# Loop over subjects:
for subject in subjects:
sess = subjects[subject]
# Loop over sessions (donepezil/placebo):
for this_s in sess:
ROI_files=[fmri_path+this_s[0]+'/Inplane/ROIs/R_V1.mat',
fmri_path+this_s[0]+'/Inplane/ROIs/L_V1.mat']
nifti_path = fmri_path + this_s[0] + '/%s_nifti/'%this_s[0]
# Get the coordinates of the ROIs, while accounting for the
# up-sampling:
#
ROI_coords = [tsv.upsample_coords(tsv.getROIcoords(f),up_samp)
for f in ROI_files]
# Initialize lists for each behavioral condition:
t_fix = []
t_left = []
t_right = []
for this_fix in this_s[1]['fix_nii']:
# Read data from each ROI, from each voxel. If you want to
# average across voxels, set the "average" kwarg to True:
# what's this filter? Does it average?
t_fix.append(load_nii(nifti_path+this_fix,
ROI_coords,
TR,
def main():
#inputs
# scanner = sys.argv[1]
# subject = sys.argv[2]
# rois = ['LLGN_ecc0','LLGN_ecc14','LLGN_polar0','LLGN_polar5',
# 'RLGN_ecc2','RLGN_ecc9','RLGN_polar2','RLGN_polar4']
rois = [
'LV1_ecc0-2', 'LV1_ecc10-18', 'LV1_polar602-026', 'LV1_polar474-526',
'RV1_ecc1-3', 'RV1_ecc7-11', 'RV1_polar174-226', 'RV1_polar374-426'
]
# rois = ['RV1','LV1','RV2v','LV2v','RV2d','LV2d','RV3v','LV3v','RV3d','LV3d']
combine_early_vis_rois = False
#paths
# sdirs = sio.loadmat('/Volumes/Plata1/LGN/Group_Analyses/subjectDirs_20121103.mat')
# if scanner=='3T':
# subject_dirs = sdirs['subjectDirs3T']
# elif scanner=='7T':
# subject_dirs = sdirs['subjectDirs7T']
#
# data_dir = '/Volumes/Plata1/LGN/Scans/{0}/{1}/{2}/'.format(
# scanner, subject_dirs[subject,0][0], subject_dirs[subject,1][0])
# nifti_dir = '{0}/{1}_nifti/'.format(data_dir, subject_dirs[subject,1][0])
# roi_dir = '{0}/Inplane/ROIs/'.format(data_dir)
# out_dir = '{0}/Masks/'.format(data_dir)
data_dir = '/Volumes/Plata1/LGN/Scans/7T/JN_20120808_Session/JN_20120808_fslDC/'
nifti_dir = os.path.join(data_dir, 'JN_20120808_fslDC_nifti/')
roi_dir = os.path.join(data_dir, 'Inplane/ROIs/')
out_dir = os.path.join(data_dir, 'Masks/')
print data_dir
#constants
up_samp = [1.0000, 1.0000, 1.0000] #upsample factor from EPI to GEM
# up_samp = [2.5606,2.5606,1.0000]
#usually epi: 2.220 x 2.220 x 2.300; gem: 0.867x0.867x2.300
example_epi = nifti_dir + 'epi01_fix_fsldc.nii.gz'
#Loop over ROIs
for roi in rois:
roi_file = roi_dir + roi + '.mat'
#get the coordinates for the ROI, accounting for upsampling
# upsample_coords takes into account 1 vs. 0-based indexing
roi_coords = tsv.upsample_coords(tsv.getROIcoords(roi_file), up_samp)
#save this to a text file for AFNI
