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MBAR_estimator.py
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MBAR_estimator.py
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# This file is part of PyEMMA.
#
# Copyright (c) 2016-2017 Computational Molecular Biology Group, Freie Universitaet Berlin (GER)
#
# PyEMMA is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import numpy as _np
from pyemma._base.estimator import Estimator as _Estimator
from pyemma._base.progress import ProgressReporter as _ProgressReporter
from pyemma._base.serialization.serialization import SerializableMixIn as _SerializableMixIn
from pyemma.thermo import MultiThermModel as _MultiThermModel
from pyemma.thermo import StationaryModel as _StationaryModel
from pyemma.thermo.estimators._base import ThermoBase
from pyemma.thermo.estimators._callback import _ConvergenceProgressIndicatorCallBack
from pyemma.util import types as _types
from pyemma.thermo.extensions import (mbar as _mbar, mbar_direct as _mbar_direct, util as _util)
__author__ = 'wehmeyer'
class MBAR(_Estimator, _MultiThermModel, ThermoBase, _SerializableMixIn):
r"""Multi-state Bennet Acceptance Ratio Method."""
__serialize_version = 0
__serialize_fields = ('biased_conf_energies_full',
'btrajs',
'conf_energies',
'increments',
'loglikelihoods',
'nthermo',
'state_counts',
'state_counts_full',
'therm_energies',
'therm_state_counts_full',
'unbiased_conf_energies_full',
)
def __init__(
self,
maxiter=10000, maxerr=1.0E-15, save_convergence_info=0,
dt_traj='1 step', direct_space=False):
r"""Multi-state Bennet Acceptance Ratio Method
Parameters
----------
maxiter : int, optional, default=10000
The maximum number of self-consistent iterations before the estimator exits unsuccessfully.
maxerr : float, optional, default=1.0E-15
Convergence criterion based on the maximal free energy change in a self-consistent
iteration step.
save_convergence_info : int, optional, default=0
Every save_convergence_info iteration steps, store the actual increment
and the actual loglikelihood; 0 means no storage.
dt_traj : str, optional, default='1 step'
Description of the physical time corresponding to the lag. May be used by analysis
algorithms such as plotting tools to pretty-print the axes. By default '1 step', i.e.
there is no physical time unit. Specify by a number, whitespace and unit. Permitted
units are (* is an arbitrary string):
| 'fs', 'femtosecond*'
| 'ps', 'picosecond*'
| 'ns', 'nanosecond*'
| 'us', 'microsecond*'
| 'ms', 'millisecond*'
| 's', 'second*'
stride : int, optional, default=1
not used
Example
-------
References
----------
"""
self.maxiter = maxiter
self.maxerr = maxerr
self.save_convergence_info = save_convergence_info
self.dt_traj = dt_traj
self.direct_space = direct_space
self.active_set = None
# set iteration variables
self.therm_energies = None
self.conf_energies = None
def estimate(self, X):
"""
Parameters
----------
X : tuple of (ttrajs, dtrajs, btrajs)
Simulation trajectories. ttrajs contain the indices of the thermodynamic state, dtrajs
contains the indices of the configurational states and btrajs contain the biases.
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.
btrajs : list of numpy.ndarray((X_i, T), dtype=numpy.float64)
For every simulation frame seen in trajectory i and time step t, btrajs[i][t,k] is the
bias energy of that frame evaluated in the k'th thermodynamic state (i.e. at the k'th
Umbrella/Hamiltonian/temperature).
"""
return super(MBAR, self).estimate(X)
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 pointwise_free_energies(self, therm_state=None):
if therm_state is not None:
assert 0 <= therm_state < self.nthermo
mu = [_np.zeros(b.shape[0], dtype=_np.float64) for b in self.btrajs]
_mbar.get_pointwise_unbiased_free_energies(therm_state,
_np.log(self.therm_state_counts_full), self.btrajs,
self.therm_energies, None, mu)
return mu