pyemma.msm.ui.ImpliedTimescales

class pyemma.msm.ui.ImpliedTimescales(dtrajs, lags=None, nits=10, connected=True, reversible=True, failfast=False)

Implied timescales for a series of lag times.

Parameters:
  • dtrajs (array-like or list of array-likes) – discrete trajectories
  • = None (lags) – integer lag times at which the implied timescales will be calculated
  • = 10 (k) – number of implied timescales to be computed. Will compute less if the number of states are smaller
  • = True (reversible) – compute the connected set before transition matrix estimation at each lag separately
  • = True – estimate the transition matrix reversibly (True) or nonreversibly (False)
  • = False (failfast) – if True, will raise an error as soon as not all requested timescales can be computed at all requested lagtimes. If False, will continue with a warning and compute the timescales/lagtimes that are possible.
__init__(dtrajs, lags=None, nits=10, connected=True, reversible=True, failfast=False)

Methods

__init__(dtrajs[, lags, nits, connected, ...])
bootstrap([nsample]) Samples ITS using bootstrapping
get_lagtimes() Return the list of lag times for which timescales were computed.
get_sample_conf([alpha, 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

Attributes

lagtimes Return the list of lag times for which timescales were computed.
number_of_timescales Return the number of timescales.
sample_lagtimes Return the list of lag times for which sample data is available
sample_mean Returns the sample means of implied timescales.
sample_number_of_timescales Return the number of timescales for which sample data is available
sample_std Returns the standard error of implied timescales.
samples_available Returns True if samples are available and thus sample
timescales Returns the implied timescale estimates
bootstrap(nsample=10)

Samples ITS using bootstrapping

get_lagtimes()

Return the list of lag times for which timescales were computed.

get_sample_conf(alpha=0.6827, process=None)

Returns the confidence interval that contains alpha % of the sample data

Use: alpha = 0.6827 for 1-sigma confidence interval alpha = 0.9545 for 2-sigma confidence interval alpha = 0.9973 for 3-sigma confidence interval etc.

Returns:
  • (L,R) ((float[],float[]) or (float[][],float[][])) – lower and upper timescales bounding the confidence interval
  • if process is None, will return two (l x k) arrays, where l is the number of lag times
  • and k is the number of computed timescales.
  • if process is an integer, will return two (l)-arrays with the
  • selected process time scale for every lag time
get_sample_mean(process=None)

Returns the sample means of implied timescales. Need to generate the samples first, e.g. by calling bootstrap

Parameters:process (int or None (default)) – index in [0:n-1] referring to the process whose timescale will be returned. By default, process = None and all computed process timescales will be returned.
Returns:
  • if process is None, will return a (l x k) array, where l is the number of lag times
  • and k is the number of computed timescales.
  • if process is an integer, will return a (l) array with the selected process time scale
  • for every lag time
get_sample_std(process=None)

Returns the standard error of implied timescales. Need to generate the samples first, e.g. by calling bootstrap

Parameters:process (int or None (default)) – index in [0:n-1] referring to the process whose timescale will be returned. By default, process = None and all computed process timescales will be returned.
Returns:
  • if process is None, will return a (l x k) array, where l is the number of lag times
  • and k is the number of computed timescales.
  • if process is an integer, will return a (l) array with the selected process time scale
  • for every lag time
get_timescales(process=None)

Returns the implied timescale estimates

Parameters:process (int or None (default)) – index in [0:n-1] referring to the process whose timescale will be returned. By default, process = None and all computed process timescales will be returned.
Returns:
  • if process is None, will return a (l x k) array, where l is the number of lag times
  • and k is the number of computed timescales.
  • if process is an integer, will return a (l) array with the selected process time scale
  • for every lag time
lagtimes

Return the list of lag times for which timescales were computed.

number_of_timescales

Return the number of timescales.

sample_lagtimes

Return the list of lag times for which sample data is available

sample_mean

Returns the sample means of implied timescales. Need to generate the samples first, e.g. by calling bootstrap

Returns:timescales – mean timescales for all processes and lag times. l is the number of lag times and k is the number of computed timescales.
Return type:ndarray((l x k), dtype=float)
sample_number_of_timescales

Return the number of timescales for which sample data is available

sample_std

Returns the standard error of implied timescales. Need to generate the samples first, e.g. by calling bootstrap

Returns:timescales – standard deviations of timescales for all processes and lag times. l is the number of lag times and k is the number of computed timescales.
Return type:ndarray((l x k), dtype=float)
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

Returns:timescales – timescales for all processes and lag times. l is the number of lag times and k is the number of computed timescales.
Return type:ndarray((l x k), dtype=float)