436 lines
17 KiB
Python
436 lines
17 KiB
Python
#!/usr/bin/env python3
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#################################################
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######## INPUT SHAPER CALIBRATION SCRIPT ########
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#################################################
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# Derived from the calibrate_shaper.py official Klipper script
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# Copyright (C) 2020 Dmitry Butyugin <dmbutyugin@google.com>
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# Copyright (C) 2020 Kevin O'Connor <kevin@koconnor.net>
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# Highly modified and improved by Frix_x#0161 #
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import optparse
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import os
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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.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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compute_spectrogram,
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detect_peaks,
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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_EFFECT_THRESHOLD = 0.12
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SPECTROGRAM_LOW_PERCENTILE_FILTER = 5
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MAX_SMOOTHING = 0.1
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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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# Find the best shaper parameters using Klipper's official algorithm selection with
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# a proper precomputed damping ratio (zeta) and using the configured printer SQV value
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def calibrate_shaper(datas, max_smoothing, scv, max_freq):
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helper = shaper_calibrate.ShaperCalibrate(printer=None)
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calibration_data = helper.process_accelerometer_data(datas)
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calibration_data.normalize_to_frequencies()
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fr, zeta, _, _ = compute_mechanical_parameters(calibration_data.psd_sum, calibration_data.freq_bins)
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# If the damping ratio computation fail, we use Klipper default value instead
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if zeta is None:
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zeta = 0.1
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compat = False
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try:
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shaper, all_shapers = helper.find_best_shaper(
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calibration_data,
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shapers=None,
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damping_ratio=zeta,
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scv=scv,
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shaper_freqs=None,
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max_smoothing=max_smoothing,
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test_damping_ratios=None,
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max_freq=max_freq,
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logger=ConsoleOutput.print,
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)
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except TypeError:
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ConsoleOutput.print(
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'[WARNING] You seem to be using an older version of Klipper that is not compatible with all the latest Shake&Tune features!'
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)
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ConsoleOutput.print(
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'Shake&Tune now runs in compatibility mode: be aware that the results may be slightly off, since the real damping ratio cannot be used to create the filter recommendations'
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)
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compat = True
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shaper, all_shapers = helper.find_best_shaper(calibration_data, max_smoothing, ConsoleOutput.print)
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ConsoleOutput.print(
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'\n-> Recommended shaper is %s @ %.1f Hz (when using a square corner velocity of %.1f and a damping ratio of %.3f)'
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% (shaper.name.upper(), shaper.freq, scv, zeta)
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)
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return shaper.name, all_shapers, calibration_data, fr, zeta, compat
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######################################################################
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# Graphing
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######################################################################
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def plot_freq_response(
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ax, calibration_data, shapers, performance_shaper, peaks, peaks_freqs, peaks_threshold, fr, zeta, max_freq
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):
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freqs = calibration_data.freqs
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psd = calibration_data.psd_sum
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px = calibration_data.psd_x
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py = calibration_data.psd_y
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pz = calibration_data.psd_z
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fontP = matplotlib.font_manager.FontProperties()
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fontP.set_size('x-small')
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ax.set_xlabel('Frequency (Hz)')
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ax.set_xlim([0, max_freq])
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ax.set_ylabel('Power spectral density')
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ax.set_ylim([0, psd.max() + psd.max() * 0.05])
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ax.plot(freqs, psd, label='X+Y+Z', color='purple', zorder=5)
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ax.plot(freqs, px, label='X', color='red')
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ax.plot(freqs, py, label='Y', color='green')
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ax.plot(freqs, pz, label='Z', color='blue')
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ax.xaxis.set_minor_locator(matplotlib.ticker.MultipleLocator(5))
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ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
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ax.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))
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ax.grid(which='major', color='grey')
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ax.grid(which='minor', color='lightgrey')
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ax2 = ax.twinx()
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ax2.yaxis.set_visible(False)
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lowvib_shaper_vibrs = float('inf')
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lowvib_shaper = None
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lowvib_shaper_freq = None
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lowvib_shaper_accel = 0
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# Draw the shappers curves and add their specific parameters in the legend
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# This adds also a way to find the best shaper with a low level of vibrations (with a resonable level of smoothing)
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for shaper in shapers:
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shaper_max_accel = round(shaper.max_accel / 100.0) * 100.0
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label = '%s (%.1f Hz, vibr=%.1f%%, sm~=%.2f, accel<=%.f)' % (
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shaper.name.upper(),
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shaper.freq,
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shaper.vibrs * 100.0,
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shaper.smoothing,
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shaper_max_accel,
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)
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ax2.plot(freqs, shaper.vals, label=label, linestyle='dotted')
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# Get the performance shaper
