161 #
+from typing import List, Optional
 
-import optparse
-import os
-from datetime import datetime
-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')
 
 from ..helpers.accelerometer import Measurement, MeasurementsManager
 from ..helpers.common_func import (
     compute_mechanical_parameters,
     compute_spectrogram,
     detect_peaks,
-    setup_klipper_import,
 )
 from ..helpers.console_output import ConsoleOutput
 from ..shaketune_config import ShakeTuneConfig
+from . import get_shaper_calibrate_module
 from .graph_creator import GraphCreator
 
 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',
-    'dark_purple': '#150140',
-    'dark_orange': '#F24130',
-    'red_pink': '#F2055C',
-}
+MAX_VIBRATIONS = 5.0
 
 
 @GraphCreator.register('input shaper')
@@ -76,18 +48,20 @@ def configure(
         self._accel_per_hz = accel_per_hz
 
     def create_graph(self, measurements_manager: MeasurementsManager) -> None:
-        if not self._scv:
-            raise ValueError('scv must be set to create the input shaper graph!')
-
-        fig = shaper_calibration(
+        computer = ShaperGraphComputation(
             measurements=measurements_manager.get_measurements(),
-            klipperdir=str(self._config.klipper_folder),
             max_smoothing=self._max_smoothing,
             scv=self._scv,
             accel_per_hz=self._accel_per_hz,
+            max_freq=self._config.max_freq,
             st_version=self._version,
         )
-        axis_label = (measurements_manager.get_measurements())[0]['name'].split('_')[1]
+        computation = computer.compute()
+        fig = self._plotter.plot_input_shaper_graph(computation)
+        try:
+            axis_label = (measurements_manager.get_measurements())[0]['name'].split('_')[1]
+        except IndexError:
+            axis_label = None
         self._save_figure(fig, measurements_manager, axis_label)
 
     def clean_old_files(self, keep_results: int = 3) -> None:
@@ -100,648 +74,255 @@ def clean_old_files(self, keep_results: int = 3) -> None:
             old_png_file.unlink()
 
 
-######################################################################
-# Computation
-######################################################################
-
-
-# Find the best shaper parameters using Klipper's official algorithm selection with
-# a proper precomputed damping ratio (zeta) and using the configured printer SQV value
-def calibrate_shaper(datas: List[np.ndarray], max_smoothing: Optional[float], scv: float, max_freq: float):
-    helper = shaper_calibrate.ShaperCalibrate(printer=None)
-    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)
-    zeta = zeta if zeta is not None else 0.1
-
-    compat = False
-    try:
-        k_shaper_choice, all_shapers = helper.find_best_shaper(
+class ShaperGraphComputation:
+    def __init__(
+        self,
+        measurements: List[Measurement],
+        accel_per_hz: Optional[float],
+        scv: float,
+        max_smoothing: Optional[float],
+        max_freq: float,
+        st_version: str,
+    ):
+        self.measurements = measurements
+        self.accel_per_hz = accel_per_hz
+        self.scv = scv
+        self.max_smoothing = max_smoothing
+        self.max_freq = max_freq
+        self.st_version = st_version
+
+    def compute(self):
+        if len(self.measurements) == 0:
+            raise ValueError('No valid data found in the provided measurements!')
+        if len(self.measurements) > 1:
+            ConsoleOutput.print('Warning: incorrect number of measurements detected. Only the first one will be used!')
+
+        datas = [np.array(m['samples']) for m in self.measurements if m['samples'] is not None]
+
+        # Compute shapers, PSD outputs and spectrogram
+        (
+            klipper_shaper_choice,
+            shapers,
+            additional_shapers,
             calibration_data,
-            shapers=None,
-            damping_ratio=zeta,
-            scv=scv,
-            shaper_freqs=None,
-            max_smoothing=max_smoothing,
-            test_damping_ratios=None,
-            max_freq=max_freq,
-            logger=None,
+            fr,
+            zeta,
+            max_smoothing_computed,
+            compat,
+        ) = self._calibrate_shaper(datas[0], self.max_smoothing, self.scv, self.max_freq)
+        pdata, bins, t = compute_spectrogram(datas[0])
+        del datas
+
+        # Select only the relevant part of the PSD data
+        freqs = calibration_data.freq_bins
+        calibration_data.psd_sum = calibration_data.psd_sum[freqs <= self.max_freq]
+        calibration_data.psd_x = calibration_data.psd_x[freqs <= self.max_freq]
+        calibration_data.psd_y = calibration_data.psd_y[freqs <= self.max_freq]
+        calibration_data.psd_z = calibration_data.psd_z[freqs <= self.max_freq]
+        calibration_data.freqs = freqs[freqs <= self.max_freq]
+
+        # Peak detection algorithm
+        peaks_threshold = [
+            PEAKS_DETECTION_THRESHOLD * calibration_data.psd_sum.max(),
+            PEAKS_EFFECT_THRESHOLD * calibration_data.psd_sum.max(),
+        ]
+        num_peaks, peaks, peaks_freqs = detect_peaks(
+            calibration_data.psd_sum, calibration_data.freqs, peaks_threshold[0]
         )
+
+        # Print the peaks info in the console
+        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'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'
-            )
+            f"Peaks detected on the graph: {num_peaks} @ {', '.join(map(str, peak_freqs_formated))} Hz ({num_peaks_above_effect_threshold} above effect threshold)"
         )
-    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!\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'
+
+        # Consolidate shaper data for plotting the table summary
+        # and data for the shaper recommendation (performance vs low vibration)
+        shaper_table_data = {
+            'shapers': [],
+            'recommendations': [],
+            'damping_ratio': zeta,
+        }
+
+        perf_shaper_choice = None
+        perf_shaper_freq = None
+        perf_shaper_accel = 0
+        for shaper in shapers:
+            shaper_info = {
+                'type': shaper.name.upper(),
+                'frequency': shaper.freq,
+                'vibrations': shaper.vibrs,
+                'smoothing': shaper.smoothing,
+                'max_accel': shaper.max_accel,
+                'vals': shaper.vals,
+            }
+            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_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:
+                perf_shaper_choice = shaper.name
+                perf_shaper_accel = shaper.max_accel
+                perf_shaper_freq = shaper.freq
+
+        # Recommendations are put in the console: 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
+        # 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 printed
+        if (
+            perf_shaper_choice is not None
+            and perf_shaper_choice != klipper_shaper_choice
+            and perf_shaper_accel >= klipper_shaper_accel
+        ):
+            perf_shaper_string = (
+                f'Recommended for performance: {perf_shaper_choice.upper()} @ {perf_shaper_freq:.1f} Hz'
             )
-        )
-        compat = True
-        k_shaper_choice, all_shapers = helper.find_best_shaper(calibration_data, max_smoothing, None)
-
-    # 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,
+            lowvibr_shaper_string = (
+                f'Recommended for low vibrations: {klipper_shaper_choice.upper()} @ {klipper_shaper_freq:.1f} Hz'
             )
-
-    # 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)
+            shaper_table_data['recommendations'].append(perf_shaper_string)
+            shaper_table_data['recommendations'].append(lowvibr_shaper_string)
+            shaper_choices = [klipper_shaper_choice.upper(), perf_shaper_choice.upper()]
+            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})')
         else:
-            _, all_shapers_bis = helper.find_best_shaper(
+            shaper_string = f'Recommended best shaper: {klipper_shaper_choice.upper()} @ {klipper_shaper_freq:.1f} Hz'
+            shaper_table_data['recommendations'].append(shaper_string)
+            shaper_choices = [klipper_shaper_choice.upper()]
+            ConsoleOutput.print(f'{shaper_string} (with a damping ratio of {zeta:.3f})')
+
+        # And finally setup the results to return them
+        computation_result = {
+            'measurements': self.measurements,
+            'compat': compat,
+            'max_smoothing_computed': max_smoothing_computed,
+            'max_freq': self.max_freq,
+            'calibration_data': calibration_data,
+            'shapers': shapers,
+            'shaper_table_data': shaper_table_data,
+            'shaper_choices': shaper_choices,
+            'peaks': peaks,
+            'peaks_freqs': peaks_freqs,
+            'peaks_threshold': peaks_threshold,
+            'fr': fr,
+            'zeta': zeta,
+            't': t,
+            'bins': bins,
+            'pdata': pdata,
+            'additional_shapers': additional_shapers,
+            'st_version': self.st_version,
+        }
+
+        return computation_result
+
+    # Find the best shaper parameters using Klipper's official algorithm selection with
+    # a proper precomputed damping ratio (zeta) and using the configured printer SQV value
+    # This function also sweep around the smoothing values to help you find the best compromise
+    def _calibrate_shaper(self, datas: List[np.ndarray], max_smoothing: Optional[float], scv: float, max_freq: float):
+        helper = get_shaper_calibrate_module().ShaperCalibrate(printer=None)
+        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)
+        zeta = zeta if zeta is not None else 0.1
+
+        compat = False
+        try:
+            k_shaper_choice, all_shapers = helper.find_best_shaper(
                 calibration_data,
                 shapers=None,
                 damping_ratio=zeta,
                 scv=scv,
                 shaper_freqs=None,
-                max_smoothing=smoothing,
+                max_smoothing=max_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 k_shaper_choice.name, all_shapers, additional_all_shapers, calibration_data, fr, zeta, max_smoothing, compat
-
-
-######################################################################
-# Graphing
-######################################################################
-
-
-def plot_freq_response(
-    ax: plt.Axes,
-    calibration_data,
-    shapers,
-    klipper_shaper_choice: str,
-    peaks: np.ndarray,
-    peaks_freqs: np.ndarray,
-    peaks_threshold: List[float],
-    fr: float,
-    zeta: float,
-    max_freq: float,
-) -> Dict[str, List[Dict[str, str]]]:
-    freqs = calibration_data.freqs
-    psd = calibration_data.psd_sum
-    px = calibration_data.psd_x
-    py = calibration_data.psd_y
-    pz = calibration_data.psd_z
-
-    fontP = matplotlib.font_manager.FontProperties()
-    fontP.set_size('x-small')
-
-    ax.set_xlabel('Frequency (Hz)')
-    ax.set_xlim([0, max_freq])
-    ax.set_ylabel('Power spectral density')
-    ax.set_ylim([0, psd.max() + psd.max() * 0.05])
-
-    ax.plot(freqs, psd, label='X+Y+Z', color='purple', zorder=5)
-    ax.plot(freqs, px, label='X', color='red')
-    ax.plot(freqs, py, label='Y', color='green')
-    ax.plot(freqs, pz, label='Z', color='blue')
-
-    ax.xaxis.set_minor_locator(matplotlib.ticker.MultipleLocator(5))
-    ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
-    ax.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))
-    ax.grid(which='major', color='grey')
-    ax.grid(which='minor', color='lightgrey')
-
-    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:
-        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
-
-        # 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:
-            perf_shaper_choice = shaper.name
-            perf_shaper_accel = shaper.max_accel
-            perf_shaper_freq = shaper.freq
-            perf_shaper_vals = shaper.vals
-
-    # 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
-    # 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
-    if (
-        perf_shaper_choice is not None
-        and perf_shaper_choice != klipper_shaper_choice
-        and perf_shaper_accel >= klipper_shaper_accel
-    ):
-        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',
-        )
-        ax.plot(
-            freqs,
-            psd * klipper_shaper_vals,
-            label=f'With {klipper_shaper_choice.upper()} applied',
-            color='lime',
-        )
-    else:
-        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,
-            label=f'With {klipper_shaper_choice.upper()} applied',
-            color='cyan',
-        )
-
-    # 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)
-    for idx, peak in enumerate(peaks):
-        if psd[peak] > peaks_threshold[1]:
-            fontcolor = 'red'
-            fontweight = 'bold'
-        else:
-            fontcolor = 'black'
-            fontweight = 'normal'
-        ax.annotate(
-            f'{idx+1}',
-            (freqs[peak], psd[peak]),
-            textcoords='offset points',
-            xytext=(8, 5),
-            ha='left',
-            fontsize=13,
-            color=fontcolor,
-            weight=fontweight,
-        )
-    ax.axhline(y=peaks_threshold[0], color='black', linestyle='--', linewidth=0.5)
-    ax.axhline(y=peaks_threshold[1], color='black', linestyle='--', linewidth=0.5)
-    ax.fill_between(freqs, 0, peaks_threshold[0], color='green', alpha=0.15, label='Relax Region')
-    ax.fill_between(freqs, peaks_threshold[0], peaks_threshold[1], color='orange', alpha=0.2, label='Warning Region')
-
-    # Add the main resonant frequency and damping ratio of the axis to the graph title
-    ax.set_title(
-        f'Axis Frequency Profile (ω0={fr:.1f}Hz, ζ={zeta:.3f})',
-        fontsize=14,
-        color=KLIPPAIN_COLORS['dark_orange'],
-        weight='bold',
-    )
-    ax.legend(loc='upper left', prop=fontP)
-    ax2.legend(loc='upper right', prop=fontP)
-
-    return shaper_table_data
-
-
-# Plot a time-frequency spectrogram to see how the system respond over time during the
-# resonnance test. This can highlight hidden spots from the standard PSD graph from other harmonics
-def plot_spectrogram(
-    ax: plt.Axes, t: np.ndarray, bins: np.ndarray, pdata: np.ndarray, peaks: np.ndarray, max_freq: float
-) -> None:
-    ax.set_title('Time-Frequency Spectrogram', fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
-
-    # We need to normalize the data to get a proper signal on the spectrogram
-    # However, while using "LogNorm" provide too much background noise, using
-    # "Normalize" make only the resonnance appearing and hide interesting elements
-    # So we need to filter out the lower part of the data (ie. find the proper vmin for LogNorm)
-    vmin_value = np.percentile(pdata, SPECTROGRAM_LOW_PERCENTILE_FILTER)
-
-    # Draw the spectrogram using imgshow that is better suited here than pcolormesh since its result is already rasterized and
-    # we doesn't need to keep vector graphics when saving to a final .png file. Using it also allow to
-    # save ~150-200MB of RAM during the "fig.savefig" operation.
-    cm = 'inferno'
-    norm = matplotlib.colors.LogNorm(vmin=vmin_value)
-    ax.imshow(
-        pdata.T,
-        norm=norm,
-        cmap=cm,
-        aspect='auto',
-        extent=[t[0], t[-1], bins[0], bins[-1]],
-        origin='lower',
-        interpolation='antialiased',
-    )
-
-    ax.set_xlim([0.0, max_freq])
-    ax.set_ylabel('Time (s)')
-    ax.set_xlabel('Frequency (Hz)')
-
-    # Add peaks lines in the spectrogram to get hint from peaks found in the first graph
-    if peaks is not None:
-        for idx, peak in enumerate(peaks):
-            ax.axvline(peak, color='cyan', linestyle='dotted', linewidth=1)
-            ax.annotate(
-                f'Peak {idx+1}',
-                (peak, bins[-1] * 0.9),
-                textcoords='data',
-                color='cyan',
-                rotation=90,
-                fontsize=10,
-                verticalalignment='top',
-                horizontalalignment='right',
+            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'
+                )
             )
-
-    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,
-                }
+        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!\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'
+                )
             )
-
-    # 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'],
+            compat = True
+            k_shaper_choice, all_shapers = helper.find_best_shaper(calibration_data, max_smoothing, None)
+
+        # 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
         )
 
