def mk_native_atlas(featdir,atlasfile,atlas_descriptor):
# make the atlas in native space if it doesn't exist
# just use linear reg, as fnirt does goofy stuff
nativeatlas=featdir+'/%s_native.nii.gz'%atlas_descriptor
if not os.path.exists(nativeatlas):
cmd='flirt -in %s -out %s -ref %s -applyxfm -init %s/reg/standard2example_func.mat -interp nearestneighbour'%(atlasfile,nativeatlas,featdir+'/example_func.nii.gz',featdir)
run_shell_cmd(cmd)
return nativeatlas
def mk_native_atlas(featdir, atlasfile, atlas_descriptor):
# make the atlas in native space if it doesn't exist
# just use linear reg, as fnirt does goofy stuff
nativeatlas = featdir + '/%s_native.nii.gz' % atlas_descriptor
if not os.path.exists(nativeatlas):
cmd = 'flirt -in %s -out %s -ref %s -applyxfm -init %s/reg/standard2example_func.mat -interp nearestneighbour' % (
atlasfile, nativeatlas, featdir + '/example_func.nii.gz', featdir)
run_shell_cmd(cmd)
return nativeatlas
except:
tarballs_to_make['model']=1
for dataset in datasets:
sublist=[s for s in os.listdir(os.path.join(basedir,dataset)) if s.find('sub')==0]
if tarballs_to_make['raw']==1:
tarcmd='cd %s; tar zcvf %s/%s_raw.tgz %s/models %s/README* %s/*.txt '%(basedir,tbdir,dataset,dataset,dataset,dataset)
for s in sublist:
l=os.path.join(dataset,s)
tarcmd=tarcmd+'%s/behav %s/model/model*/onsets %s/BOLD/*/QA %s/BOLD/*/bold.nii.gz %s/anatomy/highres*.nii.gz %s/anatomy/inplane*.nii.gz %s/README* '%(l,l,l,l,l,l,l)
print tarcmd
run_shell_cmd(tarcmd)
if tarballs_to_make['model']==1:
tarcmd='tar zcvf %s/%s_models.tgz %s/models %s/*.txt '%(tbdir,dataset,dataset,dataset)
for s in sublist:
l=dataset+'/'+s
tarcmd=tarcmd+'%s/model/model* %s/README* '%(l,l)
print tarcmd
run_shell_cmd(tarcmd)
# now make checksum file
if tarballs_to_make['raw']==1:
checksumfile='%s/%s_raw_checksums.txt'%(tbdir,dataset)
"""
import os,sys
from run_shell_cmd import *
import numpy as N
import pickle
def usage():
"""Print the docstring and exit with error."""
sys.stdout.write(__doc__)
sys.exit(2)
basedir=os.path.abspath(os.curdir)
flist=run_shell_cmd('find . -name "res4d_sc.txt"')
roidata={}
nbadrois={}
missingcount=N.zeros(305)
for f in flist:
f_s=f.split('/')
ds=f_s[1]
if not roidata.has_key(ds):
roidata[ds]={}
sub=int(f_s[2][3:6])
if not roidata[ds].has_key(sub):
roidata[ds][sub]={}
model=int(f_s[4][5:8])
if not roidata[ds][sub].has_key(model):
outdir='/corral-repl/utexas/poldracklab/openfmri/analyses/paper_analysis_April2013/classifier/' from run_shell_cmd import * from scipy.stats import scoreatpercentile import numpy as N import os cmd='cat %s/randlabel/*SVM*.txt > %s/all_svm_randlabel.txt'%(outdir,outdir) run_shell_cmd(cmd) data=N.loadtxt(os.path.join(outdir,'all_svm_randlabel.txt')) print 'mean: ',N.mean(data) print '95 pct: ',scoreatpercentile(data,95)
bolddata=bold.get_data()
for run in range(nruns):
if not os.path.exists(basedir+'BOLD/task001_run%03d'%int(run+1)):
os.mkdir(basedir+'BOLD/task001_run%03d'%int(run+1))
start=run*runlength
end=run*runlength + runlength+1
rundata=bolddata[:,:,:,start:end]
newimg=nibabel.Nifti1Image(rundata,bold.get_affine())
newimg.to_filename(basedir+'BOLD/task001_run%03d/bold.nii.gz'%int(run+1))
# deface and move anatomy
process_anatomy=1
if process_anatomy:
run_shell_cmd('deface.py %s/anat.nii.gz'%basedir)
shutil.move('%s/anat_defaced.nii.gz'%basedir,'%s/anatomy/highres001.nii.gz'%basedir)
