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added some styling and doc adjustements

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This commit is contained in:
Félix Boisselier
2023-10-18 00:09:58 +02:00
parent ff8eff9115
commit 10cf8d3566
7 changed files with 161 additions and 96 deletions
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78
K-ShakeTune/scripts/graph_belts.py
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@@ -23,8 +23,15 @@ import numpy as np
import matplotlib.pyplot, matplotlib.dates, matplotlib.font_manager
import matplotlib.ticker, matplotlib.gridspec, matplotlib.colors
import matplotlib.patches
import locale
from datetime import datetime
matplotlib.use('Agg')
try:
locale.setlocale(locale.LC_TIME, locale.getdefaultlocale())
except locale.Error:
locale.setlocale(locale.LC_TIME, 'C')
MAX_TITLE_LENGTH = 65
ALPHABET = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" # For paired peaks names
@@ -38,6 +45,14 @@ DC_MAX_UNPAIRED_PEAKS_ALLOWED = 4
# Define the SignalData namedtuple
SignalData = namedtuple('CalibrationData', ['freqs', 'psd', 'peaks', 'paired_peaks', 'unpaired_peaks'])
KLIPPAIN_COLORS = {
"purple": "#70088C",
"orange": "#FF8D32",
"dark_purple": "#150140",
"dark_orange": "#F24130",
"red_pink": "#F2055C"
}
######################################################################
# Computation of the PSD graph
@@ -140,7 +155,7 @@ def pair_peaks(peaks1, freqs1, psd1, peaks2, freqs2, psd2):
# Computation of a basic signal spectrogram
######################################################################
def calc_specgram(data):
def compute_spectrogram(data):
N = data.shape[0]
Fs = N / (data[-1,0] - data[0,0])
# Round up to a power of 2 for faster FFT
@@ -265,8 +280,8 @@ def shift_data_in_time(data, shift_amount):
# the time lag and realigning them. Finally this function combine (by substracting signals) the aligned spectrograms in a new one.
# This result of a mostly zero-ed new spectrogram with some colored zones highlighting differences in the belts paths.
def combined_spectrogram(data1, data2):
pdata1, bins1, t1 = calc_specgram(data1)
pdata2, _, _ = calc_specgram(data2)
pdata1, bins1, t1 = compute_spectrogram(data1)
pdata2, _, _ = compute_spectrogram(data2)
# Detect ridges
ridge1 = detect_ridge(pdata1)
@@ -314,9 +329,22 @@ def compute_comi(combined_data, similarity_coefficient, num_unpaired_peaks):
######################################################################
def plot_compare_frequency(ax, lognames, signal1, signal2, max_freq):
# Get the belt name for the legend to avoid putting the full file name
signal1_belt = (lognames[0].split('/')[-1]).split('_')[-1][0]
signal2_belt = (lognames[1].split('/')[-1]).split('_')[-1][0]
if signal1_belt == 'A' and signal2_belt == 'B':
signal1_belt += " (axis 1,-1)"
signal2_belt += " (axis 1, 1)"
elif signal1_belt == 'B' and signal2_belt == 'A':
signal1_belt += " (axis 1, 1)"
signal2_belt += " (axis 1,-1)"
else:
print("Warning: belts doesn't seem to have the correct name A and B (extracted from the filename.csv)")
# Plot the two belts PSD signals
ax.plot(signal1.freqs, signal1.psd, label="\n".join(wrap(lognames[0], 60)), alpha=0.6)
ax.plot(signal2.freqs, signal2.psd, label="\n".join(wrap(lognames[1], 60)), alpha=0.6)
ax.plot(signal1.freqs, signal1.psd, label="Belt " + signal1_belt, color=KLIPPAIN_COLORS['purple'])
ax.plot(signal2.freqs, signal2.psd, label="Belt " + signal2_belt, color=KLIPPAIN_COLORS['orange'])
# Trace the "relax region" (also used as a threshold to filter and detect the peaks)
psd_lowest_max = min(signal1.psd.max(), signal2.psd.max())
@@ -362,8 +390,10 @@ def plot_compare_frequency(ax, lognames, signal1, signal2, max_freq):
unpaired_peak_count += 1
# Compute the similarity (using cross-correlation of the PSD signals)
ax2 = ax.twinx() # To split the legends in two box
similarity_factor = compute_curve_similarity_factor(signal1, signal2)
ax.plot([], [], ' ', label=f'Estimated similarity: {similarity_factor:.1f}% ({unpaired_peak_count} unpaired peaks)')
ax2.plot([], [], ' ', label=f'Estimated similarity: {similarity_factor:.1f}%')
ax2.plot([], [], ' ', label=f'Number of unpaired peaks: {unpaired_peak_count}')
print(f"Belts estimated similarity: {similarity_factor:.1f}%")
# Setting axis parameters, grid and graph title
@@ -380,13 +410,12 @@ def plot_compare_frequency(ax, lognames, signal1, signal2, max_freq):
ax.grid(which='minor', color='lightgrey')
fontP = matplotlib.font_manager.FontProperties()
fontP.set_size('small')
ax.set_title('Belts Frequency Profiles (estimated similarity: {:.1f}%)'.format(similarity_factor), fontsize=14)
ax.legend(loc='best', prop=fontP)
ax.set_title('Belts Frequency Profiles (estimated similarity: {:.1f}%)'.format(similarity_factor), fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
# Print the table of offsets ontop of the graph below the original legend (upper right)
if len(offsets_table_data) > 0:
columns = ["", "Frequency delta", "Amplitude delta", ]
offset_table = ax.table(cellText=offsets_table_data, colLabels=columns, bbox=[0.67, 0.60, 0.3, 0.2], loc='upper right', cellLoc='center')
offset_table = ax.table(cellText=offsets_table_data, colLabels=columns, bbox=[0.67, 0.70, 0.3, 0.2], loc='upper right', cellLoc='center')
offset_table.auto_set_font_size(False)
offset_table.set_fontsize(8)
offset_table.auto_set_column_width([0, 1, 2])
@@ -396,6 +425,9 @@ def plot_compare_frequency(ax, lognames, signal1, signal2, max_freq):
offset_table[cell].set_facecolor('white')
offset_table[cell].set_alpha(0.6)
ax.legend(loc='upper left', prop=fontP)
ax2.legend(loc='upper right', prop=fontP)
return similarity_factor, unpaired_peak_count
@@ -407,7 +439,7 @@ def plot_difference_spectrogram(ax, data1, data2, signal1, signal2, similarity_f
