def test_rayleigh_is_rician_high_noise(self):
'''Verify rayleigh becomes rician high noise scipy's statistics module.
'''
# Rayleigh is special case of Rician (A=0)
sigma = 20
pM = rayleigh(self.M, sigma)
pM_rician = rician(self.M, 0, sigma)
self.assertTrue(np.allclose(pM, pM_rician))
def test_rician_high_noise(self):
# Generate rician distribution with high noise
# High noise is where the correction term counts!
sigma = 20
# Try out ours
pM = rician(self.M, self.A, sigma)
# Compare to scipy implementation
rv = rice(self.A)
pM_scipy = rv.pdf(self.M / (sigma**2))
# Should fail with no correction
self.assertFalse(np.allclose(pM, pM_scipy))
# Now correct
pM_scipy *= self.correction(sigma)
self.assertTrue(np.allclose(pM, pM_scipy))
def test_rician_low_noise(self):
# Generate rician distribution with low noise
sigma = .8 # too small will blow up the correction term for scipy rice.pdf function (see sigma**4 term in denominator)
# Try out ours
pM = rician(self.M, self.A, sigma)
# Compare to scipy implementation
rv = rice(self.A)
pM_scipy = rv.pdf(self.M / (sigma**2)) * self.correction(sigma)
# # Take a gander
# plt.plot(pM,label='Rician')
# plt.plot(pM_scipy,label='Rice (scipy)')
# plt.plot(pM - pM_scipy)
# plt.legend()
# plt.show()
self.assertTrue(np.allclose(pM, pM_scipy))
def test_rayleigh_is_rician_high_noise(self):
# Rayleigh is special case of Rician (A=0)
sigma = 20
pM = rayleigh(self.M, sigma)
pM_rician = rician(self.M, 0, sigma)
self.assertTrue(np.allclose(pM, pM_rician))