fout_coords = out_dir + roi + '_coords.txt'
np.savetxt(fout_coords, roi_coords.transpose())
#run afni 3dUndump command to make this into an ROI
fout_nii = out_dir + roi + '_orig.nii'
command = "3dUndump -prefix %s -master %s %s" % (fout_nii, example_epi,
fout_coords)
util.remove_previous_files(fout_nii) #remove file if it exists
print command
os.system(command)
#Looks like this is R/L flipped? Flipping to normal
print 'Flipping ROI'
fout_nii_new = out_dir + roi + '.nii'
command = "3dresample -orient LPI -prefix %s -inset %s" % (
fout_nii_new, fout_nii)
util.remove_previous_files(fout_nii_new)
os.system(command)
# we don't need the orig file anymore, it is L-R reversed
# util.remove_previous_files(fout_nii)
#Sum together ROI pairs to get a single V1,V2, and V3
if combine_early_vis_rois:
print 'Making large *all* ROIs'
roi_final = ['V1', 'V2', 'V3']
for roi in roi_final:
fout = out_dir + '_all' + roi + '.nii.gz'
fout_d = out_dir + '_all' + roi + 'd.nii.gz'
fout_v = out_dirout_dir + '_all' + roi + 'v.nii.gz'
if roi == 'V1':
#note: ispositive is used to deal with potential overlap
command = "3dcalc -a %s%s_L%s.nii.gz -b %s%s_R%s.nii.gz -expr 'ispositive(a+b)' -prefix %s" % (
out_dir, sub, roi, out_dir, sub, roi, fout)
util.remove_previous_files(fout)
os.system(command)
else:
command = "3dcalc -a %s%s_L%sd.nii.gz -b %s%s_R%sd.nii.gz -c %s%s_L%sv.nii.gz -d %s%s_R%sv.nii.gz -expr 'ispositive(a+b+c+d)' -prefix %s" % (
out_dir, sub, roi, out_dir, sub, roi, out_dir, sub, roi,
out_dir, sub, roi, fout)
util.remove_previous_files(fout)
os.system(command)
command = "3dcalc -a %s%s_L%sv.nii.gz -b %s%s_R%sv.nii.gz -expr 'ispositive(a+b)' -prefix %s" % (
out_dir, sub, roi, out_dir, sub, roi, fout_v)
util.remove_previous_files(fout_v)
os.system(command)
command = "3dcalc -a %s%s_L%sd.nii.gz -b %s%s_R%sd.nii.gz -expr 'ispositive(a+b)' -prefix %s" % (
out_dir, sub, roi, out_dir, sub, roi, fout_d)
util.remove_previous_files(fout_d)
os.system(command)
for subject in subjects:
sess = subjects[subject]
# Loop over sessions (donepezil/placebo):
for this_s in sess:
ROI_files = [
fmri_path + this_s[0] + '/Inplane/ROIs/R_V1.mat',
fmri_path + this_s[0] + '/Inplane/ROIs/L_V1.mat'
]
nifti_path = fmri_path + this_s[0] + '/%s_nifti/' % this_s[0]
# Get the coordinates of the ROIs, while accounting for the
# up-sampling:
#
ROI_coords = [
tsv.upsample_coords(tsv.getROIcoords(f), up_samp)
for f in ROI_files
]
# Initialize lists for each behavioral condition:
t_fix = []
t_left = []
t_right = []
for this_fix in this_s[1]['fix_nii']:
