842 lines
34 KiB
Python
842 lines
34 KiB
Python
#!/usr/bin/env python3
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##################################################
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#### DIRECTIONAL VIBRATIONS PLOTTING SCRIPT ######
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##################################################
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# Written by Frix_x#0161 #
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import math
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import optparse
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import os
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import re
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from collections import defaultdict
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from datetime import datetime
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import matplotlib
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import matplotlib.font_manager
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import matplotlib.gridspec
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import matplotlib.pyplot as plt
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import matplotlib.ticker
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import numpy as np
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matplotlib.use('Agg')
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from ..helpers.common_func import (
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compute_mechanical_parameters,
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detect_peaks,
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identify_low_energy_zones,
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parse_log,
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setup_klipper_import,
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)
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from ..helpers.console_output import ConsoleOutput
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PEAKS_DETECTION_THRESHOLD = 0.05
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PEAKS_RELATIVE_HEIGHT_THRESHOLD = 0.04
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CURVE_SIMILARITY_SIGMOID_K = 0.5
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SPEEDS_VALLEY_DETECTION_THRESHOLD = 0.7 # Lower is more sensitive
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SPEEDS_AROUND_PEAK_DELETION = 3 # to delete +-3mm/s around a peak
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ANGLES_VALLEY_DETECTION_THRESHOLD = 1.1 # Lower is more sensitive
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KLIPPAIN_COLORS = {
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'purple': '#70088C',
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'orange': '#FF8D32',
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'dark_purple': '#150140',
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'dark_orange': '#F24130',
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'red_pink': '#F2055C',
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}
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######################################################################
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# Computation
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######################################################################
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# Call to the official Klipper input shaper object to do the PSD computation
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def calc_freq_response(data):
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helper = shaper_calibrate.ShaperCalibrate(printer=None)
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return helper.process_accelerometer_data(data)
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# Calculate motor frequency profiles based on the measured Power Spectral Density (PSD) measurements for the machine kinematics
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# main angles and then create a global motor profile as a weighted average (from their own vibrations) of all calculated profiles
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def compute_motor_profiles(freqs, psds, all_angles_energy, measured_angles=None, energy_amplification_factor=2):
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if measured_angles is None:
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measured_angles = [0, 90]
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motor_profiles = {}
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weighted_sum_profiles = np.zeros_like(freqs)
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total_weight = 0
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conv_filter = np.ones(20) / 20
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# Creating the PSD motor profiles for each angles
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for angle in measured_angles:
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# Calculate the sum of PSDs for the current angle and then convolve
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sum_curve = np.sum(np.array([psds[angle][speed] for speed in psds[angle]]), axis=0)
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motor_profiles[angle] = np.convolve(sum_curve / len(psds[angle]), conv_filter, mode='same')
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# Calculate weights
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angle_energy = (
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all_angles_energy[angle] ** energy_amplification_factor
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) # First weighting factor is based on the total vibrations of the machine at the specified angle
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curve_area = (
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np.trapz(motor_profiles[angle], freqs) ** energy_amplification_factor
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) # Additional weighting factor is based on the area under the current motor profile at this specified angle
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total_angle_weight = angle_energy * curve_area
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# Update weighted sum profiles to get the global motor profile
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weighted_sum_profiles += motor_profiles[angle] * total_angle_weight
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total_weight += total_angle_weight
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# Creating a global average motor profile that is the weighted average of all the PSD motor profiles
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global_motor_profile = weighted_sum_profiles / total_weight if total_weight != 0 else weighted_sum_profiles
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return motor_profiles, global_motor_profile
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# Since it was discovered that there is no non-linear mixing in the stepper "steps" vibrations, instead of measuring
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# the effects of each speeds at each angles, this function simplify it by using only the main motors axes (X/Y for Cartesian
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# printers and A/B for CoreXY) measurements and project each points on the [0,360] degrees range using trigonometry
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# to "sum" the vibration impact of each axis at every points of the generated spectrogram. The result is very similar at the end.
