def test_time_units(self):
dtraj = np.random.randint(0, 4, 1000)
tau = 12
dt = 0.456
msmobj = estimate_markov_model(dtraj, lag=tau, dt_traj='%f ns' % dt)
# check MFPT consistency
mfpt_ref = msmobj.mfpt([0], [1])
tptobj = tpt(msmobj, [0], [1])
assert_allclose(tptobj.mfpt, mfpt_ref)
assert_allclose(msmana.mfpt(msmobj.P, [1], [0], tau=tau) * dt, mfpt_ref)
assert_allclose(np.dot(msmobj.stationary_distribution, tptobj.backward_committor) / tptobj.total_flux, mfpt_ref)
# check flux consistency
total_flux_ref = tptobj.total_flux
A = tptobj.A
B = tptobj.B
I = tptobj.I
assert_allclose(tptobj.gross_flux[A, :][:, B].sum() + tptobj.gross_flux[A, :][:, I].sum(),
total_flux_ref)
assert_allclose(tptobj.net_flux[A, :][:, B].sum() + tptobj.net_flux[A, :][:, I].sum(), total_flux_ref)
assert_allclose(tptobj.flux[A, :][:, B].sum() + tptobj.flux[A, :][:, I].sum(), total_flux_ref)
mf = tptobj.major_flux(1.0)
assert_allclose(mf[A, :][:, B].sum() + mf[A, :][:, I].sum(), total_flux_ref)
# check that the coarse-grained version is consistent too
_, tptobj2 = tptobj.coarse_grain([A, I, B])
assert_allclose(tptobj2.total_flux, total_flux_ref)
assert_allclose(tptobj2.mfpt, mfpt_ref)
def test_flux(self):
r = tpt(self.msm, self.A, self.B)
fig, pos = plot_flux(r)
assert type(fig) is matplotlib.figure.Figure
# matplotlib.pyplot.show(fig)
# x values should be close to committor
np.testing.assert_allclose(pos[:, 0], r.committor)
def test_state_labels_flux_auto(self):
""" ensure auto generated labels show up in the plot"""
A = [0, 1]
B = [2, 4]
flux = tpt(self.msm, A, B)
fig, pos = plot_flux(flux, state_labels='auto')
labels_in_fig = np.array(
[text.get_text() for text in fig.axes[0].texts])
self.assertEqual((labels_in_fig == "A").sum(), len(A))
self.assertEqual((labels_in_fig == "B").sum(), len(B))
def test_state_labels_flux(self):
""" ensure our labels show up in the plot"""
flux = tpt(self.msm, [0, 1], [2, 4])
labels = ['foo', '0', '1', '2', 'bar']
fig, pos = plot_flux(flux, state_labels=labels)
labels_in_fig = np.array(
[text.get_text() for text in fig.axes[0].texts])
for l in labels:
self.assertEqual((labels_in_fig == l).sum(), 1)
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_99percent = [[0, 1, 4], [0, 2, 4]]
self.ref_pathfluxes_99percent = np.array([0.00720338983051, 0.00308716707022])
self.ref_majorflux_99percent = 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.]])
msmobj = markov_model(self.P)
msmobj.mu = msmana.statdist(self.P)
msmobj.estimated = True
msmobj1 = msmobj
# Testing:
# self.tpt1 = tpt(self.P, self.A, self.B)
self.tpt1 = tpt(msmobj1, 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.statdist(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.statdist(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.]])
"""Dummy dtraj to trick trick constructor of MSM"""
dtraj = [0, 0]
tau = 1
msmobj = markov_model(self.P2)
msmobj.mu = msmana.statdist(self.P2)
msmobj.estimated = True
msmobj2 = msmobj
# Testing
self.tpt2 = tpt(msmobj2, self.A2, self.B2)