-
-######################################################################
-# Startup and main routines
-######################################################################
-
-
-def shaper_calibration(
-    measurements: List[Measurement],
-    klipperdir: str = '~/klipper',
-    max_smoothing: Optional[float] = None,
-    scv: float = 5.0,
-    max_freq: float = 200.0,
-    accel_per_hz: Optional[float] = None,
-    st_version: str = 'unknown',
-) -> plt.Figure:
-    if len(measurements) == 0:
-        raise ValueError('No valid data found in the provided measurements!')
-    if len(measurements) > 1:
-        ConsoleOutput.print('Warning: incorrect number of measurements detected. Only the first one will be used!')
-
-    global shaper_calibrate
-    shaper_calibrate = setup_klipper_import(klipperdir)
-
-    datas = [np.array(m['samples']) for m in measurements if m['samples'] is not None]
-
-    # Compute shapers, PSD outputs and spectrogram
-    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
-
-    # Select only the relevant part of the PSD data
-    freqs = calibration_data.freq_bins
-    calibration_data.psd_sum = calibration_data.psd_sum[freqs <= max_freq]
-    calibration_data.psd_x = calibration_data.psd_x[freqs <= max_freq]
-    calibration_data.psd_y = calibration_data.psd_y[freqs <= max_freq]
-    calibration_data.psd_z = calibration_data.psd_z[freqs <= max_freq]
-    calibration_data.freqs = freqs[freqs <= max_freq]
-
-    # Peak detection algorithm
-    peaks_threshold = [
-        PEAKS_DETECTION_THRESHOLD * calibration_data.psd_sum.max(),
-        PEAKS_EFFECT_THRESHOLD * calibration_data.psd_sum.max(),
-    ]
-    num_peaks, peaks, peaks_freqs = detect_peaks(calibration_data.psd_sum, calibration_data.freqs, peaks_threshold[0])
-
-    # Print the peaks info in the console
-    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"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, ax3), (ax2, ax4)) = plt.subplots(
-        2,
-        2,
-        gridspec_kw={
-            'height_ratios': [4, 3],
-            'width_ratios': [5, 4],
-            'bottom': 0.050,
-            'top': 0.890,
-            'left': 0.048,
-            'right': 0.966,
-            'hspace': 0.169,
-            'wspace': 0.150,
-        },
-    )
-    ax4.remove()
-    fig.set_size_inches(15, 11.6)
-
-    # Add a title with some test info
-    title_line1 = 'INPUT SHAPER CALIBRATION TOOL'
-    fig.text(
-        0.065, 0.965, title_line1, ha='left', va='bottom', fontsize=20, color=KLIPPAIN_COLORS['purple'], weight='bold'
-    )
-    try:
-        filename_parts = measurements[0]['name'].split('_')
-        dt = datetime.strptime(f'{filename_parts[2]} {filename_parts[3]}', '%Y%m%d %H%M%S')
-        title_line2 = dt.strftime('%x %X') + ' -- ' + filename_parts[1].upper() + ' axis'
-        if compat:
-            title_line3 = '| Older Klipper version detected, damping ratio'
-            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'| 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: measurement names look to be different than expected ({measurements[0]["name"]})'
+        return (
+            k_shaper_choice.name,
+            all_shapers,
+            additional_all_shapers,
+            calibration_data,
+            fr,
+            zeta,
+            max_smoothing,
+            compat,
         )
-        title_line2 = measurements[0]['name']
-        title_line3 = ''
-        title_line4 = ''
-        title_line5 = ''
-    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
-    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, 0.924, 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')
-
-    # Adding Shake&Tune version in the top right corner
-    if st_version != 'unknown':
-        fig.text(0.995, 0.985, st_version, ha='right', va='bottom', fontsize=8, color=KLIPPAIN_COLORS['purple'])
-
-    return fig
-
-
-def main():
-    # Parse command-line arguments
-    usage = '%prog [options] <logs>'
-    opts = optparse.OptionParser(usage)
-    opts.add_option('-o', '--output', type='string', dest='output', default=None, help='filename of output graph')
-    opts.add_option('-f', '--max_freq', type='float', default=200.0, help='maximum frequency to graph')
-    opts.add_option('-s', '--max_smoothing', type='float', default=None, help='maximum shaper smoothing to allow')
-    opts.add_option(
-        '--scv', '--square_corner_velocity', type='float', dest='scv', default=5.0, help='square corner velocity'
-    )
-    opts.add_option('--accel_per_hz', type='float', default=None, help='accel_per_hz used during the measurement')
-    opts.add_option(
-        '-k', '--klipper_dir', type='string', dest='klipperdir', default='~/klipper', help='main klipper directory'
-    )
-    options, args = opts.parse_args()
-    if len(args) < 1:
-        opts.error('Incorrect number of arguments')
-    if options.output is None:
-        opts.error('You must specify an output file.png to use the script (option -o)')
-    if options.max_smoothing is not None and options.max_smoothing < 0.05:
-        opts.error('Too small max_smoothing specified (must be at least 0.05)')
-
-    measurements_manager = MeasurementsManager(10)
-    if args[0].endswith('.csv'):
-        measurements_manager.load_from_csvs(args)
-    elif args[0].endswith('.stdata'):
-        measurements_manager.load_from_stdata(args[0])
-    else:
-        raise ValueError('Only .stdata or legacy Klipper raw accelerometer CSV files are supported!')
-
-    fig = shaper_calibration(
-        measurements_manager.get_measurements(),
-        options.klipperdir,
-        options.max_smoothing,
-        options.scv,
-        options.max_freq,
-        options.accel_per_hz,
-        'unknown',
-    )
-    fig.savefig(options.output, dpi=150)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/shaketune/graph_creators/static_graph_creator.py b/shaketune/graph_creators/static_graph_creator.py
index 2e3b8ea..7a9cc22 100644
--- a/shaketune/graph_creators/static_graph_creator.py
+++ b/shaketune/graph_creators/static_graph_creator.py
@@ -8,38 +8,16 @@
 #              issues, including computation and graphing functions for 3D printer vibration analysis.
 