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if shaper.name == performance_shaper:
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performance_shaper_freq = shaper.freq
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performance_shaper_vibr = shaper.vibrs * 100.0
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performance_shaper_vals = shaper.vals
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# Get the low vibration shaper
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if (
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shaper.vibrs * 100 < lowvib_shaper_vibrs
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or (shaper.vibrs * 100 == lowvib_shaper_vibrs and shaper_max_accel > lowvib_shaper_accel)
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) and shaper.smoothing < MAX_SMOOTHING:
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lowvib_shaper_accel = shaper_max_accel
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lowvib_shaper = shaper.name
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lowvib_shaper_freq = shaper.freq
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lowvib_shaper_vibrs = shaper.vibrs * 100
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lowvib_shaper_vals = shaper.vals
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# User recommendations are added to the legend: one is Klipper's original suggestion that is usually good for performances
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# and the other one is the custom "low vibration" recommendation that looks for a suitable shaper that doesn't have excessive
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# smoothing and that have a lower vibration level. If both recommendation are the same shaper, or if no suitable "low
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# vibration" shaper is found, then only a single line as the "best shaper" recommendation is added to the legend
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if (
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lowvib_shaper is not None
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and lowvib_shaper != performance_shaper
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and lowvib_shaper_vibrs <= performance_shaper_vibr
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):
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ax2.plot(
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[],
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[],
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' ',
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label='Recommended performance shaper: %s @ %.1f Hz'
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% (performance_shaper.upper(), performance_shaper_freq),
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)
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ax.plot(
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freqs, psd * performance_shaper_vals, label='With %s applied' % (performance_shaper.upper()), color='cyan'
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)
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ax2.plot(
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[],
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[],
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' ',
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label='Recommended low vibrations shaper: %s @ %.1f Hz' % (lowvib_shaper.upper(), lowvib_shaper_freq),
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)
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ax.plot(freqs, psd * lowvib_shaper_vals, label='With %s applied' % (lowvib_shaper.upper()), color='lime')
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else:
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ax2.plot(
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[],
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[],
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' ',
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label='Recommended best shaper: %s @ %.1f Hz' % (performance_shaper.upper(), performance_shaper_freq),
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)
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ax.plot(
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freqs, psd * performance_shaper_vals, label='With %s applied' % (performance_shaper.upper()), color='cyan'
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)
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# And the estimated damping ratio is finally added at the end of the legend
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ax2.plot([], [], ' ', label='Estimated damping ratio (ζ): %.3f' % (zeta))
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# Draw the detected peaks and name them
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# This also draw the detection threshold and warning threshold (aka "effect zone")
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ax.plot(peaks_freqs, psd[peaks], 'x', color='black', markersize=8)
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for idx, peak in enumerate(peaks):
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if psd[peak] > peaks_threshold[1]:
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fontcolor = 'red'
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fontweight = 'bold'
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else:
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fontcolor = 'black'
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fontweight = 'normal'
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ax.annotate(
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f'{idx+1}',
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(freqs[peak], psd[peak]),
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textcoords='offset points',
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xytext=(8, 5),
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ha='left',
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fontsize=13,
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color=fontcolor,
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weight=fontweight,
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)
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ax.axhline(y=peaks_threshold[0], color='black', linestyle='--', linewidth=0.5)
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ax.axhline(y=peaks_threshold[1], color='black', linestyle='--', linewidth=0.5)
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ax.fill_between(freqs, 0, peaks_threshold[0], color='green', alpha=0.15, label='Relax Region')
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ax.fill_between(freqs, peaks_threshold[0], peaks_threshold[1], color='orange', alpha=0.2, label='Warning Region')
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# Add the main resonant frequency and damping ratio of the axis to the graph title
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ax.set_title(
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'Axis Frequency Profile (ω0=%.1fHz, ζ=%.3f)' % (fr, zeta),
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fontsize=14,
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color=KLIPPAIN_COLORS['dark_orange'],
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weight='bold',
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)
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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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# Plot a time-frequency spectrogram to see how the system respond over time during the
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# resonnance test. This can highlight hidden spots from the standard PSD graph from other harmonics
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def plot_spectrogram(ax, t, bins, pdata, peaks, max_freq):
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ax.set_title('Time-Frequency Spectrogram', fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
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# We need to normalize the data to get a proper signal on the spectrogram
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# However, while using "LogNorm" provide too much background noise, using
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# "Normalize" make only the resonnance appearing and hide interesting elements
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# So we need to filter out the lower part of the data (ie. find the proper vmin for LogNorm)
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vmin_value = np.percentile(pdata, SPECTROGRAM_LOW_PERCENTILE_FILTER)
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# Draw the spectrogram using imgshow that is better suited here than pcolormesh since its result is already rasterized and
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# we doesn't need to keep vector graphics when saving to a final .png file. Using it also allow to
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# save ~150-200MB of RAM during the "fig.savefig" operation.
