Examples

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)