467 lines
21 KiB
Python
Executable File
467 lines
21 KiB
Python
Executable File
#!/usr/bin/env python3
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##################################################
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###### SPEED AND 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_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 optparse, matplotlib, re, os, operator
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from datetime import datetime
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from collections import OrderedDict
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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 compute_mechanical_parameters, detect_peaks, get_git_version, parse_log, setup_klipper_import
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PEAKS_DETECTION_THRESHOLD = 0.05
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PEAKS_RELATIVE_HEIGHT_THRESHOLD = 0.04
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VALLEY_DETECTION_THRESHOLD = 0.1 # 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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def compute_vibration_spectrogram(datas, group, max_freq):
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psd_list = []
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first_freqs = None
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signal_axes = ['x', 'y', 'z', 'all']
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for i in range(0, len(datas), group):
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# Round up to the nearest power of 2 for faster FFT
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N = datas[i].shape[0]
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T = datas[i][-1,0] - datas[i][0,0]
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M = 1 << int((N/T) * 0.5 - 1).bit_length()
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if N <= M:
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# If there is not enough lines in the array to be able to round up to the
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# nearest power of 2, we need to pad some zeros at the end of the array to
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# avoid entering a blocking state from Klipper shaper_calibrate.py
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datas[i] = np.pad(datas[i], [(0, (M-N)+1), (0, 0)], mode='constant', constant_values=0)
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freqrsp = calc_freq_response(datas[i])
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for n in range(group - 1):
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data = datas[i + n + 1]
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# Round up to the nearest power of 2 for faster FFT
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N = data.shape[0]
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T = data[-1,0] - data[0,0]
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M = 1 << int((N/T) * 0.5 - 1).bit_length()
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if N <= M:
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# If there is not enough lines in the array to be able to round up to the
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# nearest power of 2, we need to pad some zeros at the end of the array to
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# avoid entering a blocking state from Klipper shaper_calibrate.py
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data = np.pad(data, [(0, (M-N)+1), (0, 0)], mode='constant', constant_values=0)
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freqrsp.add_data(calc_freq_response(data))
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if not psd_list:
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# First group, just put it in the result list
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first_freqs = freqrsp.freq_bins
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psd = freqrsp.psd_sum[first_freqs <= max_freq]
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px = freqrsp.psd_x[first_freqs <= max_freq]
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py = freqrsp.psd_y[first_freqs <= max_freq]
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pz = freqrsp.psd_z[first_freqs <= max_freq]
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psd_list.append([psd, px, py, pz])
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else:
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# Not the first group, we need to interpolate every new signals
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# to the first one to equalize the frequency_bins between them
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signal_normalized = dict()
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freqs = freqrsp.freq_bins
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for axe in signal_axes:
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signal = freqrsp.get_psd(axe)
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signal_normalized[axe] = np.interp(first_freqs, freqs, signal)
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# Remove data above max_freq on all axes and add to the result list
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psd = signal_normalized['all'][first_freqs <= max_freq]
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px = signal_normalized['x'][first_freqs <= max_freq]
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py = signal_normalized['y'][first_freqs <= max_freq]
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pz = signal_normalized['z'][first_freqs <= max_freq]
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psd_list.append([psd, px, py, pz])
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return np.array(first_freqs[first_freqs <= max_freq]), np.array(psd_list)
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def compute_speed_profile(speeds, freqs, psd_list):
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# Preallocate arrays as psd_list is known and consistent
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pwrtot_sum = np.zeros(len(psd_list))
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pwrtot_x = np.zeros(len(psd_list))
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pwrtot_y = np.zeros(len(psd_list))
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pwrtot_z = np.zeros(len(psd_list))
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for i, psd in enumerate(psd_list):
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pwrtot_sum[i] = np.trapz(psd[0], freqs)
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pwrtot_x[i] = np.trapz(psd[1], freqs)
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pwrtot_y[i] = np.trapz(psd[2], freqs)
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pwrtot_z[i] = np.trapz(psd[3], freqs)
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# Resample the signals to get a better detection of the valleys of low energy
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# and avoid getting limited by the speed increment defined by the user
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resampled_speeds, resampled_power_sum = resample_signal(speeds, pwrtot_sum)
