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 = birth_death_chain(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 totalflux(self, a, b):
r"""The tiotal flux for the reaction A=[0,...,a] => B=[b,...,M].
Parameters
----------
a : int
State index
b : int
State index
Returns
-------
F : float
The total flux between reactant and product
"""
flux = self.flux(a, b)
A = list(range(a + 1))
from deeptime.markov.tools.flux import total_flux
return total_flux(flux, A)
def test_total_flux(tpt_scenario):
assert_almost_equal(tpt_scenario[1].total_flux, 0.0108050847458)
assert_almost_equal(total_flux(tpt_scenario[1].net_flux), 0.0108050847458)
assert_almost_equal(total_flux(tpt_scenario[1].net_flux, A=[0]),
0.0108050847458)