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cm = 'inferno'
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norm = matplotlib.colors.LogNorm(vmin=vmin_value)
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ax.imshow(
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pdata.T,
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norm=norm,
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cmap=cm,
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aspect='auto',
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extent=[t[0], t[-1], bins[0], bins[-1]],
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origin='lower',
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interpolation='antialiased',
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)
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ax.set_xlim([0.0, max_freq])
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ax.set_ylabel('Time (s)')
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ax.set_xlabel('Frequency (Hz)')
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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=1)
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ax.annotate(
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f'Peak {idx+1}',
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(peak, bins[-1] * 0.9),
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textcoords='data',
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color='cyan',
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rotation=90,
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fontsize=10,
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verticalalignment='top',
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horizontalalignment='right',
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)
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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 shaper_calibration(
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lognames,
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klipperdir='~/klipper',
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max_smoothing=None,
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scv=5.0,
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max_freq=200.0,
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accel_per_hz=None,
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st_version='unknown',
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):
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global shaper_calibrate
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shaper_calibrate = setup_klipper_import(klipperdir)
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# Parse data from the log files while ignoring CSV in the wrong format
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datas = [data for data in (parse_log(fn) for fn in lognames) if data is not None]
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if len(datas) > 1:
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ConsoleOutput.print('Warning: incorrect number of .csv files detected. Only the first one will be used!')
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# Compute shapers, PSD outputs and spectrogram
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performance_shaper, shapers, calibration_data, fr, zeta, compat = calibrate_shaper(
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datas[0], max_smoothing, scv, max_freq
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)
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pdata, bins, t = compute_spectrogram(datas[0])
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del datas
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# Select only the relevant part of the PSD data
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freqs = calibration_data.freq_bins
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calibration_data.psd_sum = calibration_data.psd_sum[freqs <= max_freq]
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calibration_data.psd_x = calibration_data.psd_x[freqs <= max_freq]
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calibration_data.psd_y = calibration_data.psd_y[freqs <= max_freq]
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calibration_data.psd_z = calibration_data.psd_z[freqs <= max_freq]
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calibration_data.freqs = freqs[freqs <= max_freq]
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# Peak detection algorithm
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peaks_threshold = [
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PEAKS_DETECTION_THRESHOLD * calibration_data.psd_sum.max(),
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PEAKS_EFFECT_THRESHOLD * calibration_data.psd_sum.max(),
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]
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num_peaks, peaks, peaks_freqs = detect_peaks(calibration_data.psd_sum, calibration_data.freqs, peaks_threshold[0])
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# Print the peaks info in the console
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peak_freqs_formated = ['{:.1f}'.format(f) for f in peaks_freqs]
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num_peaks_above_effect_threshold = np.sum(calibration_data.psd_sum[peaks] > peaks_threshold[1])
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ConsoleOutput.print(
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'\nPeaks detected on the graph: %d @ %s Hz (%d above effect threshold)'
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% (num_peaks, ', '.join(map(str, peak_freqs_formated)), num_peaks_above_effect_threshold)
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)
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# Create graph layout
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fig, (ax1, ax2) = plt.subplots(
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2,
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1,
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gridspec_kw={
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'height_ratios': [4, 3],
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'bottom': 0.050,
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'top': 0.890,
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'left': 0.085,
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'right': 0.966,
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'hspace': 0.169,
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'wspace': 0.200,
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},
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)
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fig.set_size_inches(8.3, 11.6)
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# Add a title with some test info
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title_line1 = 'INPUT SHAPER CALIBRATION TOOL'
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fig.text(
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0.12, 0.965, title_line1, ha='left', va='bottom', fontsize=20, color=KLIPPAIN_COLORS['purple'], weight='bold'
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)
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try:
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filename_parts = (lognames[0].split('/')[-1]).split('_')
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dt = datetime.strptime(f'{filename_parts[1]} {filename_parts[2]}', '%Y%m%d %H%M%S')
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title_line2 = dt.strftime('%x %X') + ' -- ' + filename_parts[3].upper().split('.')[0] + ' axis'
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if compat:
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title_line3 = '| Older Klipper version detected, damping ratio'
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title_line4 = '| and SCV are not used for filter recommendations!'
