def example2():
"""
Compute the GRADEV of a nonstationary white phase noise.
"""
N = 1000 # number of samples
f = 1 # data samples per second
s = 1 + 5 / N * np.arange(0, N)
y = s * np.random.randn(1, N)[0, :]
x = [xx for xx in np.linspace(1, len(y), len(y))]
x_ax, y_ax, (err_l, err_h), ns = allan.gradev(y,
data_type='phase',
rate=f,
taus=x)
plt.loglog(x_ax, y_ax, 'b.', label="No gaps")
y[int(0.4 * N):int(0.6 * N, )] = np.NaN # Simulate missing data
x_ax, y_ax, (err_l, err_h), ns = allan.gradev(y,
data_type='phase',
rate=f,
taus=x)
plt.loglog(x_ax, y_ax, 'g.', label="With gaps")
plt.grid()
plt.legend()
plt.xlabel('Tau / s')
plt.ylabel('Overlapping Allan deviation')
plt.show()
def example1():
"""
Compute the GRADEV of a white phase noise. Compares two different
scenarios. 1) The original data and 2) ADEV estimate with gap robust ADEV.
"""
N = 1000
f = 1
y = np.random.randn(1, N)[0, :]
x = [xx for xx in np.linspace(1, len(y), len(y))]
x_ax, y_ax, (err_l, err_h), ns = allan.gradev(y,
data_type='phase',
rate=f,
taus=x)
plt.errorbar(x_ax, y_ax, yerr=[err_l, err_h], label='GRADEV, no gaps')
y[int(np.floor(0.4 *
N)):int(np.floor(0.6 *
N))] = np.NaN # Simulate missing data
x_ax, y_ax, (err_l, err_h), ns = allan.gradev(y,
data_type='phase',
rate=f,
taus=x)
plt.errorbar(x_ax, y_ax, yerr=[err_l, err_h], label='GRADEV, with gaps')
plt.xscale('log')
plt.yscale('log')
plt.grid()
plt.legend()
plt.xlabel('Tau / s')
plt.ylabel('Overlapping Allan deviation')
plt.show()
def test_gradev():
x_ax, y_ax, (el, eh), ns = at.gradev(y, data_type='phase', rate=f, taus=x)
x_ax_gap, y_ax_gap, (el_gap, eh_gap), ns_gap = at.gradev(y_gap,
data_type='phase',
rate=f,
taus=x)
for i in range(len(x_ax)):
i_gap = np.where(x_ax_gap == x_ax[i])
if len(i_gap[0]) > 0:
# Seems like a loose condition
assert (np.log(y_ax[i] / y_ax_gap[i_gap[0][0]]) < 1)
def example2():
"""
Compute the GRADEV of a nonstationary white phase noise.
"""
N=1000 # number of samples
f = 1 # data samples per second
s=1+5/N*np.arange(0,N)
y=s*np.random.randn(1,N)[0,:]
x = [xx for xx in np.linspace(1,len(y),len(y))]
x_ax, y_ax, (err_l, err_h) , ns = allan.gradev(frequency=y,rate=f,taus=x)
plt.loglog(x_ax, y_ax,'b.',label="No gaps")
y[int(0.4*N):int(0.6*N,)] = np.NaN # Simulate missing data
x_ax, y_ax, (err_l, err_h), ns = allan.gradev(frequency=y,rate=f,taus=x)
plt.loglog(x_ax, y_ax,'g.',label="With gaps")
plt.grid()
plt.legend()
plt.xlabel('Tau / s')
plt.ylabel('Overlapping Allan deviation')
plt.show()
def example1():
"""
Compute the GRADEV of a white phase noise. Compares two different
scenarios. 1) The original data and 2) ADEV estimate with gap robust ADEV.
"""
N = 1000
f = 1
y = np.random.randn(1,N)[0,:]
x = [xx for xx in np.linspace(1,len(y),len(y))]
x_ax, y_ax, (err_l, err_h), ns = allan.gradev(frequency=y,rate=f,taus=x)
plt.errorbar(x_ax, y_ax,yerr=[err_l,err_h],label='GRADEV, no gaps')
y[np.floor(0.4*N):np.floor(0.6*N)] = np.NaN # Simulate missing data
x_ax, y_ax, (err_l, err_h) , ns = allan.gradev(frequency=y,rate=f,taus=x)
plt.errorbar(x_ax, y_ax,yerr=[err_l,err_h], label='GRADEV, with gaps')
plt.xscale('log')
plt.yscale('log')
plt.grid()
plt.legend()
plt.xlabel('Tau / s')
plt.ylabel('Overlapping Allan deviation')
plt.show()
