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2 changed files with 9 additions and 13 deletions
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@ -1 +1 @@
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__version__ = '0.1.4'
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__version__ = '0.1.5'
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@ -9,7 +9,7 @@ from scipy.stats import linregress
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from scipy.optimize import curve_fit
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def filter_signal_by_modes(time, signal, num_modes=1, bandwidth_factor=0.1, nyquist=False):
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def filter_signal_by_modes(time, signal, num_modes=1, bandwidth_factor=0.1):
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"""
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Filtre un signal pour extraire ses composantes fréquentielles dominantes.
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@ -68,17 +68,13 @@ def filter_signal_by_modes(time, signal, num_modes=1, bandwidth_factor=0.1, nyqu
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lowcut = max(0.01, freq - bandwidth) # Éviter les fréquences négatives
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highcut = freq + bandwidth
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if nyquist:
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# Éviter les fréquences supérieures à la fréquence de Nyquist
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nyq = 0.5 * fs
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highcut = min(highcut, nyq * 0.99) # Laisser une marge
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# Normaliser les fréquences
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lowcut_norm = lowcut / nyq
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highcut_norm = highcut / nyq
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else:
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lowcut_norm = lowcut
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highcut_norm = highcut
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# Éviter les fréquences supérieures à la fréquence de Nyquist
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nyq = 0.5 * fs
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highcut = min(highcut, nyq * 0.99) # Laisser une marge
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# Normaliser les fréquences
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lowcut_norm = lowcut / nyq
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highcut_norm = highcut / nyq
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print(f"Filtrage à {freq:.2f} Hz: [{lowcut_norm:.3f}, {highcut_norm:.3f}] (normalisé)")
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