# create onset files
## from the original paper:
## Each time series began and ended with 12 s of rest and contained
## eight stimulus blocks of 24-s duration, one for each category,
## sep- arated by 12-s intervals of rest. Stimuli were present- ed
## for 500 ms with an interstimulus interval of 1500 ms. Repetitions
## of meaningful stimuli were pictures of the same face or object
## photographed from dif- ferent angles.
process_behav=1
stimdur=0.5
SOA=2
import os
from run_shell_cmd import *
import nibabel
import numpy
dlh = {}
volume = {}
cdata = {}
minclust = {}
for n in [25, 50, 75, 100, 125]:
if not dlh.has_key(n):
print n, ': estimating smoothness'
l = run_shell_cmd(
'smoothest -d %d -r data/res4d_%dsubs.nii.gz -m mask.nii.gz' %
(n, n))
dlh[n] = float(l[0].split(' ')[1])
volume[n] = float(l[1].split(' ')[1])
if not os.path.exists('data/tt_%dsubs_tstat1.nii.gz' % n):
print 'running randomize'
cmd = 'randomise -i data/funcdata_%dsubs.nii.gz -o tt_%dsubs -1 -n 1' % (
n, n)
run_shell_cmd(cmd)
# convert t to z stat
tstat_img = nibabel.load('data/tt_%dsubs_tstat1.nii.gz' % n)
tstat = tstat_img.get_data()
zstat = t_to_z(tstat)
zstat_img = nibabel.Nifti1Image(zstat, tstat.get_affine())
zstat_img.to_filename('data/tt_%dsubs_zstat1.nii.gz' % n)
#!/usr/bin/env python
""" get checksums for all images
USAGE: get_image_checksums.py <dsdir>
"""
from run_shell_cmd import *
def usage():
"""Print the docstring and exit with error."""
sys.stdout.write(__doc__)
sys.exit(2)
import sys
if len(sys.argv) > 1:
taskid = sys.argv[1]
else:
taskid = 'ds001'
# usage()
o = run_shell_cmd('find %s/sub*/{BOLD,anatomy} -name "*.nii.gz"' % taskid)
checksums = []
outfile = open('checksums_%s.txt' % taskid, 'w')
for f in o:
outfile.write(run_shell_cmd('md5sum %s' % f)[0] + '\n')
outfile.close()
def run(self):
# Parse out important parameters for this block
conf = self.conf
subj = conf.subjID
nextInputFilename = conf.nextInputFilename[-1]
subjMaskDir = conf.subjMaskDir
subjfMRIDir = conf.subjfMRIDir
freesurferDir = conf.freesurferDir
hcpdata = conf.hcpData
input = conf.fs_input
subjDir = conf.subjDir
logname = conf.logname
os.chdir(subjMaskDir)
print '---------------------'
print 'Creating gray matter mask based on Freesurfer autosegmentation for subject ', subj
# adjusting for use if HCP pipeline was used
if input== 'wmparc.nii.gz':
input = 'wmparc.nii.gz'
analysisDir = subjDir + '/MNINonLinear/'
else:
input = input
analysisDir = freesurferDir + '/mri/'
print 'saving from', analysisDir, 'to', os.getcwd()
if input == 'wmparc.nii.gz':
run_shell_cmd('cp -v ' + analysisDir + input + ' ' + str(subj) + '_fs_seg.nii.gz',logname)
else:
run_shell_cmd('cp -v ' + analysisDir + input + ' .',logname)
if input != 'wmparc.nii.gz': run_shell_cmd('mri_convert -i ' + input + ' -ot nii ' + str(subj) + '_fs_seg.nii',logname)
if input != 'wmparc.nii.gz': run_shell_cmd("3dcalc -overwrite -a " + str(subj) + "_fs_seg.nii -expr 'a' -prefix " + str(subj) + "_fs_seg.nii.gz",logname)
# Delete intermediary files
run_shell_cmd('rm ' + str(subj) + '_fs_seg.nii ' + str(subj) + '_' + input + ' ' + str(subj) + 'mask_temp.nii.gz',logname)