 
-import optparse
-import os
-from datetime import datetime
 from typing import List, Optional
 
-import matplotlib
-import matplotlib.font_manager
-import matplotlib.pyplot as plt
-import matplotlib.ticker
 import numpy as np
 
-matplotlib.use('Agg')
-
 from ..helpers.accelerometer import Measurement, MeasurementsManager
 from ..helpers.common_func import compute_spectrogram
 from ..helpers.console_output import ConsoleOutput
 from ..shaketune_config import ShakeTuneConfig
 from .graph_creator import GraphCreator
 
-PEAKS_DETECTION_THRESHOLD = 0.05
-PEAKS_EFFECT_THRESHOLD = 0.12
-SPECTROGRAM_LOW_PERCENTILE_FILTER = 5
-MAX_VIBRATIONS = 5.0
-
-KLIPPAIN_COLORS = {
-    'purple': '#70088C',
-    'orange': '#FF8D32',
-    'dark_purple': '#150140',
-    'dark_orange': '#F24130',
-    'red_pink': '#F2055C',
-}
-
 
 @GraphCreator.register('static frequency')
 class StaticGraphCreator(GraphCreator):
@@ -49,185 +27,67 @@ def __init__(self, config: ShakeTuneConfig):
         self._duration: Optional[float] = None
         self._accel_per_hz: Optional[float] = None
 
-    def configure(self, freq: float, duration: float, accel_per_hz: Optional[float] = None) -> None:
+    def configure(self, freq: float = None, duration: float = None, accel_per_hz: Optional[float] = None) -> None:
         self._freq = freq
         self._duration = duration
         self._accel_per_hz = accel_per_hz
 
     def create_graph(self, measurements_manager: MeasurementsManager) -> None:
-        if not self._freq or not self._duration or not self._accel_per_hz:
-            raise ValueError('freq, duration and accel_per_hz must be set to create the static frequency graph!')
-
-        fig = static_frequency_tool(
+        computer = StaticGraphComputation(
             measurements=measurements_manager.get_measurements(),
-            klipperdir=str(self._config.klipper_folder),
             freq=self._freq,
             duration=self._duration,
-            max_freq=200.0,
+            max_freq=self._config.max_freq,
             accel_per_hz=self._accel_per_hz,
             st_version=self._version,
         )
-        axis_label = (measurements_manager.get_measurements())[0]['name'].split('_')[1]
+        computation = computer.compute()
+        fig = self._plotter.plot_static_frequency_graph(computation)
+        try:
+            axis_label = (measurements_manager.get_measurements())[0]['name'].split('_')[1]
+        except IndexError:
+            axis_label = None
         self._save_figure(fig, measurements_manager, axis_label)
 
 
-######################################################################
-# Graphing
-######################################################################
-
-
-def plot_spectrogram(ax: plt.Axes, t: np.ndarray, bins: np.ndarray, pdata: np.ndarray, max_freq: float) -> None:
-    ax.set_title('Time-Frequency Spectrogram', fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
-
-    vmin_value = np.percentile(pdata, SPECTROGRAM_LOW_PERCENTILE_FILTER)
-
-    cm = 'inferno'
-    norm = matplotlib.colors.LogNorm(vmin=vmin_value)
-    ax.imshow(
-        pdata.T,
-        norm=norm,
-        cmap=cm,
-        aspect='auto',
-        extent=[t[0], t[-1], bins[0], bins[-1]],
-        origin='lower',
-        interpolation='antialiased',
-    )
-
-    ax.set_xlim([0.0, max_freq])
-    ax.set_ylabel('Time (s)')
-    ax.set_xlabel('Frequency (Hz)')
-
-    return
-
-
-def plot_energy_accumulation(ax: plt.Axes, t: np.ndarray, bins: np.ndarray, pdata: np.ndarray) -> None:
-    # Integrate the energy over the frequency bins for each time step and plot this vertically
-    ax.plot(np.trapz(pdata, t, axis=0), bins, color=KLIPPAIN_COLORS['orange'])
-    ax.set_title('Vibrations', fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
-    ax.set_xlabel('Cumulative Energy')
-    ax.set_ylabel('Time (s)')
-    ax.set_ylim([bins[0], bins[-1]])
-
-    ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
-    ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
-    ax.ticklabel_format(axis='x', style='scientific', scilimits=(0, 0))
-    ax.grid(which='major', color='grey')
-    ax.grid(which='minor', color='lightgrey')
-    # ax.legend()
-
-
-######################################################################
-# Startup and main routines
-######################################################################
-
-
-def static_frequency_tool(
-    measurements: List[Measurement],
-    klipperdir: str = '~/klipper',
-    freq: Optional[float] = None,
-    duration: Optional[float] = None,
-    max_freq: float = 500.0,
-    accel_per_hz: Optional[float] = None,
-    st_version: str = 'unknown',
-) -> plt.Figure:
-    if freq is None or duration is None:
-        raise ValueError('Error: missing frequency or duration parameters!')
-
-    if len(measurements) == 0:
-        raise ValueError('No valid data found in the provided measurements!')
-    if len(measurements) > 1:
-        ConsoleOutput.print('Warning: incorrect number of measurements detected. Only the first one will be used!')
-
-    datas = [np.array(m['samples']) for m in measurements if m['samples'] is not None]
-
-    pdata, bins, t = compute_spectrogram(datas[0])
-    del datas
-
-    fig, ((ax1, ax3)) = plt.subplots(
-        1,
-        2,
-        gridspec_kw={
-            'width_ratios': [5, 3],
-            'bottom': 0.080,
-            'top': 0.840,
-            'left': 0.050,
-            'right': 0.985,
-            'hspace': 0.166,
-            'wspace': 0.138,
-        },
-    )
-    fig.set_size_inches(15, 7)
-
-    title_line1 = 'STATIC FREQUENCY HELPER TOOL'
-    fig.text(
-        0.060, 0.947, title_line1, ha='left', va='bottom', fontsize=20, color=KLIPPAIN_COLORS['purple'], weight='bold'
-    )
-    try:
-        filename_parts = measurements[0]['name'].split('_')
-        dt = datetime.strptime(f'{filename_parts[2]} {filename_parts[3]}', '%Y%m%d %H%M%S')
-        title_line2 = dt.strftime('%x %X') + ' -- ' + filename_parts[1].upper() + ' axis'
-        title_line3 = f'| Maintained frequency: {freq}Hz for {duration}s'
-        title_line4 = 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: measurement names look to be different than expected ({measurements[0]["name"]})'
-        )
-        title_line2 = measurements[0]['name']
-        title_line3 = ''
-        title_line4 = ''
-    fig.text(0.060, 0.939, title_line2, ha='left', va='top', fontsize=16, color=KLIPPAIN_COLORS['dark_purple'])
-    fig.text(0.55, 0.985, title_line3, ha='left', va='top', fontsize=14, color=KLIPPAIN_COLORS['dark_purple'])
-    fig.text(0.55, 0.950, title_line4, ha='left', va='top', fontsize=11, color=KLIPPAIN_COLORS['dark_purple'])
-
-    plot_spectrogram(ax1, t, bins, pdata, max_freq)
-    plot_energy_accumulation(ax3, t, bins, pdata)
-
-    ax_logo = fig.add_axes([0.001, 0.894, 0.105, 0.105], anchor='NW')
-    ax_logo.imshow(plt.imread(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'klippain.png')))
-    ax_logo.axis('off')
-
-    if st_version != 'unknown':
-        fig.text(0.995, 0.980, st_version, ha='right', va='bottom', fontsize=8, color=KLIPPAIN_COLORS['purple'])
-
-    return fig
-
-
-def main():
-    usage = '%prog [options] <logs>'
-    opts = optparse.OptionParser(usage)
-    opts.add_option('-o', '--output', type='string', dest='output', default=None, help='filename of output graph')
-    opts.add_option('-f', '--freq', type='float', default=None, help='frequency maintained during the measurement')
-    opts.add_option('-d', '--duration', type='float', default=None, help='duration of the measurement')
-    opts.add_option('--max_freq', type='float', default=500.0, help='maximum frequency to graph')
-    opts.add_option('--accel_per_hz', type='float', default=None, help='accel_per_hz used during the measurement')
-    opts.add_option(
-        '-k', '--klipper_dir', type='string', dest='klipperdir', default='~/klipper', help='main klipper directory'
-    )
-    options, args = opts.parse_args()
-    if len(args) < 1:
-        opts.error('Incorrect number of arguments')
-    if options.output is None:
-        opts.error('You must specify an output file.png to use the script (option -o)')
-
-    measurements_manager = MeasurementsManager(10)
-    if args[0].endswith('.csv'):
-        measurements_manager.load_from_csvs(args)
-    elif args[0].endswith('.stdata'):
-        measurements_manager.load_from_stdata(args[0])
-    else:
-        raise ValueError('Only .stdata or legacy Klipper raw accelerometer CSV files are supported!')
-
-    fig = static_frequency_tool(
-        measurements_manager.get_measurements(),
-        options.klipperdir,
-        options.freq,
-        options.duration,
-        options.max_freq,
-        options.accel_per_hz,
-        'unknown',
-    )
-    fig.savefig(options.output, dpi=150)
-
-
-if __name__ == '__main__':
-    main()
+class StaticGraphComputation:
+    def __init__(
+        self,
+        measurements: List[Measurement],
+        freq: float,
+        duration: float,
+        max_freq: float,
+        accel_per_hz: Optional[float],
+        st_version: str,
+    ):
+        self.measurements = measurements
+        self.freq = freq
+        self.duration = duration
+        self.max_freq = max_freq
+        self.accel_per_hz = accel_per_hz
+        self.st_version = st_version
+
+    def compute(self):
+        if len(self.measurements) == 0:
+            raise ValueError('No valid data found in the provided measurements!')
+        if len(self.measurements) > 1:
+            ConsoleOutput.print('Warning: incorrect number of measurements detected. Only the first one will be used!')
+
+        datas = [np.array(m['samples']) for m in self.measurements if m['samples'] is not None]
+
+        pdata, bins, t = compute_spectrogram(datas[0])
+        del datas
+
+        computation_result = {
+            'freq': self.freq,
+            'duration': self.duration,
+            'accel_per_hz': self.accel_per_hz,
+            'st_version': self.st_version,
+            'measurements': self.measurements,
+            't': t,
+            'bins': bins,
+            'pdata': pdata,
+            'max_freq': self.max_freq,
+        }
+
+        return computation_result
diff --git a/shaketune/graph_creators/vibrations_graph_creator.py b/shaketune/graph_creators/vibrations_graph_creator.py
index 4dcbf9e..ddf6f1b 100644
--- a/shaketune/graph_creators/vibrations_graph_creator.py
+++ b/shaketune/graph_creators/vibrations_graph_creator.py
@@ -9,31 +9,22 @@
 
 
 import math
-import optparse
 import os
 import re
-from datetime import datetime
 from typing import List, Optional, Tuple
 
-import matplotlib
-import matplotlib.font_manager
-import matplotlib.gridspec
-import matplotlib.pyplot as plt
-import matplotlib.ticker
 import numpy as np
 
-matplotlib.use('Agg')
-
 from ..helpers.accelerometer import Measurement, MeasurementsManager
 from ..helpers.common_func import (
     compute_mechanical_parameters,
     detect_peaks,
     identify_low_energy_zones,
-    setup_klipper_import,
 )
 from ..helpers.console_output import ConsoleOutput
 from ..helpers.motors_config_parser import Motor, MotorsConfigParser
 from ..shaketune_config import ShakeTuneConfig
+from . import get_shaper_calibrate_module
 from .graph_creator import GraphCreator
 
 PEAKS_DETECTION_THRESHOLD = 0.05
@@ -43,14 +34,6 @@
 SPEEDS_AROUND_PEAK_DELETION = 3  # to delete +-3mm/s around a peak
 ANGLES_VALLEY_DETECTION_THRESHOLD = 1.1  # Lower is more sensitive
 
-KLIPPAIN_COLORS = {
-    'purple': '#70088C',
-    'orange': '#FF8D32',
-    'dark_purple': '#150140',
-    'dark_orange': '#F24130',
-    'red_pink': '#F2055C',
-}
-
 
 @GraphCreator.register('vibrations profile')
 class VibrationsGraphCreator(GraphCreator):
@@ -60,884 +43,428 @@ def __init__(self, config: ShakeTuneConfig):
         self._accel: Optional[float] = None
         self._motors: Optional[List[MotorsConfigParser]] = None
 
-    def configure(self, kinematics: str, accel: float, motor_config_parser: MotorsConfigParser) -> None:
+    def configure(self, kinematics: str, accel: float, motor_config_parser: MotorsConfigParser = None) -> None:
         self._kinematics = kinematics
         self._accel = accel
-        self._motors: List[Motor] = motor_config_parser.get_motors()
+        if motor_config_parser is not None:
+            self._motors: List[Motor] = motor_config_parser.get_motors()
+        else:
+            self._motors = None
 
     def create_graph(self, measurements_manager: MeasurementsManager) -> None:
-        if not self._accel or not self._kinematics:
-            raise ValueError('accel and kinematics must be set to create the vibrations profile graph!')
-
-        fig = vibrations_profile(
+        computer = VibrationGraphComputation(
             measurements=measurements_manager.get_measurements(),
-            klipperdir=str(self._config.klipper_folder),
             kinematics=self._kinematics,
             accel=self._accel,
+            max_freq=self._config.max_freq_vibrations,
             st_version=self._version,
             motors=self._motors,
         )
+        computation = computer.compute()
+        fig = self._plotter.plot_vibrations_graph(computation)
         self._save_figure(fig, measurements_manager)
 
