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)
self.T = self.bdc.transition_matrix
"""Compute mu, qminus, qplus in constructor"""
self.tpt = reactive_flux(self.T, self.A, self.B)
"""Use precomputed mu, qminus, qplus"""
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)
self.tpt_fast = reactive_flux(self.T, self.A, self.B, stationary_distribution=self.mu,
qminus=self.qminus, qplus=self.qplus)
def test_invalid_inputs(self):
with np.testing.assert_raises(ValueError):
self.P.reactive_flux(source_states=[], target_states=[1])
with np.testing.assert_raises(ValueError):
self.P.reactive_flux(source_states=[1], target_states=[])
with np.testing.assert_raises(ValueError):
self.P.reactive_flux(source_states=np.arange(self.P.n_states + 1),
target_states=[0])
with np.testing.assert_raises(ValueError):
self.P.reactive_flux(source_states=[0],
target_states=np.arange(self.P.n_states + 1))
with np.testing.assert_raises(ValueError):
reactive_flux(np.array([[.5, .6], [.3, .7]]), [0],
[1]) # not a transition matrix
def test_with_almost_converged_stat_dist(sparse_mode):
""" test for https://github.com/markovmodel/msmtools/issues/106 """
from deeptime.markov.tools.analysis import committor, is_reversible
from deeptime.markov.tools.flux import flux_matrix, to_netflux
from deeptime.markov import reactive_flux, 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
]])
if sparse_mode:
T = csr_matrix(T)
mu = np.array([
0.2306979668517676, 0.2328013892993006, 0.2703312416016573,
0.2661694022472743
])
assert is_reversible(T)
np.testing.assert_allclose(T.T.dot(mu).T, mu)
np.testing.assert_equal(T.T.dot(mu).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 = reactive_flux(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,
stationary_distribution=mu,
qminus=qminus,
qplus=qplus,
gross_flux=grossflux)
F.pathways(1.0)
def setUp(self):
# 5-state toy system
self.P = np.array([[0.8, 0.15, 0.05, 0.0, 0.0],
[0.1, 0.75, 0.05, 0.05, 0.05],
[0.05, 0.1, 0.8, 0.0, 0.05],
[0.0, 0.2, 0.0, 0.8, 0.0],
[0.0, 0.02, 0.02, 0.0, 0.96]])
self.A = [0]
self.B = [4]
self.I = [1, 2, 3]
# REFERENCE SOLUTION FOR PATH DECOMP
self.ref_committor = np.array(
[0., 0.35714286, 0.42857143, 0.35714286, 1.])
self.ref_backwardcommittor = np.array(
[1., 0.65384615, 0.53125, 0.65384615, 0.])
self.ref_grossflux = np.array(
[[0., 0.00771792, 0.00308717, 0., 0.],
[0., 0., 0.00308717, 0.00257264, 0.00720339],
[0., 0.00257264, 0., 0., 0.00360169],
[0., 0.00257264, 0., 0., 0.], [0., 0., 0., 0., 0.]])
self.ref_netflux = np.array([[
0.00000000e+00, 7.71791768e-03, 3.08716707e-03, 0.00000000e+00,
0.00000000e+00
],
[
0.00000000e+00, 0.00000000e+00,
5.14527845e-04, 0.00000000e+00,
7.20338983e-03
],
[
0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00,
3.60169492e-03
],
[
0.00000000e+00, 4.33680869e-19,
0.00000000e+00, 0.00000000e+00,
0.00000000e+00
],
[
0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00,
0.00000000e+00
]])
self.ref_totalflux = 0.0108050847458
self.ref_kAB = 0.0272727272727
self.ref_mfptAB = 36.6666666667
self.ref_paths = [[0, 1, 4], [0, 2, 4], [0, 1, 2, 4]]
self.ref_pathfluxes = np.array(
[0.00720338983051, 0.00308716707022, 0.000514527845036])
self.ref_paths_95percent = [[0, 1, 4], [0, 2, 4]]
self.ref_pathfluxes_95percent = np.array(
[0.00720338983051, 0.00308716707022])
self.ref_majorflux_95percent = np.array(
[[0., 0.00720339, 0.00308717, 0., 0.],
[0., 0., 0., 0., 0.00720339], [0., 0., 0., 0., 0.00308717],
[0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.]])
