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Correlators for molecular and stochastic dynamics

License: CC-BY

Time correlations represent one of the most important data that one can obtain from doing molecular and stochastic dynamics. The two common methods to obtain them is via either post-processing or on-line analysis. Here I review several algorithms to compute correlation from numerical data: naive, Fourier transform and blocking scheme with illustrations from Langevin dynamics, using Python.

{% notebook 2016/correlator_post.ipynb cells[1:] %}

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