 
-######################################################################
-# Computation
-######################################################################
-
-
-# Call to the official Klipper input shaper object to do the PSD computation
-def calc_freq_response(data) -> Tuple[np.ndarray, np.ndarray]:
-    helper = shaper_calibrate.ShaperCalibrate(printer=None)
-    return helper.process_accelerometer_data(data)
-
-
-# Calculate motor frequency profiles based on the measured Power Spectral Density (PSD) measurements for the machine kinematics
-# main angles and then create a global motor profile as a weighted average (from their own vibrations) of all calculated profiles
-def compute_motor_profiles(
-    freqs: np.ndarray,
-    psds: dict,
-    all_angles_energy: dict,
-    measured_angles: Optional[List[int]] = None,
-    energy_amplification_factor: int = 2,
-) -> Tuple[dict, np.ndarray]:
-    if measured_angles is None:
-        measured_angles = [0, 90]
-
-    motor_profiles = {}
-    weighted_sum_profiles = np.zeros_like(freqs)
-    total_weight = 0
-    conv_filter = np.ones(20) / 20
-
-    # Creating the PSD motor profiles for each angles
-    for angle in measured_angles:
-        # Calculate the sum of PSDs for the current angle and then convolve
-        sum_curve = np.sum(np.array([psds[angle][speed] for speed in psds[angle]]), axis=0)
-        motor_profiles[angle] = np.convolve(sum_curve / len(psds[angle]), conv_filter, mode='same')
-
-        # Calculate weights
-        angle_energy = (
-            all_angles_energy[angle] ** energy_amplification_factor
-        )  # First weighting factor is based on the total vibrations of the machine at the specified angle
-        curve_area = (
-            np.trapz(motor_profiles[angle], freqs) ** energy_amplification_factor
-        )  # Additional weighting factor is based on the area under the current motor profile at this specified angle
-        total_angle_weight = angle_energy * curve_area
-
-        # Update weighted sum profiles to get the global motor profile
-        weighted_sum_profiles += motor_profiles[angle] * total_angle_weight
-        total_weight += total_angle_weight
-
-    # Creating a global average motor profile that is the weighted average of all the PSD motor profiles
-    global_motor_profile = weighted_sum_profiles / total_weight if total_weight != 0 else weighted_sum_profiles
-
-    return motor_profiles, global_motor_profile
-
-
-# Since it was discovered that there is no non-linear mixing in the stepper "steps" vibrations, instead of measuring
-# the effects of each speeds at each angles, this function simplify it by using only the main motors axes (X/Y for Cartesian
-# printers and A/B for CoreXY) measurements and project each points on the [0,360] degrees range using trigonometry
-# to "sum" the vibration impact of each axis at every points of the generated spectrogram. The result is very similar at the end.
-def compute_dir_speed_spectrogram(
-    measured_speeds: List[float], data: dict, kinematics: str = 'cartesian', measured_angles: Optional[List[int]] = None
-) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
-    if measured_angles is None:
-        measured_angles = [0, 90]
-
-    # We want to project the motor vibrations measured on their own axes on the [0, 360] range
-    spectrum_angles = np.linspace(0, 360, 720)  # One point every 0.5 degrees
-    spectrum_speeds = np.linspace(min(measured_speeds), max(measured_speeds), len(measured_speeds) * 6)
-    spectrum_vibrations = np.zeros((len(spectrum_angles), len(spectrum_speeds)))
-
-    def get_interpolated_vibrations(data: dict, speed: float, speeds: List[float]) -> float:
-        idx = np.clip(np.searchsorted(speeds, speed, side='left'), 1, len(speeds) - 1)
-        lower_speed = speeds[idx - 1]
-        upper_speed = speeds[idx]
-        lower_vibrations = data.get(lower_speed, 0)
-        upper_vibrations = data.get(upper_speed, 0)
-        return lower_vibrations + (speed - lower_speed) * (upper_vibrations - lower_vibrations) / (
-            upper_speed - lower_speed
-        )
-
-    # Precompute trigonometric values and constant before the loop
-    angle_radians = np.deg2rad(spectrum_angles)
-    cos_vals = np.cos(angle_radians)
-    sin_vals = np.sin(angle_radians)
-    sqrt_2_inv = 1 / math.sqrt(2)
-
-    # Compute the spectrum vibrations for each angle and speed combination
-    for target_angle_idx, (cos_val, sin_val) in enumerate(zip(cos_vals, sin_vals)):
-        for target_speed_idx, target_speed in enumerate(spectrum_speeds):
-            if kinematics in {'cartesian', 'limited_cartesian', 'corexz', 'limited_corexz'}:
-                speed_1 = np.abs(target_speed * cos_val)
-                speed_2 = np.abs(target_speed * sin_val)
-            elif kinematics in {'corexy', 'limited_corexy'}:
-                speed_1 = np.abs(target_speed * (cos_val + sin_val) * sqrt_2_inv)
-                speed_2 = np.abs(target_speed * (cos_val - sin_val) * sqrt_2_inv)
-
-            vibrations_1 = get_interpolated_vibrations(data[measured_angles[0]], speed_1, measured_speeds)
-            vibrations_2 = get_interpolated_vibrations(data[measured_angles[1]], speed_2, measured_speeds)
-            spectrum_vibrations[target_angle_idx, target_speed_idx] = vibrations_1 + vibrations_2
-
-    return spectrum_angles, spectrum_speeds, spectrum_vibrations
-
-
-def compute_angle_powers(spectrogram_data: np.ndarray) -> np.ndarray:
-    angles_powers = np.trapz(spectrogram_data, axis=1)
-
-    # Since we want to plot it on a continuous polar plot later on, we need to append parts of
-    # the array to start and end of it to smooth transitions when doing the convolution
-    # and get the same value at modulo 360. Then we return the array without the extras
-    extended_angles_powers = np.concatenate([angles_powers[-9:], angles_powers, angles_powers[:9]])
-    convolved_extended = np.convolve(extended_angles_powers, np.ones(15) / 15, mode='same')
-
-    return convolved_extended[9:-9]
-
-
-def compute_speed_powers(spectrogram_data: np.ndarray, smoothing_window: int = 15) -> np.ndarray:
-    min_values = np.amin(spectrogram_data, axis=0)
-    max_values = np.amax(spectrogram_data, axis=0)
-    var_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
-    conv_filter = np.ones(smoothing_window) / smoothing_window
-    window = int(smoothing_window / 2)
-
-    def pad_and_smooth(data: np.ndarray) -> np.ndarray:
-        data_padded = np.pad(data, (window,), mode='edge')
-        smoothed_data = np.convolve(data_padded, conv_filter, mode='valid')
-        return smoothed_data
-
-    # Stack the arrays and apply padding and smoothing in batch
-    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])
-
-    return smoothed_arrays
-
-
-# Function that filter and split the good_speed ranges. The goal is to remove some zones around
-# additional detected small peaks in order to suppress them if there is a peak, even if it's low,
-# that's probably due to a crossing in the motor resonance pattern that still need to be removed
-def filter_and_split_ranges(
-    all_speeds: np.ndarray, good_speeds: List[Tuple[int, int, float]], peak_speed_indices: dict, deletion_range: int
-) -> List[Tuple[int, int, float]]:
-    # Process each range to filter out and split based on peak indices
-    filtered_good_speeds = []
-    for start, end, energy in good_speeds:
-        start_speed, end_speed = all_speeds[start], all_speeds[end]
-        # Identify peaks that intersect with the current speed range
-        intersecting_peaks_indices = [
-            idx for speed, idx in peak_speed_indices.items() if start_speed <= speed <= end_speed
-        ]
-
-        if not intersecting_peaks_indices:
-            filtered_good_speeds.append((start, end, energy))
+class VibrationGraphComputation:
+    def __init__(
+        self,
+        measurements: List[Measurement],
+        kinematics: str,
+        accel: float,
+        max_freq: float,
+        st_version: str,
+        motors: List[str],
+    ):
+        self.measurements = measurements
+        self.kinematics = kinematics
+        self.accel = accel
+        self.max_freq = max_freq
+        self.st_version = st_version
+        self.motors = motors
+
+    def compute(self):
+        if self.kinematics in {'cartesian', 'limited_cartesian', 'corexz', 'limited_corexz'}:
+            main_angles = [0, 90]
+        elif self.kinematics in {'corexy', 'limited_corexy'}:
+            main_angles = [45, 135]
         else:
-            intersecting_peaks_indices.sort()
-            current_start = start
-
-            for peak_index in intersecting_peaks_indices:
-                before_peak_end = max(current_start, peak_index - deletion_range)
-                if current_start < before_peak_end:
-                    filtered_good_speeds.append((current_start, before_peak_end, energy))
-                current_start = peak_index + deletion_range + 1
-
-            if current_start < end:
-                filtered_good_speeds.append((current_start, end, energy))
-
-    # Sorting by start point once and then merge overlapping ranges
-    sorted_ranges = sorted(filtered_good_speeds, key=lambda x: x[0])
-    merged_ranges = [sorted_ranges[0]]
-
-    for current in sorted_ranges[1:]:
-        last_merged_start, last_merged_end, last_merged_energy = merged_ranges[-1]
-        if current[0] <= last_merged_end:
-            new_end = max(last_merged_end, current[1])
-            new_energy = min(last_merged_energy, current[2])
-            merged_ranges[-1] = (last_merged_start, new_end, new_energy)
-        else:
-            merged_ranges.append(current)
-
-    return merged_ranges
-
-
-# This function allow the computation of a symmetry score that reflect the spectrogram apparent symmetry between
-# measured axes on both the shape of the signal and the energy level consistency across both side of the signal
-def compute_symmetry_analysis(
-    all_angles: np.ndarray, spectrogram_data: np.ndarray, measured_angles: Optional[List[int]] = None
-) -> float:
-    if measured_angles is None:
-        measured_angles = [0, 90]
-
-    total_spectrogram_angles = len(all_angles)
-    half_spectrogram_angles = total_spectrogram_angles // 2
-
-    # Extend the spectrogram by adding half to the beginning (in order to not get an out of bounds error later)
-    extended_spectrogram = np.concatenate((spectrogram_data[-half_spectrogram_angles:], spectrogram_data), axis=0)
-
-    # Calculate the split index directly within the slicing
-    midpoint_angle = np.mean(measured_angles)
-    split_index = int(midpoint_angle * (total_spectrogram_angles / 360) + half_spectrogram_angles)
-    half_segment_length = half_spectrogram_angles // 2
-
-    # Slice out the two segments of the spectrogram and flatten them for comparison
-    segment_1_flattened = extended_spectrogram[split_index - half_segment_length : split_index].flatten()
-    segment_2_flattened = extended_spectrogram[split_index : split_index + half_segment_length].flatten()
-
-    # Compute the correlation coefficient between the two segments of spectrogram
-    correlation = np.corrcoef(segment_1_flattened, segment_2_flattened)[0, 1]
-    percentage_correlation_biased = (100 * np.power(correlation, 0.75)) + 10
-
-    return np.clip(0, 100, percentage_correlation_biased)
-
-
-######################################################################
-# Graphing
-######################################################################
-
-
-def plot_angle_profile_polar(
-    ax: plt.Axes,
-    angles: np.ndarray,
-    angles_powers: np.ndarray,
-    low_energy_zones: List[Tuple[int, int, float]],
-    symmetry_factor: float,
-) -> None:
-    angles_radians = np.deg2rad(angles)
-
-    ax.set_title('Polar angle energy profile', fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
-    ax.set_theta_zero_location('E')
-    ax.set_theta_direction(1)
-
-    ax.plot(angles_radians, angles_powers, color=KLIPPAIN_COLORS['purple'], zorder=5)
-    ax.fill(angles_radians, angles_powers, color=KLIPPAIN_COLORS['purple'], alpha=0.3)
-    ax.set_xlim([0, np.deg2rad(360)])
-    ymax = angles_powers.max() * 1.05
-    ax.set_ylim([0, ymax])
-    ax.set_thetagrids([theta * 15 for theta in range(360 // 15)])
-
-    ax.text(
-        0,
-        0,
-        f'Symmetry: {symmetry_factor:.1f}%',
-        ha='center',
-        va='center',
-        color=KLIPPAIN_COLORS['red_pink'],
-        fontsize=12,
-        fontweight='bold',
-        zorder=6,
-    )
-
-    for _, (start, end, _) in enumerate(low_energy_zones):
-        ax.axvline(
-            angles_radians[start],
-            angles_powers[start] / ymax,
-            color=KLIPPAIN_COLORS['red_pink'],
-            linestyle='dotted',
-            linewidth=1.5,
-        )
-        ax.axvline(
-            angles_radians[end],
-            angles_powers[end] / ymax,
-            color=KLIPPAIN_COLORS['red_pink'],
-            linestyle='dotted',
-            linewidth=1.5,
-        )
-        ax.fill_between(
-            angles_radians[start:end], angles_powers[start:end], angles_powers.max() * 1.05, color='green', alpha=0.2
-        )
+            raise ValueError('Only Cartesian, CoreXY and CoreXZ kinematics are supported by this tool at the moment!')
 
