def _estimate(self, trajs):
"""
Parameters
----------
trajs : ndarray(T, 2) or list of ndarray(T_i, 2)
Thermodynamic trajectories. Each trajectory is a (T_i, 2)-array
with T_i time steps. The first column is the thermodynamic state
index, the second column is the configuration state index.
"""
# format input if needed
if isinstance(trajs, _np.ndarray):
trajs = [trajs]
# validate input
assert _types.is_list(trajs)
for ttraj in trajs:
_types.assert_array(ttraj, ndim=2, kind='numeric')
assert _np.shape(ttraj)[1] >= 2
# harvest state counts
self.state_counts_full = _util.state_counts(
[_np.ascontiguousarray(t[:, :2]).astype(_np.intc) for t in trajs],
nthermo=self.nthermo,
nstates=self.nstates_full)
# active set
self.active_set = _np.where(self.state_counts_full.sum(axis=0) > 0)[0]
self.state_counts = _np.ascontiguousarray(
self.state_counts_full[:, self.active_set].astype(_np.intc))
self.bias_energies = _np.ascontiguousarray(
self.bias_energies_full[:, self.active_set], dtype=_np.float64)
# run estimator
self.therm_energies, self.conf_energies, self.increments, self.loglikelihoods = \
_wham.estimate(
self.state_counts, self.bias_energies,
maxiter=self.maxiter, maxerr=self.maxerr,
therm_energies=self.therm_energies, conf_energies=self.conf_energies,
save_convergence_info=self.save_convergence_info)
# get stationary models
models = [
_StationaryModel(
pi=_np.exp(self.therm_energies[K, _np.newaxis] -
self.bias_energies[K, :] - self.conf_energies),
f=self.bias_energies[K, :] + self.conf_energies,
normalize_energy=False,
label="K=%d" % K) for K in range(self.nthermo)
]
# set model parameters to self
self.set_model_params(models=models,
f_therm=self.therm_energies,
f=self.conf_energies)
# done
return self
def _estimate(self, trajs):
# check input
assert isinstance(trajs, (tuple, list))
assert len(trajs) == 2
ttrajs = trajs[0]
dtrajs = trajs[1]
# validate input
for ttraj, dtraj in zip(ttrajs, dtrajs):
_types.assert_array(ttraj, ndim=1, kind='numeric')
_types.assert_array(dtraj, ndim=1, kind='numeric')
assert _np.shape(ttraj)[0] == _np.shape(dtraj)[0]
# harvest state counts
self.state_counts_full = _util.state_counts(ttrajs,
dtrajs,
nthermo=self.nthermo,
nstates=self.nstates_full)
# active set
self.active_set = _np.where(self.state_counts_full.sum(axis=0) > 0)[0]
self.state_counts = _np.ascontiguousarray(
self.state_counts_full[:, self.active_set].astype(_np.intc))
self.bias_energies = _np.ascontiguousarray(
self.bias_energies_full[:, self.active_set], dtype=_np.float64)
# run estimator
pg = _ProgressReporter()
stage = 'WHAM'
with pg.context(stage=stage):
self.therm_energies, self.conf_energies, self.increments, self.loglikelihoods = \
_wham.estimate(
self.state_counts, self.bias_energies,
maxiter=self.maxiter, maxerr=self.maxerr,
therm_energies=self.therm_energies, conf_energies=self.conf_energies,
save_convergence_info=self.save_convergence_info,
callback=_ConvergenceProgressIndicatorCallBack(
pg, stage, self.maxiter, self.maxerr))
# get stationary models
models = [
_StationaryModel(
pi=_np.exp(self.therm_energies[K, _np.newaxis] -
self.bias_energies[K, :] - self.conf_energies),
f=self.bias_energies[K, :] + self.conf_energies,
normalize_energy=False,
label="K=%d" % K) for K in range(self.nthermo)
]
# set model parameters to self
self.set_model_params(models=models,
f_therm=self.therm_energies,
f=self.conf_energies)
# done
return self
def _estimate(self, X):
ttrajs, dtrajs_full, btrajs = X
# shape and type checks
assert len(ttrajs) == len(dtrajs_full) == len(btrajs)
for t in ttrajs:
_types.assert_array(t, ndim=1, kind='i')
for d in dtrajs_full:
_types.assert_array(d, ndim=1, kind='i')
for b in btrajs:
_types.assert_array(b, ndim=2, kind='f')
# find dimensions
self.nstates_full = max(_np.max(d) for d in dtrajs_full) + 1
self.nthermo = max(_np.max(t) for t in ttrajs) + 1
# dimensionality checks
for t, d, b, in zip(ttrajs, dtrajs_full, btrajs):