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title_line5 = f'| Accel per Hz used: {accel_per_hz} mm/s²/Hz' if accel_per_hz is not None else ''
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else:
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title_line3 = f'| Square corner velocity: {scv} mm/s'
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title_line4 = f'| Max allowed smoothing: {max_smoothing}'
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title_line5 = f'| Accel per Hz used: {accel_per_hz} mm/s²/Hz' if accel_per_hz is not None else ''
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except Exception:
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ConsoleOutput.print('Warning: CSV filename look to be different than expected (%s)' % (lognames[0]))
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title_line2 = lognames[0].split('/')[-1]
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title_line3 = ''
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title_line4 = ''
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title_line5 = ''
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fig.text(0.12, 0.957, title_line2, ha='left', va='top', fontsize=16, color=KLIPPAIN_COLORS['dark_purple'])
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fig.text(0.58, 0.965, title_line3, ha='left', va='top', fontsize=10, color=KLIPPAIN_COLORS['dark_purple'])
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fig.text(0.58, 0.951, title_line4, ha='left', va='top', fontsize=10, color=KLIPPAIN_COLORS['dark_purple'])
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fig.text(0.58, 0.919, title_line5, ha='left', va='top', fontsize=10, color=KLIPPAIN_COLORS['dark_purple'])
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# Plot the graphs
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plot_freq_response(
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ax1, calibration_data, shapers, performance_shaper, peaks, peaks_freqs, peaks_threshold, fr, zeta, max_freq
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)
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plot_spectrogram(ax2, t, bins, pdata, peaks_freqs, max_freq)
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# Adding a small Klippain logo to the top left corner of the figure
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ax_logo = fig.add_axes([0.001, 0.8995, 0.1, 0.1], anchor='NW')
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ax_logo.imshow(plt.imread(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'klippain.png')))
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ax_logo.axis('off')
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# Adding Shake&Tune version in the top right corner
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if st_version != 'unknown':
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fig.text(0.995, 0.985, st_version, ha='right', va='bottom', fontsize=8, color=KLIPPAIN_COLORS['purple'])
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return fig
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def main():
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# Parse command-line arguments
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usage = '%prog [options] <logs>'
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opts = optparse.OptionParser(usage)
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opts.add_option('-o', '--output', type='string', dest='output', default=None, help='filename of output graph')
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opts.add_option('-f', '--max_freq', type='float', default=200.0, help='maximum frequency to graph')
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opts.add_option('-s', '--max_smoothing', type='float', default=None, help='maximum shaper smoothing to allow')
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opts.add_option(
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'--scv', '--square_corner_velocity', type='float', dest='scv', default=5.0, help='square corner velocity'
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)
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opts.add_option('--accel_per_hz', type='float', default=None, help='accel_per_hz used during the measurement')
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opts.add_option(
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'-k', '--klipper_dir', type='string', dest='klipperdir', default='~/klipper', help='main klipper directory'
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)
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options, args = opts.parse_args()
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if len(args) < 1:
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opts.error('Incorrect number of arguments')
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if options.output is None:
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opts.error('You must specify an output file.png to use the script (option -o)')
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if options.max_smoothing is not None and options.max_smoothing < 0.05:
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opts.error('Too small max_smoothing specified (must be at least 0.05)')
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fig = shaper_calibration(
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args, options.klipperdir, options.max_smoothing, options.scv, options.max_freq, options.accel_per_hz, 'unknown'
|
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)
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|
fig.savefig(options.output, dpi=150)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|