deeptime.markov.tools.estimation.bootstrap_counts¶

deeptime.markov.tools.estimation.bootstrap_counts(dtrajs, lagtime, corrlength=None)¶

Generates a randomly resampled count matrix given the input coordinates.

Parameters:

dtrajs (array-like or array-like of array-like) – single or multiple discrete trajectories. Every trajectory is assumed to be a statistically independent realization. Note that this is often not true and is a weakness with the present bootstrapping approach.
lagtime (int) – the lag time at which the count matrix will be evaluated
corrlength (int, optional, default=None) – the correlation length of the discrete trajectory. N / corrlength counts will be generated, where N is the total number of frames. If set to None (default), corrlength = lagtime will be used.

Notes

This function can be called multiple times in order to generate randomly resampled realizations of count matrices. For each of these realizations you can estimate a transition matrix, and from each of them computing the observables of your interest. The standard deviation of such a sample of the observable is a model for the standard error.

The bootstrap will be generated by sampling N/corrlength counts at time tuples (t, t+lagtime), where t is uniformly sampled over all trajectory time frames in [0,n_i-lagtime]. Here, n_i is the length of trajectory i and N = sum_i n_i is the total number of frames.