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_, resampled_pwrtot_x = resample_signal(speeds, pwrtot_x)
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_, resampled_pwrtot_y = resample_signal(speeds, pwrtot_y)
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_, resampled_pwrtot_z = resample_signal(speeds, pwrtot_z)
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return resampled_speeds, [resampled_power_sum, resampled_pwrtot_x, resampled_pwrtot_y, resampled_pwrtot_z]
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def compute_motor_profile(power_spectral_densities):
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# Sum the PSD across all speeds for each frequency of the spectrogram. Basically this
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# is equivalent to sum up all the spectrogram column by column to plot the total on the right
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motor_total_vibration = np.sum([psd[0] for psd in power_spectral_densities], axis=0)
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# Then a very little smoothing of the signal is applied to avoid too much noise and sharp peaks on it and simplify
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# the resonance frequency and damping ratio estimation later on. Also, too much smoothing is bad and would alter the results
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smoothed_motor_total_vibration = np.convolve(motor_total_vibration, np.ones(10)/10, mode='same')
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return smoothed_motor_total_vibration
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# The goal is to find zone outside of peaks (flat low energy zones) to advise them as good speeds range to use in the slicer
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def identify_low_energy_zones(power_total):
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valleys = []
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# Calculate the mean and standard deviation of the entire power_total
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mean_energy = np.mean(power_total)
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std_energy = np.std(power_total)
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# Define a threshold value as mean minus a certain number of standard deviations
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threshold_value = mean_energy - VALLEY_DETECTION_THRESHOLD * std_energy
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# Find valleys in power_total based on the threshold
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in_valley = False
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start_idx = 0
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for i, value in enumerate(power_total):
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if not in_valley and value < threshold_value:
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in_valley = True
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start_idx = i
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elif in_valley and value >= threshold_value:
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in_valley = False
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valleys.append((start_idx, i))
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# If the last point is still in a valley, close the valley
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if in_valley:
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valleys.append((start_idx, len(power_total) - 1))
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max_signal = np.max(power_total)
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# Calculate mean energy for each valley as a percentage of the maximum of the signal
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valley_means_percentage = []
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for start, end in valleys:
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if not np.isnan(np.mean(power_total[start:end])):
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valley_means_percentage.append((start, end, (np.mean(power_total[start:end]) / max_signal) * 100))
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# Sort valleys based on mean percentage values
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sorted_valleys = sorted(valley_means_percentage, key=lambda x: x[2])
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return sorted_valleys
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# Resample the signal to achieve denser data points in order to get more precise valley placing and
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# avoid having to use the original sampling of the signal (that is equal to the speed increment used for the test)
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def resample_signal(speeds, power_total, new_spacing=0.1):
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new_speeds = np.arange(speeds[0], speeds[-1] + new_spacing, new_spacing)
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new_power_total = np.interp(new_speeds, speeds, power_total)
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return np.array(new_speeds), np.array(new_power_total)
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######################################################################
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# Graphing
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######################################################################
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def plot_speed_profile(ax, speeds, power_total, num_peaks, peaks, low_energy_zones):
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# For this function, we have two array for the speeds. Indeed, since the power total sum was resampled to better detect
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# the valleys of low energy later on, we also need the resampled speed array to plot it. For the rest
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ax.set_title("Machine speed 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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max_y = power_total[0].max() + power_total[0].max() * 0.05
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ax.set_xlim([speeds.min(), speeds.max()])
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ax.set_ylim([0, max_y])
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ax2.set_ylim([0, max_y])
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ax.plot(speeds, power_total[0], label="X+Y+Z", color='purple', zorder=5)
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ax.plot(speeds, power_total[1], label="X", color='red')
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ax.plot(speeds, power_total[2], label="Y", color='green')
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ax.plot(speeds, power_total[3], label="Z", color='blue')
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if peaks.size:
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ax.plot(speeds[peaks], power_total[0][peaks], "x", color='black', markersize=8)
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for idx, peak in enumerate(peaks):
