pyemma.thermo.MEMM

class pyemma.thermo.MEMM(*args, **kwargs)

Coupled set of Markov state models at multiple thermodynamic states

Parameters

• models (list of Model objects) – List of Model objects, e.g. StationaryModel or MSM objects, at the different thermodynamic states. This list may include the ground state, such that self.pi = self.models[0].pi holds. An example for that is data obtained from parallel tempering or replica-exchange, where the lowest simulated temperature is usually identical to the thermodynamic ground state. However, the list does not have to include the thermodynamic ground state. For example, when obtaining data from umbrella sampling, models might be the list of stationary models for n umbrellas (biased ensembles), while the thermodynamic ground state is the unbiased ensemble. In that case, self.pi would be different from any self.models[i].pi

• f_therm (ndarray(k)) – free energies at the different thermodynamic states

• pi (ndarray(n), default=None) – Stationary distribution of the thermodynamic ground state. If not already normalized, pi will be scaled to fulfill \(\sum_i \pi_i = 1\). If None, models[0].pi will be used

• f (ndarray(n)) – Discrete-state free energies of the thermodynamic ground state.

• label (str, default='ground state') – Human-readable description for the thermodynamic ground state or reference state of this multiensemble. May contain a temperature description, such as ‘300 K’ or a description of bias energy such as ‘unbiased’.

__init__(models, f_therm, pi=None, f=None, label='ground state')

Parameters

• models (list of model objects) – List of model objects, e.g. StationaryModel or :class:`ThermoMSM <pyemma.thermo.models.memm.ThermoMSM> objects, at the different thermodynamic states. This list may include the ground state, such that self.pi = self.models[*].pi holds, where * denotes the ground state. An example for that is data obtained from parallel tempering or replica-exchange, where the lowest simulated temperature is usually identical to the thermodynamic ground state. However, the list does not have to include the thermodynamic ground state. For example, when obtaining data from umbrella sampling, models might be the list of stationary models for n umbrellas (biased ensembles), while the thermodynamic ground state is the unbiased ensemble. In that case, self.pi would be different from any self.models[i].pi.

• f_therm (numpy.ndarray(k)) – Free energies of the different thermodynamic states.

• pi (numpy.ndarray(n), default=None) – Stationary distribution of the thermodynamic ground state. If not already normalized, pi will be scaled to fulfill \(\sum_i \pi_i = 1\). If None, models[0].pi will be used.

• f (numpy.ndarray(n)) – Discrete-state free energies of the thermodynamic ground state.

• label (str, optional, default='ground state') – Human-readable description for the thermodynamic ground state or reference state of this multiensemble. May contain a temperature description, such as ‘300 K’ or a description of bias energy such as ‘unbiased’.

Methods

_SerializableMixIn__interpolate(state, klass)
__delattr__(name, /)	Implement delattr(self, name).
__dir__()	Default dir() implementation.
__eq__(other)	Return self==value.
__format__(format_spec, /)	Default object formatter.
__ge__(value, /)	Return self>=value.
__getattribute__(name, /)	Return getattr(self, name).
__getstate__()
__gt__(value, /)	Return self>value.
__init__(models, f_therm[, pi, f, label])	param models List of model objects, e.g.
__init_subclass__(*args, **kwargs)	This method is called when a class is subclassed.
__le__(value, /)	Return self<=value.
__lt__(value, /)	Return self<value.
__my_getstate__()
__my_setstate__(state)
__ne__(value, /)	Return self!=value.
__new__(cls, *args, **kwargs)	Create and return a new object.
__reduce__()	Helper for pickle.
__reduce_ex__(protocol, /)	Helper for pickle.
__repr__()	Return repr(self).
__setattr__(name, value, /)	Implement setattr(self, name, value).
__setstate__(state)
__sizeof__()	Size of object in memory, in bytes.
__str__()	Return str(self).
__subclasshook__	Abstract classes can override this to customize issubclass().
_get_classes_to_inspect()	gets classes self derives from which 1.
_get_interpolation_map(cls)
_get_model_param_names()	Get parameter names for the model
_get_private_field(cls, name[, default])
_get_serialize_fields(cls)
_get_state_of_serializeable_fields(klass, state)	:return a dictionary {k:v} for k in self.serialize_fields and v=getattr(self, k)
_get_version(cls[, require])
_get_version_for_class_from_state(state, klass)	retrieves the version of the current klass from the state mapping from old locations to new ones.
_set_state_from_serializeable_fields_and_state(…)	set only fields from state, which are present in klass.__serialize_fields
expectation(a)	Equilibrium expectation value of a given observable.
get_model_params([deep])	Get parameters for this model.
load(file_name[, model_name])	Loads a previously saved PyEMMA object from disk.
meval(f, *args, **kw)	Evaluates the given function call for all models Returns the results of the calls in a list
save(file_name[, model_name, overwrite, …])	saves the current state of this object to given file and name.
set_model_params([models, f_therm, pi, f, label])	Call to set all basic model parameters.
update_model_params(**params)	Update given model parameter if they are set to specific values

Attributes

_MEMM__serialize_version
_MultiThermModel__serialize_version
_SerializableMixIn__serialize_fields
_SerializableMixIn__serialize_modifications_map
_SerializableMixIn__serialize_version
_StationaryModel__serialize_version
_SubSet__serialize_fields
_SubSet__serialize_version
__dict__
__doc__
__hash__
__module__
__weakref__	list of weak references to the object (if defined)
_save_data_producer
active_set	The active set of states on which all computations and estimations will be done.
f	The free energies (in units of kT) on the configuration states.
f_full_state
free_energies	The free energies (in units of kT) on the configuration states.
free_energies_full_state
label	Human-readable description for the thermodynamic state of this model.
msm	MSM of the unbiased thermodynamic state; only present when unbiased data available.
nstates	Number of active states on which all computations and estimations are done.
nstates_full	Size of the full set of states.
pi	The stationary distribution on the configuration states.
pi_full_state
stationary_distribution	The stationary distribution on the configuration states.
stationary_distribution_full_state
unbiased_state	Index of the unbiased thermodynamic state.