-    ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
-    ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
-    ax.grid(which='major', color='grey')
-    ax.grid(which='minor', color='lightgrey')
-
-    # Polar plot doesn't follow the gridspec margin, so we adjust it manually here
-    pos = ax.get_position()
-    new_pos = [pos.x0 - 0.01, pos.y0 - 0.01, pos.width, pos.height]
-    ax.set_position(new_pos)
-
-    return
-
-
-def plot_global_speed_profile(
-    ax: plt.Axes,
-    all_speeds: np.ndarray,
-    sp_min_energy: np.ndarray,
-    sp_max_energy: np.ndarray,
-    sp_variance_energy: np.ndarray,
-    vibration_metric: np.ndarray,
-    num_peaks: int,
-    peaks: np.ndarray,
-    low_energy_zones: List[Tuple[int, int, float]],
-) -> None:
-    ax.set_title('Global speed energy profile', fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
-    ax.set_xlabel('Speed (mm/s)')
-    ax.set_ylabel('Energy')
-    ax2 = ax.twinx()
-    ax2.yaxis.set_visible(False)
-
-    ax.plot(all_speeds, sp_min_energy, label='Minimum', 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_variance_energy, label='Variance', color=KLIPPAIN_COLORS['orange'], zorder=5, linestyle='--')
-    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_ylim([0, sp_max_energy.max() * 1.15])
-
-    y2min = -(vibration_metric.max() * 0.025)
-    y2max = vibration_metric.max() * 1.07
-    ax2.set_ylim([y2min, y2max])
-
-    if peaks is not None and len(peaks) > 0:
-        ax2.plot(all_speeds[peaks], vibration_metric[peaks], 'x', color='black', markersize=8, zorder=10)
-        for idx, peak in enumerate(peaks):
-            ax2.annotate(
-                f'{idx+1}',
-                (all_speeds[peak], vibration_metric[peak]),
-                textcoords='offset points',
-                xytext=(5, 5),
-                fontweight='bold',
-                ha='left',
-                fontsize=13,
-                color=KLIPPAIN_COLORS['red_pink'],
-                zorder=10,
-            )
+        psds = {}
+        psds_sum = {}
+        target_freqs_initialized = False
+        target_freqs = None
 
-    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[end], color=KLIPPAIN_COLORS['red_pink'], linestyle='dotted', linewidth=1.5, zorder=8)
-        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',
-        )
+        helper = get_shaper_calibrate_module().ShaperCalibrate(printer=None)
 
-    ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
-    ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
-    ax.grid(which='major', color='grey')
-    ax.grid(which='minor', color='lightgrey')
-
-    fontP = matplotlib.font_manager.FontProperties()
-    fontP.set_size('small')
-    ax.legend(loc='upper left', prop=fontP)
-    ax2.legend(loc='upper right', prop=fontP)
-
-    return
-
-
-def plot_angular_speed_profiles(
-    ax: plt.Axes, speeds: np.ndarray, angles: np.ndarray, spectrogram_data: np.ndarray, kinematics: str = 'cartesian'
-) -> None:
-    ax.set_title('Angular speed energy profiles', fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
-    ax.set_xlabel('Speed (mm/s)')
-    ax.set_ylabel('Energy')
-
-    # Define mappings for labels and colors to simplify plotting commands
-    angle_settings = {
-        0: ('X (0 deg)', 'purple', 10),
-        90: ('Y (90 deg)', 'dark_purple', 5),
-        45: ('A (45 deg)' if kinematics in {'corexy', 'limited_corexy'} else '45 deg', 'orange', 10),
-        135: ('B (135 deg)' if kinematics in {'corexy', 'limited_corexy'} else '135 deg', 'dark_orange', 5),
-    }
-
-    # Plot each angle using settings from the dictionary
-    for angle, (label, color, zorder) in angle_settings.items():
-        idx = np.searchsorted(angles, angle, side='left')
-        ax.plot(speeds, spectrogram_data[idx], label=label, color=KLIPPAIN_COLORS[color], zorder=zorder)
-
-    ax.set_xlim([speeds.min(), speeds.max()])
-    max_value = max(spectrogram_data[angle].max() for angle in {0, 45, 90, 135})
-    ax.set_ylim([0, max_value * 1.1])
-
-    ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
-    ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
-    ax.grid(which='major', color='grey')
-    ax.grid(which='minor', color='lightgrey')
-
-    fontP = matplotlib.font_manager.FontProperties()
-    fontP.set_size('small')
-    ax.legend(loc='upper right', prop=fontP)
-
-    return
-
-
-def plot_motor_profiles(
-    ax: plt.Axes,
-    freqs: np.ndarray,
-    main_angles: List[int],
-    motor_profiles: dict,
-    global_motor_profile: np.ndarray,
-    max_freq: float,
-) -> None:
-    ax.set_title('Motor frequency profile', fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
-    ax.set_ylabel('Energy')
-    ax.set_xlabel('Frequency (Hz)')
-
-    ax2 = ax.twinx()
-    ax2.yaxis.set_visible(False)
-
-    # Global weighted average motor profile
-    ax.plot(freqs, global_motor_profile, label='Combined', color=KLIPPAIN_COLORS['purple'], zorder=5)
-    max_value = global_motor_profile.max()
-
-    # Mapping of angles to axis names
-    angle_settings = {0: 'X', 90: 'Y', 45: 'A', 135: 'B'}
-
-    # And then plot the motor profiles at each measured angles
-    for angle in main_angles:
-        profile_max = motor_profiles[angle].max()
-        if profile_max > max_value:
-            max_value = profile_max
-        label = f'{angle_settings[angle]} ({angle} deg)' if angle in angle_settings else f'{angle} deg'
-        ax.plot(freqs, motor_profiles[angle], linestyle='--', label=label, zorder=2)
-
-    ax.set_xlim([0, max_freq])
-    ax.set_ylim([0, max_value * 1.1])
-    ax.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))
-
-    # Then add the motor resonance peak to the graph and print some infos about it
-    motor_fr, motor_zeta, motor_res_idx, lowfreq_max = compute_mechanical_parameters(global_motor_profile, freqs, 30)
-    if lowfreq_max:
-        ConsoleOutput.print(
-            '[WARNING] There are a lot of low frequency vibrations that can alter the readings. This is probably due to the test being performed at too high an acceleration!'
-        )
-        ConsoleOutput.print(
-            'Try lowering the ACCEL value and/or increasing the SIZE value before restarting the macro to ensure that only constant speeds are being recorded and that the dynamic behavior of the machine is not affecting the measurements'
-        )
-    if motor_zeta is not None:
-        ConsoleOutput.print(
-            f'Motors have a main resonant frequency at {motor_fr:.1f}Hz with an estimated damping ratio of {motor_zeta:.3f}'
-        )
-    else:
-        ConsoleOutput.print(
-            f'Motors have a main resonant frequency at {motor_fr:.1f}Hz but it was impossible to estimate a damping ratio.'
-        )
+        for measurement in self.measurements:
+            data = np.array(measurement['samples'])
+            if data is None:
+                continue  # Measurement data is not in the expected format or is empty, skip it
 
-    ax.plot(freqs[motor_res_idx], global_motor_profile[motor_res_idx], 'x', color='black', markersize=10)
-    ax.annotate(
-        'R',
-        (freqs[motor_res_idx], global_motor_profile[motor_res_idx]),
-        textcoords='offset points',
-        xytext=(15, 5),
-        ha='right',
-        fontsize=14,
-        color=KLIPPAIN_COLORS['red_pink'],
-        weight='bold',
-    )
-
-    ax2.plot([], [], ' ', label=f'Motor resonant frequency (ω0): {motor_fr:.1f}Hz')
-    if motor_zeta is not None:
-        ax2.plot([], [], ' ', label=f'Motor damping ratio (ζ): {motor_zeta:.3f}')
-    else:
-        ax2.plot([], [], ' ', label='No damping ratio computed')
-
-    ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
-    ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
-    ax.grid(which='major', color='grey')
-    ax.grid(which='minor', color='lightgrey')
-
-    fontP = matplotlib.font_manager.FontProperties()
-    fontP.set_size('small')
-    ax.legend(loc='upper left', prop=fontP)
-    ax2.legend(loc='upper right', prop=fontP)
-
-    return
-
-
-def plot_vibration_spectrogram_polar(
-    ax: plt.Axes, angles: np.ndarray, speeds: np.ndarray, spectrogram_data: np.ndarray
-) -> None:
-    angles_radians = np.radians(angles)
-
-    # Assuming speeds defines the radial distance from the center, we need to create a meshgrid
-    # for both angles and speeds to map the spectrogram data onto a polar plot correctly
-    radius, theta = np.meshgrid(speeds, angles_radians)
-
-    ax.set_title(
-        'Polar vibrations heatmap', fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold', va='bottom'
-    )
-    ax.set_theta_zero_location('E')
-    ax.set_theta_direction(1)
-
-    ax.pcolormesh(theta, radius, spectrogram_data, norm=matplotlib.colors.LogNorm(), cmap='inferno', shading='auto')
-    ax.set_thetagrids([theta * 15 for theta in range(360 // 15)])
-    ax.tick_params(axis='y', which='both', colors='white', labelsize='medium')
-    ax.set_ylim([0, max(speeds)])
-
-    # Polar plot doesn't follow the gridspec margin, so we adjust it manually here
-    pos = ax.get_position()
-    new_pos = [pos.x0 - 0.01, pos.y0 - 0.01, pos.width, pos.height]
-    ax.set_position(new_pos)
-
-    return
-
-
-def plot_vibration_spectrogram(
-    ax: plt.Axes, angles: np.ndarray, speeds: np.ndarray, spectrogram_data: np.ndarray, peaks: np.ndarray
-) -> None:
-    ax.set_title('Vibrations heatmap', fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
-    ax.set_xlabel('Speed (mm/s)')
-    ax.set_ylabel('Angle (deg)')
-
-    ax.imshow(
-        spectrogram_data,
-        norm=matplotlib.colors.LogNorm(),
-        cmap='inferno',
-        aspect='auto',
-        extent=[speeds[0], speeds[-1], angles[0], angles[-1]],
-        origin='lower',
-        interpolation='antialiased',
-    )
-
-    # Add peaks lines in the spectrogram to get hint from peaks found in the first graph
-    if peaks is not None and len(peaks) > 0:
-        for idx, peak in enumerate(peaks):
-            ax.axvline(speeds[peak], color='cyan', linewidth=0.75)
-            ax.annotate(
-                f'Peak {idx+1}',
-                (speeds[peak], angles[-1] * 0.9),
-                textcoords='data',
-                color='cyan',
-                rotation=90,
-                fontsize=10,
-                verticalalignment='top',
-                horizontalalignment='right',
-            )
+            angle, speed = self._extract_angle_and_speed(measurement['name'])
+            freq_response = helper.process_accelerometer_data(data)
+            first_freqs = freq_response.freq_bins
+            psd_sum = freq_response.psd_sum
 