# Be careful, this value is highly opinionated and is pretty experimental!
comi = compute_comi(combined_data, similarity_factor, len(signal1.unpaired_peaks) + len(signal2.unpaired_peaks))
print(f"Chances of mechanical issues: {comi:.1f}%")
ax.set_title(f"Differential Spectrogram (COMI: {comi:.1f}%)", fontsize=14)
ax.set_title(f"Differential Spectrogram (COMI: {comi:.1f}%)", fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
ax.plot([], [], ' ', label=f'Chances of mechanical issues (COMI): {comi:.1f}%')
# Draw the differential spectrogram with a specific norm to get light grey zero values and red for max values (vmin to vcenter is not used)
@@ -443,11 +475,11 @@ def plot_difference_spectrogram(ax, data1, data2, signal1, signal2, similarity_f
label = ALPHABET[idx]
x_min = min(peak1[1], peak2[1])
x_max = max(peak1[1], peak2[1])
ax.axvline(x_min, color='blue', linestyle='dotted', linewidth=1.5)
ax.axvline(x_max, color='blue', linestyle='dotted', linewidth=1.5)
ax.fill_between([x_min, x_max], 0, np.max(combined_data), color='blue', alpha=0.3)
ax.axvline(x_min, color=KLIPPAIN_COLORS['purple'], linestyle='dotted', linewidth=1.5)
ax.axvline(x_max, color=KLIPPAIN_COLORS['purple'], linestyle='dotted', linewidth=1.5)
ax.fill_between([x_min, x_max], 0, np.max(combined_data), color=KLIPPAIN_COLORS['purple'], alpha=0.3)
ax.annotate(f"Peaks {label}", (x_min, t[-1]*0.05),
textcoords="data", color='blue', rotation=90, fontsize=10,
textcoords="data", color=KLIPPAIN_COLORS['purple'], rotation=90, fontsize=10,
verticalalignment='bottom', horizontalalignment='right')
return
@@ -517,14 +549,20 @@ def belts_calibration(lognames, klipperdir="~/klipper", max_freq=200.):
gs = matplotlib.gridspec.GridSpec(2, 1, height_ratios=[4, 3])
ax1 = fig.add_subplot(gs[0])
ax2 = fig.add_subplot(gs[1])
fig.suptitle("CoreXY relative belt calibration tool", fontsize=16)
filename = lognames[0].split('/')[-1]
dt = datetime.strptime(f"{filename.split('_')[1]} {filename.split('_')[2]}", "%Y%m%d %H%M%S")
title_line1 = "RELATIVE BELT CALIBRATION TOOL"
title_line2 = dt.strftime('%x %X')
fig.text(0.88, 0.965, title_line1, ha='right', va='bottom', fontsize=20, color=KLIPPAIN_COLORS['purple'], weight='bold')
fig.text(0.88, 0.957, title_line2, ha='right', va='top', fontsize=16, color=KLIPPAIN_COLORS['dark_purple'])
similarity_factor, _ = plot_compare_frequency(ax1, lognames, signal1, signal2, max_freq)
plot_difference_spectrogram(ax2, datas[0], datas[1], signal1, signal2, similarity_factor, max_freq)
fig.set_size_inches(10, 12)
fig.set_size_inches(10, 13)
fig.tight_layout()
fig.subplots_adjust(top=0.93)
fig.subplots_adjust(top=0.90)
return fig
@@ -546,6 +584,12 @@ def main():
opts.error("You must specify an output file.png to use the script (option -o)")
fig = belts_calibration(args, options.klipperdir, options.max_freq)
# Adding a small Klippain logo to the top left corner of the figure
ax_logo = fig.add_axes([0.899, 0.899, 0.1, 0.1], anchor='NE', zorder=-1)
ax_logo.imshow(matplotlib.pyplot.imread(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'klippain.png')))
ax_logo.axis('off')
fig.savefig(options.output)