# Read data from each ROI, from each voxel. If you want to
# average across voxels, set the "average" kwarg to True:
# what's this filter? Does it average?
t_fix.append(
load_nii(nifti_path + this_fix,
for i_seed, seed_name in enumerate(seed_rois):
print '\n', seed_name, seed_roi_type
if seed_roi_type is 'mask':
seed_file = os.path.join(mask_roi_dir, '{}.nii'.format(seed_name))
# load roi mask
seed_mask = nib.load(seed_file)
# find the coordinates of the seed voxels
seed_vals = seed_mask.get_data()
seed_coords = np.array(np.where(seed_vals==1))
elif seed_roi_type is 'mrvista':
seed_file = os.path.join(mrvista_roi_dir, '{}.mat'.format(seed_name))
# get the coordinates of the seed voxels
seed_coords = tsv.upsample_coords(tsv.getROIcoords(seed_file), upsample_factor)
if flip_mrvista_x:
seed_coords[0] = (volume_shape[0]-1) - (seed_coords[0])
else:
print 'seed roi type not recognized'
# make the seed time series (mean of roi time series)
seed_ts[i_seed] = ntio.time_series_from_file(data_file,
coords=seed_coords,
TR=TR,
normalize='percent',
average=True,
filter=dict(lb=f_lb,
ub=f_ub,
method='boxcar'),
for subject in subjects:
# Get session
for sess in session:
sessName = subjects[subject][sess]
# Get ROIs
roi_names=np.array(rois[subject][sess][1])
ROI_files=[]
for roi in rois[subject][sess][1]:
ROI_files.append(fmri_path+sessName[0]+'/Inplane/ROIs/' +roi +'.mat')
# Get the coordinates of the ROIs, while accounting for the
# up-sampling:
ROI_coords = [tsv.upsample_coords(tsv.getROIcoords(f),up_samp) for f in ROI_files]
nifti_path = fmri_path +sessName[0] + '/%s_nifti/' % sessName[0]
reg_path=fmri_path+sessName[0]+'/regressors/'
# Add filtering
filterType='boxcar'
#Go through each run and save out ROIs as nifti file
for runName in allRuns:
print 'Analyzing ' + runName
# Initialize lists for each condition:
t_fix = []
print runName
saveFile=base_path+ 'fmri/Results/timeseries/'+subject+sessionName[sess]+'_'+runName+'_%sROIts_%sReg_meanROI_stc.pck' % (len(roi_names), len(nuisReg))
def main():
#inputs
# scanner = sys.argv[1]
# subject = sys.argv[2]
# rois = ['LLGN_ecc0','LLGN_ecc14','LLGN_polar0','LLGN_polar5',
# 'RLGN_ecc2','RLGN_ecc9','RLGN_polar2','RLGN_polar4']
rois = ['LV1_ecc0-2', 'LV1_ecc10-18', 'LV1_polar602-026', 'LV1_polar474-526',
'RV1_ecc1-3', 'RV1_ecc7-11', 'RV1_polar174-226', 'RV1_polar374-426']
# rois = ['RV1','LV1','RV2v','LV2v','RV2d','LV2d','RV3v','LV3v','RV3d','LV3d']
combine_early_vis_rois = False
#paths
# sdirs = sio.loadmat('/Volumes/Plata1/LGN/Group_Analyses/subjectDirs_20121103.mat')
# if scanner=='3T':
# subject_dirs = sdirs['subjectDirs3T']
# elif scanner=='7T':
# subject_dirs = sdirs['subjectDirs7T']
#
# data_dir = '/Volumes/Plata1/LGN/Scans/{0}/{1}/{2}/'.format(
# scanner, subject_dirs[subject,0][0], subject_dirs[subject,1][0])
# nifti_dir = '{0}/{1}_nifti/'.format(data_dir, subject_dirs[subject,1][0])
# roi_dir = '{0}/Inplane/ROIs/'.format(data_dir)
# out_dir = '{0}/Masks/'.format(data_dir)
data_dir = '/Volumes/Plata1/LGN/Scans/7T/JN_20120808_Session/JN_20120808_fslDC/'
nifti_dir = os.path.join(data_dir,'JN_20120808_fslDC_nifti/')
roi_dir = os.path.join(data_dir,'Inplane/ROIs/')
out_dir = os.path.join(data_dir,'Masks/')
print data_dir
#constants
up_samp = [1.0000,1.0000,1.0000] #upsample factor from EPI to GEM
# up_samp = [2.5606,2.5606,1.0000]
#usually epi: 2.220 x 2.220 x 2.300; gem: 0.867x0.867x2.300
example_epi = nifti_dir + 'epi01_fix_fsldc.nii.gz'
#Loop over ROIs
for roi in rois:
roi_file = roi_dir + roi + '.mat'
#get the coordinates for the ROI, accounting for upsampling
# upsample_coords takes into account 1 vs. 0-based indexing
roi_coords = tsv.upsample_coords(tsv.getROIcoords(roi_file),up_samp)