-    return
+            if not target_freqs_initialized:
+                target_freqs = first_freqs[first_freqs <= self.max_freq]
+                target_freqs_initialized = True
 
+            psd_sum = psd_sum[first_freqs <= self.max_freq]
+            first_freqs = first_freqs[first_freqs <= self.max_freq]
 
-def plot_motor_config_txt(fig: plt.Figure, motors: List[MotorsConfigParser], differences: Optional[str]) -> None:
-    motor_details = [(motors[0], 'X motor'), (motors[1], 'Y motor')]
+            # Initialize the angle dictionary if it doesn't exist
+            if angle not in psds:
+                psds[angle] = {}
+                psds_sum[angle] = {}
 
-    distance = 0.12
-    if motors[0].get_config('autotune_enabled'):
-        distance = 0.27
-        config_blocks = [
-            f"| {lbl}: {mot.get_config('motor').upper()} on {mot.get_config('tmc').upper()} @ {mot.get_config('voltage'):0.1f}V {mot.get_config('run_current'):0.2f}A - {mot.get_config('microsteps')}usteps"
-            for mot, lbl in motor_details
-        ]
-        config_blocks.append(
-            f'| TMC Autotune enabled (PWM freq target: X={int(motors[0].get_config("pwm_freq_target")/1000)}kHz / Y={int(motors[1].get_config("pwm_freq_target")/1000)}kHz)'
+            # Store the interpolated PSD and integral values
+            psds[angle][speed] = np.interp(target_freqs, first_freqs, psd_sum)
+            psds_sum[angle][speed] = np.trapz(psd_sum, first_freqs)
+
+        measured_angles = sorted(psds_sum.keys())
+        measured_speeds = sorted({speed for angle_speeds in psds_sum.values() for speed in angle_speeds.keys()})
+
+        for main_angle in main_angles:
+            if main_angle not in measured_angles:
+                raise ValueError('Measurements not taken at the correct angles for the specified kinematics!')
+
+        # Precompute the variables used in plot functions
+        all_angles, all_speeds, spectrogram_data = self._compute_dir_speed_spectrogram(
+            measured_speeds, psds_sum, self.kinematics, main_angles
         )
-    else:
-        config_blocks = [
-            f"| {lbl}: {mot.get_config('tmc').upper()} @ {mot.get_config('run_current'):0.2f}A - {mot.get_config('microsteps')}usteps"
-            for mot, lbl in motor_details
-        ]
-        config_blocks.append('| TMC Autotune not detected')
-
-    for idx, block in enumerate(config_blocks):
-        fig.text(
-            0.41, 0.990 - 0.015 * idx, block, ha='left', va='top', fontsize=10, color=KLIPPAIN_COLORS['dark_purple']
+        all_angles_energy = self._compute_angle_powers(spectrogram_data)
+        sp_min_energy, sp_max_energy, sp_variance_energy, vibration_metric = self._compute_speed_powers(
+            spectrogram_data
         )
-
-    tmc_registers = motors[0].get_registers()
-    idx = -1
-    for idx, (register, settings) in enumerate(tmc_registers.items()):
-        settings_str = ' '.join(f'{k}={v}' for k, v in settings.items())
-        tmc_block = f'| {register.upper()}: {settings_str}'
-        fig.text(
-            0.41 + distance,
-            0.990 - 0.015 * idx,
-            tmc_block,
-            ha='left',
-            va='top',
-            fontsize=10,
-            color=KLIPPAIN_COLORS['dark_purple'],
+        motor_profiles, global_motor_profile = self._compute_motor_profiles(
+            target_freqs, psds, all_angles_energy, main_angles
         )
 
-    if differences is not None:
-        differences_text = f'| Y motor diff: {differences}'
-        fig.text(
-            0.41 + distance,
-            0.990 - 0.015 * (idx + 1),
-            differences_text,
-            ha='left',
-            va='top',
-            fontsize=10,
-            color=KLIPPAIN_COLORS['dark_purple'],
+        # symmetry_factor = compute_symmetry_analysis(all_angles, all_angles_energy)
+        symmetry_factor = self._compute_symmetry_analysis(all_angles, spectrogram_data, main_angles)
+        ConsoleOutput.print(f'Machine estimated vibration symmetry: {symmetry_factor:.1f}%')
+
+        # 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
+        num_peaks, vibration_peaks, peaks_speeds = detect_peaks(
+            vibration_metric,
+            all_speeds,
+            PEAKS_DETECTION_THRESHOLD * vibration_metric.max(),
+            PEAKS_RELATIVE_HEIGHT_THRESHOLD,
+            10,
+            10,
+        )
+        formated_peaks_speeds = ['{:.1f}'.format(pspeed) for pspeed in peaks_speeds]
+        ConsoleOutput.print(
+            f"Vibrations peaks detected: {num_peaks} @ {', '.join(map(str, formated_peaks_speeds))} mm/s (avoid setting a speed near these values in your slicer print profile)"
         )
 
+        good_speeds = identify_low_energy_zones(vibration_metric, SPEEDS_VALLEY_DETECTION_THRESHOLD)
+        if good_speeds is not None:
+            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)}
+
+            # Filter and split ranges based on peak indices, avoiding overlaps
+            good_speeds = self._filter_and_split_ranges(all_speeds, good_speeds, peak_speed_indices, deletion_range)
+
+            # Add some logging about the good speeds found
+            ConsoleOutput.print(f'Lowest vibrations speeds ({len(good_speeds)} ranges sorted from best to worse):')
+            for idx, (start, end, _) in enumerate(good_speeds):
+                ConsoleOutput.print(f'{idx+1}: {all_speeds[start]:.1f} to {all_speeds[end]:.1f} mm/s')
+
+        # Angle low energy valleys identification (good angles ranges) and print them to the console
+        good_angles = identify_low_energy_zones(all_angles_energy, ANGLES_VALLEY_DETECTION_THRESHOLD)
+        if good_angles is not None:
+            ConsoleOutput.print(f'Lowest vibrations angles ({len(good_angles)} ranges sorted from best to worse):')
+            for idx, (start, end, energy) in enumerate(good_angles):
+                ConsoleOutput.print(
+                    f'{idx+1}: {all_angles[start]:.1f}° to {all_angles[end]:.1f}° (mean vibrations energy: {energy:.2f}% of max)'
+                )
+
+        # Motors infos and config differences check
+        if self.motors is not None and len(self.motors) == 2:
+            motors_config_differences = self.motors[0].compare_to(self.motors[1])
+            if motors_config_differences is not None and self.kinematics in {'corexy', 'limited_corexy'}:
+                ConsoleOutput.print(f'Warning: motors have different TMC configurations!\n{motors_config_differences}')
+        else:
+            motors_config_differences = None
 
-######################################################################
-# Startup and main routines
-######################################################################
-
-
-def extract_angle_and_speed(logname: str) -> Tuple[float, float]:
-    try:
-        match = re.search(r'an(\d+)_\d+sp(\d+)_\d+', os.path.basename(logname))
-        if match:
-            angle = match.group(1)
-            speed = match.group(2)
+        # Compute mechanical parameters and check the main resonant frequency of motors
+        motor_fr, motor_zeta, motor_res_idx, lowfreq_max = compute_mechanical_parameters(
+            global_motor_profile, target_freqs, 30
+        )
+        if lowfreq_max:
+            ConsoleOutput.print(
+                '[WARNING] There are a lot of low frequency vibrations that can alter the readings. This is probably due to the test being performed at too high an acceleration!'
+            )
+            ConsoleOutput.print(
+                'Try lowering the ACCEL value and/or increasing the SIZE value before restarting the macro to ensure that only constant speeds are being recorded and that the dynamic behavior of the machine is not affecting the measurements'
+            )
+        if motor_zeta is not None:
+            ConsoleOutput.print(
+                f'Motors have a main resonant frequency at {motor_fr:.1f}Hz with an estimated damping ratio of {motor_zeta:.3f}'
+            )
         else:
-            raise ValueError(f'File {logname} does not match expected format. Clean your /tmp folder and start again!')
-    except AttributeError as err:
-        raise ValueError(
-            f'File {logname} does not match expected format. Clean your /tmp folder and start again!'
-        ) from err
-    return float(angle), float(speed)
-
-
-def vibrations_profile(
-    measurements: List[Measurement],
-    klipperdir: str = '~/klipper',
-    kinematics: str = 'cartesian',
-    accel: Optional[float] = None,
-    max_freq: float = 1000.0,
-    st_version: Optional[str] = None,
-    motors: Optional[List[MotorsConfigParser]] = None,
-) -> plt.Figure:
-    global shaper_calibrate
-    shaper_calibrate = setup_klipper_import(klipperdir)
-
-    if kinematics in {'cartesian', 'limited_cartesian', 'corexz', 'limited_corexz'}:
-        main_angles = [0, 90]
-    elif kinematics in {'corexy', 'limited_corexy'}:
-        main_angles = [45, 135]
-    else:
-        raise ValueError('Only Cartesian, CoreXY and CoreXZ kinematics are supported by this tool at the moment!')
-
-    psds = {}
-    psds_sum = {}
-    target_freqs_initialized = False
-    target_freqs = None
-
-    for measurement in measurements:
-        data = np.array(measurement['samples'])
-        if data is None:
-            continue  # Measurement data is not in the expected format or is empty, skip it
-
-        angle, speed = extract_angle_and_speed(measurement['name'])
-        freq_response = calc_freq_response(data)
-        first_freqs = freq_response.freq_bins
-        psd_sum = freq_response.psd_sum
-
-        if not target_freqs_initialized:
-            target_freqs = first_freqs[first_freqs <= max_freq]
-            target_freqs_initialized = True
-
-        psd_sum = psd_sum[first_freqs <= max_freq]
-        first_freqs = first_freqs[first_freqs <= max_freq]
-
-        # Initialize the angle dictionary if it doesn't exist
-        if angle not in psds:
-            psds[angle] = {}
-            psds_sum[angle] = {}
-
-        # Store the interpolated PSD and integral values
-        psds[angle][speed] = np.interp(target_freqs, first_freqs, psd_sum)
-        psds_sum[angle][speed] = np.trapz(psd_sum, first_freqs)
-
-    measured_angles = sorted(psds_sum.keys())
-    measured_speeds = sorted({speed for angle_speeds in psds_sum.values() for speed in angle_speeds.keys()})
-
-    for main_angle in main_angles:
-        if main_angle not in measured_angles:
-            raise ValueError('Measurements not taken at the correct angles for the specified kinematics!')
-
-    # 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_energy = compute_angle_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)
-
-    # symmetry_factor = compute_symmetry_analysis(all_angles, all_angles_energy)
-    symmetry_factor = compute_symmetry_analysis(all_angles, spectrogram_data, main_angles)
-    ConsoleOutput.print(f'Machine estimated vibration symmetry: {symmetry_factor:.1f}%')
-
-    # 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
-    num_peaks, vibration_peaks, peaks_speeds = detect_peaks(
-        vibration_metric,
-        all_speeds,
-        PEAKS_DETECTION_THRESHOLD * vibration_metric.max(),
-        PEAKS_RELATIVE_HEIGHT_THRESHOLD,
-        10,
-        10,
-    )
-    formated_peaks_speeds = ['{:.1f}'.format(pspeed) for pspeed in peaks_speeds]
-    ConsoleOutput.print(
-        f"Vibrations peaks detected: {num_peaks} @ {', '.join(map(str, formated_peaks_speeds))} mm/s (avoid setting a speed near these values in your slicer print profile)"
-    )
-
-    good_speeds = identify_low_energy_zones(vibration_metric, SPEEDS_VALLEY_DETECTION_THRESHOLD)
-    if good_speeds is not None:
-        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)}
-
-        # Filter and split ranges based on peak indices, avoiding overlaps
-        good_speeds = filter_and_split_ranges(all_speeds, good_speeds, peak_speed_indices, deletion_range)
-
-        # Add some logging about the good speeds found
-        ConsoleOutput.print(f'Lowest vibrations speeds ({len(good_speeds)} ranges sorted from best to worse):')
-        for idx, (start, end, _) in enumerate(good_speeds):
-            ConsoleOutput.print(f'{idx+1}: {all_speeds[start]:.1f} to {all_speeds[end]:.1f} mm/s')
-
-    # Angle low energy valleys identification (good angles ranges) and print them to the console
-    good_angles = identify_low_energy_zones(all_angles_energy, ANGLES_VALLEY_DETECTION_THRESHOLD)
-    if good_angles is not None:
-        ConsoleOutput.print(f'Lowest vibrations angles ({len(good_angles)} ranges sorted from best to worse):')
-        for idx, (start, end, energy) in enumerate(good_angles):
             ConsoleOutput.print(
-                f'{idx+1}: {all_angles[start]:.1f}° to {all_angles[end]:.1f}° (mean vibrations energy: {energy:.2f}% of max)'
+                f'Motors have a main resonant frequency at {motor_fr:.1f}Hz but it was impossible to estimate a damping ratio.'
             )
 
