# load CSV file
data=load_csv(input_csv)
# columns:
# Subject,Visit,Filename,Age,Gender,Scale
#
# setup R objects for performing linear modelling
Subject = ro.FactorVector(data['Subject'])
Visit = ro.FactorVector(data['Visit'])
Age = ro.FloatVector(data['Age'])
Gender = ro.FactorVector(data['Gender'])
Scale = ro.FloatVector(data['Scale'])
inp=pyezminc.parallel_input_iterator()
out=pyezminc.parallel_output_iterator()
# allocate R formula, saves time for interpreter
fixed_effects = ro.Formula('Jacobian ~ I(Age^2) + Gender:I(Age^2) + Age + Gender:Age + Gender')
random_effects = ro.Formula('~1|Subject')
inp.open(data['Filename'],mask_file )
out.open(["output_roi_Intercept.mnc","output_roi_Age2.mnc","output_roi_Gender_Age2.mnc","output_roi_Age.mnc","output_roi_Gender_Age.mnc","output_roi_Gender.mnc",
"output_roi_Intercept_t.mnc","output_roi_Age2_t.mnc","output_roi_Gender_Age2_t.mnc","output_roi_Age_t.mnc","output_roi_Gender_Age_t.mnc","output_roi_Gender_t.mnc"
],mask_file)
# allocate space for input
jacobian=np.zeros(shape=[inp.dim()],dtype=np.float64,order='C')
# allocate space for output
# probably model didn't converge
pass
return result
if __name__ == "__main__":
max_jobs_queue = 100
inp = pyezminc.parallel_input_iterator()
inp.open(data['Filename'], mask_file)
# setup output iterator
out = pyezminc.parallel_output_iterator()
out.open([
"output_Intercept.mnc", "output_Age2.mnc", "output_Gender_Age2.mnc",
"output_Age.mnc", "output_Gender_Age.mnc", "output_Gender.mnc",
"output_Intercept_t.mnc", "output_Age2_t.mnc",
"output_Gender_Age2_t.mnc", "output_Age_t.mnc",
"output_Gender_Age_t.mnc", "output_Gender_t.mnc"
], mask_file)
l = None
# start iterators, not really needed
inp.begin()
out.begin()