import plotly.graph_objects as go
import numpy as np
from scipy.interpolate.interpolate import interp1d
from Helper import read_wav, get_pulses_area, split_pulses, signal_to_pulses, get_frontier #, save_wav, get_frontier
from scipy.signal import savgol_filter
# os.path.dirname(os.path.dirname(path))
'''==============='''
''' Read wav file '''
'''==============='''
fig = go.Figure()
parent_path = str(Path(os.path.abspath('./')).parents[1])
print(parent_path)
name = "piano33"
W, fps = read_wav(f"{parent_path}/Python/Samples/{name}.wav")
W = W[100103:100775]
W = W - np.average(W)
amplitude = np.max(np.abs(W))
W = W / amplitude
n = W.size
print(n)
X = np.arange(n)
pulses = signal_to_pulses(W)
areas_X, areas_Y = get_pulses_area(pulses)
pos_pulses, neg_pulses, pos_noises, neg_noises = split_pulses(pulses)
class Pulse:
# pulses = []
# max_length = 0
def __init__(self, x0, W):
# Pulse.pulses.append(self)
self.start = x0 - .5
self.end = self.start + W.shape[0]
self.n = W.shape[0]
self.W = W
self.normalized_W = W / np.max(np.abs(W))
self.a = np.average(W)
self.group = None
W, fps = read_wav("Samples/piano33.wav")
W = W - np.average(W)
a = np.max(np.abs(W))
W = W / a
# W = savgol_filter(W, 5, 3)
W = W[70000:80000]
n = W.shape[0]
X = np.arange(n)
## Split the signal into pulses
pulses = []
sign = np.sign(W[0])
x0 = 0
s = (a + b + c) / 2.0
area = np.sqrt(s * (s - a) * (s - b) * (s - c))
circum_r = a * b * c / (4.0 * area)
if circum_r < alpha:
add_edge(edges, ia, ib)
add_edge(edges, ib, ic)
add_edge(edges, ic, ia)
return edges
'''==============='''
''' Read wav file '''
'''==============='''
name = "piano33"
W, fps = read_wav(f"Samples/{name}.wav")
W = W - np.average(W)
amplitude = np.max(np.abs(W))
W = W / amplitude
# W = W [ : W.size // 10]
pulses = signal_to_pulses(W)
# avg_len = np.average([p.len for p in pulses])
# avg_y = np.average([abs(p.y) for p in pulses])
# for p in pulses:
# p.len = p.len / avg_len
# p.y = p.y / avg_y
areas_X, areas_Y = get_pulses_area(pulses)
self.normalized_W = W / np.max(np.abs(W))
self.avg = np.average(W)
self.var = np.var(W)
centered_W = W - np.average(W)
FT = np.fft.rfft(centered_W)
self.f = np.argmax(np.abs(FT))
self.p = np.angle(FT[self.f])
sign = np.sign(W[0])
self.zeros = 0
for w in centered_W:
if sign != np.sign(w):
self.zeros += 1
sign = np.sign(w)
W, fps = read_wav("Samples/tom.wav")
W = W - np.average(W)
a = np.max(np.abs(W))
W = W / a
# W = savgol_filter(W, 5, 3)
# W = W [ : 5000]
n = W.shape[0]
X = np.arange(n)
## Split the signal into pulses
pulses = []
sign = np.sign(W[0])
x0 = 0
