pyemma.thermo.StationaryModel¶

class pyemma.thermo.StationaryModel(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.
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’

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

Methods

__init__([pi, f, normalize_energy, label])

expectation(a) Equilibrium expectation value of a given observable.

get_model_params([deep]) Get parameters for this model.

set_model_params([pi, f, normalize_f])

param pi:	Stationary distribution. If not already normalized, pi will be

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

Attributes

`active_set`
`f_full_state`	The free energies of discrete states
`free_energies`
`free_energies_full_state`
`nstates`	Number of active states on which all computations and estimations are done
`nstates_full`
`pi_full_state`
`stationary_distribution`	The stationary distribution
`stationary_distribution_full_state`

expectation(a)¶

Equilibrium expectation value of a given observable. :param a: Observable vector :type a: (M,) ndarray

Returns:	val – Equilibrium expectation value of the given observable
Return type:	float

Notes

The equilibrium expectation value of an observable a is defined as follows

\[\mathbb{E}_{\mu}[a] = \sum_i \mu_i a_i\]

\(\mu=(\mu_i)\) is the stationary vector of the transition matrix \(T\).

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

nstates¶: Number of active states on which all computations and estimations are done

set_model_params(pi=None, f=None, normalize_f=True)¶

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.
normalize_f (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’

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