def test_noise():
N = 500
rate = 1.0
w = noise.white(N)
b = noise.brown(N)
v = noise.violet(N)
p = noise.pink(N)
assert len(w) == N
assert len(b) == N
assert len(v) == N-1 # why?
assert len(p) == N
def test_noise():
N = 500
rate = 1.0
w = noise.white(N)
b = noise.brown(N)
v = noise.violet(N)
p = noise.pink(N)
assert len(w) == N
assert len(b) == N
assert len(v) == N - 1 # why?
assert len(p) == N
def add_brown_noise(filename):
print "Adding Brown Noise..."
fps, snd_array = wavfile.read('../audio/transformed/' + filename)
noise1 = noise.brown(len(snd_array))
noise1 = map(lambda x: int(x/.01), noise1)
noise2 = np.array([noise1,noise1]).T
noisy_array = noise2 + snd_array
filename, file_extension = os.path.splitext(filename)
filename = "../audio/transformed/"+filename+"brownnoise.wav"
scaled = np.int16(noisy_array/np.max(np.abs(noisy_array)) * 32767)
write(filename, 44100, scaled)
return
def test_noise():
N = 500
#rate = 1.0
w = noise.white(N)
b = noise.brown(N)
v = noise.violet(N)
p = noise.pink(N)
# check output length
assert len(w) == N
assert len(b) == N
assert len(v) == N
assert len(p) == N
# check output type
for x in [w, b, v, p]:
assert type(x) == numpy.ndarray, "%s is not numpy.ndarray" % (type(x))
if __name__ == "__main__":
print("allatools tests with various noise types")
# we test ADEV etc. by calculations on synthetic data
# with known slopes of ADEV
t = [tt for tt in numpy.logspace(0, 3, 50)] # tau values from 1 to 1000
plt.subplot(111, xscale="log", yscale="log")
N = 100000
# Colors: http://en.wikipedia.org/wiki/Colors_of_noise
# Brownian a.k.a random walk frequency => sqrt(tau) ADEV
print("Random Walk frequency noise - should have sqrt(tau) ADEV")
freq_rw = noise.brown(N)
phase_rw_rw = numpy.cumsum(noise.brown(N)) # integrate to get phase
plotallan(plt, freq_rw, 1, t, 'm.')
plotallan_phase(plt, phase_rw_rw, 1, t, 'mo',label='random walk frequency')
plotline(plt, +0.5, t, 'm',label="f^(+1/2)")
# pink frequency noise => constant ADEV
print("Pink frequency noise - should have constant ADEV")
freq_pink = noise.pink(N)
phase_p = numpy.cumsum(noise.pink(N)) # integrate to get phase, color??
plotallan_phase(plt, phase_p, 1, t, 'co',label="pink/flicker frequency noise")
plotallan(plt, freq_pink, 1, t, 'c.')
plotline(plt, 0, t, 'c',label="f^0")
# white frequency modulation => 1/sqrt(tau) ADEV
print("White frequency noise - should have 1/sqrt(tau) ADEV")
if __name__ == "__main__":
print("allatools tests with various noise types")
# we test ADEV etc. by calculations on synthetic data
# with known slopes of ADEV
t = [tt for tt in numpy.logspace(0, 3, 50)] # tau values from 1 to 1000
plt.subplot(111, xscale="log", yscale="log")
N = 100000
# Colors: http://en.wikipedia.org/wiki/Colors_of_noise
# Brownian a.k.a random walk frequency => sqrt(tau) ADEV
print("Random Walk frequency noise - should have sqrt(tau) ADEV")
freq_rw = noise.brown(N)
phase_rw_rw = numpy.cumsum(noise.brown(N)) # integrate to get phase
plotallan(plt, freq_rw, 1, t, 'm.')
plotallan_phase(plt,
phase_rw_rw,
1,
t,
'mo',
label='random walk frequency')
plotline(plt, +0.5, t, 'm', label="f^(+1/2)")
# pink frequency noise => constant ADEV
print("Pink frequency noise - should have constant ADEV")
freq_pink = noise.pink(N)
phase_p = numpy.cumsum(noise.pink(N)) # integrate to get phase, color??
