def test_timescales(self): P_dense = self.bdc.transition_matrix() P = self.bdc.transition_matrix_sparse() ev = eigvals(P_dense) """Sort with decreasing magnitude""" ev = ev[np.argsort(np.abs(ev))[::-1]] ts = -1.0 / np.log(np.abs(ev)) """k=None""" with self.assertRaises(ValueError): tsn = timescales(P) """k is not None""" tsn = timescales(P, k=self.k) assert_allclose(ts[1:self.k], tsn[1:]) """k is not None, ncv is not None""" tsn = timescales(P, k=self.k, ncv=self.ncv) assert_allclose(ts[1:self.k], tsn[1:]) """tau=7""" """k is not None""" tsn = timescales(P, k=self.k, tau=7) assert_allclose(7 * ts[1:self.k], tsn[1:])
def test_timescales(self): P=self.bdc.transition_matrix() ev=eigvals(P) """Sort with decreasing magnitude""" ev=ev[np.argsort(np.abs(ev))[::-1]] ts=-1.0/np.log(np.abs(ev)) """k=None""" tsn=timescales(P) self.assertTrue(np.allclose(ts[1:], tsn[1:])) """k is not None""" tsn=timescales(P, k=self.k) self.assertTrue(np.allclose(ts[1:self.k], tsn[1:])) """tau=7""" """k=None""" tsn=timescales(P, tau=7) self.assertTrue(np.allclose(7*ts[1:], tsn[1:])) """k is not None""" tsn=timescales(P, k=self.k, tau=7) self.assertTrue(np.allclose(7*ts[1:self.k], tsn[1:]))
def test_4_2(self): t4 = timescales(self.P4)[1] lags = [int(t4)] its = ImpliedTimescales([self.dtraj4_2], lags=lags) est = its.timescales[0] assert (np.alltrue(est < t4 + 20.0)) assert (np.alltrue(est > t4 - 20.0))
def setUp(self): """Store state of the rng""" self.state = np.random.mtrand.get_state() """Reseed the rng to enforce 'deterministic' behavior""" np.random.mtrand.seed(42) """Meta-stable birth-death chain""" b = 2 q = np.zeros(7) p = np.zeros(7) q[1:] = 0.5 p[0:-1] = 0.5 q[2] = 1.0 - 10**(-b) q[4] = 10**(-b) p[2] = 10**(-b) p[4] = 1.0 - 10**(-b) bdc = BirthDeathChain(q, p) P = bdc.transition_matrix() self.dtraj = generate_traj(P, 10000, start=0) self.tau = 1 """Estimate MSM""" self.C_MSM = cmatrix(self.dtraj, self.tau, sliding=True) self.lcc_MSM = largest_connected_set(self.C_MSM) self.Ccc_MSM = connected_cmatrix(self.C_MSM, lcc=self.lcc_MSM) self.P_MSM = tmatrix(self.Ccc_MSM, reversible=True) self.mu_MSM = statdist(self.P_MSM) self.k = 3 self.ts = timescales(self.P_MSM, k=self.k, tau=self.tau)
def setUp(self): self.k=4 p=np.zeros(10) q=np.zeros(10) p[0:-1]=0.5 q[1:]=0.5 p[4]=0.01 q[6]=0.1 self.bdc=BirthDeathChain(q, p) self.mu = self.bdc.stationary_distribution() self.T = self.bdc.transition_matrix_sparse() """Test matrix-vector product against spectral decomposition""" R, D, L=rdl_decomposition(self.T, k=self.k) self.L=L self.R=R self.ts=timescales(self.T, k=self.k) self.times=np.array([1, 5, 10, 20, 100]) ev=np.diagonal(D) self.ev_t=ev[np.newaxis,:]**self.times[:,np.newaxis] """Observable""" obs1 = np.zeros(10) obs1[0] = 1 obs1[1] = 1 self.obs=obs1 """Initial distribution""" w0=np.zeros(10) w0[0:4]=0.25 self.p0=w0
