pyemma.thermo.MEMM¶

class pyemma.thermo.MEMM(models, f_therm, pi=None, f=None, label='ground state')¶

Coupled set of Markov state models at multiple thermodynamic states

Parameters:

models (list of Model objects) – List of Model objects, e.g. StationaryModel or MSM objects, at the different thermodynamic states. This list may include the ground state, such that self.pi = self.models[0].pi holds. An example for that is data obtained from parallel tempering or replica-exchange, where the lowest simulated temperature is usually identical to the thermodynamic ground state. However, the list does not have to include the thermodynamic ground state. For example, when obtaining data from umbrella sampling, models might be the list of stationary models for n umbrellas (biased ensembles), while the thermodynamic ground state is the unbiased ensemble. In that case, self.pi would be different from any self.models[i].pi
f_therm (ndarray(k)) – free energies at the different thermodynamic states
pi (ndarray(n), default=None) – Stationary distribution of the thermodynamic ground state. If not already normalized, pi will be scaled to fulfill \(\sum_i \pi_i = 1\). If None, models[0].pi will be used
f (ndarray(n)) – Discrete-state free energies of the thermodynamic ground state.
label (str, default='ground state') – Human-readable description for the thermodynamic ground state or reference state of this multiensemble. May contain a temperature description, such as ‘300 K’ or a description of bias energy such as ‘unbiased’.

__init__(models, f_therm, pi=None, f=None, label='ground state')¶

Methods

`__init__`(models, f_therm[, pi, f, label])
`expectation`(a)	Equilibrium expectation value of a given observable.
`get_model_params`([deep])	Get parameters for this model.
`meval`(f, args, *kw)	Evaluates the given function call for all models
`set_model_params`([models, f_therm, pi, f, label])
`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`
`model_active_set`
`msm`
`msm_active_set`
`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`
`unbiased_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

meval(f, *args, **kw)¶: Evaluates the given function call for all models Returns the results of the calls in a list

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

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