def test_get_output(self):
O = self.inp.get_output()
assert types.is_list(O)
assert len(O) == 1
assert types.is_float_matrix(O[0])
assert O[0].shape[0] == 100
assert O[0].shape[1] == self.inp.dimension()
def test_get_output(self):
O = self.pca_obj.get_output()
assert types.is_list(O)
assert len(O) == 1
assert types.is_float_matrix(O[0])
assert O[0].shape[0] == self.T
assert O[0].shape[1] == self.pca_obj.dimension()
def test_get_output(self):
c = self.ass
O = c.get_output()
assert types.is_list(O)
assert len(O) == 1
assert types.is_int_matrix(O[0])
assert O[0].shape[0] == self.T
assert O[0].shape[1] == 1
def test_get_output(self):
for c in self.cl:
O = c.get_output()
assert types.is_list(O)
assert len(O) == 1
assert types.is_int_matrix(O[0])
assert O[0].shape[0] == self.T
assert O[0].shape[1] == 1
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):
"""
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 transition counts
self.count_matrices_full = _util.count_matrices(
[_np.ascontiguousarray(t[:, :2]).astype(_np.intc) for t in trajs],
self.lag,
sliding=self.count_mode,
sparse_return=False,
nstates=self.nstates_full)
# harvest state counts (for WHAM)
self.state_counts_full = _util.state_counts(trajs,
nthermo=self.nthermo,
nstates=self.nstates_full)
# restrict to connected set
C_sum = self.count_matrices_full.sum(axis=0)
# TODO: use improved cset
cset = _largest_connected_set(C_sum, directed=True)
self.active_set = cset
# correct counts
self.count_matrices = self.count_matrices_full[:, cset[:, _np.newaxis],
cset]
self.count_matrices = _np.require(self.count_matrices,
dtype=_np.intc,
requirements=['C', 'A'])
# correct bias matrix
self.bias_energies = self.bias_energies_full[:, cset]
self.bias_energies = _np.require(self.bias_energies,
dtype=_np.float64,
requirements=['C', 'A'])
# correct state counts
self.state_counts = self.state_counts_full[:, cset]
self.state_counts = _np.require(self.state_counts,
dtype=_np.intc,
requirements=['C', 'A'])
# run initialisation
if self.init is not None:
if self.init == 'wham':
self.therm_energies, self.conf_energies, _increments, _loglikelihoods = \
_wham.estimate(
self.state_counts, self.bias_energies,
maxiter=5000, maxerr=1.0E-8, save_convergence_info=0,
therm_energies=self.therm_energies, conf_energies=self.conf_energies)
# run estimator
self.therm_energies, self.conf_energies, self.log_lagrangian_mult, \
self.increments, self.loglikelihoods = _dtram.estimate(
self.count_matrices, self.bias_energies,
maxiter=self.maxiter, maxerr=self.maxerr,
log_lagrangian_mult=self.log_lagrangian_mult,
conf_energies=self.conf_energies,
save_convergence_info=self.save_convergence_info)
# compute models
models = [
_dtram.estimate_transition_matrix(
self.log_lagrangian_mult, self.bias_energies,
self.conf_energies, self.count_matrices,
_np.zeros(shape=self.conf_energies.shape,
dtype=_np.float64), K) for K in range(self.nthermo)
]
self.model_active_set = [
_largest_connected_set(msm, directed=False) for msm in models
]
models = [
_np.ascontiguousarray((msm[lcc, :])[:, lcc])
for msm, lcc in zip(models, self.model_active_set)
]
# set model parameters to self
self.set_model_params(models=[
_MSM(msm, dt_model=self.timestep_traj.get_scaled(self.lag))
for msm in models
],
f_therm=self.therm_energies,
f=self.conf_energies)
# done
return self
