def test_rms(self):
l = np.array([2, 3, 4.5])
ans = 3.329164
self.assertAlmostEqual(ans, rms(l), places=5)
l = [[[1, 2, 3], [4, 5, 6]], [[2, 3, 4], [5, 6, 7]]]
ans = 2.1602469
self.assertAlmostEqual(ans, rms(l, axis=-1)[0, 0], places=5)
def test_rms(self):
l = np.array([2, 3, 4.5])
ans = 3.329164
self.assertAlmostEqual(ans, rms(l), places=5)
l = [[[1, 2, 3], [4, 5, 6]], [[2,3,4], [5, 6, 7]]]
ans = 2.1602469
self.assertAlmostEqual(ans, rms(l, axis=-1)[0, 0], places=5)
def noise_db(a, snr):
"""
Takes an array of seismic amplitudes and SNR in dB.
Args:
a (ndarray): seismic amplitude array.
snr (int): signal to noise ratio.
Returns: Noise array, the same shape as the input.
Note: it does *not* return the input array with the noise added.
"""
# Get the amplitude of the signal
sigmean = rms(a)
# Calculate the amp of the noise,
# given the desired SNR
noisemean = sigmean / 10.0**(snr / 20.0)
# Normal noise, centered on 0,
# SD=sqrt(var), same shape as input
noise = noisemean * np.random.normal(0.0, 1.0, a.shape)
return noise
def noise_db(a, snr):
"""
Takes an array of seismic amplitudes and SNR in dB.
Args:
a (array) : seismic amplitude array.
snr (int): signal to noise ratio.
Returns: Noise array, the same shape as the input.
Note: it does *not* return the input array with the noise added.
"""
# Get the amplitude of the signal
sigmean = rms(a)
# Calculate the amp of the noise,
# given the desired SNR
noisemean = sigmean / 10.0**(snr/20.0)
# Normal noise, centered on 0,
# SD=sqrt(var), same shape as input
noise = noisemean * np.random.normal(0.0, 1.0, a.shape)
return noise
def test_rms(self):
l = np.array([2, 3, 4.5])
ans = 3.329164
self.assertAlmostEqual(ans, rms(l), places=5)
def test_rms(self):
l = np.array([2, 3, 4.5])
ans = 3.329164
self.assertAlmostEqual(ans, rms(l), places=5)