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def compute_dir_speed_spectrogram(measured_speeds, data, kinematics='cartesian', measured_angles=None):
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if measured_angles is None:
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measured_angles = [0, 90]
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# We want to project the motor vibrations measured on their own axes on the [0, 360] range
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spectrum_angles = np.linspace(0, 360, 720) # One point every 0.5 degrees
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spectrum_speeds = np.linspace(min(measured_speeds), max(measured_speeds), len(measured_speeds) * 6)
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spectrum_vibrations = np.zeros((len(spectrum_angles), len(spectrum_speeds)))
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def get_interpolated_vibrations(data, speed, speeds):
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idx = np.clip(np.searchsorted(speeds, speed, side='left'), 1, len(speeds) - 1)
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lower_speed = speeds[idx - 1]
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upper_speed = speeds[idx]
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lower_vibrations = data.get(lower_speed, 0)
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upper_vibrations = data.get(upper_speed, 0)
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return lower_vibrations + (speed - lower_speed) * (upper_vibrations - lower_vibrations) / (
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upper_speed - lower_speed
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)
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# Precompute trigonometric values and constant before the loop
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angle_radians = np.deg2rad(spectrum_angles)
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cos_vals = np.cos(angle_radians)
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sin_vals = np.sin(angle_radians)
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sqrt_2_inv = 1 / math.sqrt(2)
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# Compute the spectrum vibrations for each angle and speed combination
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for target_angle_idx, (cos_val, sin_val) in enumerate(zip(cos_vals, sin_vals)):
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for target_speed_idx, target_speed in enumerate(spectrum_speeds):
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if kinematics == 'cartesian':
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speed_1 = np.abs(target_speed * cos_val)
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speed_2 = np.abs(target_speed * sin_val)
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elif kinematics == 'corexy':
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speed_1 = np.abs(target_speed * (cos_val + sin_val) * sqrt_2_inv)
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speed_2 = np.abs(target_speed * (cos_val - sin_val) * sqrt_2_inv)
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vibrations_1 = get_interpolated_vibrations(data[measured_angles[0]], speed_1, measured_speeds)
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vibrations_2 = get_interpolated_vibrations(data[measured_angles[1]], speed_2, measured_speeds)
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spectrum_vibrations[target_angle_idx, target_speed_idx] = vibrations_1 + vibrations_2
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return spectrum_angles, spectrum_speeds, spectrum_vibrations
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def compute_angle_powers(spectrogram_data):
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angles_powers = np.trapz(spectrogram_data, axis=1)
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# Since we want to plot it on a continuous polar plot later on, we need to append parts of
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# the array to start and end of it to smooth transitions when doing the convolution
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# and get the same value at modulo 360. Then we return the array without the extras
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extended_angles_powers = np.concatenate([angles_powers[-9:], angles_powers, angles_powers[:9]])
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convolved_extended = np.convolve(extended_angles_powers, np.ones(15) / 15, mode='same')
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return convolved_extended[9:-9]
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def compute_speed_powers(spectrogram_data, smoothing_window=15):
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min_values = np.amin(spectrogram_data, axis=0)
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max_values = np.amax(spectrogram_data, axis=0)
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var_values = np.var(spectrogram_data, axis=0)
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# rescale the variance to the same range as max_values to plot it on the same graph
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var_values = var_values / var_values.max() * max_values.max()
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# Create a vibration metric that is the product of the max values and the variance to quantify the best
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# speeds that have at the same time a low global energy level that is also consistent at every angles
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vibration_metric = max_values * var_values
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# utility function to pad and smooth the data avoiding edge effects
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conv_filter = np.ones(smoothing_window) / smoothing_window
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window = int(smoothing_window / 2)
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def pad_and_smooth(data):
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data_padded = np.pad(data, (window,), mode='edge')
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smoothed_data = np.convolve(data_padded, conv_filter, mode='valid')
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return smoothed_data
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# Stack the arrays and apply padding and smoothing in batch
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data_arrays = np.stack([min_values, max_values, var_values, vibration_metric])
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smoothed_arrays = np.array([pad_and_smooth(data) for data in data_arrays])
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return smoothed_arrays
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# Function that filter and split the good_speed ranges. The goal is to remove some zones around
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# additional detected small peaks in order to suppress them if there is a peak, even if it's low,
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# that's probably due to a crossing in the motor resonance pattern that still need to be removed
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def filter_and_split_ranges(all_speeds, good_speeds, peak_speed_indices, deletion_range):
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# Process each range to filter out and split based on peak indices
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filtered_good_speeds = []
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for start, end, energy in good_speeds:
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start_speed, end_speed = all_speeds[start], all_speeds[end]
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# Identify peaks that intersect with the current speed range
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intersecting_peaks_indices = [
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idx for speed, idx in peak_speed_indices.items() if start_speed <= speed <= end_speed
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]
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if not intersecting_peaks_indices:
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filtered_good_speeds.append((start, end, energy))
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else:
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intersecting_peaks_indices.sort()
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current_start = start
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for peak_index in intersecting_peaks_indices:
