if x1 - x0 >= 4:
yx = interpolate.interp1d(np.linspace(0, 1, x1 - x0), W[x0:x1],
"cubic")
# Ylocal = yx(Xlocal)
pos_norm_pcs.append(yx(Xlocal))
pos_avgpc = np.average(np.array(pos_norm_pcs), 0)
return pos_avgpc, pos_orig_pcs, pos_norm_pcs
'''==============='''
''' Read wav file '''
'''==============='''
name = "alto"
W, fps = se.read_wav(f"Samples/{name}.wav")
W = W - np.average(W)
amp = np.max(np.abs(W))
n = W.size
X = np.arange(n)
Xpos_orig, Xneg_orig = se.get_frontiers(W)
Xpos = hp.refine_frontier_iter(Xpos_orig, W)
Xneg = hp.refine_frontier_iter(Xneg_orig, W)
siz = min(Xpos.size, Xneg.size)
Average_ref_X = (Xpos[:siz] + Xneg[:siz]) / 2
Average_ref_X_smooth = np.round(savgol_filter(Average_ref_X, 51,
3)).astype(np.int)
Average_ref_X_smooth = np.unique(np.abs(Average_ref_X_smooth))
import numpy as np
from scipy.signal import butter, filtfilt
def butter_lowpass_filter(data, fps, cutoff = 2000, order = 2):
nyq = 0.5 * fps
normal_cutoff = cutoff / nyq
# Get the filter coefficients
b, a = butter(order, normal_cutoff, btype='low', analog=False)
y = filtfilt(b, a, data)
return y
'''==============='''
''' Read wav file '''
'''==============='''
name = "alto"
W, fps = se.read_wav(f"C:/Users/tesse/Desktop/Files/Dropbox/0_Science/reps/envelope/test_samples/{name}.wav")
W = W - np.average(W)
amplitude = np.max(np.abs(W))
W = W / amplitude
n = W.size
print(f"n={n}")
X = np.arange(n)
y = butter_lowpass_filter(W, fps)
fig = go.Figure()
fig.add_trace(go.Scatter(
y = W,
# line = dict(shape = 'spline' ),
import sys
# sys.path.append("signal_envelope/")
import numpy as np
import signal_envelope as se
from scipy import interpolate
import plotly.graph_objects as go
name = "alto"
# name="bend"
# name="spoken_voice"
W, _ = se.read_wav(f"test_samples/{name}.wav")
amp = np.max(np.abs(W))
W = 4 * W / amp
X = np.arange(W.size)
Xpos, Xneg = se.get_frontiers(W, 0)
E = se.get_frontiers(W, 1)
f = interpolate.interp1d(E, np.abs(W[E]), kind="linear", fill_value="extrapolate")
E = f(X)
for i in range(E.size):
if E[i] < 0.1:
E[i] = 0.1
C = W / E
'''============================================================================'''
''' PLOT '''
'''============================================================================'''
def to_plot(Matrix):
X = []
Y = []
for line in Matrix:
for x, y in enumerate(line):
X.append(x)
Y.append(y)
X.append(None)
Y.append(None)
return X, Y
name = "alto"
'''###### Read wav file ######'''
W, fps = se.read_wav(f"{name}.wav")
W = W - np.average(W)
amp = np.max(np.abs(W))
W = W / amp
n = W.size
'''###### Read Pseudo-cycles info ######'''
Xpc = np.genfromtxt(name + ".csv", delimiter=",")
X_xpcs, Y_xpcs = [], []
for x in Xpc:
X_xpcs.append(x)
X_xpcs.append(x)
X_xpcs.append(None)
Y_xpcs.append(-1)
Y_xpcs.append(1)
Y_xpcs.append(None)
