Data = dataIO.readData(options.data_FN)
# first reshape the data into trajectories.
Lens = Proj["TrajLengths"]
Trajs = []
sum = 0
for i in range(len(Lens)):
Trajs.append(Data[sum : sum + Lens[i]])
sum += Lens[i]
Folds = []
Unfolds = []
for traj in Trajs:
(a, b) = msmTools.calcRawFoldTime(traj, options.f_cut, options.u_cut, low_is_folded=options.low_is_folded)
Folds.extend(a)
Unfolds.extend(b)
# FoldsDist = bincount( Folds )
# UnfoldsDist = bincount( Unfolds )
figure()
subplot(211)
foldHist = hist(Folds, bins=100, color="blue", label="Fold")
vlines(mean(Folds), 0, ylim()[1], color="black", linewidth=3)
ylabel("Frequency")
legend()
xFolds = xlim()
subplot(212)
unfoldHist = hist(Unfolds, bins=100, color="red", label="Unfold")
options, args = parser.parse_args()
from numpy import *
from pyschwancr import dataIO, msmTools
import os, sys, re
# first make the xvg.
os.system('echo "0 0" | g_rms -f %s -s %s -o %s_rmsd.xvg' % ( options.traj_FN, options.struc_FN, options.traj_FN ) )
xvgIn = open( '%s_rmsd.xvg' % options.traj_FN, 'r' )
dat = []
for line in xvgIn:
if line[0] in [ '#', '@' ]:
continue
else:
dat.append( [ float( i ) for i in line.split() ] )
dat = array( dat )
hist( dat[:,1], bins=100 )
savefig('%s_dist.pdf' % options.traj_FN )
#Find the correct cutoffs to use... I'm going to simply use the two maxima.
Folds, Unfolds = msmTools.calcRawFoldTime( dat[:,1], options.f_cut, options.u_cut, low_is_folded = True )
savetxt( options.out_FN[:-4] + '_FoldTimes.dat', Folds )
savetxt( options.out_FN[:-4] + '_UnfoldTimes.dat', Unfolds )