def runglmperun(self, subject, trtimeinsec):
s = SpecifyModel()
# loop on all runs and models within each run
modelfiles = subject._modelfiles
for model in modelfiles:
# Make directory results to store the results of the model
results_dir = os.path.join(subject._path, 'model', model[0],
'results', model[1])
dir_util.mkpath(results_dir)
os.chdir(results_dir)
s.inputs.event_files = model[2]
s.inputs.input_units = 'secs'
s.inputs.functional_runs = os.path.join(subject._path, 'BOLD',
model[1],
'bold_mcf_hp.nii.gz')
# use nibable to get the tr of from the .nii file
s.inputs.time_repetition = trtimeinsec
s.inputs.high_pass_filter_cutoff = 128.
# find par file that has motion
motionfiles = glob(
os.path.join(subject._path, 'BOLD', model[1], "*.par"))
s.inputs.realignment_parameters = motionfiles
#info = [Bunch(conditions=['cond1'], onsets=[[2, 50, 100, 180]], durations=[[1]]), Bunch(conditions=['cond1'], onsets=[[30, 40, 100, 150]], durations=[[1]])]
#s.inputs.subject_info = None
res = s.run()
res.runtime.cwd
print ">>>> preparing evs for model " + model[
1] + "and run " + model[0]
sessionInfo = res.outputs.session_info
level1design = Level1Design()
level1design.inputs.interscan_interval = trtimeinsec
level1design.inputs.bases = {'dgamma': {'derivs': False}}
level1design.inputs.session_info = sessionInfo
level1design.inputs.model_serial_correlations = True
#TODO: add contrasts to level 1 design so that I have just condition vs rest for each ev
#TODO: Look into changign this to FILM instead of FEAT - this also has the option of setting output directory
# http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT/UserGuide#Contrasts
#http://nipy.org/nipype/interfaces/generated/nipype.interfaces.fsl.model.html#filmgls
resLevel = level1design.run()
featModel = FEATModel()
featModel.inputs.fsf_file = resLevel.outputs.fsf_files
featModel.inputs.ev_files = resLevel.outputs.ev_files
resFeat = featModel.run()
print ">>>> creating fsf design files for " + model[
1] + "and run " + model[0]
# TODO: give mask here
glm = fsl.GLM(in_file=s.inputs.functional_runs[0],
design=resFeat.outputs.design_file,
output_type='NIFTI')
print ">>>> running glm for " + model[1] + "and run " + model[0]
resGlm = glm.run()
print ">>>> finished running glm for " + model[
1] + "and run " + model[0]
cont1 = ['Bundling-Control', 'T', ['Bundling', 'Control'], [1, -1]]
s = SpecifyModel()
s.inputs.input_units = 'secs'
s.inputs.functional_runs = results.outputs.func
s.inputs.time_repetition = 2
s.inputs.high_pass_filter_cutoff = 128.
s.inputs.event_files = results.outputs.evs
model = s.run()
level1design = Level1Design()
level1design.inputs.interscan_interval = 2.5
level1design.inputs.bases = {'dgamma': {'derivs': False}}
level1design.inputs.model_serial_correlations = False
level1design.inputs.session_info = model.outputs.session_info
level1design.inputs.contrasts = [cont1]
l1d = level1design.run()
print l1d.outputs.ev_files
modelgen = FEATModel()
modelgen.inputs.ev_files = l1d.outputs.ev_files
modelgen.inputs.fsf_file = l1d.outputs.fsf_files
model = modelgen.run()
fgls = fsl.FILMGLS()
fgls.inputs.in_file = results.outputs.func
fgls.inputs.design_file = model.outputs.design_file
fgls.inputs.threshold = 10
fgls.inputs.results_dir = 'stats'
res = fgls.run()