print "Any error after this output should be noted and checked... there shouldn't be errors after this flag"
if input == 'wmparc.nii.gz':
# use wmparc maskval set
maskValSet="8 9 10 11 12 13 16 17 18 19 20 26 27 28 47 48 49 50 51 52 53 54 55 56 58 59 60 96 97 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035"
maskValSet = maskValSet.split(' ')
else:
# Using aseg (not aparc+aseg)
maskValSet = [8, 9, 10, 11, 12, 13, 16,17, 18, 19, 20, 26,27, 28, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 58, 59, 60, 96, 97, 3, 42]
#Add segments to mask
maskNum = 1
for maskval in maskValSet:
if maskNum == 1:
run_shell_cmd('3dcalc -a ' + str(subj) + '_fs_seg.nii.gz -expr "equals(a,' + str(maskval) + ')" -prefix ' + str(subj) + 'mask_temp.nii.gz -overwrite',logname)
else:
run_shell_cmd('3dcalc -a ' + str(subj) + '_fs_seg.nii.gz -b ' + str(subj) + 'mask_temp.nii.gz -expr "equals(a,' + str(maskval) + ')+b" -prefix ' + str(subj) + 'mask_temp.nii.gz -overwrite',logname)
maskNum += 1
# Make mask binary
run_shell_cmd("3dcalc -a " + str(subj) + "mask_temp.nii.gz -expr 'ispositive(a)' -prefix " + str(subj) + '_gmMask.nii.gz -overwrite',logname)
if hcpdata == False:
# Transform to TLRC space
# First convert files from NIfTI to AFNI
run_shell_cmd('3dcopy ' + subjMaskDir + str(subj) + '_gmMask.nii.gz ' + subjMaskDir + str(subj) + '_gmMask', logname)
run_shell_cmd('rm -v ' + subjMaskDir + str(subj) + '_gmMask.nii.gz',logname)
# Talairach command using AFNI functions
run_shell_cmd('@auto_tlrc -apar ' + subjfMRIDir + 'anat_mprage_skullstripped+tlrc -input ' + str(subj) + '_gmMask+orig -overwrite',logname)
run_shell_cmd('3dresample -overwrite -master ' + subjfMRIDir + nextInputFilename + '.nii.gz -inset ' + str(subj) + '_gmMask+tlrc -prefix ' + str(subj) + '_gmMask_func+tlrc',logname)
run_shell_cmd('3dcopy ' + subjMaskDir + str(subj) + '_gmMask+tlrc ' + subjMaskDir + str(subj) + '_gmMask.nii.gz', logname)
run_shell_cmd('3dcopy ' + subjMaskDir + str(subj) + '_gmMask_func+tlrc ' + subjMaskDir + str(subj) + '_gmMask_func.nii.gz', logname)
run_shell_cmd('rm -v ' + subjMaskDir + str(subj) + '_gmMask+tlrc* ' + subjMaskDir + '_gmMask_func+tlrc*', logname)
else:
# Resample to functional space
run_shell_cmd('3dresample -overwrite -master ' + subjfMRIDir + nextInputFilename + '.nii.gz -inset ' + str(subj) + '_gmMask.nii.gz -prefix ' + str(subj) + '_gmMask_func.nii.gz',logname)
# Dilate mask by 1 functional voxel (just in case the resampled anatomical mask is off by a bit)
run_shell_cmd("3dLocalstat -overwrite -nbhd 'SPHERE(-1)' -stat 'max' -prefix " + str(subj) + '_gmMask_func_dil1vox.nii.gz ' + str(subj) + '_gmMask_func.nii.gz',logname)
run_shell_cmd('rm ' + str(subj) + 'mask_temp.nii.gz',logname)
def run(self):
# Parse out important parameters for this block
conf = self.conf
subj = conf.subjID
nextInputFilename = conf.nextInputFilename[-1]
subjMaskDir = conf.subjMaskDir
subjfMRIDir = conf.subjfMRIDir
freesurferDir = conf.freesurferDir
hcpdata = conf.hcpData
input = conf.fs_input
logname = conf.logname
subjDir = conf.subjDir
os.chdir(subjMaskDir)
print '---------------------'
