pyemma.thermo.WHAM¶

class pyemma.thermo.WHAM(bias_energies_full, stride=1, maxiter=10000, maxerr=1e-15, dt_traj='1 step', save_convergence_info=0)¶

Weighted Histogram Analysis Method

Parameters:

bias_energies_full (ndarray(K, n)) – bias_energies_full[j,i] is the bias energy for each discrete state i at thermodynamic state j.
maxiter (int, optional, default=10000) – The maximum number of self-consistent iterations before the estimator exits unsuccessfully.
maxerr (float, optional, default=1E-15) – Convergence criterion based on the maximal free energy change in a self-consistent iteration step.
dt_traj (str, optional, default='1 step') –
Description of the physical time corresponding to the lag. May be used by analysis algorithms such as plotting tools to pretty-print the axes. By default ‘1 step’, i.e. there is no physical time unit. Specify by a number, whitespace and unit. Permitted units are (* is an arbitrary string):

‘fs’, ‘femtosecond*’

‘ps’, ‘picosecond*’

‘ns’, ‘nanosecond*’

‘us’, ‘microsecond*’

‘ms’, ‘millisecond*’

‘s’, ‘second*’
save_convergence_info (int, optional, default=0) – Every save_convergence_info iteration steps, store the actual increment and the actual loglikelihood; 0 means no storage.
TODO (stride) –

Example

>>> from pyemma.thermo import WHAM
>>> import numpy as np
>>> B = np.array([[0, 0],[0.5, 1.0]])
>>> wham = WHAM(B)
>>> traj1 = np.array([[0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,1,1,0,0,0]]).T
>>> traj2 = np.array([[1,1,1,1,1,1,1,1,1,1],[0,1,0,1,0,1,1,0,0,1]]).T
>>> wham = wham.estimate([traj1, traj2])
>>> wham.log_likelihood() 
-6.6...
>>> wham.state_counts 
array([[7, 3],
       [5, 5]])
>>> wham.stationary_distribution 
array([ 0.5...,  0.4...])
>>> wham.meval('stationary_distribution') 
[array([ 0.5...,  0.4...]), array([ 0.6...,  0.3...])]

__init__(bias_energies_full, stride=1, maxiter=10000, maxerr=1e-15, dt_traj='1 step', save_convergence_info=0)¶

Methods

`__init__`(bias_energies_full[, stride, ...])
`estimate`(X, **params)	Estimates the model given the data X
`expectation`(a)	Equilibrium expectation value of a given observable.
`fit`(X)	Estimates parameters - for compatibility with sklearn.
`get_model_params`([deep])	Get parameters for this model.
`get_params`([deep])	Get parameters for this estimator.
`log_likelihood`()
`meval`(f, args, *kw)	Evaluates the given function call for all models
`set_model_params`([models, f_therm, pi, f, label])
`set_params`(**params)	Set the parameters of this estimator.
`update_model_params`(**params)	Update given model parameter if they are set to specific values

Attributes

`free_energies`	The free energies of discrete states
`logger`	The logger for this class instance
`model`	The model estimated by this Estimator
`name`	The name of this instance
`nstates`	Number of active states on which all computations and estimations are done
`stationary_distribution`	The stationary distribution

estimate(X, **params)¶

Estimates the model given the data X

Parameters:	X (object) – A reference to the data from which the model will be estimated params (dict) – New estimation parameter values. The parameters must that have been announced in the __init__ method of this estimator. The present settings will overwrite the settings of parameters given in the __init__ method, i.e. the parameter values after this call will be those that have been used for this estimation. Use this option if only one or a few parameters change with respect to the __init__ settings for this run, and if you don’t need to remember the original settings of these changed parameters.
Returns:	estimator – The estimated estimator with the model being available.
Return type:	object

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 \(T\).

fit(X)¶

Estimates parameters - for compatibility with sklearn.

Parameters:	X (object) – A reference to the data from which the model will be estimated
Returns:	estimator – The estimator (self) with estimated model.
Return type:	object

free_energies¶: The free energies of discrete states

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

get_params(deep=True)¶

Get parameters for this estimator.

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

logger¶: The logger for this class instance

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

model¶: The model estimated by this Estimator

name¶: The name of this instance

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

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 former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. :returns: :rtype: self

stationary_distribution¶: The stationary distribution

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