Desmond Tutorial

Analyzing Trajectories Using VMD
Big trajectories
# Get positions of CA atoms
pos = vmdnumpy.positions()
ca_pos = pos[self.inds]
# Accumulate positions into average and increment count
if self.avg is None: self.avg = ca_pos[:]
else: self.avg += ca_pos
self.count += 1
def finish(self):
self.avg /= self.count
self.rmsfile.flush()
The most obvious difference between the BigTrajectory version of the script and the
original version is that weʹve packaged the data for the task into a Python class. This
means we have to access our data structures through the self reference, rather than as
global variables. Another difference is that, rather than simply appending the RMSD values
to a list, we write them to a file; this keeps us from running ourselves out of memory if
we end up processing a huge number of frames.
To use this task in our own analysis script, letʹs assume that the contents of Figure 9.5 are
stored in a Python module called Analysis. Rather than copying and pasting the task
into our script, we import the definition of the task into our workspace, then call BigTrajectory.analyze
to apply the task to our own files. Figure 9.6 illustrates how this
might look.
Figure 9.6 Analysis script using Figure 9.5 and BigTrajectory
import Analysis
import BigTrajectory
import sys
inputfiles = sys.argv[1:]
rmsfile = open('rms.dat', 'w')
task = Analysis.AnalysisTask(rmsfile)
BigTrajectory.analyze( inputfiles, task )
exit()
September 2008 D. E. Shaw Research 95