-    # Create graph layout
-    fig, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = plt.subplots(
-        2,
-        3,
-        gridspec_kw={
-            'height_ratios': [1, 1],
-            'width_ratios': [4, 8, 6],
-            'bottom': 0.050,
-            'top': 0.890,
-            'left': 0.040,
-            'right': 0.985,
-            'hspace': 0.166,
-            'wspace': 0.138,
-        },
-    )
-
-    # Transform ax3 and ax4 to polar plots
-    ax1.remove()
-    ax1 = fig.add_subplot(2, 3, 1, projection='polar')
-    ax4.remove()
-    ax4 = fig.add_subplot(2, 3, 4, projection='polar')
-
-    # Set the global .png figure size
-    fig.set_size_inches(20, 11.5)
-
-    # Add title
-    title_line1 = 'MACHINE VIBRATIONS ANALYSIS TOOL'
-    fig.text(
-        0.060, 0.965, title_line1, ha='left', va='bottom', fontsize=20, color=KLIPPAIN_COLORS['purple'], weight='bold'
-    )
-    try:
-        filename_parts = measurements[0]['name'].split('_')
-        dt = datetime.strptime(f"{filename_parts[4]} {filename_parts[5].split('-')[0]}", '%Y%m%d %H%M%S')
-        title_line2 = dt.strftime('%x %X')
-        if accel is not None:
-            title_line2 += ' at ' + str(accel) + ' mm/s² -- ' + kinematics.upper() + ' kinematics'
-    except Exception:
-        ConsoleOutput.print(
-            f'Warning: measurement names look to be different than expected ({measurements[0]["name"]})'
-        )
-        title_line2 = measurements[0]['name']
-    fig.text(0.060, 0.957, title_line2, ha='left', va='top', fontsize=16, color=KLIPPAIN_COLORS['dark_purple'])
-
-    # Add the motors infos to the top of the graph
-    if motors is not None and len(motors) == 2:
-        differences = motors[0].compare_to(motors[1])
-        plot_motor_config_txt(fig, motors, differences)
-        if differences is not None and kinematics in {'corexy', 'limited_corexy'}:
-            ConsoleOutput.print(f'Warning: motors have different TMC configurations!\n{differences}')
-
-    # Plot the graphs
-    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_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_vibration_spectrogram(ax5, all_angles, all_speeds, spectrogram_data, vibration_peaks)
-
-    plot_motor_profiles(ax6, target_freqs, main_angles, motor_profiles, global_motor_profile, max_freq)
-
-    # Adding a small Klippain logo to the top left corner of the figure
-    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')
-
-    # Adding Shake&Tune version in the top right corner
-    if st_version != 'unknown':
-        fig.text(0.995, 0.985, st_version, ha='right', va='bottom', fontsize=8, color=KLIPPAIN_COLORS['purple'])
-
-    return fig
-
-
-def main():
-    # Parse command-line arguments
-    usage = '%prog [options] <raw logs>'
-    opts = optparse.OptionParser(usage)
-    opts.add_option('-o', '--output', type='string', dest='output', default=None, help='filename of output graph')
-    opts.add_option(
-        '-c', '--accel', type='int', dest='accel', default=None, help='accel value to be printed on the graph'
-    )
-    opts.add_option('-f', '--max_freq', type='float', default=1000.0, help='maximum frequency to graph')
-    opts.add_option(
-        '-k', '--klipper_dir', type='string', dest='klipperdir', default='~/klipper', help='main klipper directory'
-    )
-    opts.add_option(
-        '-m',
-        '--kinematics',
-        type='string',
-        dest='kinematics',
-        default='cartesian',
-        help='machine kinematics configuration',
-    )
-    options, args = opts.parse_args()
-    if len(args) < 1:
-        opts.error('No measurements to analyse')
-    if options.output is None:
-        opts.error('You must specify an output file.png to use the script (option -o)')
-    if options.kinematics not in {
-        'cartesian',
-        'limited_cartesian',
-        'corexy',
-        'limited_corexy',
-        'corexz',
-        'limited_corexz',
-    }:
-        opts.error(
-            'Only cartesian, limited_cartesian, corexy, limited_corexy, corexz, limited_corexz kinematics are supported by this tool at the moment!'
-        )
+        computation_results = {
+            'measurements': self.measurements,
+            'all_speeds': all_speeds,
+            'all_angles': all_angles,
+            'all_angles_energy': all_angles_energy,
+            'good_speeds': good_speeds,
+            'good_angles': good_angles,
+            'kinematics': self.kinematics,
+            'accel': self.accel,
+            'motors': self.motors,
+            'motors_config_differences': motors_config_differences,
+            'symmetry_factor': symmetry_factor,
+            'spectrogram_data': spectrogram_data,
+            'sp_min_energy': sp_min_energy,
+            'sp_max_energy': sp_max_energy,
+            'sp_variance_energy': sp_variance_energy,
+            'vibration_metric': vibration_metric,
+            'motor_profiles': motor_profiles,
+            'global_motor_profile': global_motor_profile,
+            'num_peaks': num_peaks,
+            'vibration_peaks': vibration_peaks,
+            'target_freqs': target_freqs,
+            'main_angles': main_angles,
+            'max_freq': self.max_freq,
+            'motor_fr': motor_fr,
+            'motor_zeta': motor_zeta,
+            'motor_res_idx': motor_res_idx,
+            'st_version': self.st_version,
+        }
+
+        return computation_results
+
+    # Calculate motor frequency profiles based on the measured Power Spectral Density (PSD) measurements for the machine kinematics
+    # main angles and then create a global motor profile as a weighted average (from their own vibrations) of all calculated profiles
+    def _compute_motor_profiles(
+        self,
+        freqs: np.ndarray,
+        psds: dict,
+        all_angles_energy: dict,
+        measured_angles: Optional[List[int]] = None,
+        energy_amplification_factor: int = 2,
+    ) -> Tuple[dict, np.ndarray]:
+        if measured_angles is None:
+            measured_angles = [0, 90]
+
+        motor_profiles = {}
+        weighted_sum_profiles = np.zeros_like(freqs)
+        total_weight = 0
+        conv_filter = np.ones(20) / 20
+
+        # Creating the PSD motor profiles for each angles
+        for angle in measured_angles:
+            # Calculate the sum of PSDs for the current angle and then convolve
+            sum_curve = np.sum(np.array([psds[angle][speed] for speed in psds[angle]]), axis=0)
+            motor_profiles[angle] = np.convolve(sum_curve / len(psds[angle]), conv_filter, mode='same')
+
+            # Calculate weights
+            angle_energy = (
+                all_angles_energy[angle] ** energy_amplification_factor
+            )  # First weighting factor is based on the total vibrations of the machine at the specified angle
+            curve_area = (
+                np.trapz(motor_profiles[angle], freqs) ** energy_amplification_factor
+            )  # Additional weighting factor is based on the area under the current motor profile at this specified angle
+            total_angle_weight = angle_energy * curve_area
+
+            # Update weighted sum profiles to get the global motor profile
+            weighted_sum_profiles += motor_profiles[angle] * total_angle_weight
+            total_weight += total_angle_weight
+
+        # Creating a global average motor profile that is the weighted average of all the PSD motor profiles
+        global_motor_profile = weighted_sum_profiles / total_weight if total_weight != 0 else weighted_sum_profiles
+
+        return motor_profiles, global_motor_profile
+
+    # Since it was discovered that there is no non-linear mixing in the stepper "steps" vibrations, instead of measuring
+    # the effects of each speeds at each angles, this function simplify it by using only the main motors axes (X/Y for Cartesian
+    # printers and A/B for CoreXY) measurements and project each points on the [0,360] degrees range using trigonometry
+    # to "sum" the vibration impact of each axis at every points of the generated spectrogram. The result is very similar at the end.
+    def _compute_dir_speed_spectrogram(
+        self,
+        measured_speeds: List[float],
+        data: dict,
+        kinematics: str = 'cartesian',
+        measured_angles: Optional[List[int]] = None,
+    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+        if measured_angles is None:
+            measured_angles = [0, 90]
+
+        # We want to project the motor vibrations measured on their own axes on the [0, 360] range
+        spectrum_angles = np.linspace(0, 360, 720)  # One point every 0.5 degrees
+        spectrum_speeds = np.linspace(min(measured_speeds), max(measured_speeds), len(measured_speeds) * 6)
+        spectrum_vibrations = np.zeros((len(spectrum_angles), len(spectrum_speeds)))
+
+        def get_interpolated_vibrations(data: dict, speed: float, speeds: List[float]) -> float:
+            idx = np.clip(np.searchsorted(speeds, speed, side='left'), 1, len(speeds) - 1)
+            lower_speed = speeds[idx - 1]
+            upper_speed = speeds[idx]
+            lower_vibrations = data.get(lower_speed, 0)
+            upper_vibrations = data.get(upper_speed, 0)
+            return lower_vibrations + (speed - lower_speed) * (upper_vibrations - lower_vibrations) / (
+                upper_speed - lower_speed
+            )
 
