def test_fingerprint_relaxation(self):
one_vec = np.ones(self.T.shape[0])
"""k=None"""
relax_amp = np.dot(self.p0, self.R) * np.dot(self.L, self.obs1)
tsn, relax_ampn = fingerprint_relaxation(self.T, self.p0, self.obs1)
assert_allclose(tsn, self.ts)
assert_allclose(relax_ampn, relax_amp)
"""k=4"""
k = self.k
relax_amp = np.dot(self.p0, self.R[:, 0:k]) * np.dot(
self.L[0:k, :], self.obs1)
tsn, relax_ampn = fingerprint_relaxation(self.T,
self.p0,
self.obs1,
k=k)
assert_allclose(tsn, self.ts[0:k])
assert_allclose(relax_ampn, relax_amp)
def test_fingerprint_relaxation(self):
one_vec = np.ones(self.T.shape[0])
relax_amp = np.dot(self.p0, self.R) * np.dot(self.L, self.obs1)
tsn, relax_ampn = fingerprint_relaxation(self.T,
self.p0,
self.obs1,
k=self.k)
assert_allclose(tsn, self.ts)
assert_allclose(relax_ampn, relax_amp)
def test_fingerprint_relaxation(fingerprints_data, observables):
obs1, obs2 = observables
T = fingerprints_data.transition_matrix
ts = timescales(T, k=4 if fingerprints_data.sparse else None)
R, D, L = rdl_decomposition(T, k=4 if fingerprints_data.sparse else None)
"""Initial vector for relaxation"""
p0 = np.zeros(10)
p0[0:4] = 0.25
if not fingerprints_data.sparse:
"""k=None"""
relax_amp = np.dot(p0, R) * np.dot(L, obs1)
tsn, relax_ampn = fingerprint_relaxation(T, p0, obs1)
assert_allclose(tsn, ts)
assert_allclose(relax_ampn, relax_amp)
"""k=4"""
k = 4
relax_amp = np.dot(p0, R[:, 0:k]) * np.dot(L[0:k, :], obs1)
tsn, relax_ampn = fingerprint_relaxation(T, p0, obs1, k=k)
assert_allclose(tsn, ts[0:k])
assert_allclose(relax_ampn, relax_amp)