def setUp(self):
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.A = [0, 1]
self.B = [8, 9]
self.a = 1
self.b = 8
self.bdc = BirthDeathChain(q, p)
T_dense = self.bdc.transition_matrix()
T_sparse = csr_matrix(T_dense)
self.T = T_sparse
self.mu = self.bdc.stationary_distribution()
self.qminus = self.bdc.committor_backward(self.a, self.b)
self.qplus = self.bdc.committor_forward(self.a, self.b)
# present results
self.fluxn = flux.flux_matrix(self.T,
self.mu,
self.qminus,
self.qplus,
netflux=False)
self.netfluxn = flux.flux_matrix(self.T,
self.mu,
self.qminus,
self.qplus,
netflux=True)
self.totalfluxn = flux.total_flux(self.netfluxn, self.A)
self.raten = flux.rate(self.totalfluxn, self.mu, self.qminus)
def test_with_almost_converged_stat_dist(self):
""" test for #106 """
from msmtools.analysis import committor, is_reversible
from msmtools.flux import tpt, flux_matrix, to_netflux, ReactiveFlux
T = np.array([[
0.2576419223095193, 0.2254214623509954, 0.248270708174756,
0.2686659071647294
],
[
0.2233847186210225, 0.2130434781715344,
0.2793477268264001, 0.284224076381043
],
[
0.2118717275169231, 0.2405661227681972,
0.2943396213976011, 0.2532225283172787
],
[
0.2328617711043517, 0.2485926610067547,
0.2571819311236834, 0.2613636367652102
]])
mu = np.array([
0.2306979668517676, 0.2328013892993006, 0.2703312416016573,
0.2661694022472743
])
assert is_reversible(T)
np.testing.assert_allclose(mu.dot(T), mu)
np.testing.assert_equal(mu.dot(T), T.T.dot(mu))
A = [0]
B = [1]
# forward committor
qplus = committor(T, A, B, forward=True, mu=mu)
# backward committor
if is_reversible(T, mu=mu):
qminus = 1.0 - qplus
else:
qminus = committor(T, A, B, forward=False, mu=mu)
tpt_obj = tpt(T, A, B)
tpt_obj.major_flux(1.0)
# gross flux
grossflux = flux_matrix(T, mu, qminus, qplus, netflux=False)
# net flux
netflux = to_netflux(grossflux)
F = ReactiveFlux(A,
B,
netflux,
mu=mu,
qminus=qminus,
qplus=qplus,
gross_flux=grossflux)
F.pathways(1.0)