pyemma.thermo.DTRAM

class pyemma.thermo.DTRAM(*args, **kwargs)

Discrete Transition(-based) Reweighting Analysis Method.

__init__(bias_energies_full, lag, count_mode='sliding', connectivity='reversible_pathways', 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='reversible_pathways') –

  One of ‘reversible_pathways’, ‘summed_count_matrix’ or None. Defines what should be considered a connected set in the joint (product) space of conformations and thermodynamic ensembles. * ‘reversible_pathways’ : requires that every state in the connected set

  can be reached by following a pathway of reversible transitions. A reversible transition between two Markov states (within the same thermodynamic state k) is a pair of Markov states that belong to the same strongly connected component of the count matrix (from thermodynamic state k). A pathway of reversible transitions is a list of reversible transitions [(i_1, i_2), (i_2, i_3),…, (i_(N-2), i_(N-1)), (i_(N-1), i_N)]. The thermodynamic state where the reversible transitions happen, is ignored in constructing the reversible pathways. This is equivalent to assuming that two ensembles overlap at some Markov state whenever there exist frames from both ensembles in that Markov state.

  • ’summed_count_matrix’ : all thermodynamic states are assumed to overlap. The connected set is then computed by summing the count matrices over all thermodynamic states and taking it’s largest strongly connected set. Not recommended!

  • None : assume that everything is connected. For debugging.

  For more details see pyemma.thermo.extensions.cset.compute_csets_dTRAM().

• 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 log-likelihood; 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

Methods

_Loggable__create_logger()
_SerializableMixIn__interpolate(state, klass)
__delattr__(name, /)	Implement delattr(self, name).
__dir__()	Default dir() implementation.
__eq__(other)	Return self==value.
__format__(format_spec, /)	Default object formatter.
__ge__(value, /)	Return self>=value.
__getattribute__(name, /)	Return getattr(self, name).
__getstate__()
__gt__(value, /)	Return self>value.
__init__(bias_energies_full, lag[, …])	Discrete Transition(-based) Reweighting Analysis Method
__init_subclass__(*args, **kwargs)	This method is called when a class is subclassed.
__le__(value, /)	Return self<=value.
__lt__(value, /)	Return self<value.
__my_getstate__()
__my_setstate__(state)
__ne__(value, /)	Return self!=value.
__new__(cls, *args, **kwargs)	Create and return a new object.
__reduce__()	Helper for pickle.
__reduce_ex__(protocol, /)	Helper for pickle.
__repr__()	Return repr(self).
__setattr__(name, value, /)	Implement setattr(self, name, value).
__setstate__(state)
__sizeof__()	Size of object in memory, in bytes.
__str__()	Return str(self).
__subclasshook__	Abstract classes can override this to customize issubclass().
_check_estimated()
_cleanup_logger(logger_id, logger_name)
_estimate(trajs)
_get_classes_to_inspect()	gets classes self derives from which 1.
_get_interpolation_map(cls)
_get_model_param_names()	Get parameter names for the model
_get_param_names()	Get parameter names for the estimator
_get_private_field(cls, name[, default])
_get_serialize_fields(cls)
_get_state_of_serializeable_fields(klass, state)	:return a dictionary {k:v} for k in self.serialize_fields and v=getattr(self, k)
_get_version(cls[, require])
_get_version_for_class_from_state(state, klass)	retrieves the version of the current klass from the state mapping from old locations to new ones.
_logger_is_active(level)	@param level: int log level (debug=10, info=20, warn=30, error=40, critical=50)
_set_state_from_serializeable_fields_and_state(…)	set only fields from state, which are present in klass.__serialize_fields
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[, y])	Estimates parameters - for compatibility with sklearn.
get_model_params([deep])	Get parameters for this model.
get_params([deep])	Get parameters for this estimator.
load(file_name[, model_name])	Loads a previously saved PyEMMA object from disk.
log_likelihood()
meval(f, *args, **kw)	Evaluates the given function call for all models Returns the results of the calls in a list
save(file_name[, model_name, overwrite, …])	saves the current state of this object to given file and name.
set_model_params([models, f_therm, pi, f, label])	Call to set all basic model parameters.
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

_DTRAM__serialize_fields
_DTRAM__serialize_version
_Estimator__serialize_fields
_Loggable__ids
_Loggable__refs
_MEMM__serialize_version
_MultiThermModel__serialize_version
_SerializableMixIn__serialize_fields
_SerializableMixIn__serialize_modifications_map
_SerializableMixIn__serialize_version
_StationaryModel__serialize_version
_SubSet__serialize_fields
_SubSet__serialize_version
_ThermoBase__serialize_fields
_ThermoBase__serialize_version
__dict__
__doc__
__hash__
__module__
__weakref__	list of weak references to the object (if defined)
_estimated
_loglevel_CRITICAL
_loglevel_DEBUG
_loglevel_ERROR
_loglevel_INFO
_loglevel_WARN
_save_data_producer
active_set	The active set of states on which all computations and estimations will be done.
dt_traj
f	The free energies (in units of kT) on the configuration states.
f_full_state
force_constants	The individual force matrices labelled accordingly to ttrajs.
free_energies	The free energies (in units of kT) on the configuration states.
free_energies_full_state
label	Human-readable description for the thermodynamic state of this model.
logger	The logger for this class instance
model	The model estimated by this Estimator
msm	MSM of the unbiased thermodynamic state; only present when unbiased data available.
name	The name of this instance
nstates	Number of active states on which all computations and estimations are done.
nstates_full	Size of the full set of states.
pi	The stationary distribution on the configuration states.
pi_full_state
stationary_distribution	The stationary distribution on the configuration states.
stationary_distribution_full_state
temperatures	The individual temperatures labelled accordingly to ttrajs.
umbrella_centers	The individual umbrella centers labelled accordingly to ttrajs.
unbiased_state	Index of the unbiased thermodynamic state.