pyemma.thermo.DTRAM¶

class pyemma.thermo.DTRAM(bias_energies_full, lag, count_mode='sliding', connectivity='largest', maxiter=10000, maxerr=1e-15, save_convergence_info=0, dt_traj='1 step', init=None, init_maxiter=10000, init_maxerr=1e-08)¶

Discrete Transition(-based) Reweighting Analysis Method

Parameters:

bias_energies_full (numpy.ndarray(shape=(num_therm_states, num_conf_states)) object) – bias_energies_full[j, i] is the bias energy in units of kT for each discrete state i at thermodynamic state j.
lag (int) – Integer lag time at which transitions are counted.
count_mode (str, optional, default='sliding') –
Mode to obtain count matrices from discrete trajectories. Should be one of: * ‘sliding’ : a trajectory of length T will have \(T-\tau\) counts at time indexes

\[(0 \rightarrow \tau), (1 \rightarrow \tau+1), ..., (T-\tau-1 \rightarrow T-1)\]
- ‘sample’
  : a trajectory of length T will have \(T/\tau\) counts at time indexes
  \[(0 \rightarrow \tau), (\tau \rightarrow 2 \tau), ..., ((T/\tau-1) \tau \rightarrow T)\]
Currently only ‘sliding’ is supported.
connectivity (str, optional, default='largest') – Defines what should be considered a connected set in the joint space of conformations and thermodynamic ensembles. Currently only ‘largest’ is supported.
maxiter (int, optional, default=10000) – The maximum number of self-consistent iterations before the estimator exits unsuccessfully.
maxerr (float, optional, default=1.0E-15) – Convergence criterion based on the maximal free energy change in a self-consistent iteration step.
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.
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*’
init (str, optional, default=None) –
Use a specific initialization for self-consistent iteration:

None: use a hard-coded guess for free energies and Lagrangian multipliers

‘wham’: perform a short WHAM estimate to initialize the free energies
init_maxiter (int, optional, default=10000) – The maximum number of self-consistent iterations during the initialization.
init_maxerr (float, optional, default=1.0E-8) – Convergence criterion for the initialization.

Example

>>> from pyemma.thermo import DTRAM
>>> import numpy as np
>>> B = np.array([[0, 0],[0.5, 1.0]])
>>> dtram = DTRAM(B, 1)
>>> ttrajs = [np.array([0,0,0,0,0,0,0,0,0,0]),np.array([1,1,1,1,1,1,1,1,1,1])]
>>> dtrajs = [np.array([0,0,0,0,1,1,1,0,0,0]),np.array([0,1,0,1,0,1,1,0,0,1])]
>>> dtram = dtram.estimate((ttrajs, dtrajs))
>>> dtram.log_likelihood() 
-9.805...
>>> dtram.count_matrices 
array([[[5, 1],
        [1, 2]],

[[1, 4],

[3, 1]]], dtype=int32)

>>> dtram.stationary_distribution 
array([ 0.38...,  0.61...])
>>> dtram.meval('stationary_distribution') 
[array([ 0.38...,  0.61...]), array([ 0.50...,  0.49...])]

References

[1]	Wu, H. et al 2014 Statistically optimal analysis of state-discretized trajectory data from multiple thermodynamic states J. Chem. Phys. 141, 214106

__init__(bias_energies_full, lag, count_mode='sliding', connectivity='largest', maxiter=10000, maxerr=1e-15, save_convergence_info=0, dt_traj='1 step', init=None, init_maxiter=10000, init_maxerr=1e-08)¶

Methods

__init__(bias_energies_full, lag[, ...])

estimate(trajs)

param X:	Simulation trajectories. ttrajs contain the indices of the thermodynamic state and

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

register_progress_callback(call_back[, stage]) Registers the progress reporter.

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

`active_set`
`f_full_state`	The free energies of discrete states
`free_energies`
`free_energies_full_state`
`logger`	The logger for this class instance
`model`	The model estimated by this Estimator
`model_active_set`
`msm`
`msm_active_set`
`name`	The name of this instance
`nstates`	Number of active states on which all computations and estimations are done
`nstates_full`
`pi_full_state`
`show_progress`	whether to show the progress of heavy calculations on this object.
`stationary_distribution`	The stationary distribution
`stationary_distribution_full_state`
`unbiased_state`

estimate(trajs)¶

Parameters:

X (tuple of (ttrajs, dtrajs)) –

Simulation trajectories. ttrajs contain the indices of the thermodynamic state and dtrajs contains the indices of the configurational states.

ttrajs: Every elements is a trajectory (time series). ttrajs[i][t] is the index of the thermodynamic state visited in trajectory i at time step t.
dtrajs: dtrajs[i][t] is the index of the configurational state (Markov state) visited in trajectory i at time step t.

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 \(T\).

f_full_state¶: The free energies of discrete states

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

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

register_progress_callback(call_back, stage=0)¶

Registers the progress reporter.

Parameters:	call_back (function) – This function will be called with the following arguments: stage (int) instance of pyemma.utils.progressbar.ProgressBar optional args and named keywords (kw), for future changes stage* (int, optional, default=0) – The stage you want the given call back function to be fired.

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

show_progress¶: whether to show the progress of heavy calculations on this object.

stationary_distribution¶: The stationary distribution

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