def test_autocorrelation(self):
"""test_autocorrelation"""
num_samples = 4096
test_signal = testsignals.noisy_sine_wave(num_samples)
order = 5
# calculate autocorrelation coefficients
r = np.zeros(order + 1)
for i in range(order + 1):
for k in range(num_samples - i):
r[i] += test_signal[k] * test_signal[k + i]
# form matrices
rows = []
for i in range(order):
rows.append([r[abs(i - k)] for k in range(order)])
A = np.matrix(rows)
B = np.matrix(np.array([[-r[i]] for i in range(1, order + 1)]))
coefs = np.linalg.solve(A, B)
ld_coefs = autocorrelation(test_signal, order)
# compare coefficients
self.assertEquals(order, coefs.T.A[0].size)
self.assertEquals(order, ld_coefs.size)
for i in range(order):
self.assertAlmostEquals(coefs.T.A[0][i],
ld_coefs[i],
places=FLOAT_PRECISION)
def test_covariance(self):
num_samples = 4096
test_signal = testsignals.noisy_sine_wave(num_samples)
order = 5
n = 50
coefs = covariance(test_signal, order, n)
predict(test_signal, coefs, 3)
def test_covariance(self):
num_samples = 4096
test_signal = testsignals.noisy_sine_wave(num_samples)
order = 5
n = 50
coefs = covariance(test_signal, order, n)
predict(test_signal, coefs, 3)
def test_covariance(self):
"""test_covariance"""
num_samples = 4096
test_signal = testsignals.noisy_sine_wave(num_samples)
order = 5
n = 50
coefs = covariance(test_signal, order, n)
cp = predict(test_signal, coefs, 3)
raise Exception("NotYetImplemented")
def test_covariance(self):
"""test_covariance"""
num_samples = 4096
test_signal = testsignals.noisy_sine_wave(num_samples)
order = 5
n = 50
coefs = covariance(test_signal, order, n)
cp = predict(test_signal, coefs, 3)
raise Exception("NotYetImplemented")
def test_burg(self):
"""test_burg"""
num_samples = 1024
test_signal = testsignals.noisy_sine_wave(num_samples)
order = 5
b_coefs = burg(test_signal, order)
brec_coefs = burg_rec(test_signal, order)
self.assertEquals(b_coefs.size, brec_coefs.size)
for i in range(order):
self.assertAlmostEquals(b_coefs[i], brec_coefs[i],
places=FLOAT_PRECISION)
def test_burg(self):
"""test_burg"""
num_samples = 1024
test_signal = testsignals.noisy_sine_wave(num_samples)
order = 5
b_coefs = burg(test_signal, order)
brec_coefs = burg_rec(test_signal, order)
self.assertEquals(b_coefs.size, brec_coefs.size)
for i in range(order):
self.assertAlmostEquals(b_coefs[i],
brec_coefs[i],
places=FLOAT_PRECISION)
def test_autocorrelation(self):
num_samples = 4096
test_signal = testsignals.noisy_sine_wave(num_samples)
order = 5
# calculate autocorrelation coefficients
r = np.zeros(order + 1)
for i in range(order + 1):
for k in range(num_samples - i):
r[i] += test_signal[k] * test_signal[k + i]
# form matrices
rows = []
for i in range(order):
rows.append([r[abs(i - k)] for k in range(order)])
A = np.matrix(rows)
B = np.matrix(np.array([[-r[i]] for i in range(1, order + 1)]))
coefs = np.linalg.solve(A, B)
ld_coefs = autocorrelation(test_signal, order)
# compare coefficients
self.assertEquals(order, coefs.T.A[0].size)
self.assertEquals(order, ld_coefs.size)
for i in range(order):
self.assertAlmostEquals(coefs.T.A[0][i], ld_coefs[i],
places=FLOAT_PRECISION)
def test_burg(self):
num_samples = 1024
test_signal = testsignals.noisy_sine_wave(num_samples)
order = 5
burg(test_signal, order)
def test_burg(self):
num_samples = 1024
test_signal = testsignals.noisy_sine_wave(num_samples)
order = 5
burg(test_signal, order)
