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	Also updated instructions to install python3-numpy and python3-matplotlib Python packages. Signed-off-by: Dmitry Butyugin <dmbutyugin@google.com>
		
			
				
	
	
		
			175 lines
		
	
	
		
			6.9 KiB
		
	
	
	
		
			Python
		
	
	
		
			Executable File
		
	
	
	
	
			
		
		
	
	
			175 lines
		
	
	
		
			6.9 KiB
		
	
	
	
		
			Python
		
	
	
		
			Executable File
		
	
	
	
	
| #!/usr/bin/env python3
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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 importlib, 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'))
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| shaper_calibrate = importlib.import_module('.shaper_calibrate', 'extras')
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| 
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| MAX_TITLE_LENGTH=65
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| 
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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 = shaper_calibrate.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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| ######################################################################
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| # Shaper calibration
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| ######################################################################
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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 = shaper_calibrate.ShaperCalibrate(printer=None)
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|     if isinstance(datas[0], shaper_calibrate.CalibrationData):
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|         calibration_data = datas[0]
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|         for data in datas[1:]:
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|             calibration_data.add_data(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.add_data(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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| ######################################################################
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| # Plot frequency response and suggested input shapers
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| ######################################################################
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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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| 
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|     fontP = matplotlib.font_manager.FontProperties()
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|     fontP.set_size('x-small')
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| 
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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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| 
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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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| 
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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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| 
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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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| 
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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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| 
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|     fig.tight_layout()
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|     return fig
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| 
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| ######################################################################
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| # Startup
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| ######################################################################
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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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| 
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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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| 
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|     # Parse data
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|     datas = [parse_log(fn) for fn in args]
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| 
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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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| 
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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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| 
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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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| 
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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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| 
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| if __name__ == '__main__':
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|     main()
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