pyemma.msm.ImpliedTimescales

class pyemma.msm.ImpliedTimescales(*args, **kwargs)

__init__(estimator, lags=None, nits=None, n_jobs=None, show_progress=True, only_timescales=False)

Initialize self. See help(type(self)) for accurate signature.

Methods

_Loggable__create_logger()
_SerializableMixIn__interpolate(state, klass)
__delattr__(name, /)	Implement delattr(self, name).
__dir__()	Default dir() implementation.
__eq__(value, /)	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.
__hash__()	Return hash(self).
__init__(estimator[, lags, nits, n_jobs, …])	Initialize self.
__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(dtrajs)
_estimator_produces_samples()
_get_classes_to_inspect()	gets classes self derives from which 1.
_get_interpolation_map(cls)
_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)
_postprocess_results(models)
_set_state_from_serializeable_fields_and_state(…)	set only fields from state, which are present in klass.__serialize_fields
estimate(X, **params)	param X discrete trajectories
fit(X[, y])	Estimates parameters - for compatibility with sklearn.
get_params([deep])	Get parameters for this estimator.
get_sample_conf([conf, process])	Returns the confidence interval that contains alpha % of the sample data
get_sample_mean([process])	Returns the sample means of implied timescales.
get_sample_std([process])	Returns the standard error of implied timescales.
get_timescales([process])	Returns the implied timescale estimates
load(file_name[, model_name])	Loads a previously saved PyEMMA object from disk.
save(file_name[, model_name, overwrite, …])	saves the current state of this object to given file and name.
set_params(**params)	Set the parameters of this estimator.

Attributes

_Estimator__serialize_fields
_ImpliedTimescales__serialize_fields
_ImpliedTimescales__serialize_version
_Loggable__ids
_Loggable__refs
_SerializableMixIn__serialize_fields
_SerializableMixIn__serialize_modifications_map
_SerializableMixIn__serialize_version
__dict__
__doc__
__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
estimators	Returns the estimators for all lagtimes.
fraction_of_frames	Returns the fraction of frames used to compute the count matrix at each lag time.
lags	Return the list of lag times for which timescales were computed.
lagtimes	Return the list of lag times for which timescales were computed.
logger	The logger for this class instance
model	The model estimated by this Estimator
models	Returns the models for all lagtimes.
n_jobs	Returns number of jobs/threads to use during assignment of data.
name	The name of this instance
nits	Return the number of timescales.
number_of_timescales	Return the number of timescales.
sample_mean	Returns the sample means of implied timescales.
sample_std	Returns the standard error of implied timescales.
samples_available	Returns True if samples are available and thus sample means, standard errors and confidence intervals can be obtained
timescales	Returns the implied timescale estimates