# pyblock API¶

pyblock is a python module for analysis of correlated data.

pyblock.blocking implements the reblocking algorithm [1] and an algorithm [2], [3] for suggesting the most appropriate block size (and thus estimate of the standard error in the data set) for data contained within numpy arrays. pyblock.pd_utils provides a nice wrapper around this using pandas, and it is highly recommended to use this if possible.

pyblock.error contains functions for simple error propagation and formatting of output of a value and it’s associated error.

## References¶

 [1] “Error estimates on averages of correlated data”, H. Flyvbjerg and H.G. Petersen, J. Chem. Phys. 91, 461 (1989).
 [2] “Monte Carlo errors with less errors”, U. Wolff, Comput. Phys. Commun. 156, 143 (2004) and arXiv:hep-lat/0306017.
 [3] “Strategies for improving the efficiency of quantum Monte Carlo calculations”, R. M. Lee, G. J. Conduit, N. Nemec, P. Lopez Rios, and N. D. Drummond, Phys. Rev. E. 83, 066706 (2011).