add option dominant freqs
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d007702712
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2 changed files with 20 additions and 18 deletions
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@ -1 +1 @@
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__version__ = '0.1.23'
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__version__ = '0.1.24'
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@ -12,7 +12,7 @@ from scipy.stats import linregress
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from scipy.optimize import curve_fit
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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, required_cycles=100):
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def filter_signal_by_modes(time, signal, num_modes=1, bandwidth_factor=0.1, required_cycles=100, dominant_freqs=None):
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"""
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"""
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Filtre un signal pour extraire ses composantes fréquentielles dominantes.
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Filtre un signal pour extraire ses composantes fréquentielles dominantes.
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@ -20,6 +20,7 @@ def filter_signal_by_modes(time, signal, num_modes=1, bandwidth_factor=0.1, requ
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time (array-like): Array contenant les données temporelles.
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time (array-like): Array contenant les données temporelles.
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signal (array-like): Array contenant les données du signal.
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signal (array-like): Array contenant les données du signal.
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num_modes (int): Nombre de modes fréquentiels à extraire.
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num_modes (int): Nombre de modes fréquentiels à extraire.
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dominant_freqs: les frequences des modes, if None, computed internally
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Returns:
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Returns:
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tuple: (filtered_signals, frequencies, time_filtered)
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tuple: (filtered_signals, frequencies, time_filtered)
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@ -38,6 +39,7 @@ def filter_signal_by_modes(time, signal, num_modes=1, bandwidth_factor=0.1, requ
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# Supprimer la composante DC
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# Supprimer la composante DC
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signal_clean = signal - np.mean(signal)
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signal_clean = signal - np.mean(signal)
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if dominant_freqs is None:
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# Calculer la FFT
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# Calculer la FFT
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n = len(time)
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n = len(time)
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fft_signal = fft(signal_clean)
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fft_signal = fft(signal_clean)
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