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before_peak_end = max(current_start, peak_index - deletion_range)
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if current_start < before_peak_end:
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filtered_good_speeds.append((current_start, before_peak_end, energy))
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current_start = peak_index + deletion_range + 1
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if current_start < end:
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filtered_good_speeds.append((current_start, end, energy))
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# Sorting by start point once and then merge overlapping ranges
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sorted_ranges = sorted(filtered_good_speeds, key=lambda x: x[0])
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merged_ranges = [sorted_ranges[0]]
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for current in sorted_ranges[1:]:
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last_merged_start, last_merged_end, last_merged_energy = merged_ranges[-1]
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if current[0] <= last_merged_end:
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new_end = max(last_merged_end, current[1])
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new_energy = min(last_merged_energy, current[2])
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merged_ranges[-1] = (last_merged_start, new_end, new_energy)
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else:
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merged_ranges.append(current)
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return merged_ranges
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# This function allow the computation of a symmetry score that reflect the spectrogram apparent symmetry between
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# measured axes on both the shape of the signal and the energy level consistency across both side of the signal
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def compute_symmetry_analysis(all_angles, spectrogram_data, measured_angles=None):
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if measured_angles is None:
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measured_angles = [0, 90]
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total_spectrogram_angles = len(all_angles)
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half_spectrogram_angles = total_spectrogram_angles // 2
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# Extend the spectrogram by adding half to the beginning (in order to not get an out of bounds error later)
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extended_spectrogram = np.concatenate((spectrogram_data[-half_spectrogram_angles:], spectrogram_data), axis=0)
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# Calculate the split index directly within the slicing
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midpoint_angle = np.mean(measured_angles)
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split_index = int(midpoint_angle * (total_spectrogram_angles / 360) + half_spectrogram_angles)
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half_segment_length = half_spectrogram_angles // 2
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# Slice out the two segments of the spectrogram and flatten them for comparison
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segment_1_flattened = extended_spectrogram[split_index - half_segment_length : split_index].flatten()
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segment_2_flattened = extended_spectrogram[split_index : split_index + half_segment_length].flatten()
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# Compute the correlation coefficient between the two segments of spectrogram
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correlation = np.corrcoef(segment_1_flattened, segment_2_flattened)[0, 1]
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percentage_correlation_biased = (100 * np.power(correlation, 0.75)) + 10
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return np.clip(0, 100, percentage_correlation_biased)
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######################################################################
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# Graphing
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######################################################################
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def plot_angle_profile_polar(ax, angles, angles_powers, low_energy_zones, symmetry_factor):
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angles_radians = np.deg2rad(angles)
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ax.set_title('Polar angle energy profile', fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
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ax.set_theta_zero_location('E')
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ax.set_theta_direction(1)
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ax.plot(angles_radians, angles_powers, color=KLIPPAIN_COLORS['purple'], zorder=5)
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ax.fill(angles_radians, angles_powers, color=KLIPPAIN_COLORS['purple'], alpha=0.3)
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ax.set_xlim([0, np.deg2rad(360)])
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ymax = angles_powers.max() * 1.05
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ax.set_ylim([0, ymax])
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ax.set_thetagrids([theta * 15 for theta in range(360 // 15)])
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ax.text(
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0,
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0,
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f'Symmetry: {symmetry_factor:.1f}%',
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ha='center',
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va='center',
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color=KLIPPAIN_COLORS['red_pink'],
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fontsize=12,
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fontweight='bold',
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zorder=6,
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)
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for _, (start, end, _) in enumerate(low_energy_zones):
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ax.axvline(
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angles_radians[start],
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angles_powers[start] / ymax,
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color=KLIPPAIN_COLORS['red_pink'],
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linestyle='dotted',
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linewidth=1.5,
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)
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ax.axvline(
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angles_radians[end],
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angles_powers[end] / ymax,
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color=KLIPPAIN_COLORS['red_pink'],
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linestyle='dotted',
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linewidth=1.5,
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)
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ax.fill_between(
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angles_radians[start:end], angles_powers[start:end], angles_powers.max() * 1.05, color='green', alpha=0.2
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)
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ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
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ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
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ax.grid(which='major', color='grey')
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ax.grid(which='minor', color='lightgrey')
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# Polar plot doesn't follow the gridspec margin, so we adjust it manually here
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pos = ax.get_position()
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new_pos = [pos.x0 - 0.01, pos.y0 - 0.01, pos.width, pos.height]
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ax.set_position(new_pos)
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return