print 'Create white matter mask, and erode it for subject', subj, '(MAKE SURE EROSIAN DOESN\'T REMOVE ALL VENTRICLE VOXELS)'
# adjusting for use if HCP pipeline was used
if input== 'wmparc.nii.gz':
input = 'wmparc.nii.gz'
analysisDir = subjDir + '/MNINonLinear/'
else:
input = input
analysisDir = freesurferDir + '/mri/'
print 'saving from', analysisDir, 'to', os.getcwd()
if input == 'wmparc.nii.gz':
run_shell_cmd('cp -v ' + analysisDir + input + ' ' + str(subj) + '_fs_seg.nii.gz',logname)
else:
#run_shell_cmd('cp -v ' + analysisDir + input + ' ' + str(subj) + '_fs_seg.nii',logname)
run_shell_cmd('cp -v ' + analysisDir + input + ' .',logname)
if input != 'wmparc.nii.gz': run_shell_cmd('mri_convert -i ' + input + ' -ot nii ' + str(subj) + '_fs_seg.nii',logname)
if input != 'wmparc.nii.gz': run_shell_cmd("3dcalc -overwrite -a " + str(subj) + "_fs_seg.nii -expr 'a' -prefix " + str(subj) + "_fs_seg.nii.gz",logname)
# Delete intermediary files
run_shell_cmd('rm ' + str(subj) + '_fs_seg.nii ' + str(subj) + '_' + input + ' ' + str(subj) + 'mask_temp.nii.gz',logname)
if input == 'wmparc.nii.gz':
# use wmparc.nii.gz maskvals
maskValSet="250 251 252 253 254 255 3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 3028 3029 3030 3031 3032 3033 3034 3035 4000 4001 4002 4003 4004 4005 4006 4007 4008 4009 4010 4011 4012 4013 4014 4015 4016 4017 4018 4019 4020 4021 4022 4023 4024 4025 4026 4027 4028 4029 4030 4031 4032 4033 4034 4035 5001 5002"
maskValSet = maskValSet.split(' ')
else:
# Using aseg (not aparc+aseg)
# including all white matter
maskValSet = [2, 7, 41, 46]
# Add segments to mask
maskNum = 1
for maskval in maskValSet:
if maskNum == 1:
run_shell_cmd("3dcalc -a " + str(subj) + '_fs_seg.nii.gz -expr "equals(a,' + str(maskval) + ')" -prefix ' + str(subj) + 'mask_temp.nii.gz -overwrite',logname)
else:
run_shell_cmd('3dcalc -a ' + str(subj) + '_fs_seg.nii.gz -b ' + str(subj) + 'mask_temp.nii.gz -expr "equals(a,' + str(maskval) + ')+b" -prefix ' + str(subj) + 'mask_temp.nii.gz -overwrite',logname)
maskNum += 1
# Make mask binary
run_shell_cmd("3dcalc -a " + str(subj) + "mask_temp.nii.gz -expr 'ispositive(a)' -prefix " + str(subj) + '_wmMask.nii.gz -overwrite',logname)
# Transform to TLRC space
if hcpdata == False:
# Transform to TLRC space
# First convert files from NIfTI to AFNI
run_shell_cmd('3dcopy ' + subjMaskDir + str(subj) + '_wmMask.nii.gz ' + subjMaskDir + str(subj) + '_wmMask', logname)
run_shell_cmd('rm -v ' + subjMaskDir + str(subj) + '_wmMask.nii.gz',logname)
# Talairach command using AFNI functions
run_shell_cmd('@auto_tlrc -apar ' + subjfMRIDir + 'anat_mprage_skullstripped+tlrc -input ' + str(subj) + '_wmMask+orig -overwrite',logname)
run_shell_cmd('3dresample -overwrite -master ' + subjfMRIDir + nextInputFilename + '.nii.gz -inset ' + str(subj) + '_wmMask+tlrc -prefix ' + str(subj) + '_wmMask_func+tlrc',logname)
run_shell_cmd('3dcopy ' + subjMaskDir + str(subj) + '_wmMask+tlrc ' + subjMaskDir + str(subj) + '_wmMask.nii.gz', logname)
run_shell_cmd('3dcopy ' + subjMaskDir + str(subj) + '_wmMask_func+tlrc ' + subjMaskDir + str(subj) + '_wmMask_func.nii.gz', logname)
