def bellenModel(h, params, alg = ['rk4Neutral', 2], tref = [], pref = []) :
h /= 10
t0 = np.arange(-1., 0, h)
X0 = np.zeros((len(t0), 1))
X0[:, 0] = 1-t0
start = process_time()
t, p = dde.rk4Delay(t0, X0, 4., '../examples/bellen/bellen.c', params, alg = alg[0], interpOrder=alg[1])
stop = process_time()
p = p.reshape((len(p)))
if (len(tref) > 0) :
interp = np.abs(p-np.interp(t, tref, pref))/max(abs(pref))
return([h, max(interp), stop-start])
else :
return(t, p)
def stateArtModel(h, params, alg = 'rk4Neutral', tref = [], pref = []) :
h /= 44100
t0 = np.arange(-1e-3, h, h)
X0 = np.zeros((len(t0), 2*K+2))
for i in range(2*K+2) :
if i % 2 == 0 :
X0[:, i] = 1e-6*np.cos(2*np.pi*t0*omega1/3.5)
else :
X0[:, i] = -1e-6*np.sin(2*np.pi*t0*omega1/3.5)
t0 *= omega1
start = process_time()
t, X = dde.rk4Delay(t0, X0, omega1*0.4, '../examples/stateArt/stateArt.c', params, alg = alg[0], interpOrder=alg[1])
stop = process_time()
p = np.sum(X[:, ::2], axis = 1)
if (len(tref) > 0) :
interp = np.abs(p-np.interp(t, tref, pref))/max(abs(pref))
return([omega1*h, max(interp), stop-start])
else :
return(t, p)
def bellen(TMax, h):
t0 = np.arange(-1., 0, h)
X0 = np.zeros((len(t0), 1))
X0[:, 0] = 1 - t0
# t, X = dde.rk4Delay(t0, X0, TMax, 'bellen.c', [], alg = 'rk4Neutral')
# plt.plot(t, X, label="rk4")
t, X = dde.rk4Delay(t0,
X0,
TMax,
'bellen.c', [],
alg='eulerNeutral',
interpOrder=3)
plt.plot(t, X, label='euler order 3')
t, X = dde.rk4Delay(t0,
X0,
TMax,
'bellen.c', [],
alg='eulerNeutral',
interpOrder=2)
plt.plot(t, X, label='euler order 2')
t, X = dde.rk4Delay(t0,
X0,
TMax,
'bellen.c', [],
alg='eulerNeutral',
interpOrder=1)
plt.plot(t, X, label='euler order 1')
# t, X = dde.rk4Delay(t0, X0, TMax, 'bellen.c', [], alg = 'eulerImpNeutral')
# plt.plot(t, X, label='eulerImp')
# t, X = dde.rk4Delay(t0, X0, TMax, 'bellen.c', [], alg = 'impTrNeutral')
# plt.plot(t, X, label='impTrNeutral')
plt.legend()
def toyModel(TMax, h):
omega1 = 2764.
t0 = np.arange(-5, 0, h)
X0 = np.zeros((len(t0), 4))
X0[:, 0] = 1e-1 + 0 * np.cos(100 * np.pi * t0)
X0[:, 1] = 0 * np.cos(3 * np.pi / 2 * t0)
T = TMax * omega1
t, X = dde.rk4Delay(t0, X0, T, 'toy.c', [30, T])
p = X[:, 0] + X[:, 2]
#plt.plot(t, p)
# Calcul les fréquences
w_len = 1024 * 4
w_step = 256 * 4
f0_min = 0.05
f0_max = 3
harmo_thresh = 0.85
sr = int(1 / (t[1] - t[0]))
pitches, harmonic_rates, argmins, times = yin.compute_yin(
p[len(t0):], sr, None, w_len, w_step, f0_min, f0_max, harmo_thresh)
pitches = np.array(pitches)
times = np.array(times)
# Changements de signes
""" Calcul bourrin
I = np.where(p[1:]*p[:-1] < 0)[0]
F = t[I]
treal = 0.1+(T/2-abs(t[I[2:]]-T/2))*30/T
Freal = omega1/(F[2:]-F[:-2])
plt.plot(treal[:len(treal)//2], Freal[:len(treal)//2])
plt.plot(treal[len(treal)//2:], Freal[len(Freal)//2:])"""
tau = 0.1 + (T / 2 - abs(times - T / 2)) * 30 / T
#print(times[-1], t[-1])
plt.plot(tau[:len(tau) // 2], omega1 * np.array(pitches[:len(tau) // 2]))
#plt.plot(tau[len(tau)//2:], omega1*np.array(pitches[len(tau)//2:]))
from scipy.io.wavfile import write
scaled = np.int16(p / np.max(np.abs(p)) * 32767)
write('test.wav', int(sr * omega1), scaled)
def bellen(TMax, h):
t0 = np.arange(-1., 0, h)
X0 = np.zeros((len(t0), 1))
X0[:, 0] = 1 - t0
t, X = dde.rk4Delay(t0, X0, TMax, 'ode.c', [], alg='rk4Neutral')
plt.plot(t, X, label="rk4")
t, X = dde.rk4Delay(t0, X0, TMax, 'ode.c', [], alg='eulerNeutral')
plt.plot(t, X, label='euler')
t, X = dde.rk4Delay(t0, X0, TMax, 'ode.c', [], alg='eulerImpNeutral')
plt.plot(t, X, label='eulerImp')
t, X = dde.rk4Delay(t0, X0, TMax, 'ode.c', [], alg='impTrNeutral')
plt.plot(t, X, label='impTrNeutral')
plt.legend()
def stateArtModel(TMax, h, params, alg='rk4Neutral', interpOrder=2):
t0 = np.arange(-1e-2, h, h)
X0 = np.zeros((len(t0), 2 * K + 2))
label = 'h= ' + str(h) + ', ' + alg #str(h) + ', ' + str(params) +
for i in range(2 * K + 2):
if i % 2 == 0:
X0[:, i] = 1e-6 * np.cos(2 * np.pi * t0 * omega1 / 3.5)
else:
X0[:, i] = -1e-6 * np.sin(2 * np.pi * t0 * omega1 / 3.5)
t0 *= omega1
T = TMax * omega1
t, X = dde.rk4Delay(t0,
X0,
T,
'stateArt.c',
params,
alg=alg,
interpOrder=interpOrder)
p = np.sum(X[:, ::2], axis=1)
# plt.figure("Wave")
# plt.plot(t/omega1, p, label=label)
# plt.legend()
# Calcul les fréquences
w_len = 1024 * 8
w_step = 256 // 2
f0_min = 200
f0_max = 2500
harmo_thresh = 0.6
sr = int(omega1 / (t[1] - t[0]))
pitches, harmonic_rates, argmins, times = yin.compute_yin(
p[len(t0):], sr, None, w_len, w_step, f0_min, f0_max, harmo_thresh)
pitches = np.array(pitches)
times = np.array(times)
Pm = params[0] + (TMax / 2 - abs(times - TMax / 2)) / TMax * (params[1] -
params[0])
plt.figure("Yin")
ax = plt.gca()
color = next(ax._get_lines.prop_cycler)['color']
plt.plot(Pm[:len(Pm) // 2],
np.array(pitches[:len(Pm) // 2]),
'-',
c=color,
label=label)
plt.plot(Pm[len(Pm) // 2:], np.array(pitches[len(Pm) // 2:]), ':', c=color)
plt.legend()
from scipy.io.wavfile import write
scaled = np.int16(p / np.max(np.abs(p)) * 32767)
write('test' + '.'.join(str(e) for e in params) + '.wav', int(sr), scaled)