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"""
To use:
import profile_dti as p
import dipy.reconst.dti as dti
lprun -f dti.restore_fit_tensor -f p.tm.fit_method p.func()
"""
import nibabel as nib
import dipy.core.gradients as grad
import dipy.data as dpd
import dipy.reconst.dti as dti
data, bvals, bvecs = dpd.get_data('small_25')
dd = nib.load(data).get_data()
gtab = grad.gradient_table(bvals, bvecs)
fit_method = 'restore' # 'NLLS'
jac = True # False
# To profile RESTORE, set some of the signals to be outliers (otherwise comment
# out the following line):
dd[..., 5] = 1.0
tm = dti.TensorModel(gtab, fit_method=fit_method, jac=True, sigma=10)
def func():
tf = tm.fit(dd)
if __name__=="__main__":
func()
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