assert t.shape[0] == d.shape[0] == b.shape[0]
assert b.shape[1] == self.nthermo
# cast types and change axis order if needed
ttrajs = [
_np.require(t, dtype=_np.intc, requirements='C') for t in ttrajs
]
dtrajs_full = [
_np.require(d, dtype=_np.intc, requirements='C')
for d in dtrajs_full
]
btrajs = [
_np.require(b, dtype=_np.float64, requirements='C') for b in btrajs
]
# find state visits
self.state_counts_full = _util.state_counts(ttrajs, dtrajs_full)
self.therm_state_counts_full = self.state_counts_full.sum(axis=1)
self.active_set = _np.sort(
_np.where(self.state_counts_full.sum(axis=0) > 0)[0])
self.state_counts = _np.ascontiguousarray(
self.state_counts_full[:, self.active_set].astype(_np.intc))
if self.direct_space:
mbar = _mbar_direct
else:
mbar = _mbar
pg = _ProgressReporter()
with pg.context():
self.therm_energies, self.unbiased_conf_energies_full, self.biased_conf_energies_full, \
self.increments = mbar.estimate(
self.state_counts_full.sum(axis=1), btrajs, dtrajs_full,
maxiter=self.maxiter, maxerr=self.maxerr,
save_convergence_info=self.save_convergence_info,
callback=_ConvergenceProgressIndicatorCallBack(
pg, 'MBAR', self.maxiter, self.maxerr),
n_conf_states=self.nstates_full)
try:
self.loglikelihoods = _np.nan * self.increments
except TypeError:
self.loglikelihoods = None
# get stationary models
models = [
_StationaryModel(f=self.biased_conf_energies_full[K,
self.active_set],
normalize_energy=False,
label="K=%d" % K) for K in range(self.nthermo)
]
# set model parameters to self
self.set_model_params(
models=models,
f_therm=self.therm_energies,
f=self.unbiased_conf_energies_full[self.active_set])
self.btrajs = btrajs
# done
return self
def _estimate(self, trajs):
# TODO: fix docstring
"""
Parameters
----------
X : tuple of (ttrajs, dtrajs)
Simulation trajectories. ttrajs contain the indices of the thermodynamic state and
dtrajs contains the indices of the configurational states.
ttrajs : list of numpy.ndarray(X_i, dtype=int)
Every elements is a trajectory (time series). ttrajs[i][t] is the index of the
thermodynamic state visited in trajectory i at time step t.
dtrajs : list of numpy.ndarray(X_i, dtype=int)
dtrajs[i][t] is the index of the configurational state (Markov state) visited in
trajectory i at time step t.
"""
# check input
assert isinstance(trajs, (tuple, list))
assert len(trajs) == 2
ttrajs = trajs[0]
dtrajs = trajs[1]
# validate input
for ttraj, dtraj in zip(ttrajs, dtrajs):
_types.assert_array(ttraj, ndim=1, kind='numeric')
_types.assert_array(dtraj, ndim=1, kind='numeric')
assert _np.shape(ttraj)[0] == _np.shape(dtraj)[0]
# harvest state counts
self.state_counts_full = _util.state_counts(ttrajs,
dtrajs,
nthermo=self.nthermo,
nstates=self.nstates_full)
# active set
self.active_set = _np.where(self.state_counts_full.sum(axis=0) > 0)[0]
self.state_counts = _np.ascontiguousarray(
self.state_counts_full[:, self.active_set].astype(_np.intc))
self.bias_energies = _np.ascontiguousarray(
self.bias_energies_full[:, self.active_set], dtype=_np.float64)
# run estimator
self.therm_energies, self.conf_energies, self.increments, self.loglikelihoods = \
_wham.estimate(
self.state_counts, self.bias_energies,
maxiter=self.maxiter, maxerr=self.maxerr,
therm_energies=self.therm_energies, conf_energies=self.conf_energies,
save_convergence_info=self.save_convergence_info,
callback=_ConvergenceProgressIndicatorCallBack(
self, 'WHAM', self.maxiter, self.maxerr))
self._progress_force_finish(stage='WHAM')
# get stationary models
models = [
_StationaryModel(
pi=_np.exp(self.therm_energies[K, _np.newaxis] -
self.bias_energies[K, :] - self.conf_energies),
f=self.bias_energies[K, :] + self.conf_energies,
normalize_energy=False,
label="K=%d" % K) for K in range(self.nthermo)
]
# set model parameters to self
self.set_model_params(models=models,
f_therm=self.therm_energies,
f=self.conf_energies)
# done
return self