def setUp(self): self.k = 4 p = np.zeros(10) q = np.zeros(10) p[0:-1] = 0.5 q[1:] = 0.5 p[4] = 0.01 q[6] = 0.1 self.bdc = BirthDeathChain(q, p) self.mu = self.bdc.stationary_distribution() self.T = self.bdc.transition_matrix_sparse() R, D, L = rdl_decomposition(self.T, k=self.k) self.L = L self.R = R self.ts = timescales(self.T, k=self.k) self.times = np.array([1, 5, 10, 20, 100]) ev = np.diagonal(D) self.ev_t = ev[np.newaxis, :]**self.times[:, np.newaxis] obs1 = np.zeros(10) obs1[0] = 1 obs1[1] = 1 obs2 = np.zeros(10) obs2[8] = 1 obs2[9] = 1 self.obs1 = obs1 self.obs2 = obs2 self.one_vec = np.ones(10)
def setUp(self): self.k=4 p=np.zeros(10) q=np.zeros(10) p[0:-1]=0.5 q[1:]=0.5 p[4]=0.01 q[6]=0.1 self.bdc=BirthDeathChain(q, p) self.mu = self.bdc.stationary_distribution() self.T = self.bdc.transition_matrix_sparse() R, D, L=rdl_decomposition(self.T, k=self.k) self.L=L self.R=R self.ts=timescales(self.T, k=self.k) self.times=np.array([1, 5, 10, 20, 100]) ev=np.diagonal(D) self.ev_t=ev[np.newaxis,:]**self.times[:,np.newaxis] obs1 = np.zeros(10) obs1[0] = 1 obs1[1] = 1 obs2 = np.zeros(10) obs2[8] = 1 obs2[9] = 1 self.obs1=obs1 self.obs2=obs2 self.one_vec=np.ones(10)
def setUp(self): self.k = 4 p = np.zeros(10) q = np.zeros(10) p[0:-1] = 0.5 q[1:] = 0.5 p[4] = 0.01 q[6] = 0.1 self.bdc = BirthDeathChain(q, p) self.mu = self.bdc.stationary_distribution() self.T = self.bdc.transition_matrix_sparse() """Test matrix-vector product against spectral decomposition""" R, D, L = rdl_decomposition(self.T, k=self.k) self.L = L self.R = R self.ts = timescales(self.T, k=self.k) self.times = np.array([1, 5, 10, 20, 100]) ev = np.diagonal(D) self.ev_t = ev[np.newaxis, :]**self.times[:, np.newaxis] """Observable""" obs1 = np.zeros(10) obs1[0] = 1 obs1[1] = 1 self.obs = obs1 """Initial distribution""" w0 = np.zeros(10) w0[0:4] = 0.25 self.p0 = w0
def test_2(self): t2 = timescales(self.P2)[1] lags = [1, 2, 3, 4, 5] its = ImpliedTimescales([self.dtraj2], lags=lags) est = its.timescales[0] assert (np.alltrue(est < t2 + 2.0)) assert (np.alltrue(est > t2 - 2.0))
def setUp(self): """Store state of the rng""" self.state = np.random.mtrand.get_state() """Reseed the rng to enforce 'deterministic' behavior""" np.random.mtrand.seed(42) """Meta-stable birth-death chain""" b = 2 q = np.zeros(7) p = np.zeros(7) q[1:] = 0.5 p[0:-1] = 0.5 q[2] = 1.0 - 10 ** (-b) q[4] = 10 ** (-b) p[2] = 10 ** (-b) p[4] = 1.0 - 10 ** (-b) bdc = BirthDeathChain(q, p) P = bdc.transition_matrix() self.dtraj = generate_traj(P, 10000, start=0) self.tau = 1 """Estimate MSM""" self.C_MSM = cmatrix(self.dtraj, self.tau, sliding=True) self.lcc_MSM = largest_connected_set(self.C_MSM) self.Ccc_MSM = connected_cmatrix(self.C_MSM, lcc=self.lcc_MSM) self.P_MSM = tmatrix(self.Ccc_MSM, reversible=True) self.mu_MSM = statdist(self.P_MSM) self.k = 3 self.ts = timescales(self.P_MSM, k=self.k, tau=self.tau)
