175 lines
6.8 KiB
Python
Executable File
175 lines
6.8 KiB
Python
Executable File
#!/usr/bin/env python2
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# Shaper auto-calibration script
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#
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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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#
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# This file may be distributed under the terms of the GNU GPLv3 license.
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from __future__ import print_function
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import optparse, os, sys
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from textwrap import wrap
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import numpy as np, matplotlib
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sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)),
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'..', 'klippy', 'extras'))
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from shaper_calibrate import CalibrationData, ShaperCalibrate
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MAX_TITLE_LENGTH=65
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def parse_log(logname):
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with open(logname) as f:
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for header in f:
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if not header.startswith('#'):
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break
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if not header.startswith('freq,psd_x,psd_y,psd_z,psd_xyz'):
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# Raw accelerometer data
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return np.loadtxt(logname, comments='#', delimiter=',')
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# Parse power spectral density data
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data = np.loadtxt(logname, skiprows=1, comments='#', delimiter=',')
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calibration_data = CalibrationData(
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freq_bins=data[:,0], psd_sum=data[:,4],
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psd_x=data[:,1], psd_y=data[:,2], psd_z=data[:,3])
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calibration_data.set_numpy(np)
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# If input shapers are present in the CSV file, the frequency
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# response is already normalized to input frequencies
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if 'mzv' not in header:
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calibration_data.normalize_to_frequencies()
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return calibration_data
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######################################################################
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# Shaper calibration
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######################################################################
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# Find the best shaper parameters
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def calibrate_shaper(datas, csv_output, max_smoothing):
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helper = ShaperCalibrate(printer=None)
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if isinstance(datas[0], CalibrationData):
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calibration_data = datas[0]
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for data in datas[1:]:
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calibration_data.join(data)
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else:
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# Process accelerometer data
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calibration_data = helper.process_accelerometer_data(datas[0])
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for data in datas[1:]:
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calibration_data.join(helper.process_accelerometer_data(data))
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calibration_data.normalize_to_frequencies()
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shaper, all_shapers = helper.find_best_shaper(
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calibration_data, max_smoothing, print)
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print("Recommended shaper is %s @ %.1f Hz" % (shaper.name, shaper.freq))
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if csv_output is not None:
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helper.save_calibration_data(
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csv_output, calibration_data, all_shapers)
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return shaper.name, all_shapers, calibration_data
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######################################################################
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# Plot frequency response and suggested input shapers
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######################################################################
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def plot_freq_response(lognames, calibration_data, shapers,
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selected_shaper, max_freq):
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freqs = calibration_data.freq_bins
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psd = calibration_data.psd_sum[freqs <= max_freq]
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px = calibration_data.psd_x[freqs <= max_freq]
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py = calibration_data.psd_y[freqs <= max_freq]
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pz = calibration_data.psd_z[freqs <= max_freq]
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freqs = freqs[freqs <= max_freq]
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fontP = matplotlib.font_manager.FontProperties()
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fontP.set_size('x-small')
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fig, ax = matplotlib.pyplot.subplots()
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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.plot(freqs, psd, label='X+Y+Z', color='purple')
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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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title = "Frequency response and shapers (%s)" % (', '.join(lognames))
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ax.set_title("\n".join(wrap(title, MAX_TITLE_LENGTH)))
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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.set_ylabel('Shaper vibration reduction (ratio)')
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best_shaper_vals = None
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for shaper in shapers:
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label = "%s (%.1f Hz, vibr=%.1f%%, sm~=%.2f, accel<=%.f)" % (
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shaper.name.upper(), shaper.freq,
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shaper.vibrs * 100., shaper.smoothing,
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round(shaper.max_accel / 100.) * 100.)
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linestyle = 'dotted'
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if shaper.name == selected_shaper:
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linestyle = 'dashdot'
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best_shaper_vals = shaper.vals
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ax2.plot(freqs, shaper.vals, label=label, linestyle=linestyle)
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ax.plot(freqs, psd * best_shaper_vals,
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label='After\nshaper', color='cyan')
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# A hack to add a human-readable shaper recommendation to legend
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ax2.plot([], [], ' ',
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label="Recommended shaper: %s" % (selected_shaper.upper()))
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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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fig.tight_layout()
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return fig
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######################################################################
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# Startup
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######################################################################
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def setup_matplotlib(output_to_file):
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global matplotlib
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if output_to_file:
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matplotlib.rcParams.update({'figure.autolayout': True})
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matplotlib.use('Agg')
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import matplotlib.pyplot, matplotlib.dates, matplotlib.font_manager
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import matplotlib.ticker
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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",
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default=None, help="filename of output graph")
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opts.add_option("-c", "--csv", type="string", dest="csv",
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default=None, help="filename of output csv file")
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opts.add_option("-f", "--max_freq", type="float", default=200.,
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help="maximum frequency to graph")
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opts.add_option("-s", "--max_smoothing", type="float", default=None,
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help="maximum shaper smoothing to allow")
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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.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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# Parse data
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datas = [parse_log(fn) for fn in args]
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# Calibrate shaper and generate outputs
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selected_shaper, shapers, calibration_data = calibrate_shaper(
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datas, options.csv, options.max_smoothing)
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if not options.csv or options.output:
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# Draw graph
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setup_matplotlib(options.output is not None)
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fig = plot_freq_response(args, calibration_data, shapers,
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selected_shaper, options.max_freq)
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# Show graph
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if options.output is None:
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matplotlib.pyplot.show()
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else:
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fig.set_size_inches(8, 6)
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fig.savefig(options.output)
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if __name__ == '__main__':
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main()
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