reijii/klippain-shaketune-telegramm
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locale helpers are now on their own file

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This commit is contained in:
Félix Boisselier
2023-12-26 23:47:14 +01:00
parent f385bd98e3
commit 0ff63edec8
5 changed files with 52 additions and 91 deletions
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31
K-ShakeTune/scripts/graph_shaper.py
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@@ -20,7 +20,7 @@ import numpy as np
import scipy
import matplotlib.pyplot, matplotlib.dates, matplotlib.font_manager
import matplotlib.ticker, matplotlib.gridspec
import locale
from locale_utils import set_locale, print_with_c_locale
from datetime import datetime
matplotlib.use('Agg')
@@ -38,24 +38,6 @@ KLIPPAIN_COLORS = {
}
# Set the best locale for time and date formating (generation of the titles)
try:
current_locale = locale.getlocale(locale.LC_TIME)
if current_locale is None or current_locale[0] is None:
locale.setlocale(locale.LC_TIME, 'C')
except locale.Error:
locale.setlocale(locale.LC_TIME, 'C')
# Override the built-in print function to avoid problem in Klipper due to locale settings
original_print = print
def print_with_c_locale(*args, **kwargs):
original_locale = locale.setlocale(locale.LC_ALL, None)
locale.setlocale(locale.LC_ALL, 'C')
original_print(*args, **kwargs)
locale.setlocale(locale.LC_ALL, original_locale)
print = print_with_c_locale
######################################################################
# Computation
######################################################################
@@ -70,14 +52,14 @@ def calibrate_shaper_with_damping(datas, max_smoothing):
calibration_data.normalize_to_frequencies()
shaper, all_shapers = helper.find_best_shaper(calibration_data, max_smoothing, print)
shaper, all_shapers = helper.find_best_shaper(calibration_data, max_smoothing, print_with_c_locale)
freqs = calibration_data.freq_bins
psd = calibration_data.psd_sum
fr, zeta = compute_damping_ratio(psd, freqs)
print("Recommended shaper is %s @ %.1f Hz" % (shaper.name, shaper.freq))
print("Axis has a main resonant frequency at %.1fHz with an estimated damping ratio of %.3f" % (fr, zeta))
print_with_c_locale("Recommended shaper is %s @ %.1f Hz" % (shaper.name, shaper.freq))
print_with_c_locale("Axis has a main resonant frequency at %.1fHz with an estimated damping ratio of %.3f" % (fr, zeta))
return shaper.name, all_shapers, calibration_data, fr, zeta
@@ -148,7 +130,7 @@ def detect_peaks(psd, freqs, window_size=5, vicinity=3):
num_peaks = len(refined_peaks)
num_peaks_above_effect_threshold = np.sum(psd[refined_peaks] > effect_threshold)
print("Peaks detected on the graph: %d @ %s Hz (%d above effect threshold)" % (num_peaks, ", ".join(map(str, peak_freqs)), num_peaks_above_effect_threshold))
print_with_c_locale("Peaks detected on the graph: %d @ %s Hz (%d above effect threshold)" % (num_peaks, ", ".join(map(str, peak_freqs)), num_peaks_above_effect_threshold))
return np.array(refined_peaks), num_peaks, num_peaks_above_effect_threshold
@@ -319,6 +301,7 @@ def setup_klipper_import(kdir):
def shaper_calibration(lognames, klipperdir="~/klipper", max_smoothing=None, max_freq=200.):
set_locale()
setup_klipper_import(klipperdir)
# Parse data
@@ -340,7 +323,7 @@ def shaper_calibration(lognames, klipperdir="~/klipper", max_smoothing=None, max
dt = datetime.strptime(f"{filename_parts[1]} {filename_parts[2]}", "%Y%m%d %H%M%S")
title_line2 = dt.strftime('%x %X') + ' -- ' + filename_parts[3].upper().split('.')[0] + ' axis'
except:
print("Warning: CSV filename look to be different than expected (%s)" % (lognames[0]))
print_with_c_locale("Warning: CSV filename look to be different than expected (%s)" % (lognames[0]))
title_line2 = lognames[0].split('/')[-1]
fig.text(0.12, 0.957, title_line2, ha='left', va='top', fontsize=16, color=KLIPPAIN_COLORS['dark_purple'])