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François Boulogne 2025-08-29 18:22:06 +02:00
parent dea64a7b7f
commit e1ae1c6236
2 changed files with 9 additions and 13 deletions
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2
mysignal/__init__.py
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@ -1 +1 @@
__version__ = '0.1.4' __version__ = '0.1.5'

20
mysignal/phasefreq.py
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@ -9,7 +9,7 @@ from scipy.stats import linregress
from scipy.optimize import curve_fit from scipy.optimize import curve_fit
def filter_signal_by_modes(time, signal, num_modes=1, bandwidth_factor=0.1, nyquist=False): def filter_signal_by_modes(time, signal, num_modes=1, bandwidth_factor=0.1):
""" """
Filtre un signal pour extraire ses composantes fréquentielles dominantes. Filtre un signal pour extraire ses composantes fréquentielles dominantes.
@ -68,17 +68,13 @@ def filter_signal_by_modes(time, signal, num_modes=1, bandwidth_factor=0.1, nyqu
lowcut = max(0.01, freq - bandwidth) # Éviter les fréquences négatives lowcut = max(0.01, freq - bandwidth) # Éviter les fréquences négatives
highcut = freq + bandwidth highcut = freq + bandwidth
if nyquist: # Éviter les fréquences supérieures à la fréquence de Nyquist
# Éviter les fréquences supérieures à la fréquence de Nyquist nyq = 0.5 * fs
nyq = 0.5 * fs highcut = min(highcut, nyq * 0.99) # Laisser une marge
highcut = min(highcut, nyq * 0.99) # Laisser une marge
# Normaliser les fréquences
# Normaliser les fréquences lowcut_norm = lowcut / nyq
lowcut_norm = lowcut / nyq highcut_norm = highcut / nyq
highcut_norm = highcut / nyq
else:
lowcut_norm = lowcut
highcut_norm = highcut
print(f"Filtrage à {freq:.2f} Hz: [{lowcut_norm:.3f}, {highcut_norm:.3f}] (normalisé)") print(f"Filtrage à {freq:.2f} Hz: [{lowcut_norm:.3f}, {highcut_norm:.3f}] (normalisé)")