#save this to a text file for AFNI
fout_coords = out_dir + roi + '_coords.txt'
np.savetxt(fout_coords,roi_coords.transpose())
#run afni 3dUndump command to make this into an ROI
fout_nii = out_dir + roi + '_orig.nii'
command = "3dUndump -prefix %s -master %s %s" %(fout_nii,
example_epi,
fout_coords)
util.remove_previous_files(fout_nii) #remove file if it exists
print command
os.system(command)
#Looks like this is R/L flipped? Flipping to normal
print 'Flipping ROI'
fout_nii_new = out_dir + roi + '.nii'
command = "3dresample -orient LPI -prefix %s -inset %s" %(fout_nii_new,
fout_nii)
util.remove_previous_files(fout_nii_new)
os.system(command)
# we don't need the orig file anymore, it is L-R reversed
# util.remove_previous_files(fout_nii)
#Sum together ROI pairs to get a single V1,V2, and V3
if combine_early_vis_rois:
print 'Making large *all* ROIs'
roi_final = ['V1','V2','V3']
for roi in roi_final:
fout = out_dir + '_all' + roi + '.nii.gz'
fout_d = out_dir + '_all' + roi + 'd.nii.gz'
fout_v = out_dirout_dir + '_all' + roi + 'v.nii.gz'
if roi == 'V1':
#note: ispositive is used to deal with potential overlap
command = "3dcalc -a %s%s_L%s.nii.gz -b %s%s_R%s.nii.gz -expr 'ispositive(a+b)' -prefix %s" %(out_dir,sub,roi,out_dir,sub,roi,fout)
util.remove_previous_files(fout)
os.system(command)
else:
command = "3dcalc -a %s%s_L%sd.nii.gz -b %s%s_R%sd.nii.gz -c %s%s_L%sv.nii.gz -d %s%s_R%sv.nii.gz -expr 'ispositive(a+b+c+d)' -prefix %s" %(out_dir,sub,roi,out_dir,sub,roi,out_dir,sub,roi,out_dir,sub,roi,fout)
util.remove_previous_files(fout)
os.system(command)
command = "3dcalc -a %s%s_L%sv.nii.gz -b %s%s_R%sv.nii.gz -expr 'ispositive(a+b)' -prefix %s" %(out_dir,sub,roi,out_dir,sub,roi,fout_v)
util.remove_previous_files(fout_v)
os.system(command)
command = "3dcalc -a %s%s_L%sd.nii.gz -b %s%s_R%sd.nii.gz -expr 'ispositive(a+b)' -prefix %s" %(out_dir,sub,roi,out_dir,sub,roi,fout_d)
util.remove_previous_files(fout_d)
os.system(command)
# load data
data = nib.load(data_file)
volume_shape = data.shape[:-1]
n_TRs = data.shape[-1]
# Get seed data
# initialize seed time series
seed_ts = np.zeros((len(seed_rois), n_TRs))
for i_seed, seed_name in enumerate(seed_rois):
print '\n', seed_name
seed_file = os.path.join(roi_dir, '{}.mat'.format(seed_name))
# get the coordinates of the seed voxels
seed_coords = tsv.upsample_coords(tsv.getROIcoords(seed_file), upsample_factor)
if flip_x:
seed_coords[0] = (volume_shape[0]-1) - (seed_coords[0])
# make the seed time series (mean of roi time series)
# this is a little odd - reads the data as a TimeSeries, then just takes the data ...
seed_ts[i_seed] = ntio.time_series_from_file(data_file,
coords=seed_coords,
TR=TR,
normalize=None,
average=True,
filter=dict(lb=f_lb,
ub=f_ub,
method='boxcar'),
verbose=True).data
for i_seed, seed_name in enumerate(seed_rois):
print '\n', seed_name, seed_roi_type
if seed_roi_type is 'mask':
seed_file = os.path.join(mask_roi_dir, '{}.nii'.format(seed_name))
# load roi mask
seed_mask = nib.load(seed_file)
# find the coordinates of the seed voxels
seed_vals = seed_mask.get_data()
seed_coords = np.array(np.where(seed_vals == 1))
elif seed_roi_type is 'mrvista':
seed_file = os.path.join(mrvista_roi_dir, '{}.mat'.format(seed_name))
# get the coordinates of the seed voxels
seed_coords = tsv.upsample_coords(tsv.getROIcoords(seed_file),
upsample_factor)
if flip_mrvista_x:
seed_coords[0] = (volume_shape[0] - 1) - (seed_coords[0])
else:
print 'seed roi type not recognized'
# make the seed time series (mean of roi time series)
seed_ts[i_seed] = ntio.time_series_from_file(data_file,
coords=seed_coords,
TR=TR,
normalize='percent',
average=True,
filter=dict(lb=f_lb,
ub=f_ub,