def test_its_estimated_with_only_ts(self):
its = ImpliedTimescales(estimator=self.estimator,
lags=[1, 2, 3],
only_timescales=True,
n_jobs=1)
its.estimate(self.dtraj)
plot_implied_timescales(its)
def test_its_estimated_with_only_ts_samples(self):
from pyemma.msm import BayesianMSM
its = ImpliedTimescales(estimator=BayesianMSM(nsamples=2),
lags=[1, 2, 3],
only_timescales=True)
its.estimate(self.dtraj)
plot_implied_timescales(its)
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 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 compute_nice(self, reversible):
"""
Tests if standard its estimates run without errors
:return:
"""
for i in range(len(self.dtrajs)):
its = ImpliedTimescales(self.dtrajs[i], reversible=reversible)
def test_too_large_lagtime(self):
dtraj = [[0, 1, 1, 1, 0]]
lags = [1, 2, 3, 4, 5, 6, 7, 8]
expected_lags = [
1, 2, 3
] # 4 is impossible because only one state remains and no finite timescales.
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
its = ImpliedTimescales(dtraj, lags=lags, reversible=False)
assert issubclass(w[-1].category, UserWarning)
got_lags = its.lagtimes
assert (np.shape(got_lags) == np.shape(expected_lags))
assert (np.allclose(got_lags, expected_lags))
def setUpClass(cls):
P = np.array([[0.5, .25, .25, 0.],
[0., .25, .5, .25],
[.25, .25, .5, 0],
[.25, .25, .25, .25],
])
# bogus its object
lags = [1, 2, 3, 5, 10]
dtraj = generate_traj(P, 1000)
estimator = MaximumLikelihoodMSM(dt_traj='10 ps')
cls.its = ImpliedTimescales(estimator=estimator)
cls.its.estimate(dtraj, lags=lags)
cls.refs = cls.its.timescales[-1]
return cls
def setUpClass(cls):
P = np.array([
[0.5, .25, .25, 0.],
[0., .25, .5, .25],
[.25, .25, .5, 0],
[.25, .25, .25, .25],
])
# bogus its object
lags = [1, 2, 3, 5, 10]
cls.dtraj = MarkovStateModel(P).simulate(1000)
cls.estimator = MaximumLikelihoodMSM(dt_traj='10 ps')
cls.its = ImpliedTimescales(estimator=cls.estimator, n_jobs=1)
cls.its.estimate(cls.dtraj, lags=lags)
cls.refs = cls.its.timescales[-1]
return cls