-    measurements_manager = MeasurementsManager(10)
-    if args[0].endswith('.csv'):
-        measurements_manager.load_from_csvs(args)
-    elif args[0].endswith('.stdata'):
-        measurements_manager.load_from_stdata(args[0])
-    else:
-        raise ValueError('Only .stdata or legacy Klipper raw accelerometer CSV files are supported!')
-
-    fig = vibrations_profile(
-        measurements_manager.get_measurements(),
-        options.klipperdir,
-        options.kinematics,
-        options.accel,
-        options.max_freq,
-    )
-    fig.savefig(options.output, dpi=150)
-
-
-if __name__ == '__main__':
-    main()
+        # Precompute trigonometric values and constant before the loop
+        angle_radians = np.deg2rad(spectrum_angles)
+        cos_vals = np.cos(angle_radians)
+        sin_vals = np.sin(angle_radians)
+        sqrt_2_inv = 1 / math.sqrt(2)
+
+        # Compute the spectrum vibrations for each angle and speed combination
+        for target_angle_idx, (cos_val, sin_val) in enumerate(zip(cos_vals, sin_vals)):
+            for target_speed_idx, target_speed in enumerate(spectrum_speeds):
+                if kinematics in {'cartesian', 'limited_cartesian', 'corexz', 'limited_corexz'}:
+                    speed_1 = np.abs(target_speed * cos_val)
+                    speed_2 = np.abs(target_speed * sin_val)
+                elif kinematics in {'corexy', 'limited_corexy'}:
+                    speed_1 = np.abs(target_speed * (cos_val + sin_val) * sqrt_2_inv)
+                    speed_2 = np.abs(target_speed * (cos_val - sin_val) * sqrt_2_inv)
+
+                vibrations_1 = get_interpolated_vibrations(data[measured_angles[0]], speed_1, measured_speeds)
+                vibrations_2 = get_interpolated_vibrations(data[measured_angles[1]], speed_2, measured_speeds)
+                spectrum_vibrations[target_angle_idx, target_speed_idx] = vibrations_1 + vibrations_2
+
+        return spectrum_angles, spectrum_speeds, spectrum_vibrations
+
+    def _compute_angle_powers(self, spectrogram_data: np.ndarray) -> np.ndarray:
+        angles_powers = np.trapz(spectrogram_data, axis=1)
+
+        # Since we want to plot it on a continuous polar plot later on, we need to append parts of
+        # the array to start and end of it to smooth transitions when doing the convolution
+        # and get the same value at modulo 360. Then we return the array without the extras
+        extended_angles_powers = np.concatenate([angles_powers[-9:], angles_powers, angles_powers[:9]])
+        convolved_extended = np.convolve(extended_angles_powers, np.ones(15) / 15, mode='same')
+
+        return convolved_extended[9:-9]
+
+    def _compute_speed_powers(self, spectrogram_data: np.ndarray, smoothing_window: int = 15) -> np.ndarray:
+        min_values = np.amin(spectrogram_data, axis=0)
+        max_values = np.amax(spectrogram_data, axis=0)
+        var_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
+        conv_filter = np.ones(smoothing_window) / smoothing_window
+        window = int(smoothing_window / 2)
+
+        def pad_and_smooth(data: np.ndarray) -> np.ndarray:
+            data_padded = np.pad(data, (window,), mode='edge')
+            smoothed_data = np.convolve(data_padded, conv_filter, mode='valid')
+            return smoothed_data
+
+        # Stack the arrays and apply padding and smoothing in batch
+        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])
+
+        return smoothed_arrays
+
+    # Function that filter and split the good_speed ranges. The goal is to remove some zones around
+    # additional detected small peaks in order to suppress them if there is a peak, even if it's low,
+    # that's probably due to a crossing in the motor resonance pattern that still need to be removed
+    def _filter_and_split_ranges(
+        self,
+        all_speeds: np.ndarray,
+        good_speeds: List[Tuple[int, int, float]],
+        peak_speed_indices: dict,
+        deletion_range: int,
+    ) -> List[Tuple[int, int, float]]:
+        # Process each range to filter out and split based on peak indices
+        filtered_good_speeds = []
+        for start, end, energy in good_speeds:
+            start_speed, end_speed = all_speeds[start], all_speeds[end]
+            # Identify peaks that intersect with the current speed range
+            intersecting_peaks_indices = [
+                idx for speed, idx in peak_speed_indices.items() if start_speed <= speed <= end_speed
+            ]
+
+            if not intersecting_peaks_indices:
+                filtered_good_speeds.append((start, end, energy))
+            else:
+                intersecting_peaks_indices.sort()
+                current_start = start
+
+                for peak_index in intersecting_peaks_indices:
+                    before_peak_end = max(current_start, peak_index - deletion_range)
+                    if current_start < before_peak_end:
+                        filtered_good_speeds.append((current_start, before_peak_end, energy))
+                    current_start = peak_index + deletion_range + 1
+
+                if current_start < end:
+                    filtered_good_speeds.append((current_start, end, energy))
+
+        # Sorting by start point once and then merge overlapping ranges
+        sorted_ranges = sorted(filtered_good_speeds, key=lambda x: x[0])
+        merged_ranges = [sorted_ranges[0]]
+
+        for current in sorted_ranges[1:]:
+            last_merged_start, last_merged_end, last_merged_energy = merged_ranges[-1]
+            if current[0] <= last_merged_end:
+                new_end = max(last_merged_end, current[1])
+                new_energy = min(last_merged_energy, current[2])
+                merged_ranges[-1] = (last_merged_start, new_end, new_energy)
+            else:
+                merged_ranges.append(current)
+
+        return merged_ranges
+
+    # This function allow the computation of a symmetry score that reflect the spectrogram apparent symmetry between
+    # measured axes on both the shape of the signal and the energy level consistency across both side of the signal
+    def _compute_symmetry_analysis(
+        self, all_angles: np.ndarray, spectrogram_data: np.ndarray, measured_angles: Optional[List[int]] = None
+    ) -> float:
+        if measured_angles is None:
+            measured_angles = [0, 90]
+
+        total_spectrogram_angles = len(all_angles)
+        half_spectrogram_angles = total_spectrogram_angles // 2
+
+        # Extend the spectrogram by adding half to the beginning (in order to not get an out of bounds error later)
+        extended_spectrogram = np.concatenate((spectrogram_data[-half_spectrogram_angles:], spectrogram_data), axis=0)
+
+        # Calculate the split index directly within the slicing
+        midpoint_angle = np.mean(measured_angles)
+        split_index = int(midpoint_angle * (total_spectrogram_angles / 360) + half_spectrogram_angles)
+        half_segment_length = half_spectrogram_angles // 2
+
+        # Slice out the two segments of the spectrogram and flatten them for comparison
+        segment_1_flattened = extended_spectrogram[split_index - half_segment_length : split_index].flatten()
+        segment_2_flattened = extended_spectrogram[split_index : split_index + half_segment_length].flatten()
+
+        # Compute the correlation coefficient between the two segments of spectrogram
+        correlation = np.corrcoef(segment_1_flattened, segment_2_flattened)[0, 1]
+        percentage_correlation_biased = (100 * np.power(correlation, 0.75)) + 10
+
+        return np.clip(0, 100, percentage_correlation_biased)
+
+    # Extract from the measurement name the angle and speed of the tested movement
+    def _extract_angle_and_speed(self, logname: str) -> Tuple[float, float]:
+        try:
+            match = re.search(r'an(\d+)_\d+sp(\d+)_\d+', os.path.basename(logname))
+            if match:
+                angle = match.group(1)
+                speed = match.group(2)
+            else:
+                raise ValueError(
+                    f'File {logname} does not match expected format. Clean your /tmp folder and start again!'
+                )
+        except AttributeError as err:
+            raise ValueError(
+                f'File {logname} does not match expected format. Clean your /tmp folder and start again!'
+            ) from err
+        return float(angle), float(speed)
diff --git a/shaketune/helpers/common_func.py b/shaketune/helpers/common_func.py
index 3e69a45..79feadc 100644
--- a/shaketune/helpers/common_func.py
+++ b/shaketune/helpers/common_func.py
@@ -28,6 +28,7 @@
 ]
 
 
+# TODO: remove this function when the refactoring is finished
 def setup_klipper_import(kdir):
     kdir = os.path.expanduser(kdir)
     sys.path.append(os.path.join(kdir, 'klippy'))
diff --git a/shaketune/shaketune.py b/shaketune/shaketune.py
index 23eaef5..70bcaad 100644
--- a/shaketune/shaketune.py
+++ b/shaketune/shaketune.py
@@ -28,6 +28,7 @@
 DEFAULT_FOLDER = '~/printer_data/config/ShakeTune_results'
 DEFAULT_NUMBER_OF_RESULTS = 10
 DEFAULT_KEEP_RAW_DATA = False
+DEFAULT_MAX_FREQ = 200.0
 DEFAULT_DPI = 150
 DEFAULT_TIMEOUT = 600
 DEFAULT_SHOW_MACROS = True
@@ -75,9 +76,12 @@ def _initialize_config(self, config) -> None:
         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_raw_data = config.getboolean('keep_raw_data', default=DEFAULT_KEEP_RAW_DATA)
+        max_freq = config.getfloat('max_freq', default=DEFAULT_MAX_FREQ, minval=100.0)
         dpi = config.getint('dpi', default=DEFAULT_DPI, minval=100, maxval=500)
         m_chunk_size = config.getint('measurements_chunk_size', default=DEFAULT_MEASUREMENTS_CHUNK_SIZE, minval=2)
-        self._st_config = ShakeTuneConfig(result_folder_path, keep_n_results, keep_raw_data, m_chunk_size, dpi)
+        self._st_config = ShakeTuneConfig(
+            result_folder_path, keep_n_results, keep_raw_data, m_chunk_size, max_freq, dpi
+        )
 
         self.timeout = config.getfloat('timeout', DEFAULT_TIMEOUT, above=0.0)
         self._show_macros = config.getboolean('show_macros_in_webui', default=DEFAULT_SHOW_MACROS)
diff --git a/shaketune/shaketune_config.py b/shaketune/shaketune_config.py
index c3d1885..fad5d2d 100644
--- a/shaketune/shaketune_config.py
+++ b/shaketune/shaketune_config.py
@@ -31,6 +31,7 @@ def __init__(
         keep_n_results: int = 10,
         keep_raw_data: bool = False,
         chunk_size: int = 2,
+        max_freq: float = 200.0,
         dpi: int = 150,
     ) -> None:
         self._result_folder = result_folder
@@ -38,6 +39,8 @@ def __init__(
         self.keep_n_results = keep_n_results
         self.keep_raw_data = keep_raw_data
         self.chunk_size = chunk_size
+        self.max_freq = max_freq
+        self.max_freq_vibrations = max_freq * 5  # 1000Hz is the default (5 * 200.0)
         self.dpi = dpi
 
         self.klipper_folder = KLIPPER_FOLDER