def test_timescales_2(self): """Eigenvalues with non-zero imaginary part""" ts = np.array([np.inf, 0.971044, 0.971044]) with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") tsn = timescales(0.5 * self.T + 0.5 * self.P) assert_allclose(tsn, ts) assert issubclass(w[-1].category, ImaginaryEigenValueWarning)
def test_timescales(self): P = self.bdc.transition_matrix() ev = eigvals(P) """Sort with decreasing magnitude""" ev = ev[np.argsort(np.abs(ev))[::-1]] ts = -1.0 / np.log(np.abs(ev)) """k=None""" tsn = timescales(P) assert_allclose(ts[1:], tsn[1:]) """k is not None""" tsn = timescales(P, k=self.k) assert_allclose(ts[1:self.k], tsn[1:]) """tau=7""" """k=None""" tsn = timescales(P, tau=7) assert_allclose(7 * ts[1:], tsn[1:]) """k is not None""" tsn = timescales(P, k=self.k, tau=7) assert_allclose(7 * ts[1:self.k], tsn[1:])
def test_timescales_1(self): """Multiple eigenvalues of magnitude one, eigenvalues with non-zero imaginary part""" ts = np.array([np.inf, np.inf]) with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") tsn = timescales(self.W) assert_allclose(tsn, ts) assert issubclass(w[-1].category, SpectralWarning)
def _estimate_ts_tau(self, C, tau): r"""Estimate timescales from the given count matrix. """ # connected set C = (connected_cmatrix(C)).toarray() if (len(C) > 1): # estimate transition matrix T = tmatrix(C, reversible=self._reversible) # timescales ts = timescales(T, tau, k=min(self._nits, len(T)) + 1)[1:] return ts else: return None # no timescales available
def setUp(self): self.k=4 p=np.zeros(10) q=np.zeros(10) p[0:-1]=0.5 q[1:]=0.5 p[4]=0.01 q[6]=0.1 self.bdc=BirthDeathChain(q, p) self.mu = self.bdc.stationary_distribution() self.T = self.bdc.transition_matrix_sparse() R, D, L=rdl_decomposition(self.T, k=self.k) self.L=L self.R=R self.ts=timescales(self.T, k=self.k) self.times=np.array([1, 5, 10, 20]) ev=np.diagonal(D) self.ev_t=ev[np.newaxis,:]**self.times[:,np.newaxis] self.tau=7.5 """Observables""" obs1 = np.zeros(10) obs1[0] = 1 obs1[1] = 1 obs2 = np.zeros(10) obs2[8] = 1 obs2[9] = 1 self.obs1=obs1 self.obs2=obs2 """Initial vector for relaxation""" w0=np.zeros(10) w0[0:4]=0.25 self.p0=w0
def setUp(self): self.k = 4 p = np.zeros(10) q = np.zeros(10) p[0:-1] = 0.5 q[1:] = 0.5 p[4] = 0.01 q[6] = 0.1 self.bdc = BirthDeathChain(q, p) self.mu = self.bdc.stationary_distribution() self.T = self.bdc.transition_matrix_sparse() R, D, L = rdl_decomposition(self.T, k=self.k) self.L = L self.R = R self.ts = timescales(self.T, k=self.k) self.times = np.array([1, 5, 10, 20]) ev = np.diagonal(D) self.ev_t = ev[np.newaxis, :]**self.times[:, np.newaxis] self.tau = 7.5 """Observables""" obs1 = np.zeros(10) obs1[0] = 1 obs1[1] = 1 obs2 = np.zeros(10) obs2[8] = 1 obs2[9] = 1 self.obs1 = obs1 self.obs2 = obs2 """Initial vector for relaxation""" w0 = np.zeros(10) w0[0:4] = 0.25 self.p0 = w0
def get_timescales(self, k): ts=timescales(self.T, k=k, tau=self.lagtime) return ts
def get_timescales(self, k): ts = timescales(self.T, k=k, tau=self.lagtime) return ts