def main():
data = dataIO.readData(options.data_FN)
data = prepData( data )
maxVal = data[:,1].max()
minX = data[:,0].min()
maxX = data[:,0].max()
temp = (maxX - minX) * 0.1
p0 = [ maxVal, temp, temp, maxVal, maxX - temp, temp ]
pF, success = optimize.leastsq( errFnc, p0[:], args=( data[:,0], data[:,1] ), maxfev=100000, xtol=1E-10 )
print pF
xFit = linspace(data[:,0].min(),data[:,0].max(),100)
yFit = fitFnc( abs(pF), xFit )
plot( data[:,0], data[:,1],'.',label='Data')
plot( xFit, yFit, '-r',lw=2,label='Fit')
legend()
if options.out_FN:
ttl = '.'.join( options.out_FN.split('.')[:-1] )
else:
ttl = '.'.join( options.data_FN.split('/')[-1].split('.')[:-1]) + 'Fit to Gaussians'
xlabel('.'.join( options.data_FN.split('/')[-1].split('.')[:-1] ) )
ylabel('Frequency')
title(ttl)
if options.out_FN:
savefig(options.out_FN[:-4]+'.pdf')
else:
savefig('.'.join( options.data_FN.split('/')[-1].split('.')[:-1] ) + 'fit2gauss.pdf')
print "Wrote parameters to %s" % dataIO.writeData( [ options.out_FN ], pF )
return
def main():
# First load in all the data:
print "Loading Data..."
ass = Serializer.LoadData(options.ass_FN)
tProb = mmread(options.trans_FN)
Proj = Project.LoadFromHDF(options.proj_FN)
rawAry = dataIO.readData(options.raw_FN)
msmAry = dataIO.readData(options.msm_FN)
if options.low_is_folded:
testFcn = lambda x: x <= options.fCut
else:
testFcn = lambda x: x >= options.fCut
print "Calculating the raw folded over time..."
# Now break up the raw data by trajectory:
sum = 0
rawTrajs = []
for i in range( len( Proj['TrajLengths'] ) ):
rawTrajs.append( rawAry[ sum : sum + Proj['TrajLengths'][i] ] )
sum += Proj['TrajLengths'][i]
# Now calculate the steps it takes to fold for each trajectory:
time2fold_raw = []
for trj in rawTrajs:
count = 0
for frame in trj:
if testFcn(frame):
break
count += 1
time2fold_raw.append( count )
time2fold_raw = array( time2fold_raw )
rawName = dataIO.writeData( [ "RAW_FractionFolded" ], time2fold_raw )
# Now calculate the msm fraction folded (using msmTools.calcFracFold)
x0 = zeros( tProb.shape[0] ) # This start vector is based on the first frame of all the trajectories
for i in range( ass.shape[0] ):
x0[ ass[i,0] ] += 1.
x0 /= float(ass.shape[0])
print "Defining the folded state and calculating the MSM folded over time"
# Need to define the folded state. Will use a cutoff, but if there are no states below/above the cutoff then pick the min/max value
Fstates = []
for index,stateAvg in enumerate(msmAry[:,1]):
if testFcn( stateAvg ):
Fstates.append( index )
Fstates = array( Fstates )
if not Fstates.any():
if options.low_is_folded:
Fstates = array([ where( msmAry[:,1] == msmAry[:,1].min() ) ] )
else:
Fstates = array([ where( msmAry[:,1] == msmAry[:,1].max() ) ] )
N = time2fold_raw.max() / options.lag + 1
time2fold_msm = msmTools.calcFracFold( Fstates, tProb, x0, N = N )
datName = dataIO.writeData( [ "MSM_FractionFolded", str(options.lag) ], time2fold_msm )
print "Saved data to %s" % datName
# Now plot everything
print "Making plot ..."
hist( time2fold_raw, bins=100, histtype='step',label="Raw Data",cumulative=True,normed=True)
plot( arange( N ) * options.lag , time2fold_msm, label="MSM")
hlines( 1.0, xmin=0, xmax=time2fold_raw.max(),color='red' )
xlim([0,time2fold_raw.max()])
ylim([0,1.25])
legend()
xlabel( 'Time (frames)' )
ylabel( 'Fraction Folded' )
if options.title:
title( 'Fraction folded over time (%s)' % options.title)
else:
title( 'Fraction folded over time' )
text( 0.75 * time2fold_raw.max(), 0.2, "N = %d" % ass.shape[0] )
savefig( "FracFolded_%s_rawVsMsm.pdf" % '.'.join( options.raw_FN.split('/')[-1].split('.')[:-1] ) )
print "Plot saved to %s" % ("FracFolded_%s_rawVsMsm.pdf" % '.'.join( options.raw_FN.split('/')[-1].split('.')[:-1] ) )
exit()
Total = []
nameList =[ 'CombinedData' ]
for i in range( len( options.data_FNs ) ):
fn = options.data_FNs[i]
C = options.coefs[i]
if C % 1: # Nonzero return from mod 1 means this is not an integer, so write as a float
nameList.append('%.1e'%C)
else: # It's an integer!
nameList.append('%d'%int(C))
nameList.append(fn)
dat = dataIO.readData( fn )
if len( dat.shape ) > 1:
print "Using first column of data... re-shape the data if this doesn't work."
dat = dat[:,0]
if len( dat.shape ) == 0:
dat = np.array([ dat ])
Total.append( C * dat )
Total = np.array( Total ).sum(axis=0)
name = dataIO.writeData( nameList, Total, txt=False )
print "Wrote the combination to %s" % name
