def test_count_3(self):
S1 = np.array([0, 1, 2, 3, 4])
S2 = np.array([2, 2, 2, 2, 6])
H = np.array([1, 1, 5, 1, 1, 0, 1])
assert (dt.number_of_states([S1, S2]) == 7)
assert (dt.number_of_states([S1, S2], only_used=True) == 6)
assert (np.allclose(dt.count_states([S1, S2]), H))
def test_count_3(self):
S1 = np.array([0, 1, 2, 3, 4])
S2 = np.array([2, 2, 2, 2, 6])
H = np.array([1, 1, 5, 1, 1, 0, 1])
assert(dt.number_of_states([S1,S2]) == 7)
assert(dt.number_of_states([S1,S2], only_used=True) == 6)
assert(np.allclose(dt.count_states([S1,S2]),H))
def active_count_fraction(self):
"""The fraction of counts in the largest connected set.
"""
self._check_is_estimated()
from pyemma.util.discrete_trajectories import count_states
hist = count_states(self._dtrajs_full)
hist_active = hist[self.active_set]
return float(_np.sum(hist_active)) / float(_np.sum(hist))
def _active_state_indexes(self, msm):
I = msm.active_state_indexes
assert (len(I) == msm.nstates)
# compare to histogram
import pyemma.util.discrete_trajectories as dt
hist = dt.count_states(msm.discrete_trajectories_full)
# number of frames should match on active subset
A = msm.active_set
for i in range(A.shape[0]):
assert (I[i].shape[0] == hist[A[i]])
assert (I[i].shape[1] == 2)
def _active_state_indexes(self, msm):
I = msm.active_state_indexes
assert (len(I) == msm.nstates)
# compare to histogram
import pyemma.util.discrete_trajectories as dt
hist = dt.count_states(msm.discrete_trajectories_full)
# number of frames should match on active subset
A = msm.active_set
for i in range(A.shape[0]):
assert (I[i].shape[0] == hist[A[i]])
assert (I[i].shape[1] == 2)
def test_observable_state_indexes(self):
hmsm = self.hmsm_lag10
I = hmsm.observable_state_indexes
assert (len(I) == hmsm.nstates_obs)
# compare to histogram
import pyemma.util.discrete_trajectories as dt
hist = dt.count_states(hmsm.discrete_trajectories_full)
# number of frames should match on active subset
A = hmsm.observable_set
for i in range(A.shape[0]):
assert (I[i].shape[0] == hist[A[i]])
assert (I[i].shape[1] == 2)
def test_count_1(self):
S = np.array([0, 0, 0, 0, 0, 0])
H = np.array([6])
assert (dt.number_of_states(S) == 1)
assert (dt.number_of_states(S, only_used=True) == 1)
assert (np.allclose(dt.count_states(S), H))
def test_count_big(self):
import pyemma.datasets
dtraj = pyemma.datasets.load_2well_discrete().dtraj_T100K_dt10
dt.number_of_states(dtraj)
dt.count_states(dtraj)
def test_count_2(self):
S = np.array([1, 1, 1, 1, 1, 1])
H = np.array([0,6])
assert(dt.number_of_states(S) == 2)
assert(dt.number_of_states(S, only_used=True) == 1)
assert(np.allclose(dt.count_states(S),H))
def test_count_big(self):
dtraj = dt.read_discrete_trajectory(testpath+'2well_traj_100K.dat')
# just run these to see if there's any exception
dt.number_of_states(dtraj)
dt.count_states(dtraj)
