def setUpClass(cls):
# load observations
import pyemma.datasets
data = pyemma.datasets.load_2well_discrete()
obs_micro = data.dtraj_T100K_dt10
# stationary distribution
pi_micro = data.msm.stationary_distribution
pi_macro = np.zeros(2)
pi_macro[0] = pi_micro[0:50].sum()
pi_macro[1] = pi_micro[50:].sum()
# coarse-grain microstates to two metastable states
cg = np.zeros(100, dtype=int)
cg[50:] = 1
obs_macro = cg[obs_micro]
# hidden states
cls.nstates = 2
# samples
cls.nsamples = 100
cls.lag = 100
cls.bmsm_rev = bayesian_markov_model(obs_macro,
cls.lag,
dt_traj='4 fs',
reversible=True,
nsamples=cls.nsamples)
cls.bmsm_revpi = bayesian_markov_model(obs_macro,
cls.lag,
dt_traj='4 fs',
reversible=True,
statdist=pi_macro,
nsamples=cls.nsamples)
def test_bmsm(self):
from pyemma.msm import bayesian_markov_model
msm = bayesian_markov_model(self.dtraj,
lag=1,
mincount_connectivity='1/n')
msm_restricted = bayesian_markov_model(
self.dtraj,
lag=1,
mincount_connectivity=self.mincount_connectivity)
self._test_connectivity(msm, msm_restricted)
def test_its_bmsm(self):
BMSM = msm.bayesian_markov_model(
[self.double_well_data.dtraj_T100K_dt10_n6good], 40)
ck = BMSM.cktest(2, mlags=[0, 1, 10])
estref = np.array([[[1., 0.], [0., 1.]],
[[0.89722931, 0.10277069], [0.10070029,
0.89929971]],
[[0.64668027, 0.35331973], [0.34369109,
0.65630891]]])
predref = np.array([[[1., 0.], [0., 1.]],
[[0.89722931, 0.10277069],
[0.10070029, 0.89929971]],
[[0.62568693, 0.37431307],
[0.36677222, 0.63322778]]])
predLref = np.array([[[1., 0.], [0., 1.]],
[[0.89398296, 0.09942586],
[0.09746008, 0.89588256]],
[[0.6074675, 0.35695492],
[0.34831224, 0.61440531]]])
predRref = np.array([[[1., 0.], [0., 1.]],
[[0.90070139, 0.10630301],
[0.10456111, 0.90255169]],
[[0.64392557, 0.39258944],
[0.38762444, 0.65176265]]])
# rough agreement
assert np.allclose(ck.estimates, estref, rtol=0.1, atol=10.0)
assert ck.estimates_conf[0] is None
assert ck.estimates_conf[1] is None
assert np.allclose(ck.predictions, predref, rtol=0.1, atol=10.0)
assert np.allclose(ck.predictions[0], predLref, rtol=0.1, atol=10.0)
assert np.allclose(ck.predictions[1], predRref, rtol=0.1, atol=10.0)
def buildMSM(self):
""" Estimate a MSM from the trajectories using a provided lagtime that
should be big enough so that the relevant processes have converged.
self.error: whether to estimate errors or not
"""
if self.error:
self.MSM_object = msm.bayesian_markov_model(self.dtrajs, self.lagtime)
else:
self.MSM_object = msm.estimate_markov_model(self.dtrajs, self.lagtime)
def estimateMSM(trajectories, lagtime, error_est=False):
""" Estimate a MSM from the trajectories using a provided lagtime that
should be big enough so that the relevant processes have converged.
Return a MaximumLikelihoodMSM object"""
if error_est:
print "Computing msm with bayes error calc"
MSM_object = MSM.bayesian_markov_model(trajectories, lagtime)
else:
print "Computing msm with no error calc"
MSM_object = MSM.estimate_markov_model(trajectories,
lagtime,
count_mode='sliding')
return MSM_object
def setUpClass(cls):
# load observations
import pyemma.datasets
obs_micro = pyemma.datasets.load_2well_discrete().dtraj_T100K_dt10
# coarse-grain microstates to two metastable states
cg = np.zeros(100, dtype=int)
cg[50:] = 1
obs_macro = cg[obs_micro]
# hidden states
cls.nstates = 2
# samples
cls.nsamples = 100
cls.lag = 100
cls.sampled_msm_lag100 = bayesian_markov_model(obs_macro,
cls.lag,
reversible=True,
nsamples=cls.nsamples)
def setUpClass(cls):
data = datasets.load_2well_discrete()
cls.obs_micro = data.dtraj_T100K_dt10
# coarse-grain microstates to two metastable states
cg = np.zeros(100, dtype=int)
cg[50:] = 1
cls.obs_macro = cg[cls.obs_micro]
# hidden states
cls.nstates = 2
# samples
cls.nsamples = 10
cls.lag = 100
cls.msm = datasets.load_2well_discrete().msm
cls.bmsm_rev = bayesian_markov_model(cls.obs_macro,
cls.lag,
reversible=True,
nsamples=cls.nsamples)
import matplotlib
matplotlib.use('Agg')
import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
sns.set_style('ticks')
colors = sns.color_palette()
import pickle
start_time = time.time()
## Load
meta, ktrajs = load_trajs('../../ktrajs_cen_30_100_5')
dtrajs = list(ktrajs.values())
type(dtrajs[0])
## Fit
msm = bayesian_markov_model(dtrajs, lag=16, nsamples=100000)
print('done with bmm')
## Load
kmeans = load_generic('../../kcenters_30_100_5.pickl')
msm2 = load_generic('../msm_kcen_30_100_5_16.pickl')
meta, ttrajs = load_trajs('../../../ttrajs_a0_30')
txx = np.concatenate(list(ttrajs.values()))
a1 = ttrajs[14]
print('done with load')
print('active_count_fraction is ', msm.active_count_fraction)
print('active_state_fraction is ', msm.active_state_fraction)
print('shape of transition matrix is ', msm.P.shape)
delG_nstates_prob = {}
delG_interval = {}
for system in systems:
print('calculating free energy landscape for %s' % system)
delG_nstates_prob[system] = []
Y = np.load('%s/tica_projection.npy' % file_paths[system])
Y1 = [y[:, 0] for y in Y]
Y2 = [y[:, 1] for y in Y]
clkmeans_dtrajs = np.load('%s/clkmeans_dtrajs.npy' % file_paths[system])
clkmeans_dtrajs = clkmeans_dtrajs.tolist()
MSM = msm.bayesian_markov_model(clkmeans_dtrajs, lag=200, nsamples=1000)
#Calculate delG
Y1_hstack = np.hstack(Y1)
Y2_hstack = np.hstack(Y2)
MSM_weights_hstack = np.hstack(MSM.trajectory_weights())
Y1_DFG_in = []
Y2_DFG_in = []
Y1_DFG_out = []
Y2_DFG_out = []
weights_DFG_in = []
weights_DFG_out = []
for i, y1 in enumerate(Y1_hstack):
this_y1 = y1