run_shell_cmd('rm -v ' + subjMaskDir + str(subj) + '_wmMask+tlrc* ' + subjMaskDir + '_wmMask_func+tlrc*', logname)
else:
# Resample to functional space
run_shell_cmd('3dresample -overwrite -master ' + subjfMRIDir + nextInputFilename + '.nii.gz -inset ' + str(subj) + '_wmMask.nii.gz -prefix ' + str(subj) + '_wmMask_func.nii.gz',logname)
# Subtract gray matter mask from white matter mask (avoiding negative #s)
run_shell_cmd('3dcalc -a ' + str(subj) + '_wmMask_func.nii.gz -b ' + str(subj) + "_gmMask_func_dil1vox.nii.gz -expr 'step(a-b)' -prefix " + str(subj) + '_wmMask_func_eroded.nii.gz -overwrite',logname)
run_shell_cmd('rm ' + str(subj) + 'mask_temp.nii.gz',logname)
print 'cannot open %s' % dscon
outfilename = td + '/group_stats_report.html'
outfile = open(outfilename, 'w')
outfile.write('<html>\n<body>\n')
copes = glob.glob(os.path.join(td, 'cope[0-9][0-9][0-9].nii.gz'))
copes.sort()
for c in copes:
copenum = int(os.path.basename(c).split('.')[0].lstrip('cope'))
print '%d: %s' % (copenum, con_names[copenum - 1])
overlay_cmd = 'overlay 0 0 %s -a %s 2.3 5 %s' % (
args.bgimg, c.replace('.nii.gz', '_tstat1.nii.gz'),
c.replace('.nii.gz', '_tstat1_overlay.nii.gz'))
print overlay_cmd
run_shell_cmd(overlay_cmd)
slicer_cmd = 'slicer %s -S 4 1000 %s' % (
c.replace('.nii.gz', '_tstat1_overlay.nii.gz'),
c.replace('.nii.gz', '_tstat1_overlay.png'))
print slicer_cmd
run_shell_cmd(slicer_cmd)
outfile.write('%s cope%03d (%s)<br>\n' %
(td, copenum, con_names[copenum - 1]))
outfile.write('<img src="%s" width=100%%/>\n' %
c.replace('.nii.gz', '_tstat1_overlay.png'))
outfile.write('</body>\n</html>\n')
outfile.close()
#!/usr/bin/env python
""" get checksums for all images
USAGE: get_image_checksums.py <dsdir>
"""
from run_shell_cmd import *
def usage():
"""Print the docstring and exit with error."""
sys.stdout.write(__doc__)
sys.exit(2)
import sys
if len(sys.argv)>1:
taskid=sys.argv[1]
else:
taskid='ds001'
# usage()
o=run_shell_cmd('find %s/sub*/{BOLD,anatomy} -name "*.nii.gz"'%taskid)
checksums=[]
outfile=open('checksums_%s.txt'%taskid,'w')
for f in o:
outfile.write(run_shell_cmd('md5sum %s'%f)[0]+'\n')
outfile.close()
def main():
# first run N4 bias correction
imgfile=sys.argv[1]
subdir=imgfile.rstrip('highres001.nii.gz')
if not os.path.exists(subdir):
print '%s does not exist!'%subdir
sys.exit(0)
cmd='%sN4BiasFieldCorrection -i %s/highres001.nii.gz -d 3 -o %s/highres001_bfc.nii.gz -c [50,0.0001]'%(ANTSPATH,subdir,subdir)
print cmd
if not test:
run_shell_cmd(cmd,cwd=subdir)
# then align bias-corrected whole-head image to template
PARAMS="-r Gauss[3,0] -t SyN[0.25] -i 30x90x20 --use-Histogram-Matching --number-of-affine-iterations 10000x10000x10000x10000x10000 --rigid-affine false"
template_wholehead='%s/data/standard/MNI152_T1_2mm.nii.gz'%FSLDIR
template_brain='%s/data/standard/MNI152_T1_2mm_brain.nii.gz'%FSLDIR
template_mask='%s/data/standard/MNI152_T1_2mm_brain_mask.nii.gz'%FSLDIR
cmd='%sANTS 3 -m CC[%s,%shighres001_bfc.nii.gz,1,4] -o highres001_ANTS.nii.gz %s'%(ANTSPATH,template_wholehead,subdir,PARAMS)
print cmd
if not test:
run_shell_cmd(cmd,cwd=subdir)
