diff --git a/scripts/calibrate_shaper.py b/scripts/calibrate_shaper.py
index 22b32b236..82c15ed18 100755
--- a/scripts/calibrate_shaper.py
+++ b/scripts/calibrate_shaper.py
@@ -7,11 +7,14 @@
# This file may be distributed under the terms of the GNU GPLv3 license.
from __future__ import print_function
import optparse, os, sys
+from textwrap import wrap
import numpy as np, matplotlib
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)),
'..', 'klippy', 'extras'))
from shaper_calibrate import CalibrationData, ShaperCalibrate
+MAX_TITLE_LENGTH=65
+
def parse_log(logname):
with open(logname) as f:
for header in f:
@@ -61,7 +64,7 @@ def calibrate_shaper(datas, csv_output):
# Plot frequency response and suggested input shapers
######################################################################
-def plot_freq_response(calibration_data, shapers_vals,
+def plot_freq_response(lognames, calibration_data, shapers_vals,
selected_shaper, max_freq):
freqs = calibration_data.freq_bins
psd = calibration_data.psd_sum[freqs <= max_freq]
@@ -83,10 +86,8 @@ def plot_freq_response(calibration_data, shapers_vals,
ax.plot(freqs, py, label='Y', color='green')
ax.plot(freqs, pz, label='Z', color='blue')
- if shapers_vals:
- ax.set_title("Frequency response and shapers")
- else:
- ax.set_title("Frequency response")
+ title = "Frequency response and shapers (%s)" % (', '.join(lognames))
+ ax.set_title("\n".join(wrap(title, MAX_TITLE_LENGTH)))
ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
@@ -94,23 +95,25 @@ def plot_freq_response(calibration_data, shapers_vals,
ax.ticklabel_format(axis='y', style='scientific', scilimits=(0,0))
ax.grid(which='major', color='grey')
ax.grid(which='minor', color='lightgrey')
- ax.legend(loc='upper right', prop=fontP)
- if shapers_vals:
- ax2 = ax.twinx()
- ax2.set_ylabel('Shaper vibration reduction (ratio)')
- best_shaper_vals = None
- for name, freq, vals in shapers_vals:
- label = "%s (%.1f Hz)" % (name.upper(), freq)
- linestyle = 'dotted'
- if name == selected_shaper:
- label += ' (selected)'
- linestyle = 'dashdot'
- best_shaper_vals = vals
- ax2.plot(freqs, vals, label=label, linestyle=linestyle)
- ax.plot(freqs, psd * best_shaper_vals,
- label='After\nshaper', color='cyan')
- ax2.legend(loc='upper left', prop=fontP)
+ ax2 = ax.twinx()
+ ax2.set_ylabel('Shaper vibration reduction (ratio)')
+ best_shaper_vals = None
+ for name, freq, vals in shapers_vals:
+ label = "%s (%.1f Hz)" % (name.upper(), freq)
+ linestyle = 'dotted'
+ if name == selected_shaper:
+ linestyle = 'dashdot'
+ best_shaper_vals = vals
+ ax2.plot(freqs, vals, label=label, linestyle=linestyle)
+ ax.plot(freqs, psd * best_shaper_vals,
+ label='After\nshaper', color='cyan')
+ # A hack to add a human-readable shaper recommendation to legend
+ ax2.plot([], [], ' ',
+ label="Recommended shaper: %s" % (selected_shaper.upper()))
+
+ ax.legend(loc='upper left', prop=fontP)
+ ax2.legend(loc='upper right', prop=fontP)
fig.tight_layout()
return fig
@@ -152,7 +155,7 @@ def main():
# Draw graph
setup_matplotlib(options.output is not None)
- fig = plot_freq_response(calibration_data, shapers_vals,
+ fig = plot_freq_response(args, calibration_data, shapers_vals,
selected_shaper, options.max_freq)
# Show graph
diff --git a/scripts/graph_accelerometer.py b/scripts/graph_accelerometer.py
index 0e975bf55..f6d7cf896 100755
--- a/scripts/graph_accelerometer.py
+++ b/scripts/graph_accelerometer.py
@@ -12,10 +12,20 @@ sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)),
'..', 'klippy', 'extras'))
from shaper_calibrate import ShaperCalibrate
-MAX_TITLE_LENGTH=80
+MAX_TITLE_LENGTH=65
-def parse_log(logname):
- return np.loadtxt(logname, comments='#', delimiter=',')
+def parse_log(logname, opts):
+ with open(logname) as f:
+ for header in f:
+ if not header.startswith('#'):
+ break
+ if not header.startswith('freq,psd_x,psd_y,psd_z,psd_xyz'):
+ # Raw accelerometer data
+ return np.loadtxt(logname, comments='#', delimiter=',')
+ # Power spectral density data or shaper calibration data
+ opts.error("File %s does not contain raw accelerometer data and therefore "
+ "is not supported by graph_accelerometer.py script. Please use "
+ "calibrate_shaper.py script to process it instead." % (logname,))
######################################################################
# Raw accelerometer graphing
@@ -185,7 +195,7 @@ def setup_matplotlib(output):
def main():
# Parse command-line arguments
- usage = "%prog [options] <logs>"
+ 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")
@@ -205,7 +215,7 @@ def main():
opts.error("Incorrect number of arguments")
# Parse data
- datas = [parse_log(fn) for fn in args]
+ datas = [parse_log(fn, opts) for fn in args]
setup_matplotlib(options.output)
@@ -215,6 +225,8 @@ def main():
if options.compare:
opts.error("comparison mode is not supported with csv output")
if options.specgram:
+ if len(args) > 1:
+ opts.error("Only 1 input is supported in specgram mode")
pdata, bins, t = calc_specgram(datas[0], options.axis)
write_specgram(pdata, bins, t, options.output)
else:
@@ -223,8 +235,12 @@ def main():
# Draw graph
if options.raw:
+ if len(args) > 1:
+ opts.error("Only 1 input is supported in raw mode")
fig = plot_accel(datas[0], args[0])
elif options.specgram:
+ if len(args) > 1:
+ opts.error("Only 1 input is supported in specgram mode")
fig = plot_specgram(datas[0], args[0], options.max_freq, options.axis)
elif options.compare:
fig = plot_compare_frequency(datas, args, options.max_freq)