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#!/usr/bin/env python3
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
''' Single subject analysis script for SPM / FIAC '''
import sys
from glob import glob
from os.path import join as pjoin
import numpy as np
from nipy.interfaces.spm import (
fltcols,
fname_presuffix,
fnames_presuffix,
make_job,
run_jobdef,
scans_for_fnames,
spm_info,
)
def get_fdata(data_path, subj_id):
data_def = {}
subject_path = pjoin(data_path, f'fiac{subj_id}')
data_def['functionals'] = sorted(
glob(pjoin(subject_path, 'functional_*.nii')))
anatomicals = glob(pjoin(subject_path, 'anatomical.nii'))
if len(anatomicals) == 1:
data_def['anatomical'] = anatomicals[0]
elif len(anatomicals) == 0:
data_def['anatomical'] = None
else:
raise ValueError('Too many anatomicals')
return data_def
def slicetime(data_def):
sess_scans = scans_for_fnames(data_def['functionals'])
stinfo = make_job('temporal', 'st', {
'scans': sess_scans,
'so':range(1,31,2) + range(2,31,2),
'tr':2.5,
'ta':2.407,
'nslices':float(30),
'refslice':1
})
run_jobdef(stinfo)
def realign(data_def):
sess_scans = scans_for_fnames(fnames_presuffix(data_def['functionals'], 'a'))
rinfo = make_job('spatial', 'realign', [{
'estimate':{
'data':sess_scans,
'eoptions':{
'quality':0.9,
'sep':4.0,
'fwhm':5.0,
'rtm':True,
'interp':2.0,
'wrap':[0.0,0.0,0.0],
'weight':[]
}
}
}])
run_jobdef(rinfo)
def reslice(data_def):
sess_scans = scans_for_fnames(fnames_presuffix(data_def['functionals'], 'a'))
rsinfo = make_job('spatial', 'realign', [{
'write':{
'data': np.vstack(sess_scans.flat),
'roptions':{
'which':[2, 1],
'interp':4.0,
'wrap':[0.0,0.0,0.0],
'mask':True,
}
}
}])
run_jobdef(rsinfo)
def coregister(data_def):
func1 = data_def['functionals'][0]
mean_fname = fname_presuffix(func1, 'meana')
crinfo = make_job('spatial', 'coreg', [{
'estimate':{
'ref': [mean_fname],
'source': [data_def['anatomical']],
'other': [[]],
'eoptions':{
'cost_fun':'nmi',
'sep':[4.0, 2.0],
'tol':np.array(
[0.02,0.02,0.02,
0.001,0.001,0.001,
0.01,0.01,0.01,
0.001,0.001,0.001]).reshape(1,12),
'fwhm':[7.0, 7.0]
}
}
}])
run_jobdef(crinfo)
def segnorm(data_def):
def_tpms = np.zeros((3,1), dtype=object)
spm_path = spm_info.spm_path
def_tpms[0] = pjoin(spm_path, 'tpm', 'grey.nii'),
def_tpms[1] = pjoin(spm_path, 'tpm', 'white.nii'),
def_tpms[2] = pjoin(spm_path, 'tpm', 'csf.nii')
data = np.zeros((1,), dtype=object)
data[0] = data_def['anatomical']
sninfo = make_job('spatial', 'preproc', {
'data': data,
'output':{
'GM':fltcols([0,0,1]),
'WM':fltcols([0,0,1]),
'CSF':fltcols([0,0,0]),
'biascor':1.0,
'cleanup':False,
},
'opts':{
'tpm':def_tpms,
'ngaus':fltcols([2,2,2,4]),
'regtype':'mni',
'warpreg':1.0,
'warpco':25.0,
'biasreg':0.0001,
'biasfwhm':60.0,
'samp':3.0,
'msk':np.array([], dtype=object),
}
})
run_jobdef(sninfo)
def norm_write(data_def):
sess_scans = scans_for_fnames(fnames_presuffix(data_def['functionals'], 'a'))
matname = fname_presuffix(data_def['anatomical'],
suffix='_seg_sn.mat',
use_ext=False)
subj = {
'matname': np.zeros((1,), dtype=object),
'resample': np.vstack(sess_scans.flat),
}
subj['matname'][0] = matname
roptions = {
'preserve':False,
'bb':np.array([[-78,-112, -50],[78,76,85.0]]),
'vox':fltcols([2.0,2.0,2.0]),
'interp':1.0,
'wrap':[0.0,0.0,0.0],
}
nwinfo = make_job('spatial', 'normalise', [{
'write':{
'subj': subj,
'roptions': roptions,
}
}])
run_jobdef(nwinfo)
# knock out the list of images, replacing with only one
subj['resample'] = np.zeros((1,), dtype=object)
subj['resample'][0] = data_def['anatomical']
roptions['interp'] = 4.0
run_jobdef(nwinfo)
def smooth(data_def, fwhm=8.0):
try:
len(fwhm)
except TypeError:
fwhm = [fwhm] * 3
fwhm = np.asarray(fwhm, dtype=np.float64).reshape(1,3)
sess_scans = scans_for_fnames(fnames_presuffix(data_def['functionals'], 'wa'))
sinfo = make_job('spatial', 'smooth',
{'data':np.vstack(sess_scans.flat),
'fwhm':fwhm,
'dtype':0})
run_jobdef(sinfo)
def process_subject(ddef):
if not ddef['anatomical']:
return
slicetime(ddef)
realign(ddef)
reslice(ddef)
coregister(ddef)
segnorm(ddef)
norm_write(ddef)
smooth(ddef)
def process_subjects(data_path, subj_ids):
for subj_id in subj_ids:
ddef = get_fdata(data_path, subj_id)
process_subject(ddef)
if __name__ == '__main__':
try:
data_path = sys.argv[1]
except IndexError:
raise OSError('Need FIAC data path as input')
try:
subj_ids = sys.argv[2:]
except IndexError:
subj_ids = range(16)
process_subjects(data_path, subj_ids)
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