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def plot_global_speed_profile(
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ax,
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all_speeds,
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sp_min_energy,
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sp_max_energy,
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sp_variance_energy,
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vibration_metric,
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num_peaks,
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peaks,
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low_energy_zones,
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):
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ax.set_title('Global speed energy profile', fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
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ax.set_xlabel('Speed (mm/s)')
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ax.set_ylabel('Energy')
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ax2 = ax.twinx()
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ax2.yaxis.set_visible(False)
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ax.plot(all_speeds, sp_min_energy, label='Minimum', color=KLIPPAIN_COLORS['dark_purple'], zorder=5)
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ax.plot(all_speeds, sp_max_energy, label='Maximum', color=KLIPPAIN_COLORS['purple'], zorder=5)
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ax.plot(all_speeds, sp_variance_energy, label='Variance', color=KLIPPAIN_COLORS['orange'], zorder=5, linestyle='--')
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ax2.plot(
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all_speeds,
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vibration_metric,
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label=f'Vibration metric ({num_peaks} bad peaks)',
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color=KLIPPAIN_COLORS['red_pink'],
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zorder=5,
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)
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ax.set_xlim([all_speeds.min(), all_speeds.max()])
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ax.set_ylim([0, sp_max_energy.max() * 1.15])
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y2min = -(vibration_metric.max() * 0.025)
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y2max = vibration_metric.max() * 1.07
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ax2.set_ylim([y2min, y2max])
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if peaks is not None and len(peaks) > 0:
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ax2.plot(all_speeds[peaks], vibration_metric[peaks], 'x', color='black', markersize=8, zorder=10)
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for idx, peak in enumerate(peaks):
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ax2.annotate(
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f'{idx+1}',
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(all_speeds[peak], vibration_metric[peak]),
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textcoords='offset points',
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xytext=(5, 5),
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fontweight='bold',
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ha='left',
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fontsize=13,
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color=KLIPPAIN_COLORS['red_pink'],
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zorder=10,
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)
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for idx, (start, end, _) in enumerate(low_energy_zones):
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# ax2.axvline(all_speeds[start], color=KLIPPAIN_COLORS['red_pink'], linestyle='dotted', linewidth=1.5, zorder=8)
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# ax2.axvline(all_speeds[end], color=KLIPPAIN_COLORS['red_pink'], linestyle='dotted', linewidth=1.5, zorder=8)
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ax2.fill_between(
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all_speeds[start:end],
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y2min,
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vibration_metric[start:end],
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color='green',
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alpha=0.2,
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label=f'Zone {idx+1}: {all_speeds[start]:.1f} to {all_speeds[end]:.1f} mm/s',
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)
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ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
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ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
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ax.grid(which='major', color='grey')
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ax.grid(which='minor', color='lightgrey')
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fontP = matplotlib.font_manager.FontProperties()
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fontP.set_size('small')
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ax.legend(loc='upper left', prop=fontP)
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ax2.legend(loc='upper right', prop=fontP)
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return
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def plot_angular_speed_profiles(ax, speeds, angles, spectrogram_data, kinematics='cartesian'):
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ax.set_title('Angular speed energy profiles', fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
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ax.set_xlabel('Speed (mm/s)')
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ax.set_ylabel('Energy')
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# Define mappings for labels and colors to simplify plotting commands
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angle_settings = {
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0: ('X (0 deg)', 'purple', 10),
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90: ('Y (90 deg)', 'dark_purple', 5),
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45: ('A (45 deg)' if kinematics == 'corexy' else '45 deg', 'orange', 10),
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135: ('B (135 deg)' if kinematics == 'corexy' else '135 deg', 'dark_orange', 5),
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}
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# Plot each angle using settings from the dictionary
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for angle, (label, color, zorder) in angle_settings.items():
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idx = np.searchsorted(angles, angle, side='left')
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ax.plot(speeds, spectrogram_data[idx], label=label, color=KLIPPAIN_COLORS[color], zorder=zorder)
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ax.set_xlim([speeds.min(), speeds.max()])
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max_value = max(spectrogram_data[angle].max() for angle in [0, 45, 90, 135])
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ax.set_ylim([0, max_value * 1.1])
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ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
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ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
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ax.grid(which='major', color='grey')
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ax.grid(which='minor', color='lightgrey')
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fontP = matplotlib.font_manager.FontProperties()
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fontP.set_size('small')
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ax.legend(loc='upper right', prop=fontP)
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return
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def plot_motor_profiles(ax, freqs, main_angles, motor_profiles, global_motor_profile, max_freq):
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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(
|
|
'Motors have a main resonant frequency at %.1fHz with an estimated damping ratio of %.3f'
|
|
% (motor_fr, motor_zeta)
|
|
)
|
|
else:
|
|
ConsoleOutput.print(
|
|
'Motors have a main resonant frequency at %.1fHz but it was impossible to estimate a damping ratio.'
