class ReactiveFlux¶

class deeptime.markov.ReactiveFlux(source_states, target_states, net_flux, stationary_distribution=None, qminus=None, qplus=None, gross_flux=None)¶

The 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 using transition path theory - see [1] and deeptime.markov.tools.tpt().

Parameters:

source_states (array_like) – List of integer state labels for set A
target_states (array_like) – List of integer state labels for set B
net_flux ((n,n) ndarray or scipy sparse matrix) – effective or net flux of A->B pathways
stationary_distribution ((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

reactive_flux: Method that produces ReactiveFlux instances
deeptime.markov.msm.MarkovStateModel.reactive_flux: TPT analysis based on a Markov state model

References

Attributes

`backward_committor`	backward committor probability
`forward_committor`	forward committor probability
`gross_flux`	Gross \(A\\rightarrow B\) flux.
`intermediate_states`	set of intermediate states
`mfpt`	Mean-first-passage-time (inverse rate) of \(A\rightarrow B\) transitions.
`n_states`	number of states.
`net_flux`	Effective or net flux.
`rate`	Rate (inverse mfpt) of \(A\\rightarrow B\) transitions in units of \(1/ \mathrm{time}\).
`source_states`	set of reactant (source) states.
`stationary_distribution`	stationary distribution
`target_states`	set of product (target) states
`total_flux`	The total flux.

Methods

`coarse_grain`(user_sets)	Coarse-grains the flux onto user-defined sets.
`copy`()	Makes a deep copy of this model.
`get_params`([deep])	Get the parameters.
`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.
`set_params`(**params)	Set the parameters of this estimator.

coarse_grain(user_sets)¶

Coarse-grains the flux onto user-defined sets.

Parameters:: user_sets (list of int-iterables) – sets of states that shall be distinguished in the coarse-grained flux.
Returns:: (sets, tpt) – sets contains the sets tpt is computed on. The tpt states of the new tpt object correspond to these sets of states in this order. Sets might be identical, if the user has already provided a complete partition that respects the boundary between A, B and the intermediates. If not, Sets will have more members than provided by the user, containing the “remainder” states and reflecting the splitting at the A and B boundaries. tpt contains a new tpt object for the coarse-grained flux. All its quantities (gross_flux, net_flux, A, B, committor, backward_committor) are coarse-grained to sets.
Return type:: (list of int-iterables, ReactiveFlux)

Notes

All user-specified sets will be split (if necessary) to preserve the boundary between A, B and the intermediate states.

copy() → Model¶

Makes a deep copy of this model.

Returns:: A new copy of this model.
Return type:: copy

get_params(deep=False)¶

Get the parameters.

Returns:: params – Parameter names mapped to their values.
Return type:: mapping of string to any

major_flux(fraction=0.9)¶: Returns the main pathway part of the net flux comprising at most the requested fraction of the full flux.

pathways(fraction=1.0, maxiter=1000)¶

Decompose flux network into dominant reaction paths.

Parameters:

fraction (float, optional) – Fraction of total flux to assemble in pathway decomposition
maxiter (int, optional) – Maximum number of pathways for decomposition

Returns:

paths (list) – List of dominant reaction pathways
capacities (list) – List of capacities corresponding to each reactions pathway in paths

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:: **params (dict) – Estimator parameters.
Returns:: self – Estimator instance.
Return type:: object

property backward_committor¶: backward committor probability

property forward_committor¶: forward committor probability

property gross_flux¶: Gross \(A\\rightarrow B\) flux. Units are \(1/ \mathrm{time}\).

property intermediate_states¶: set of intermediate states

property mfpt¶: Mean-first-passage-time (inverse rate) of \(A\rightarrow B\) transitions.

property n_states¶: number of states.

property net_flux¶: Effective or net flux. Units are \(1/ \mathrm{time}\).

property rate¶: Rate (inverse mfpt) of \(A\\rightarrow B\) transitions in units of \(1/ \mathrm{time}\).

property source_states¶: set of reactant (source) states.

property stationary_distribution¶: stationary distribution

property target_states¶: set of product (target) states

property total_flux¶: The total flux. Units are \(1/ \mathrm{time}\).