import elikopy
f_path="/CHEMIN/VERS/LETUDE"
patient_list=None
#patient_list=["Case1","Case2","Case3","Control1","Control2","Control3"]
study = elikopy.core.Elikopy(f_path)
#Génération de la liste des sujets
study.patient_list()
# Preprocessing
study.preproc(eddy=True,topup=True,denoising=True,reslice=False,gibbs=False,biasfield=False,patient_list_m=patient_list,starting_state=None)
study.white_mask()
# Microstructure
study.dti(patient_list_m=patient_list, use_wm=False)
study.noddi(use_wm=False)
dic_path="/home/users/microstructure/fixed_rad_dist.mat"
study.fingerprinting(dic_path,use_wm=False)
# Stats
grp1=[1,2]
grp2=[3,4]
study.regall_FA(grp1=grp1, grp2=grp2, registration_type='-T', postreg_type='-S')
additional_metrics={'_noddi_odi':'noddi','_mf_fvf_tot':'mf'}
study.regall(grp1=grp1,grp2=grp2, metrics_dic=additional_metrics)
metrics={'dti':'FA','_noddi_odi':'noddi','_mf_fvf_tot':'mf'}
study.randomise_all(metrics_dic=metrics,randomise_numberofpermutation=5000, skeletonised=True, additional_atlases={'AtlasName':["path to xml","path to nifti"], 'AtlasName2':["path to xml2","path to nifti2"]})
# Export
study.export(preprocessing=True,dti=True,noddi=True,mf=True,wm_mask=False,report=False,raw=False)