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fontcolor = 'red'
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fontweight = 'bold'
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ax.annotate(f"{idx+1}", (speeds[peak], power_total[0][peak]),
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textcoords="offset points", xytext=(8, 5),
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ha='left', fontsize=13, color=fontcolor, weight=fontweight)
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ax2.plot([], [], ' ', label=f'Number of peaks: {num_peaks}')
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else:
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ax2.plot([], [], ' ', label=f'No peaks detected')
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for idx, (start, end, energy) in enumerate(low_energy_zones):
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ax.axvline(speeds[start], color='red', linestyle='dotted', linewidth=1.5)
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ax.axvline(speeds[end], color='red', linestyle='dotted', linewidth=1.5)
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ax2.fill_between(speeds[start:end], 0, power_total[0][start:end], color='green', alpha=0.2, label=f'Zone {idx+1}: {speeds[start]:.1f} to {speeds[end]:.1f} mm/s (mean energy: {energy:.2f}%)')
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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_vibration_spectrogram(ax, speeds, freqs, power_spectral_densities, peaks, fr, max_freq):
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# Prepare the spectrum data
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spectrum = np.empty([len(freqs), len(speeds)])
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for i in range(len(speeds)):
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for j in range(len(freqs)):
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spectrum[j, i] = power_spectral_densities[i][0][j]
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ax.set_title("Vibrations spectrogram", fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
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# ax.pcolormesh(speeds, freqs, spectrum, norm=matplotlib.colors.LogNorm(),
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# cmap='inferno', shading='gouraud')
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ax.imshow(spectrum, norm=matplotlib.colors.LogNorm(), cmap='inferno',
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aspect='auto', extent=[speeds[0], speeds[-1], freqs[0], freqs[-1]],
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origin='lower', interpolation='antialiased')
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# Add peaks lines in the spectrogram to get hint from peaks found in the first graph
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if peaks is not None:
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for idx, peak in enumerate(peaks):
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ax.axvline(peak, color='cyan', linestyle='dotted', linewidth=0.75)
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ax.annotate(f"Peak {idx+1}", (peak, freqs[-1]*0.9),
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textcoords="data", color='cyan', rotation=90, fontsize=10,
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verticalalignment='top', horizontalalignment='right')
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# Add motor resonance line
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if fr is not None and fr > 25:
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ax.axhline(fr, color='cyan', linestyle='dotted', linewidth=1)
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ax.annotate(f"Motor resonance", (speeds[-1]*0.95, fr+2),
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textcoords="data", color='cyan', fontsize=10,
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verticalalignment='bottom', horizontalalignment='right')
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ax.set_ylim([0., max_freq])
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ax.set_ylabel('Frequency (hz)')
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ax.set_xlabel('Speed (mm/s)')
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return
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def plot_motor_profile(ax, freqs, motor_vibration_power, motor_fr, motor_zeta, motor_max_power_index):
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ax.set_title("Motors frequency profile", fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
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ax.set_xlabel('Energy')
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ax.set_ylabel('Frequency (hz)')
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ax2 = ax.twinx()
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ax2.yaxis.set_visible(False)
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ax.set_ylim([freqs.min(), freqs.max()])
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ax.set_xlim([0, motor_vibration_power.max() + motor_vibration_power.max() * 0.1])
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# Plot the profile curve
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ax.plot(motor_vibration_power, freqs, color=KLIPPAIN_COLORS['orange'])
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# Tag the resonance peak
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ax.plot(motor_vibration_power[motor_max_power_index], freqs[motor_max_power_index], "x", color='black', markersize=8)
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fontcolor = KLIPPAIN_COLORS['purple']
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fontweight = 'bold'
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ax.annotate(f"R", (motor_vibration_power[motor_max_power_index], freqs[motor_max_power_index]),
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textcoords="offset points", xytext=(8, 8),
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ha='right', fontsize=13, color=fontcolor, weight=fontweight)
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# Add the legend
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ax2.plot([], [], ' ', label="Motor resonant frequency (ω0): %.1fHz" % (motor_fr))
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ax2.plot([], [], ' ', label="Motor damping ratio (ζ): %.3f" % (motor_zeta))
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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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ax2.legend(loc='upper right', prop=fontP)
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return
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######################################################################
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# Startup and main routines
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######################################################################
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def extract_speed(logname):
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try:
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speed = re.search('sp(.+?)n', os.path.basename(logname)).group(1).replace('_','.')
