changed repo architecture to decouple python and Klipper macros
This commit is contained in:
Félix Boisselier
603
src/graph_vibrations.py
Executable file
603
src/graph_vibrations.py
Executable file
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#!/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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# Be sure to make this script executable using SSH: type 'chmod +x ./graph_dir_vibrations.py' when in the folder !
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#####################################################################
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################ !!! DO NOT EDIT BELOW THIS LINE !!! ################
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#####################################################################
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import math
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import optparse, matplotlib, re, os
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from datetime import datetime
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from collections import defaultdict
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.font_manager, matplotlib.ticker, matplotlib.gridspec
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matplotlib.use('Agg')
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from locale_utils import set_locale, print_with_c_locale
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from common_func import get_git_version, parse_log, setup_klipper_import, identify_low_energy_zones, compute_curve_similarity_factor, compute_mechanical_parameters, detect_peaks
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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=[0, 90], energy_amplification_factor=2):
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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 = all_angles_energy[angle] ** energy_amplification_factor # First weighting factor is based on the total vibrations of the machine at the specified angle
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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
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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=[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) / (upper_speed - lower_speed)
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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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# 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=[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(0, 0, f'Symmetry: {symmetry_factor:.1f}%', ha='center', va='center', color=KLIPPAIN_COLORS['red_pink'], fontsize=12, fontweight='bold', zorder=6)
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for _, (start, end, _) in enumerate(low_energy_zones):
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ax.axvline(angles_radians[start], angles_powers[start]/ymax, color=KLIPPAIN_COLORS['red_pink'], linestyle='dotted', linewidth=1.5)
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ax.axvline(angles_radians[end], angles_powers[end]/ymax, color=KLIPPAIN_COLORS['red_pink'], linestyle='dotted', linewidth=1.5)
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ax.fill_between(angles_radians[start:end], angles_powers[start:end], angles_powers.max() * 1.05, color='green', alpha=0.2)
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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(ax, all_speeds, sp_min_energy, sp_max_energy, sp_variance_energy, vibration_metric, num_peaks, peaks, low_energy_zones):
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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(all_speeds, vibration_metric, label=f'Vibration metric ({num_peaks} bad peaks)', color=KLIPPAIN_COLORS['red_pink'], zorder=5)
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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:
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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(f"{idx+1}", (all_speeds[peak], vibration_metric[peak]),
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textcoords="offset points", xytext=(5, 5), fontweight='bold',
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ha='left', fontsize=13, color=KLIPPAIN_COLORS['red_pink'], zorder=10)
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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(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')
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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')
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ax.set_ylabel('Energy')
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ax.set_xlabel('Frequency (Hz)')
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ax2 = ax.twinx()
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ax2.yaxis.set_visible(False)
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# Global weighted average motor profile
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ax.plot(freqs, global_motor_profile, label="Combined", color=KLIPPAIN_COLORS['purple'], zorder=5)
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max_value = global_motor_profile.max()
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# Mapping of angles to axis names
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angle_settings = {
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0: "X",
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90: "Y",
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45: "A",
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135: "B"
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}
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# And then plot the motor profiles at each measured angles
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for angle in main_angles:
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profile_max = motor_profiles[angle].max()
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if profile_max > max_value:
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max_value = profile_max
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label = f"{angle_settings[angle]} ({angle} deg)" if angle in angle_settings else f"{angle} deg"
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ax.plot(freqs, motor_profiles[angle], linestyle='--', label=label, zorder=2)
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ax.set_xlim([0, max_freq])
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ax.set_ylim([0, max_value * 1.1])
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ax.ticklabel_format(axis='y', style='scientific', scilimits=(0,0))
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# Then add the motor resonance peak to the graph and print some infos about it
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motor_fr, motor_zeta, motor_res_idx, lowfreq_max = compute_mechanical_parameters(global_motor_profile, freqs, 30)
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if lowfreq_max:
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print_with_c_locale("[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!")
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print_with_c_locale("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")
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if motor_zeta is not None:
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print_with_c_locale("Motors have a main resonant frequency at %.1fHz with an estimated damping ratio of %.3f" % (motor_fr, motor_zeta))
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else:
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print_with_c_locale("Motors have a main resonant frequency at %.1fHz but it was impossible to estimate a damping ratio." % (motor_fr))
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ax.plot(freqs[motor_res_idx], global_motor_profile[motor_res_idx], "x", color='black', markersize=10)
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ax.annotate(f"R", (freqs[motor_res_idx], global_motor_profile[motor_res_idx]),
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textcoords="offset points", xytext=(15, 5),
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ha='right', fontsize=14, color=KLIPPAIN_COLORS['red_pink'], weight='bold')
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ax2.plot([], [], ' ', label="Motor resonant frequency (ω0): %.1fHz" % (motor_fr))
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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:
|
||||
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
|
||||
|
||||
|
||||
######################################################################
|
||||
# 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)
|
||||
except AttributeError:
|
||||
raise ValueError(f"File {logname} does not match expected format.")
|
||||
return float(angle), float(speed)
|
||||
|
||||
|
||||
def vibrations_profile(lognames, klipperdir="~/klipper", kinematics="cartesian", accel=None, max_freq=1000.):
|
||||
set_locale()
|
||||
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)
|
||||
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)
|
||||
print_with_c_locale(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]
|
||||
print_with_c_locale("Vibrations peaks detected: %d @ %s mm/s (avoid setting a speed near these values in your slicer print profile)" % (num_peaks, ", ".join(map(str, formated_peaks_speeds))))
|
||||
|
||||
good_speeds = identify_low_energy_zones(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)}
|
||||
|
||||
filtered_good_speeds = []
|
||||
for start, end, energy in good_speeds:
|
||||
# Check for peaks within the current good speed range
|
||||
start_speed, end_speed = all_speeds[start], all_speeds[end]
|
||||
intersecting_peaks_indices = [idx for speed, idx in peak_speed_indices.items() if start_speed <= speed <= end_speed]
|
||||
|
||||
# If no peaks intersect any good_speed range, add it as is, else iterate through intersecting peaks to split the range
|
||||
if not intersecting_peaks_indices: filtered_good_speeds.append((start, end, energy))
|
||||
else:
|
||||
for peak_index in intersecting_peaks_indices:
|
||||
before_peak_end = max(start, peak_index - deletion_range)
|
||||
after_peak_start = min(end, peak_index + deletion_range)
|
||||
if start < before_peak_end:
|
||||
filtered_good_speeds.append((start, before_peak_end, energy))
|
||||
if after_peak_start < end:
|
||||
filtered_good_speeds.append((after_peak_start, end, energy))
|
||||
|
||||
good_speeds = filtered_good_speeds
|
||||
print_with_c_locale(f'Lowest vibrations speeds ({len(good_speeds)} ranges sorted from best to worse):')
|
||||
for idx, (start, end, energy) in enumerate(good_speeds):
|
||||
print_with_c_locale(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:
|
||||
print_with_c_locale(f'Lowest vibrations angles ({len(good_angles)} ranges sorted from best to worse):')
|
||||
for idx, (start, end, energy) in enumerate(good_angles):
|
||||
print_with_c_locale(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:
|
||||
print_with_c_locale("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'])
|
||||
|
||||
# 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
|
||||
st_version = get_git_version()
|
||||
if st_version is not None:
|
||||
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.,
|
||||
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()
|
||||
Reference in New Issue
Block a user