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#!/usr/bin/env python
# coding=utf-8
MDAnalysis example: Simple blocking analysis
Calculate the radius of gyration for increasing number of blocks over
a trajectory.
See: H. Flyvbjerg and H. G. Petersen. Error estimates on averages of
correlated data. 91(1):461-466, 1989.
import numpy as np
import MDAnalysis
def blocked(universe, nblocks, analyze):
size = universe.trajectory.numframes / nblocks
blocks = []
for block in xrange(nblocks):
a = []
for ts in u.trajectory[block * size:(block + 1) * size]:
blockaverage = np.average(blocks)
blockstd = np.std(blocks)
return nblocks, size, blockaverage, blockstd
def rgyr(universe):
return universe.select_atoms('protein').radius_of_gyration()
if __name__ == "__main__":
from MDAnalysis.tests.datafiles import PSF, DCD
import matplotlib
matplotlib.use('agg') # no interactive plotting, only save figures
from pylab import errorbar, subplot, xlabel, ylabel, savefig
have_matplotlib = True
except ImportError:
have_matplotlib = False
u = MDAnalysis.Universe(PSF, DCD)
results = []
for nblocks in xrange(2, 10):
results.append(blocked(u, nblocks, rgyr))
r = np.array(results)
if have_matplotlib:
errorbar(r[:, 0], r[:, 2], yerr=r[:, 3])
xlabel("number of blocks")
ylabel(r"$\langle R_{\rm{gyr}} \rangle$ ($\AA$)")
errorbar(r[:, 1], r[:, 2], yerr=r[:, 3])
xlabel("block size")
ylabel(r"$\langle R_{\rm{gyr}} \rangle$ ($\AA$)")
print "Wrote ./figures/blocks.{{pdf,png}}".format(*vars())