pyemma.thermo.StationaryModel

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

StationaryModel combines a stationary vector with discrete-state free energies.

__init__(pi=None, f=None, normalize_energy=True, label='ground state')

StationaryModel combines a stationary vector with discrete-state free energies.

Parameters

• pi (ndarray(n)) – Stationary distribution. If not already normalized, pi will be scaled to fulfill \(\sum_i \pi_i = 1\). The free energies f will be computed from pi via \(f_i = - \log(\pi_i)\). Only if normalize_f is True, a constant will be added to ensure consistency with \(\sum_i \pi_i = 1\).

• f (ndarray(n)) – Discrete-state free energies. If normalized_f = True, a constant will be added to normalize the stationary distribution. Otherwise f is left as given. If both (pi and f) are given, f takes precedence.

• normalize_energy (bool, default=True) – If parametrized by free energy f, normalize them such that \(\sum_i \pi_i = 1\), which is achieved by \(\log \sum_i \exp(-f_i) = 0\).

• label (str, default='ground state') – Human-readable description for the thermodynamic state of this model. May contain a temperature description, such as ‘300 K’ or a description of bias energy such as ‘unbiased’ or ‘Umbrella 1’

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__([pi, f, normalize_energy, label])	StationaryModel combines a stationary vector with discrete-state free energies.
__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.
save(file_name[, model_name, overwrite, …])	saves the current state of this object to given file and name.
set_model_params([pi, f, normalize_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

_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.
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