# Testing:
self.tpt1 = reactive_flux(self.P, self.A, self.B)
# 16-state toy system
P2_nonrev = np.array([[
0.5, 0.2, 0.0, 0.0, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0
],
[
0.2, 0.5, 0.1, 0.0, 0.0, 0.2, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
],
[
0.0, 0.1, 0.5, 0.2, 0.0, 0.0, 0.2, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
],
[
0.0, 0.0, 0.1, 0.5, 0.0, 0.0, 0.0, 0.4, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
],
[
0.3, 0.0, 0.0, 0.0, 0.5, 0.1, 0.0, 0.0, 0.1,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
],
[
0.0, 0.1, 0.0, 0.0, 0.2, 0.5, 0.1, 0.0, 0.0,
0.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
],
[
0.0, 0.0, 0.1, 0.0, 0.0, 0.1, 0.5, 0.2, 0.0,
0.0, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0
],
[
0.0, 0.0, 0.0, 0.1, 0.0, 0.0, 0.3, 0.5, 0.0,
0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.0
],
[
0.0, 0.0, 0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.5,
0.1, 0.0, 0.0, 0.3, 0.0, 0.0, 0.0
],
[
0.0, 0.0, 0.0, 0.0, 0.0, 0.1, 0.0, 0.0, 0.2,
0.5, 0.1, 0.0, 0.0, 0.1, 0.0, 0.0
],
[
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1, 0.0, 0.0,
0.1, 0.5, 0.1, 0.0, 0.0, 0.2, 0.0
],
[
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1, 0.0,
0.0, 0.2, 0.5, 0.0, 0.0, 0.0, 0.2
],
[
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.3,
0.0, 0.0, 0.0, 0.5, 0.2, 0.0, 0.0
],
[
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.1, 0.0, 0.0, 0.3, 0.5, 0.1, 0.0
],
[
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.2, 0.0, 0.0, 0.1, 0.5, 0.2
],
[
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.3, 0.0, 0.0, 0.2, 0.5
]])
pstat2_nonrev = msmana.stationary_distribution(P2_nonrev)
# make reversible
C = np.dot(np.diag(pstat2_nonrev), P2_nonrev)
Csym = C + C.T
self.P2 = Csym / np.sum(Csym, axis=1)[:, np.newaxis]
pstat2 = msmana.stationary_distribution(self.P2)
self.A2 = [0, 4]
self.B2 = [11, 15]
self.coarsesets2 = [
[2, 3, 6, 7],
[10, 11, 14, 15],
[0, 1, 4, 5],
[8, 9, 12, 13],
]
# REFERENCE SOLUTION CG
self.ref2_tpt_sets = [
set([0, 4]),
set([2, 3, 6, 7]),
set([10, 14]),
set([1, 5]),
set([8, 9, 12, 13]),
set([11, 15])
]
self.ref2_cgA = [0]
self.ref2_cgI = [1, 2, 3, 4]
self.ref2_cgB = [5]
self.ref2_cgpstat = np.array([
0.15995388, 0.18360442, 0.12990937, 0.11002342, 0.31928127,
0.09722765
])
self.ref2_cgcommittor = np.array(
[0., 0.56060272, 0.73052426, 0.19770537, 0.36514272, 1.])
self.ref2_cgbackwardcommittor = np.array(
[1., 0.43939728, 0.26947574, 0.80229463, 0.63485728, 0.])
self.ref2_cggrossflux = np.array(
[[0., 0., 0., 0.00427986, 0.00282259, 0.],
[0., 0, 0.00234578, 0.00104307, 0., 0.00201899],
[0., 0.00113892, 0, 0., 0.00142583, 0.00508346],
[0., 0.00426892, 0., 0, 0.00190226, 0.],
[0., 0., 0.00530243, 0.00084825, 0, 0.], [0., 0., 0., 0., 0.,
0.]])
self.ref2_cgnetflux = np.array(
[[0., 0., 0., 0.00427986, 0.00282259, 0.],
[0., 0., 0.00120686, 0., 0., 0.00201899],
[0., 0., 0., 0., 0., 0.00508346],
[0., 0.00322585, 0., 0., 0.00105401, 0.],
[0., 0., 0.0038766, 0., 0., 0.], [0., 0., 0., 0., 0., 0.]])
# Testing
self.tpt2 = reactive_flux(self.P2, self.A2, self.B2)