#Load needed libraries
import numpy as np, nibabel as nib, nagini, sys, scipy.optimize as opt
import scipy.interpolate as interp, matplotlib.pyplot as plt, matplotlib.gridspec as grid
import scipy.ndimage.filters as filt, scipy.spatial as spat
from tqdm import tqdm
#Ignore invalid and overflow warnings
np.seterr(invalid='ignore', over='ignore')
#########################
###Data Pre-Processing###
#########################
print('Loading images...')
#Load in the dta file
dta = nagini.loadDta(args.dta[0])
#Load in the info file
info = nagini.loadInfo(args.info[0])
#Load image headers
pet = nagini.loadHeader(args.pet[0])
roi = nagini.loadHeader(args.roi[0])
cbf = nagini.loadHeader(args.cbf[0])
cbv = nagini.loadHeader(args.cbv[0])
#Check to make sure dimensions match
if pet.shape[3] != info.shape[0] or pet.shape[0:3] != roi.shape[
0:3] or pet.shape[0:3] != cbf.shape[0:3] or pet.shape[
0:3] != cbv.shape[0:3]:
print 'ERROR: Data dimensions do not match. Please check...'
#Load needed libraries
import numpy as np, nibabel as nib, nagini, sys, scipy.optimize as opt
import scipy.interpolate as interp, matplotlib.pyplot as plt, matplotlib.gridspec as grid
import scipy.ndimage.filters as filt
from tqdm import tqdm
#Ignore invalid and overflow warnings
np.seterr(invalid='ignore',over='ignore')
#########################
###Data Pre-Processing###
#########################
print ('Loading images...')
#Load in the dta file
dta = nagini.loadDta(args.dta[0])
#Load in the info file
info = nagini.loadInfo(args.info[0])
#Load image headers
pet = nagini.loadHeader(args.pet[0])
cbf = nagini.loadHeader(args.cbf[0])
cbv = nagini.loadHeader(args.cbv[0])
#Check to make sure dimensions match
if pet.shape[3] != info.shape[0] or pet.shape[0:3] != cbf.shape[0:3] or pet.shape[0:3] != cbv.shape[0:3] :
print 'ERROR: Data dimensions do not match. Please check...'
sys.exit()
#Brain mask logic