def main(): print "Reading data...", print " %s."% options.rawFN, raw = rd( options.rawFN ) print " %s." % options.projFN, P = Serializer.LoadFromHDF( options.projFN ) print " %s." % options.msmFN msmDict = Serializer.LoadFromHDF( options.msmFN ) lens = [ ( P['TrajLengths'][i], i ) for i in range( P['TrajLengths'].shape[0] ) ] lens = sorted(lens,reverse=True) print lens trajList = [ b for (a,b) in lens[:options.nTraj] ] print trajList # Generate the trajectory data: trajData = [] for traj in trajList: trajData.append( array(rawObs( traj, P['TrajLengths'], raw ) ) ) # Generate the autocorrelations: print "Calculating the convolutions ... " trajCorr = [ autocorrelate.fft_autocorrelate( thing ) for thing in trajData ] print "Plotting the data ... " plotData( msmDict, trajCorr, trajList )
def main():
print "Reading data...",
print " %s."% options.rawFN,
raw = rd( options.rawFN )
print " %s." % options.msmFN,
msm = rd( options.msmFN )
msm = msm[:,1] # Use the center column (This is how my data is formated.)
print " %s." % options.AsFN,
As = Serializer.LoadData( options.AsFN )
print " %s." % options.projFN,
P = Serializer.LoadFromHDF( options.projFN )
print " %s." % options.tFN
T = mmread( options.tFN )
lens = [ ( P['TrajLengths'][i], i ) for i in range( P['TrajLengths'].shape[0] ) ]
lens = sorted(lens,reverse=True)
trajList = [ b for (a,b) in lens[:options.nTraj] ]
rawFmt = ones( As.shape ) * -1
tempSum = 0
print raw.shape
for i in range( len( P['TrajLengths'] ) ):
rawFmt[i][ : P['TrajLengths'][i] ] = raw[ tempSum : tempSum + P['TrajLengths'][i] ]
tempSum += P['TrajLengths'][i]
# Generate the trajectory data:
trajData = []
msmData = []
N = int( P['TrajLengths'].max() / options.lag ) + 1
for traj in trajList:
print "\nCalculating trajectory %d" % traj,
print "Raw data.",
trajData.append( array(rawObs( traj, P['TrajLengths'], raw ) ) )
print "MSM Average."
start = zeros( len(msm) )
start[ As[ traj ][0] ] += 1
Cor, Traj, ObsTraj = Correlation.RawMSMCorrelation( T, rawFmt, As, Steps = N, StartingState = As[ traj ][0] )
msmData.append( Cor )
# Generate the autocorrelations:
print "Calculating the convolutions ... "
trajCorr = [ autocorrelate.fft_autocorrelate( thing ) for thing in trajData ]
msmCorr = [ autocorrelate.fft_autocorrelate( thing ) for thing in msmData ]
msmCorr = [ thing for thing in msmData ]
for i in range(1000):
if not os.path.exists('Autocorr%d'%i):
outDir = 'Autocorr%d'%i
os.mkdir( outDir )
break
if not outDir:
print "You have a lot of Autocorr directories..."
print "Writing output to ./ which could overwrite other data!"
outDir = './'
print "Writing the autocorrelation data"
for index, traj in enumerate( trajList ):
wd( [ 'autocorr','traj%d'%traj,'msm','lag%d'%options.lag ], msmCorr[index], dir=outDir )
wd( [ 'autocorr','traj%d'%traj,'raw'], trajCorr[index], dir=outDir )
plotData( msmCorr, trajCorr, trajList, options.lag )
tProb = mmread(args.tProb)
try:
raw_data = io.loadh(args.raw_data)["arr_0"]
except:
raw_data = io.loadh(args.raw_data)["Data"]
num_frames = raw_data.shape[1]
num_lagtimes = num_frames / args.lagtime
# msm_acf = msm_analysis.msm_acf(tProb, msm_data, np.arange(num_lagtimes),
# num_modes=args.num_modes)
sampled_traj = msm_analysis.sample(tProb, np.random.randint(tProb.shape[0]), num_lagtimes)
data_traj = msm_data[sampled_traj]
msm_acf = autocorrelate.fft_autocorrelate(data_traj)
raw_acfs = []
for i in xrange(np.max([raw_data.shape[0], 10])):
max_non_neg = np.where(raw_data[i] != -1)[0].max()
row = raw_data[i][: max_non_neg + 1]
raw_acfs.append(autocorrelate.fft_autocorrelate(row))
figure()
axes((0.18, 0.18, 0.72, 0.72))
raw_label = "Raw Data"
for i in xrange(len(raw_acfs)):