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except AttributeError:
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raise ValueError("File %s does not contain speed in its name and therefore "
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"is not supported by this script." % (logname,))
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return float(speed)
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def sort_and_slice(raw_speeds, raw_datas, remove):
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# Sort to get the speeds and their datas aligned and in ascending order
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raw_speeds, raw_datas = zip(*sorted(zip(raw_speeds, raw_datas), key=operator.itemgetter(0)))
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# Optionally remove the beginning and end of each data file to get only
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# the constant speed part of the segments and remove the start/stop phase
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sliced_datas = []
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for data in raw_datas:
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sliced = round((len(data) * remove / 100) / 2)
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sliced_datas.append(data[sliced:len(data)-sliced])
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return raw_speeds, sliced_datas
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def speed_vibrations_profile(lognames, klipperdir="~/klipper", axisname=None, accel=None, max_freq=1000., remove=0):
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set_locale()
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global shaper_calibrate
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shaper_calibrate = setup_klipper_import(klipperdir)
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# Parse the raw data and get them ready for analysis
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raw_datas = [parse_log(filename) for filename in lognames]
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raw_speeds = [extract_speed(filename) for filename in lognames]
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speeds, datas = sort_and_slice(raw_speeds, raw_datas, remove)
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del raw_datas, raw_speeds
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# As we assume that we have the same number of file for each speed increment, we can group
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# the PSD results by this number (to combine all the segments of the pattern at a constant speed)
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group_by = speeds.count(speeds[0])
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# Remove speeds duplicates and graph the processed datas
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speeds = list(OrderedDict((x, True) for x in speeds).keys())
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# Compute speed profile, vibration spectrogram and motor resonance profile
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freqs, psd = compute_vibration_spectrogram(datas, group_by, max_freq)
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upsampled_speeds, speeds_powers = compute_speed_profile(speeds, freqs, psd)
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motor_vibration_power = compute_motor_profile(psd)
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# Peak detection and low energy valleys (good speeds) identification between the peaks
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num_peaks, vibration_peaks, peaks_speeds = detect_peaks(
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speeds_powers[0], upsampled_speeds,
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PEAKS_DETECTION_THRESHOLD * speeds_powers[0].max(),
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PEAKS_RELATIVE_HEIGHT_THRESHOLD, 10, 10
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)
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low_energy_zones = identify_low_energy_zones(speeds_powers[0])
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# Print the vibration peaks info in the console
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formated_peaks_speeds = ["{:.1f}".format(pspeed) for pspeed in peaks_speeds]
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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))))
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# Motor resonance estimation
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motor_fr, motor_zeta, motor_max_power_index = compute_mechanical_parameters(motor_vibration_power, freqs)
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if motor_fr > 25:
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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("The detected resonance frequency of the motors is too low (%.1fHz). This is probably due to the test run with too high acceleration!" % motor_fr)
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print_with_c_locale("Try lowering the ACCEL value before restarting the macro to ensure that only constant speeds are recorded and that the dynamic behavior in the corners is not impacting the measurements.")
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# Create graph layout
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fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, gridspec_kw={
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'height_ratios':[4, 3],
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'width_ratios':[5, 3],
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'bottom':0.050,
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'top':0.890,
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'left':0.057,
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'right':0.985,
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'hspace':0.166,
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'wspace':0.138
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})
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ax2.remove() # top right graph is not used and left blank for now...
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fig.set_size_inches(14, 11.6)
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# Add title
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|
title_line1 = "VIBRATIONS MEASUREMENT TOOL"
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fig.text(0.075, 0.965, title_line1, ha='left', va='bottom', fontsize=20, color=KLIPPAIN_COLORS['purple'], weight='bold')
|
|
try:
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|
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 axisname is not None:
|
|
title_line2 += ' -- ' + str(axisname).upper() + ' axis'
|
|
if accel is not None:
|
|
title_line2 += ' at ' + str(accel) + ' mm/s²'
|
|
except:
|
|
print_with_c_locale("Warning: CSV filename look to be different than expected (%s)" % (lognames[0]))
|
|
title_line2 = lognames[0].split('/')[-1]
|
|
fig.text(0.075, 0.957, title_line2, ha='left', va='top', fontsize=16, color=KLIPPAIN_COLORS['dark_purple'])
|
|
|
|
# Plot the graphs
|
|
plot_speed_profile(ax1, upsampled_speeds, speeds_powers, num_peaks, vibration_peaks, low_energy_zones)
|
|
plot_motor_profile(ax4, freqs, motor_vibration_power, motor_fr, motor_zeta, motor_max_power_index)
|
|
plot_vibration_spectrogram(ax3, speeds, freqs, psd, peaks_speeds, motor_fr, 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("-a", "--axis", type="string", dest="axisname",
|
|
default=None, help="axis name to be printed on the 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("-r", "--remove", type="int", default=0,
|
|
help="percentage of data removed at start/end of each CSV files")
|
|
opts.add_option("-k", "--klipper_dir", type="string", dest="klipperdir",
|
|
default="~/klipper", help="main klipper directory")
|
|
options, args = opts.parse_args()
|
|
if len(args) < 1:
|
|
opts.error("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.remove > 50 or options.remove < 0:
|
|
opts.error("You must specify a correct percentage (option -r) in the 0-50 range")
|
|
|
|
fig = speed_vibrations_profile(args, options.klipperdir, options.axisname, options.accel, options.max_freq, options.remove)
|
|
fig.savefig(options.output, dpi=150)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|