pyemma.msm.ReactiveFlux¶

class pyemma.msm.ReactiveFlux(A, B, flux, mu=None, qminus=None, qplus=None, gross_flux=None, dt_model='1 step')¶

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

`__init__`(A, B, flux[, mu, qminus, qplus, …])	Initialize self.
`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
`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

A¶: Returns the set of reactant (source) states.

B¶: Returns the set of product (target) states

I¶: Returns the set of intermediate states

backward_committor¶: Returns the backward committor probability

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, tpt-object)

Notes

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

committor¶: Returns the forward committor probability

flux¶: Returns the effective or net flux

forward_committor¶: Returns the forward committor probability

get_model_params(deep=True)¶

Get parameters for this model.

Parameters: deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params – Parameter names mapped to their values.
Return type: mapping of string to any

gross_flux¶: Returns the gross A–>B flux

classmethod load(file_name, model_name='default')¶

Loads a previously saved PyEMMA object from disk.

Parameters

file_name (str or file like object (has to provide read method)) – The file like object tried to be read for a serialized object.
model_name (str, default='default') – if multiple models are contained in the file, these can be accessed by their name. Use pyemma.list_models() to get a representation of all stored models.

Returns

obj

Return type

the de-serialized object

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

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

References

1: P. Metzner, C. Schuette and E. Vanden-Eijnden. Transition Path Theory for Markov Jump Processes. Multiscale Model Simul 7: 1192-1219 (2009)

qminus¶: Returns the backward committor probability

qplus¶: Returns the forward committor probability

rate¶: Returns the rate (inverse mfpt) of A–>B transitions

save(file_name, model_name='default', overwrite=False, save_streaming_chain=False)¶

saves the current state of this object to given file and name.

Parameters

file_name (str) – path to desired output file
model_name (str, default='default') – creates a group named ‘model_name’ in the given file, which will contain all of the data. If the name already exists, and overwrite is False (default) will raise a RuntimeError.
overwrite (bool, default=False) – Should overwrite existing model names?
save_streaming_chain (boolean, default=False) – if True, the data_producer(s) of this object will also be saved in the given file.

Examples

>>> import pyemma, numpy as np
>>> from pyemma.util.contexts import named_temporary_file
>>> m = pyemma.msm.MSM(P=np.array([[0.1, 0.9], [0.9, 0.1]]))

>>> with named_temporary_file() as file: # doctest: +SKIP
...    m.save(file, 'simple') # doctest: +SKIP
...    inst_restored = pyemma.load(file, 'simple') # doctest: +SKIP
>>> np.testing.assert_equal(m.P, inst_restored.P) # doctest: +SKIP

stationary_distribution¶: Returns the stationary distribution

total_flux¶: Returns the total flux

update_model_params(**params)¶: Update given model parameter if they are set to specific values