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Merge pull request #138 from Frix-x/develop

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v4.1.0
This commit is contained in:
Félix Boisselier
2024-06-30 22:41:30 +02:00
committed by GitHub
parent 6e49c2c607 8cf81bcb44
commit c12653e1f7
1 changed files with 41 additions and 53 deletions
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94
shaketune/graph_creators/belts_graph_creator.py
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@@ -19,6 +19,7 @@ import matplotlib.font_manager
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import matplotlib.ticker import matplotlib.ticker
import numpy as np import numpy as np
from scipy.stats import pearsonr
matplotlib.use('Agg') matplotlib.use('Agg')
@@ -343,14 +344,12 @@ def plot_versus_belts(
common_freqs: np.ndarray, common_freqs: np.ndarray,
signal1: SignalData, signal1: SignalData,
signal2: SignalData, signal2: SignalData,
interp_psd1: np.ndarray,
interp_psd2: np.ndarray,
signal1_belt: str, signal1_belt: str,
signal2_belt: str, signal2_belt: str,
) -> None: ) -> None:
ax.set_title('Cross-belts comparison plot', fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold') ax.set_title('Cross-belts comparison plot', fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
max_psd = max(np.max(interp_psd1), np.max(interp_psd2)) max_psd = max(np.max(signal1.psd), np.max(signal2.psd))
ideal_line = np.linspace(0, max_psd * 1.1, 500) ideal_line = np.linspace(0, max_psd * 1.1, 500)
green_boundary = ideal_line + (0.35 * max_psd * np.exp(-ideal_line / (0.6 * max_psd))) green_boundary = ideal_line + (0.35 * max_psd * np.exp(-ideal_line / (0.6 * max_psd)))
ax.fill_betweenx(ideal_line, ideal_line, green_boundary, color='green', alpha=0.15) ax.fill_betweenx(ideal_line, ideal_line, green_boundary, color='green', alpha=0.15)
@@ -364,8 +363,8 @@ def plot_versus_belts(
linewidth=2, linewidth=2,
) )
ax.plot(interp_psd1, interp_psd2, color='dimgrey', marker='o', markersize=1.5) ax.plot(signal1.psd, signal2.psd, color='dimgrey', marker='o', markersize=1.5)
ax.fill_betweenx(interp_psd2, interp_psd1, color=KLIPPAIN_COLORS['red_pink'], alpha=0.1) ax.fill_betweenx(signal2.psd, signal1.psd, color=KLIPPAIN_COLORS['red_pink'], alpha=0.1)
paired_peak_count = 0 paired_peak_count = 0
unpaired_peak_count = 0 unpaired_peak_count = 0
@@ -374,31 +373,27 @@ def plot_versus_belts(
label = ALPHABET[paired_peak_count] label = ALPHABET[paired_peak_count]
freq1 = signal1.freqs[peak1[0]] freq1 = signal1.freqs[peak1[0]]
freq2 = signal2.freqs[peak2[0]] freq2 = signal2.freqs[peak2[0]]
nearest_idx1 = np.argmin(np.abs(common_freqs - freq1))
nearest_idx2 = np.argmin(np.abs(common_freqs - freq2))
if nearest_idx1 == nearest_idx2: if abs(freq1 - freq2) < 1:
psd1_peak_value = interp_psd1[nearest_idx1] ax.plot(signal1.psd[peak1[0]], signal2.psd[peak2[0]], marker='o', color='black', markersize=7)
psd2_peak_value = interp_psd2[nearest_idx1]
ax.plot(psd1_peak_value, psd2_peak_value, marker='o', color='black', markersize=7)
ax.annotate( ax.annotate(
f'{label}1/{label}2', f'{label}1/{label}2',
(psd1_peak_value, psd2_peak_value), (signal1.psd[peak1[0]], signal2.psd[peak2[0]]),
textcoords='offset points', textcoords='offset points',
xytext=(-7, 7), xytext=(-7, 7),
fontsize=13, fontsize=13,
color='black', color='black',
) )
else: else:
psd1_peak_value = interp_psd1[nearest_idx1] ax.plot(
psd1_on_peak = interp_psd1[nearest_idx2] signal1.psd[peak2[0]], signal2.psd[peak2[0]], marker='o', color=KLIPPAIN_COLORS['orange'], markersize=7
psd2_peak_value = interp_psd2[nearest_idx2] )
psd2_on_peak = interp_psd2[nearest_idx1] ax.plot(
ax.plot(psd1_on_peak, psd2_peak_value, marker='o', color=KLIPPAIN_COLORS['orange'], markersize=7) signal1.psd[peak1[0]], signal2.psd[peak1[0]], marker='o', color=KLIPPAIN_COLORS['purple'], markersize=7