# create a version of the MNI mask aligned to subject space
cmd='WarpImageMultiTransform 3 %s %shighres001_brain_mask.nii.gz -R %shighres001.nii.gz -i %shighres001_ANTSAffine.txt %shighres001_ANTSInverseWarp.nii.gz --use-NN'%(template_mask,subdir,subdir,subdir,subdir)
print cmd
if not test:
run_shell_cmd(cmd,cwd=subdir)
cmd='fslmaths %shighres001.nii.gz -mul %shighres001_brain_mask.nii.gz %shighres001_brain.nii.gz'%(subdir,subdir,subdir)
print cmd
if not test:
run_shell_cmd(cmd,cwd=subdir)
# rerun warp from stripped highres to stripped template
cmd='%sANTS 3 -m CC[%s,%shighres001_brain.nii.gz,1,4] -o highres001_ANTSstd.nii.gz %s'%(ANTSPATH,template_brain,subdir,PARAMS)
print cmd
if not test:
run_shell_cmd(cmd,cwd=subdir)
cmd='WarpImageMultiTransform 3 %shighres001_brain.nii.gz %shighres001_reg2std_ANTS.nii.gz -R %s %shighres001_ANTSWarp.nii.gz %shighres001_ANTSAffine.txt'%(subdir,subdir,template_brain,subdir,subdir)
print cmd
if not test:
run_shell_cmd(cmd,cwd=subdir)
for run in range(nruns):
if not os.path.exists(basedir + 'BOLD/task001_run%03d' % int(run + 1)):
os.mkdir(basedir + 'BOLD/task001_run%03d' % int(run + 1))
start = run * runlength
end = run * runlength + runlength + 1
rundata = bolddata[:, :, :, start:end]
newimg = nibabel.Nifti1Image(rundata, bold.get_affine())
newimg.to_filename(basedir +
'BOLD/task001_run%03d/bold.nii.gz' % int(run + 1))
# deface and move anatomy
process_anatomy = 1
if process_anatomy:
run_shell_cmd('deface.py %s/anat.nii.gz' % basedir)
shutil.move('%s/anat_defaced.nii.gz' % basedir,
'%s/anatomy/highres001.nii.gz' % basedir)
# create onset files
## from the original paper:
## Each time series began and ended with 12 s of rest and contained
## eight stimulus blocks of 24-s duration, one for each category,
## sep- arated by 12-s intervals of rest. Stimuli were present- ed
## for 500 ms with an interstimulus interval of 1500 ms. Repetitions
## of meaningful stimuli were pictures of the same face or object
## photographed from dif- ferent angles.
process_behav = 1
stimdur = 0.5
SOA = 2
(suffix, comp))
posthresh_dis[comp] = N.min(corrdata_thresh[corrdata_thresh > 0])
negthresh_dis[comp] = N.max(corrdata_thresh[corrdata_thresh < 0])
posmax_dis[comp] = N.max(corrdata_thresh[corrdata_thresh > 0])
negmax_dis[comp] = N.min(corrdata_thresh[corrdata_thresh < 0])
# make slice images
if 1 == 0:
for comp in range(nccacomps):
if posthresh[comp] > 0:
# first take back to 3mm space
cmd = '/work/01329/poldrack/software_lonestar/fsl/bin/flirt -in /scratch/01329/poldrack/textmining/paper/CCA_corr/cca%s_comp%03d_6mm_thresh.nii.gz -applyxfm -init /work/01329/poldrack/software_lonestar/fsl/etc/flirtsch/ident.mat -out %sCCA_corr/cca%s_comp%03d_thresh.nii.gz -paddingsize 0.0 -interp trilinear -ref /scratch/01329/poldrack/textmining/paper/data_preparation/all_peakimages.nii.gz' % (
suffix, comp, basedir, suffix, comp)
print cmd
run_shell_cmd(cmd)
cmd = 'overlay 1 0 /work/01329/poldrack/software_lonestar/atlases/MNI152lin_3mm.nii.gz -a %sCCA_corr/cca%s_comp%03d_thresh.nii.gz %f %f %sCCA_corr/cca%s_comp%03d_thresh.nii.gz %f %f cbar.png ysb %sCCA_corr/cca%s_comp%03d_rend' % (
