pyemma.msm.ReactiveFlux

class pyemma.msm.ReactiveFlux(*args, **kwargs)

A->B reactive flux from transition path theory (TPT)

This object describes a reactive flux, i.e. a network of fluxes from a set of source states A, to a set of sink states B, via a set of intermediate nodes. Every node has three properties: the stationary probability mu, the forward committor qplus and the backward committor qminus. Every pair of edges has the following properties: a flux, generally a net flux that has no unnecessary back-fluxes, and optionally a gross flux.

Flux objects can be used to compute transition pathways (and their weights) from A to B, the total flux, the total transition rate or mean first passage time, and they can be coarse-grained onto a set discretization of the node set.

Fluxes can be computed in EMMA using transition path theory - see msmtools.tpt()

Parameters

• A (array_like) – List of integer state labels for set A

• B (array_like) – List of integer state labels for set B

• flux ((n,n) ndarray or scipy sparse matrix) – effective or net flux of A->B pathways

• mu ((n,) ndarray (optional)) – Stationary vector

• qminus ((n,) ndarray (optional)) – Backward committor for A->B reaction

• qplus ((n,) ndarray (optional)) – Forward committor for A-> B reaction

• gross_flux ((n,n) ndarray or scipy sparse matrix) – gross flux of A->B pathways, if available

Notes

Reactive flux contains a flux network from educt states (A) to product states (B).

See also

msmtools.tpt

__init__(A, B, flux, mu=None, qminus=None, qplus=None, gross_flux=None, dt_model='1 step')

Initialize self. See help(type(self)) for accurate signature.

Methods

_SerializableMixIn__interpolate(state, klass)
__delattr__(name, /)	Implement delattr(self, name).
__dir__()	Default dir() implementation.
__eq__(value, /)	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.
__hash__()	Return hash(self).
__init__(A, B, flux[, mu, qminus, qplus, …])	Initialize self.
__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().
_compute_coarse_sets(user_sets)	Computes the sets to coarse-grain the tpt flux to.
_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.
_pathways_to_flux(paths, pathfluxes[, n])	Sums up the flux from the pathways given
_set_state_from_serializeable_fields_and_state(…)	set only fields from state, which are present in klass.__serialize_fields
coarse_grain(user_sets)	Coarse-grains the flux onto user-defined sets.
get_model_params([deep])	Get parameters for this model.
load(file_name[, model_name])	Loads a previously saved PyEMMA object from disk.
major_flux([fraction])	Returns the main pathway part of the net flux comprising at most the requested fraction of the full flux.
pathways([fraction, maxiter])	Decompose flux network into dominant reaction paths.
save(file_name[, model_name, overwrite, …])	saves the current state of this object to given file and name.
set_model_params(A, B, flux, mu[, qminus, …])
update_model_params(**params)	Update given model parameter if they are set to specific values

Attributes

A	Returns the set of reactant (source) states.
B	Returns the set of product (target) states
I	Returns the set of intermediate states
_ReactiveFlux__serialize_version
_SerializableMixIn__serialize_fields
_SerializableMixIn__serialize_modifications_map
_SerializableMixIn__serialize_version
__dict__
__doc__
__module__
__weakref__	list of weak references to the object (if defined)
_save_data_producer
backward_committor	Returns the backward committor probability
committor	Returns the forward committor probability
dt_model
flux	Returns the effective or net flux
forward_committor	Returns the forward committor probability
gross_flux	Returns the gross A–>B flux
mfpt	Returns the mean-first-passage-time (inverse rate) of A–>B transitions
mu	Returns the stationary distribution
net_flux	Returns the effective or net flux
nstates	Returns the number of states.
qminus	Returns the backward committor probability
qplus	Returns the forward committor probability
rate	Returns the rate (inverse mfpt) of A–>B transitions
stationary_distribution	Returns the stationary distribution
total_flux	Returns the total flux