pyemma.thermo.DTRAM¶
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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
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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 : list of numpy.ndarray(X_i, dtype=int)
- 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 : list of numpy.ndarray(X_i, dtype=int)
- dtrajs[i][t] is the index of the configurational state (Markov state) visited in trajectory i at time step t.
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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 \(T\).
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f_full_state
¶ The free energies of discrete states
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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
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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
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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
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logger
¶ The logger for this class instance
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meval
(f, *args, **kw)¶ Evaluates the given function call for all models Returns the results of the calls in a list
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model
¶ The model estimated by this Estimator
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name
¶ The name of this instance
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nstates
¶ Number of active states on which all computations and estimations are done
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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.
- call_back (function) –
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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
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show_progress
¶ whether to show the progress of heavy calculations on this object.
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stationary_distribution
¶ The stationary distribution
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update_model_params
(**params)¶ Update given model parameter if they are set to specific values