basedir, suffix, comp, posthresh[comp], posmax[comp], basedir,
suffix, comp, negthresh[comp], negmax[comp], basedir, suffix,
comp)
print cmd
run_shell_cmd(cmd)
cmd = 'slicer %sCCA_corr/cca%s_comp%03d_rend.nii.gz -u -S 2 750 %sCCA_corr/cca%s_comp%03d_slices.png' % (
basedir, suffix, comp, basedir, suffix, comp)
print cmd
run_shell_cmd(cmd)
# make slice images for disorders
if 1 == 0:
for comp in range(nccacomps):
if posthresh_dis[comp] > 0:
def run(self):
# Parse out important parameters for this block
conf = self.conf
logname = conf.logname
subj = conf.subjID
nextInputFilename = conf.nextInputFilename[-1]
subjMaskDir = conf.subjMaskDir
subjfMRIDir = conf.subjfMRIDir
freesurferDir = conf.freesurferDir
subjDir = conf.subjDir
hcpdata = conf.hcpData
input = conf.fs_input
os.chdir(subjMaskDir)
print '---------------------'
print 'Create ventricle mask, and erode it for subject ' + str(subj) + ' (MAKE SURE EROSION DOESN\'T REMOVE ALL VENTRICLE VOXELS)'
# adjusting for use if HCP pipeline was used
if input== 'wmparc.nii.gz':
input = 'wmparc.nii.gz'
analysisDir = subjDir + '/MNINonLinear/'
else:
input = input
analysisDir = freesurferDir + '/mri/'
print 'saving from', analysisDir, 'to', os.getcwd()
if input == 'wmparc.nii.gz':
run_shell_cmd('cp -v ' + analysisDir + input + ' ' + str(subj) + '_fs_seg.nii.gz',logname)
else:
run_shell_cmd('cp -v ' + analysisDir + input + ' .',logname)
#run_shell_cmd('cp -v ' + analysisDir + input + ' ' + str(subj) + '_fs_seg.nii',logname)
if input != 'wmparc.nii.gz': run_shell_cmd('mri_convert -i ' + input + ' -ot nii ' + str(subj) + '_fs_seg.nii',logname)
if input != 'wmparc.nii.gz': run_shell_cmd("3dcalc -overwrite -a " + str(subj) + "_fs_seg.nii -expr 'a' -prefix " + str(subj) + "_fs_seg.nii.gz",logname)
# Delete intermediary files
run_shell_cmd('rm ' + str(subj) + '_fs_seg.nii ' + str(subj) + '_' + input + ' ' + str(subj) + 'mask_temp.nii.gz',logname)
# Using aseg (not aparc+aseg)
# including ventricles
maskValSet = [4, 43, 14, 15]
# Add segments to mask
maskNum = 1
for maskval in maskValSet:
if maskNum == 1:
run_shell_cmd("3dcalc -a " + str(subj) + '_fs_seg.nii.gz -expr "equals(a,' + str(maskval) + ')" -prefix ' + str(subj) + 'mask_temp.nii.gz -overwrite',logname)
else:
run_shell_cmd('3dcalc -a ' + str(subj) + '_fs_seg.nii.gz -b ' + str(subj) + 'mask_temp.nii.gz -expr "equals(a,' + str(maskval) + ')+b" -prefix ' + str(subj) + 'mask_temp.nii.gz -overwrite',logname)
maskNum += 1
# Make mask binary
run_shell_cmd("3dcalc -a " + str(subj) + "mask_temp.nii.gz -expr 'ispositive(a)' -prefix " + str(subj) + '_ventricles.nii.gz -overwrite',logname)
# Transform to TLRC space
if hcpdata == False:
# Transform to TLRC space
# First convert files from NIfTI to AFNI
run_shell_cmd('3dcopy ' + subjMaskDir + str(subj) + '_ventricles.nii.gz ' + subjMaskDir + str(subj) + '_ventricles', logname)
run_shell_cmd('rm -v ' + subjMaskDir + str(subj) + '_ventricles.nii.gz',logname)
# Talairach command using AFNI functions
run_shell_cmd('@auto_tlrc -apar ' + subjfMRIDir + 'anat_mprage_skullstripped+tlrc -input ' + str(subj) + '_ventricles+orig -overwrite',logname)