ax.plot(psd1_peak_value, psd2_on_peak, marker='o', color=KLIPPAIN_COLORS['purple'], markersize=7) )
ax.annotate( ax.annotate(
f'{label}1', f'{label}1',
(psd1_peak_value, psd2_on_peak), (signal1.psd[peak1[0]], signal2.psd[peak1[0]]),
textcoords='offset points', textcoords='offset points',
xytext=(0, 7), xytext=(0, 7),
fontsize=13, fontsize=13,
@@ -406,7 +401,7 @@ def plot_versus_belts(
) )
ax.annotate( ax.annotate(
f'{label}2', f'{label}2',
(psd1_on_peak, psd2_peak_value), (signal1.psd[peak2[0]], signal2.psd[peak2[0]]),
textcoords='offset points', textcoords='offset points',
xytext=(0, 7), xytext=(0, 7),
fontsize=13, fontsize=13,
@@ -415,16 +410,12 @@ def plot_versus_belts(
paired_peak_count += 1 paired_peak_count += 1
for _, peak_index in enumerate(signal1.unpaired_peaks): for _, peak_index in enumerate(signal1.unpaired_peaks):
freq1 = signal1.freqs[peak_index] ax.plot(
freq2 = signal2.freqs[peak_index] signal1.psd[peak_index], signal2.psd[peak_index], marker='o', color=KLIPPAIN_COLORS['purple'], markersize=7
nearest_idx1 = np.argmin(np.abs(common_freqs - freq1)) )
nearest_idx2 = np.argmin(np.abs(common_freqs - freq2))
psd1_peak_value = interp_psd1[nearest_idx1]
psd2_peak_value = interp_psd2[nearest_idx1]
ax.plot(psd1_peak_value, psd2_peak_value, marker='o', color=KLIPPAIN_COLORS['purple'], markersize=7)
ax.annotate( ax.annotate(
str(unpaired_peak_count + 1), str(unpaired_peak_count + 1),
(psd1_peak_value, psd2_peak_value), (signal1.psd[peak_index], signal2.psd[peak_index]),
textcoords='offset points', textcoords='offset points',
fontsize=13, fontsize=13,
weight='bold', weight='bold',
@@ -434,16 +425,12 @@ def plot_versus_belts(
unpaired_peak_count += 1 unpaired_peak_count += 1
for _, peak_index in enumerate(signal2.unpaired_peaks): for _, peak_index in enumerate(signal2.unpaired_peaks):
freq1 = signal1.freqs[peak_index] ax.plot(
freq2 = signal2.freqs[peak_index] signal1.psd[peak_index], signal2.psd[peak_index], marker='o', color=KLIPPAIN_COLORS['orange'], markersize=7
nearest_idx1 = np.argmin(np.abs(common_freqs - freq1)) )
nearest_idx2 = np.argmin(np.abs(common_freqs - freq2))
psd1_peak_value = interp_psd1[nearest_idx1]
psd2_peak_value = interp_psd2[nearest_idx1]
ax.plot(psd1_peak_value, psd2_peak_value, marker='o', color=KLIPPAIN_COLORS['orange'], markersize=7)
ax.annotate( ax.annotate(
str(unpaired_peak_count + 1), str(unpaired_peak_count + 1),
(psd1_peak_value, psd2_peak_value), (signal1.psd[peak_index], signal2.psd[peak_index]),
textcoords='offset points', textcoords='offset points',
fontsize=13, fontsize=13,
weight='bold', weight='bold',
@@ -476,16 +463,21 @@ def plot_versus_belts(
# Original Klipper function to get the PSD data of a raw accelerometer signal # Original Klipper function to get the PSD data of a raw accelerometer signal
def compute_signal_data(data: np.ndarray, max_freq: float) -> SignalData: def compute_signal_data(data: np.ndarray, common_freqs: np.ndarray, max_freq: float) -> SignalData:
helper = shaper_calibrate.ShaperCalibrate(printer=None) helper = shaper_calibrate.ShaperCalibrate(printer=None)
calibration_data = helper.process_accelerometer_data(data) calibration_data = helper.process_accelerometer_data(data)
freqs = calibration_data.freq_bins[calibration_data.freq_bins <= max_freq] freqs = calibration_data.freq_bins[calibration_data.freq_bins <= max_freq]
psd = calibration_data.get_psd('all')[calibration_data.freq_bins <= max_freq] psd = calibration_data.get_psd('all')[calibration_data.freq_bins <= max_freq]
_, peaks, _ = detect_peaks(psd, freqs, PEAKS_DETECTION_THRESHOLD * psd.max()) # Re-interpolate the PSD signal to a common frequency range to be able to plot them one against the other
interp_psd = np.interp(common_freqs, freqs, psd)
return SignalData(freqs=freqs, psd=psd, peaks=peaks) _, peaks, _ = detect_peaks(
interp_psd, common_freqs, PEAKS_DETECTION_THRESHOLD * interp_psd.max(), window_size=20, vicinity=15