plotallan_phase(plt,
if __name__ == "__main__":
print("allatools tests with various noise types")
# we test ADEV etc. by calculations on synthetic data
# with known slopes of ADEV
t = [tt for tt in numpy.logspace(0, 3, 50)] # tau values from 1 to 1000
plt.subplot(111, xscale="log", yscale="log")
N = 100000
# Colors: http://en.wikipedia.org/wiki/Colors_of_noise
# Brownian a.k.a random walk frequency => sqrt(tau) ADEV
print("Random Walk frequency noise - should have sqrt(tau) ADEV")
freq_rw = noise.brown(N)
phase_rw_rw = numpy.cumsum(noise.brown(N)) # integrate to get phase
plotallan(plt, freq_rw, 1, t, 'm.')
plotallan_phase(plt,
phase_rw_rw,
1,
t,
'mo',
label='random walk frequency')
plotline(plt, +0.5, t, 'm', label="f^(+1/2)")
# pink frequency noise => constant ADEV
print("Pink frequency noise - should have constant ADEV")
freq_pink = noise.pink(N)
phase_p = numpy.cumsum(noise.pink(N)) # integrate to get phase, color??
plotallan_phase(plt,
# the predicted S_y, S_fi, S_x, and ADEV given in the table
# AW 2015-08-06
# from the ieee1139 table
# PSD_y(f) = h2 * f^-2 fractional frequency PSD
# PSD_fi(f) = h2 * vo^2 * f^-4 phase (radians) PSD
# PSD_x(f) = h2 * (2 pi)^-2 * f^-4 phase (time) PSD
# ADEV_y(tau) = sqrt{ 2*pi^2/3 * h2 * tau } Allan deviation
fs=12.8 # sampling frequency in Hz (code should work for any non-zero value here)
h2=2e-20 # PSD f^-2 coefficient
N=10*4096 # number of samples
v0 = 1.2345e6 # nominal oscillator frequency
y = noise.brown(N=N,b2=h2,fs=fs) # fractional frequency
x = allantools.frequency2phase(y,fs) # phase in seconds
fi = [2*math.pi*v0*xx for xx in x] # phase in radians
t = np.linspace(0, (1.0/fs)*N, len(y)) # time-series time axis
# time-series figure
plt.figure()
plt.plot(t,y,label='y')
plt.plot(t,x[1:],label='x')
plt.legend()
plt.xlabel('Time / s')
plt.ylabel('Fractional frequency')
# note: calculating the PSD of an 1/f^4 signal using fft seems to be difficult
# the welch method returns a correct 1/f^4 shaped PSD, but fft often does not
# things that may help
# the predicted S_y, S_fi, S_x, and ADEV given in the table
# AW 2015-08-06
# from the ieee1139 table
# PSD_y(f) = h2 * f^-2 fractional frequency PSD
# PSD_fi(f) = h2 * vo^2 * f^-4 phase (radians) PSD
# PSD_x(f) = h2 * (2 pi)^-2 * f^-4 phase (time) PSD
# ADEV_y(tau) = sqrt{ 2*pi^2/3 * h2 * tau } Allan deviation
fs=12.8 # sampling frequency in Hz (code should work for any non-zero value here)
h2=2e-20 # PSD f^-2 coefficient
N=10*4096 # number of samples
v0 = 1.2345e6 # nominal oscillator frequency
y = noise.brown(num_points=N, b2=h2, fs=fs) # fractional frequency
x = allantools.frequency2phase(y,fs) # phase in seconds
fi = [2*math.pi*v0*xx for xx in x] # phase in radians
t = np.linspace(0, (1.0/fs)*N, len(y)) # time-series time axis
# time-series figure
plt.figure()
fig, ax1 = plt.subplots()
ax1.plot(t, y, label='y')
ax2 = ax1.twinx()
ax2.plot(t, x[1:], label='x')
plt.legend()
plt.xlabel('Time / s')
plt.ylabel('Fractional frequency')
# note: calculating the PSD of an 1/f^4 signal using fft seems to be difficult
