def temporal_envelope(s, sample_rate, cutoff_freq=200.0, resample_rate=None):
"""
Get the temporal envelope from the sound pressure waveform.
s: the signal
sample_rate: the sample rate of the signal
cutoff_freq: the cutoff frequency of the low pass filter used to create the envelope
Returns the temporal envelope of the signal, with same sample rate or downsampled.
"""
#rectify
srect = np.abs(s)
#low pass filter
if cutoff_freq is not None:
srect = lowpass_filter(srect, sample_rate, cutoff_freq, filter_order=4)
srect[srect < 0] = 0
if resample_rate is not None:
lensound = len(srect)
t=(np.array(range(lensound),dtype=float))/sample_rate
lenresampled = int(round(float(lensound)*resample_rate/sample_rate))
(srectresampled, tresampled) = resample(srect, lenresampled, t=t, axis=0, window=None)
return (srectresampled, tresampled)
else:
return srect
def bandpass_timefreq(s, frequencies, sample_rate):
"""
Bandpass filter signal s at the given frequency bands, and then use the Hilber transform
to produce a complex-valued time-frequency representation of the bandpass filtered signal.
"""
freqs = sorted(frequencies)
tf_raw = np.zeros([len(frequencies), len(s)], dtype='float')
tf_freqs = list()
for k,f in enumerate(freqs):
#bandpass filter signal
if k == 0:
tf_raw[k, :] = lowpass_filter(s, sample_rate, f)
tf_freqs.append( (0.0, f) )
else:
tf_raw[k, :] = bandpass_filter(s, sample_rate, freqs[k-1], f)
tf_freqs.append( (freqs[k-1], f) )
#compute analytic signal
tf = hilbert(tf_raw, axis=1)
#print 'tf_raw.shape=',tf_raw.shape
#print 'tf.shape=',tf.shape
return np.array(tf_freqs),tf_raw,tf
# Testing the coherence import numpy as np import matplotlib.pyplot as plt from lasp.coherence import coherence_jn from lasp.signal import lowpass_filter # Make two gaussian signals sample_rate = 1000.0 tlen = 2.0 # 2 second signal # Make space for both signals s1 = np.random.normal(0, 1, int(tlen*sample_rate)) s2 = lowpass_filter(s1, sample_rate, 250.0) + np.random.normal(0, 1, int(tlen*sample_rate)) freq1,c_amp,c_var_amp,c_phase,c_phase_var, cohe_unbiased, cohe_se = coherence_jn(s1, s2, sample_rate, 0.1, 0.05) plt.figure() plt.plot(freq1, cohe_unbiased, 'k-', linewidth=2.0, alpha=0.9) plt.plot(freq1, cohe_unbiased+2*(cohe_se), 'g-', linewidth=2.0, alpha=0.75) plt.plot(freq1, cohe_unbiased-2*(cohe_se), 'c-', linewidth=2.0, alpha=0.75) plt.show()