)
return SignalData(freqs=common_freqs, psd=interp_psd, peaks=peaks)
###################################################################### ######################################################################
@@ -517,8 +509,9 @@ def belts_calibration(
signal2_belt += belt_info.get(signal2_belt, '') signal2_belt += belt_info.get(signal2_belt, '')
# Compute calibration data for the two datasets with automatic peaks detection # Compute calibration data for the two datasets with automatic peaks detection
signal1 = compute_signal_data(datas[0], max_freq) common_freqs = np.linspace(0, max_freq, 500)
signal2 = compute_signal_data(datas[1], max_freq) signal1 = compute_signal_data(datas[0], common_freqs, max_freq)
signal2 = compute_signal_data(datas[1], common_freqs, max_freq)
del datas del datas
# Pair the peaks across the two datasets # Pair the peaks across the two datasets
@@ -526,18 +519,13 @@ def belts_calibration(
signal1 = signal1._replace(paired_peaks=pairing_result.paired_peaks, unpaired_peaks=pairing_result.unpaired_peaks1) signal1 = signal1._replace(paired_peaks=pairing_result.paired_peaks, unpaired_peaks=pairing_result.unpaired_peaks1)
signal2 = signal2._replace(paired_peaks=pairing_result.paired_peaks, unpaired_peaks=pairing_result.unpaired_peaks2) signal2 = signal2._replace(paired_peaks=pairing_result.paired_peaks, unpaired_peaks=pairing_result.unpaired_peaks2)
# Re-interpolate the PSD signals to a common frequency range to be able to plot them one against the other point by point # R² proved to be pretty instable to compute the similarity between the two belts
common_freqs = np.linspace(0, max_freq, 500) # So now, we use the Pearson correlation coefficient to compute the similarity
interp_psd1 = np.interp(common_freqs, signal1.freqs, signal1.psd) correlation, _ = pearsonr(signal1.psd, signal2.psd)
interp_psd2 = np.interp(common_freqs, signal2.freqs, signal2.psd) similarity_factor = correlation * 100
similarity_factor = np.clip(similarity_factor, 0, 100)
# Calculating R^2 to y=x line to compute the similarity between the two belts
ss_res = np.sum((interp_psd2 - interp_psd1) ** 2)
ss_tot = np.sum((interp_psd2 - np.mean(interp_psd2)) ** 2)
similarity_factor = (1 - (ss_res / ss_tot)) * 100
ConsoleOutput.print(f'Belts estimated similarity: {similarity_factor:.1f}%') ConsoleOutput.print(f'Belts estimated similarity: {similarity_factor:.1f}%')
# mhi = compute_mhi(similarity_factor, num_peaks, num_unpaired_peaks)
mhi = compute_mhi(similarity_factor, signal1, signal2) mhi = compute_mhi(similarity_factor, signal1, signal2)
ConsoleOutput.print(f'[experimental] Mechanical health: {mhi}') ConsoleOutput.print(f'[experimental] Mechanical health: {mhi}')
@@ -582,11 +570,11 @@ def belts_calibration(
# Add the accel_per_hz value to the title # Add the accel_per_hz value to the title
title_line5 = f'| Accel per Hz used: {accel_per_hz} mm/s²/Hz' title_line5 = f'| Accel per Hz used: {accel_per_hz} mm/s²/Hz'
fig.text(0.55, 0.915, title_line5, ha='left', va='top', fontsize=14, color=KLIPPAIN_COLORS['dark_purple']) fig.text(0.551, 0.915, title_line5, ha='left', va='top', fontsize=10, color=KLIPPAIN_COLORS['dark_purple'])
# Plot the graphs # Plot the graphs
plot_compare_frequency(ax1, signal1, signal2, signal1_belt, signal2_belt, max_freq) plot_compare_frequency(ax1, signal1, signal2, signal1_belt, signal2_belt, max_freq)
plot_versus_belts(ax3, common_freqs, signal1, signal2, interp_psd1, interp_psd2, signal1_belt, signal2_belt) plot_versus_belts(ax3, common_freqs, signal1, signal2, signal1_belt, signal2_belt)
# Adding a small Klippain logo to the top left corner of the figure # Adding a small Klippain logo to the top left corner of the figure
ax_logo = fig.add_axes([0.001, 0.894, 0.105, 0.105], anchor='NW') ax_logo = fig.add_axes([0.001, 0.894, 0.105, 0.105], anchor='NW')