run_shell_cmd('3dresample -overwrite -master ' + subjfMRIDir + nextInputFilename + '.nii.gz -inset ' + str(subj) + '_ventricles+tlrc -prefix ' + str(subj) + '_ventricles_func+tlrc',logname)
run_shell_cmd('3dcopy ' + subjMaskDir + str(subj) + '_ventricles+tlrc ' + subjMaskDir + str(subj) + '_ventricles.nii.gz', logname)
run_shell_cmd('3dcopy ' + subjMaskDir + str(subj) + '_ventricles_func+tlrc ' + subjMaskDir + str(subj) + '_ventricles_func.nii.gz', logname)
run_shell_cmd('rm -v ' + subjMaskDir + str(subj) + '_ventricles+tlrc* ' + subjMaskDir + '_ventricles_func+tlrc*', logname)
else:
# Resample to functional space
run_shell_cmd('3dresample -overwrite -master ' + subjfMRIDir + nextInputFilename + '.nii.gz -inset ' + str(subj) + '_ventricles.nii.gz -prefix ' + str(subj) + '_ventricles_func.nii.gz',logname)
# Subtract gray matter mask from white matter mask (avoiding negative #s)
run_shell_cmd('3dcalc -a ' + str(subj) + '_ventricles_func.nii.gz -b ' + str(subj) + "_gmMask_func_dil1vox.nii.gz -expr 'step(a-b)' -prefix " + str(subj) + '_ventricles_func_eroded.nii.gz -overwrite',logname)
run_shell_cmd('rm ' + str(subj) + 'mask_temp.nii.gz',logname)
from run_shell_cmd import *
datasets=['ds001', 'ds002', 'ds003', 'ds005', 'ds006', 'ds007', 'ds008', 'ds011', 'ds017A', 'ds017B', 'ds018', 'ds051', 'ds052', 'ds101', 'ds102', 'ds105', 'ds107']
tarballs_to_make={'raw':0,'model':1}
for dataset in datasets:
sublist=[s for s in os.listdir(basedir+dataset) if s.find('sub')==0]
if tarballs_to_make['raw']==1:
tarcmd='tar zcvf %s/%s_raw.tgz %s/models %s/*.txt '%(tbdir,dataset,dataset,dataset)
for s in sublist:
l=dataset+'/'+s
tarcmd=tarcmd+'%s/behav %s/model/model*/onsets %s/BOLD/*/bold.nii.gz %s/anatomy/highres*.nii.gz %s/anatomy/inplane*.nii.gz %s/README* '%(l,l,l,l,l,l)
print tarcmd
run_shell_cmd(tarcmd)
if tarballs_to_make['model']==1:
tarcmd='tar zcvf %s/%s_models.tgz %s/models %s/*.txt '%(tbdir,dataset,dataset,dataset)
for s in sublist:
l=dataset+'/'+s
tarcmd=tarcmd+'%s/model/model* %s/BOLD/*/bold_mcf.par* %s/README* '%(l,l,l)
print tarcmd
run_shell_cmd(tarcmd)
if os.path.exists(dscon):
con_info=open(dscon).readlines()
con_names=[i.strip().split('\t')[1] for i in con_info if i.find('ContrastName')>-1]
else:
print 'cannot open %s'%dscon
outfilename=td+'/group_stats_report.html'
outfile=open(outfilename,'w')
outfile.write('<html>\n<body>\n')
copes=glob.glob(os.path.join(td,'cope[0-9][0-9][0-9].nii.gz'))
copes.sort()
for c in copes:
copenum=int(os.path.basename(c).split('.')[0].lstrip('cope'))
print '%d: %s'%(copenum,con_names[copenum-1])
overlay_cmd='overlay 0 0 %s -a %s 2.3 5 %s'%(args.bgimg,c.replace('.nii.gz','_tstat1.nii.gz'),c.replace('.nii.gz','_tstat1_overlay.nii.gz'))
print overlay_cmd
run_shell_cmd(overlay_cmd)
slicer_cmd='slicer %s -S 4 1000 %s'%(c.replace('.nii.gz','_tstat1_overlay.nii.gz'),c.replace('.nii.gz','_tstat1_overlay.png'))
print slicer_cmd
run_shell_cmd(slicer_cmd)
outfile.write('%s cope%03d (%s)<br>\n'%(td,copenum,con_names[copenum-1]))
outfile.write('<img src="%s" width=100%%/>\n'%c.replace('.nii.gz','_tstat1_overlay.png'))
outfile.write('</body>\n</html>\n')
outfile.close()
