/usr/lib/python2.7/dist-packages/csb/statistics/samplers/mc/__init__.py is in python-csb 1.2.2+dfsg-2ubuntu1.
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Abstract Monte Carlo samplers.
"""
import numpy.random
import csb.numeric
import csb.core
from abc import ABCMeta, abstractmethod, abstractproperty
from csb.statistics.samplers import AbstractSampler, AbstractState, State, EnsembleState
class AbstractMC(AbstractSampler):
"""
Abstract Monte Carlo sampler class. Subclasses implement various
Monte carlo equilibrium sampling schemes.
@param state: Initial state
@type state: L{AbstractState}
"""
__metaclass__ = ABCMeta
def __init__(self, state):
self._state = None
self.state = state
def _checkstate(self, state):
if not isinstance(state, AbstractState):
raise TypeError(state)
@abstractproperty
def energy(self):
"""
Energy of the current state.
"""
pass
@property
def state(self):
"""
Current state.
"""
return self._state
@state.setter
def state(self, value):
self._checkstate(value)
self._state = value
@abstractmethod
def sample(self):
"""
Draw a sample.
@rtype: L{AbstractState}
"""
pass
class AbstractPropagationResult(object):
"""
Abstract class providing the interface for the result
of a deterministic or stochastic propagation of a state.
"""
__metaclass__ = ABCMeta
@abstractproperty
def initial(self):
"""
Initial state
"""
pass
@abstractproperty
def final(self):
"""
Final state
"""
pass
@abstractproperty
def heat(self):
"""
Heat produced during propagation
@rtype: float
"""
pass
class PropagationResult(AbstractPropagationResult):
"""
Describes the result of a deterministic or stochastic
propagation of a state.
@param initial: Initial state from which the
propagation started
@type initial: L{State}
@param final: Final state in which the propagation
resulted
@type final: L{State}
@param heat: Heat produced during propagation
@type heat: float
"""
def __init__(self, initial, final, heat=0.0):
if not isinstance(initial, AbstractState):
raise TypeError(initial)
if not isinstance(final, AbstractState):
raise TypeError(final)
self._initial = initial
self._final = final
self._heat = None
self.heat = heat
def __iter__(self):
return iter([self._initial, self.final])
@property
def initial(self):
return self._initial
@property
def final(self):
return self._final
@property
def heat(self):
return self._heat
@heat.setter
def heat(self, value):
self._heat = float(value)
class Trajectory(csb.core.CollectionContainer, AbstractPropagationResult):
"""
Ordered collection of states, representing a phase-space trajectory.
@param items: list of states defining a phase-space trajectory
@type items: list of L{AbstractState}
@param heat: heat produced during the trajectory
@type heat: float
@param work: work produced during the trajectory
@type work: float
"""
def __init__(self, items, heat=0.0, work=0.0):
super(Trajectory, self).__init__(items, type=AbstractState)
self._heat = heat
self._work = work
@property
def initial(self):
return self[0]
@property
def final(self):
return self[self.last_index]
@property
def heat(self):
return self._heat
@heat.setter
def heat(self, value):
self._heat = float(value)
@property
def work(self):
return self._work
@work.setter
def work(self, value):
self._work = float(value)
class TrajectoryBuilder(object):
"""
Allows to build a Trajectory object step by step.
@param heat: heat produced over the trajectory
@type heat: float
@param work: work produced during the trajectory
@type work: float
"""
def __init__(self, heat=0.0, work=0.0):
self._heat = heat
self._work = work
self._states = []
@staticmethod
def create(full=True):
"""
Trajectory builder factory.
@param full: if True, a TrajectoryBuilder instance designed
to build a full trajectory with initial state,
intermediate states and a final state. If False,
a ShortTrajectoryBuilder instance designed to
hold only the initial and the final state is
returned
@type full: boolean
"""
if full:
return TrajectoryBuilder()
else:
return ShortTrajectoryBuilder()
@property
def product(self):
"""
The L{Trajectory} instance build by a specific instance of
this class
"""
return Trajectory(self._states, heat=self._heat, work=self._work)
def add_initial_state(self, state):
"""
Inserts a state at the beginning of the trajectory
@param state: state to be added
@type state: L{State}
"""
self._states.insert(0, state.clone())
def add_intermediate_state(self, state):
"""
Adds a state to the end of the trajectory
@param state: state to be added
@type state: L{State}
"""
self._states.append(state.clone())
def add_final_state(self, state):
"""
Adds a state to the end of the trajectory
@param state: state to be added
@type state: L{State}
"""
self._states.append(state.clone())
class ShortTrajectoryBuilder(TrajectoryBuilder):
def add_intermediate_state(self, state):
pass
@property
def product(self):
"""
The L{PropagationResult} instance built by a specific instance of
this class
"""
if len(self._states) != 2:
raise ValueError("Can't create a product, two states required")
initial, final = self._states
return PropagationResult(initial, final, heat=self._heat)
class MCCollection(csb.core.BaseCollectionContainer):
"""
Collection of single-chain samplers.
@param items: samplers
@type items: list of L{AbstractSingleChainMC}
"""
def __init__(self, items):
from csb.statistics.samplers.mc.singlechain import AbstractSingleChainMC
super(MCCollection, self).__init__(items, type=AbstractSingleChainMC)
def augment_state(state, temperature=1.0, mass_matrix=None):
"""
Augments a state with only positions given by momenta drawn
from the Maxwell-Boltzmann distribution.
@param state: State to be augmented
@type state: L{State}
@param temperature: Temperature of the desired Maxwell-Boltzmann
distribution
@type temperature: float
@param mass_matrix: Mass matrix to be used in the Maxwell-Boltzmann
distribution; None defaults to a unity matrix
@type mass_matrix: L{InvertibleMatrix}
@return: The initial state augmented with momenta
@rtype: L{State}
"""
d = len(state.position)
mm_unity = None
if mass_matrix is None:
mm_unity = True
if mm_unity == None:
mm_unity = mass_matrix.is_unity_multiple
if mm_unity == True:
momentum = numpy.random.normal(scale=numpy.sqrt(temperature),
size=d)
else:
covariance_matrix = temperature * mass_matrix
momentum = numpy.random.multivariate_normal(mean=numpy.zeros(d),
cov=covariance_matrix)
state.momentum = momentum
return state
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