|
|
% (motor_fr)
|
|
)
|
|
|
|
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='Motor resonant frequency (ω0): %.1fHz' % (motor_fr))
|
|
if motor_zeta is not None:
|
|
ax2.plot([], [], ' ', label='Motor damping ratio (ζ): %.3f' % (motor_zeta))
|
|
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, angles, speeds, spectrogram_data):
|
|
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, angles, speeds, spectrogram_data, peaks):
|
|
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',
|
|
)
|
|
|
|
return
|
|
|
|
|
|
def plot_motor_config_txt(fig, motors, differences):
|
|
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('| TMC Autotune enabled')
|
|
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']
|
|
)
|
|
|
|
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'],
|
|
)
|
|
|
|
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'],
|
|
)
|
|
|
|
|
|
######################################################################
|
|
# Startup and main routines
|
|
######################################################################
|
|
|
|
|
|
def extract_angle_and_speed(logname):
|
|
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)
|
|
|
|
|
|
def vibrations_profile(
|
|
lognames, klipperdir='~/klipper', kinematics='cartesian', accel=None, max_freq=1000.0, st_version=None, motors=None
|
|
):
|
|
global shaper_calibrate
|
|
shaper_calibrate = setup_klipper_import(klipperdir)
|
|
|
|
if kinematics == 'cartesian':
|
|
main_angles = [0, 90]
|
|
elif kinematics == 'corexy':
|
|
main_angles = [45, 135]
|
|
else:
|
|
raise ValueError('Only Cartesian and CoreXY kinematics are supported by this tool at the moment!')
|
|
|
|
psds = defaultdict(lambda: defaultdict(list))
|
|
psds_sum = defaultdict(lambda: defaultdict(list))
|
|
target_freqs_initialized = False
|
|
|
|
for logname in lognames:
|
|
data = parse_log(logname)
|
|
if data is None:
|
|
continue # File is not in the expected format, skip it
|
|
angle, speed = extract_angle_and_speed(logname)
|
|
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]
|
|
|
|
# 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(
|
|
'Vibrations peaks detected: %d @ %s mm/s (avoid setting a speed near these values in your slicer print profile)'
|
|
% (num_peaks, ', '.join(map(str, formated_peaks_speeds)))
|
|
)
|
|
|
|
good_speeds = identify_low_energy_zones(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)'
|
|
)
|
|
|
|
# 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 = (lognames[0].split('/')[-1]).split('_')
|
|
dt = datetime.strptime(f"{filename_parts[1]} {filename_parts[2].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('Warning: CSV filenames appear to be different than expected (%s)' % (lognames[0]))
|
|
title_line2 = lognames[0].split('/')[-1]
|
|
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 == '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 CSV file(s) 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', 'corexy']:
|
|
opts.error('Only cartesian and corexy kinematics are supported by this tool at the moment!')
|
|
|
|
fig = vibrations_profile(args, options.klipperdir, options.kinematics, options.accel, options.max_freq)
|
|
fig.savefig(options.output, dpi=150)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|