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switched to pearson coefficient for belts similarity

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Félix Boisselier
2024-06-30 22:27:46 +02:00
parent 6712506862
commit 92a651b6a6
1 changed files with 6 additions and 4 deletions
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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')
@@ -518,10 +519,11 @@ 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)
# Calculating R^2 to y=x line to compute the similarity between the two belts # R² proved to be pretty instable to compute the similarity between the two belts
ss_res = np.sum((signal2.psd - signal1.psd) ** 2) # So now, we use the Pearson correlation coefficient to compute the similarity
ss_tot = np.sum((signal2.psd - np.mean(signal2.psd)) ** 2) correlation, _ = pearsonr(signal1.psd, signal2.psd)
similarity_factor = (1 - (ss_res / ss_tot)) * 100 similarity_factor = correlation * 100
similarity_factor = np.clip(similarity_factor, 0, 100)
ConsoleOutput.print(f'Belts estimated similarity: {similarity_factor:.1f}%') ConsoleOutput.print(f'Belts estimated similarity: {similarity_factor:.1f}%')
mhi = compute_mhi(similarity_factor, signal1, signal2) mhi = compute_mhi(similarity_factor, signal1, signal2)