verbose=True

if len(sys.argv)>2:

infile=sys.argv[1]

TR=float(sys.argv[2])

else:

error_and_exit('')

#infile='/corral-repl/utexas/poldracklab/openfmri/shared2/ds105/sub001/BOLD/task001_run001/bold_mcf.nii.gz'

#TR=2.5

save_sfnr=True

if os.path.dirname(infile)=='':

basedir=os.getcwd()

infile=os.path.join(basedir,infile)

elif os.path.dirname(infile)=='.':

basedir=os.getcwd()

infile=os.path.join(basedir,infile.replace('./',''))

else:

basedir=os.path.dirname(infile)

if outdir==None:

outdir=basedir

qadir=os.path.join(outdir,'QA')

if not infile.find('mcf.nii.gz')>0:

error_and_exit('infile must be of form XXX_mcf.nii.gz')

if not os.path.exists(infile):

error_and_exit('%s does not exist!'%infile)

if maskfile==None:

maskfile=infile.replace('mcf.nii','mcf_brain_mask.nii')

if not os.path.exists(maskfile):

error_and_exit('%s does not exist!'%maskfile)

if motfile==None:

motfile=infile.replace('mcf.nii.gz','mcf.par')

if not os.path.exists(motfile):

error_and_exit('%s does not exist!'%motfile)

if not os.path.exists(qadir):

os.mkdir(qadir)

else:

print 'QA dir already exists - overwriting!'

if verbose:

print 'infile:',infile

print 'maskfile:',maskfile

print 'motfile:',motfile

print 'outdir:',outdir

print 'computing image stats'

img=nib.load(infile)

imgdata=img.get_data()

nslices=imgdata.shape[2]

ntp=imgdata.shape[3]

maskimg=nib.load(maskfile)

maskdata=maskimg.get_data()

maskvox=N.where(maskdata>0)

nonmaskvox=N.where(maskdata==0)

if verbose:

print 'nmaskvox:',len(maskvox[0])

# load motion parameters and compute FD and identify bad vols for

# potential scrubbing (ala Power et al.)

motpars=N.loadtxt(motfile)

fd=compute_fd(motpars)

N.savetxt(os.path.join(qadir,'fd.txt'),fd)

voxmean=N.mean(imgdata,3)

voxstd=N.std(imgdata,3)

voxcv=voxstd/N.abs(voxmean)

voxcv[N.isnan(voxcv)]=0

voxcv[voxcv>1]=1

# compute timepoint statistics

maskmedian=N.zeros(imgdata.shape[3])

maskmean=N.zeros(imgdata.shape[3])

maskmad=N.zeros(imgdata.shape[3])

maskcv=N.zeros(imgdata.shape[3])

imgsnr=N.zeros(imgdata.shape[3])

for t in range(imgdata.shape[3]):

tmp=imgdata[:,:,:,t]

tmp_brain=tmp[maskvox]

tmp_nonbrain=tmp[nonmaskvox]

maskmad[t]=MAD(tmp_brain)

maskmedian[t]=N.median(tmp_brain)

maskmean[t]=N.mean(tmp_brain)

maskcv[t]=maskmad[t]/maskmedian[t]

imgsnr[t]=maskmean[t]/N.std(tmp_nonbrain)

# perform Greve et al./fBIRN spike detection

#1. Remove mean and temporal trend from each voxel.

#2. Compute temporal Z-score for each voxel.

#3. Average the absolute Z-score (AAZ) within a each slice and time point separately.

# This gives a matrix with number of rows equal to the number of slices (nSlices)

# and number of columns equal to the number of time points (nFrames).

#4. Compute new Z-scores using a jackknife across the slices (JKZ). For a given time point,

# remove one of the slices, compute the average and standard deviation of the AAZ across

# the remaining slices. Use these two numbers to compute a Z for the slice left out

# (this is the JKZ). The final Spike Measure is the absolute value of the JKZ (AJKZ).

# Repeat for all slices. This gives a new nSlices-by-nFrames matrix (see Figure 8).

# This procedure tends to remove components that are common across slices and so rejects motion.

if verbose:

print 'computing spike stats'

detrended_zscore=N.zeros(imgdata.shape)

detrended_data=N.zeros(imgdata.shape)

for i in range(len(maskvox[0])):

tmp=imgdata[maskvox[0][i],maskvox[1][i],maskvox[2][i],:]

tmp_detrended=detrend(tmp)

detrended_data[maskvox[0][i],maskvox[1][i],maskvox[2][i],:]=tmp_detrended

detrended_zscore[maskvox[0][i],maskvox[1][i],maskvox[2][i],:]=(tmp_detrended - N.mean(tmp_detrended))/N.std(tmp_detrended)

loo=sklearn.cross_validation.LeaveOneOut(nslices)

AAZ=N.zeros((nslices,ntp))

for s in range(nslices):

for t in range(ntp):

AAZ[s,t]=N.mean(N.abs(detrended_zscore[:,:,s,t]))

JKZ=N.zeros((nslices,ntp))

if verbose:

print 'computing outliers'

for train,test in loo:

for tp in range(ntp):

train_mean=N.mean(AAZ[train,tp])

train_std=N.std(AAZ[train,tp])

JKZ[test,tp]=(AAZ[test,tp] - train_mean)/train_std

AJKZ=N.abs(JKZ)

spikes=[]

if N.max(AJKZ)>AJKZ_thresh:

print 'Possible spike: Max AJKZ = %f'%N.max(AJKZ)

spikes=N.where(N.max(AJKZ,0)>AJKZ_thresh)[0]

if len(spikes)>0:

N.savetxt(os.path.join(qadir,'spikes.txt'),spikes)

voxmean_detrended=N.mean(detrended_data,3)

voxstd_detrended=N.std(detrended_data,3)

voxsfnr=voxmean/voxstd

meansfnr=N.mean(voxsfnr[maskvox])

# create plots

#

#imgdata_flat=imgdata.reshape(N.prod(imgdata.shape))

#imgdata_nonzero=imgdata_flat[imgdata_flat>0.0]

scaledmean=(maskmean - N.mean(maskmean))/N.std(maskmean)

mean_running_diff=N.zeros(maskmad.shape)

mean_running_diff=(maskmean[1:]-maskmean[:-1])/((maskmean[1:]+maskmean[:-1])/2.0)

DVARS=N.zeros(fd.shape)

DVARS[1:]=N.sqrt(mean_running_diff**2)*100.0

N.savetxt(os.path.join(qadir,'dvars.txt'),DVARS)

badvol_index_orig=N.where((fd>FDthresh)*(DVARS>DVARSthresh))[0]

#print badvol_index_orig

badvols=N.zeros(len(DVARS))

badvols[badvol_index_orig]=1

badvols_expanded=badvols.copy()

for i in badvol_index_orig:

if i>(nback-1):

start=i-nback

else:

start=0

if i<(len(badvols)-nforward):

end=i+nforward+1

else:

end=len(badvols)

#print i,start,end

badvols_expanded[start:end]=1

badvols_expanded_index=N.where(badvols_expanded>0)[0]

#print badvols_expanded_index

if len(badvols_expanded_index)>0:

N.savetxt(os.path.join(qadir,'scrubvols.txt'),badvols_expanded_index,fmt='%d')

# make scrubing design matrix - one colum per scrubbed timepoint

scrubdes=N.zeros((len(DVARS),len(badvols_expanded_index)))

for i in range(len(badvols_expanded_index)):

scrubdes[badvols_expanded_index[i],i]=1

N.savetxt(os.path.join(qadir,'scrubdes.txt'),scrubdes,fmt='%d')

else:

scrubdes=[]

# save out complete confound file

confound_mtx=N.zeros((len(DVARS),14))

confound_mtx[:,0:6]=motpars

confound_mtx[1:,6:12]=motpars[:-1,:]-motpars[1:,:] # derivs

confound_mtx[:,12]=fd

confound_mtx[:,13]=DVARS

if not scrubdes==[]:

confound_mtx=N.hstack((confound_mtx,scrubdes))

N.savetxt(os.path.join(qadir,'confound.txt'),confound_mtx)

#plot_timeseries(scaledmean,'Mean in-mask signal (Z-scored)',

# os.path.join(qadir,'scaledmaskmean.png'),spikes,'Potential spikes')

datavars={'imgsnr':imgsnr,'meansfnr':meansfnr,'spikes':spikes,'badvols':badvols_expanded_index}

if plot_data:

print 'before plot'

trend=plot_timeseries(maskmean,'Mean signal (unfiltered)',os.path.join(qadir,'maskmean.png'),

plottrend=True,ylabel='Mean MR signal')

print 'after plot'

datavars['trend']=trend

plot_timeseries(maskmad,'Median absolute deviation (robust SD)',

os.path.join(qadir,'mad.png'),ylabel='MAD')

plot_timeseries(DVARS,'DVARS (root mean squared signal derivative over brain mask)',

os.path.join(qadir,'DVARS.png'),plotline=0.5,ylabel='DVARS')

plot_timeseries(fd,'Framewise displacement',os.path.join(qadir,'fd.png'),

badvols_expanded_index,'Timepoints to scrub (%d total)'%len(badvols),

plotline=0.5,ylims=[0,1],ylabel='FD')

psd=matplotlib.mlab.psd(maskmean,NFFT=128,noverlap=96,Fs=1/TR)

plt.clf()

fig=plt.figure(figsize=[10,3])

fig.subplots_adjust(bottom=0.15)

plt.plot(psd[1][2:],N.log(psd[0][2:]))

plt.title('Log power spectrum of mean signal across mask')

plt.xlabel('frequency (secs)')

plt.ylabel('log power')

plt.savefig(os.path.join(qadir,'meanpsd.png'),bbox_inches='tight')

plt.close()

plt.clf()

plt.imshow(AJKZ,vmin=0,vmax=AJKZ_thresh)

plt.xlabel('timepoints')

plt.ylabel('slices')

plt.title('Spike measure (absolute jackknife Z)')

plt.savefig(os.path.join(qadir,'spike.png'),bbox_inches='tight')

plt.close()

if img.shape[0]<img.shape[1] and img.shape[0]<img.shape[2]:

orientation='saggital'

else:

orientation='axial'

mk_slice_mosaic(voxmean,os.path.join(qadir,'voxmean.png'),'Image mean (with mask)',contourdata=maskdata)

mk_slice_mosaic(voxcv,os.path.join(qadir,'voxcv.png'),'Image CV')

mk_slice_mosaic(voxsfnr,os.path.join(qadir,'voxsfnr.png'),'Image SFNR')

mk_report(infile,qadir,datavars)

# def save_vars(infile,qadir,datavars):

datafile=os.path.join(qadir,'qadata.csv')

f=open(datafile,'w')

f.write('SNR,%f\n'%N.mean(datavars['imgsnr']))

f.write('SFNR,%f\n'%datavars['meansfnr'])

#f.write('drift,%f\n'%datavars['trend'].params[1])

f.write('nspikes,%d\n'%len(datavars['spikes']))

f.write('nscrub,%d\n'%len(datavars['badvols']))

f.close()

if save_sfnr:

sfnrimg=nib.Nifti1Image(voxsfnr,img.get_affine())

sfnrimg.to_filename(os.path.join(qadir,'voxsfnr.nii.gz'))