3 d !@sddljZddlmZmZmZmZmZddlm Z m Z Gddde Z Gdd d eZ Gd d d e Z Gd d d e ZGdddeZGddde ZGdddeZGdddeZdS)N)traits TraitedSpecFileInputMultiObject isdefined)MRTrix3BaseInputSpec MRTrix3Basec@sreZdZeddddddZeddddddd Zedd d d Zejd ddddddZ ej ddddZ edddZ dS)FitTensorInputSpecTz%srzinput diffusion weighted images)existsargstr mandatorypositiondesczdti.mifrz!the output diffusion tensor image)r rr usedefaultrz-mask %szEonly perform computation within the specified binary brain mask image)r r rZ nonlinearZ loglinearZsechZricianz -method %sz)select method used to perform the fitting)r rz-regularisation %fz0.3.13zspecify the strength of the regularisation term on the magnitude of the tensor elements (default = 5000). This only applies to the non-linear methods)r Zmax_verrz-predicted_signal %szvspecify a file to contain the predicted signal from the tensor fits. This can be used to calculate the residual signalN) __name__ __module__ __qualname__rin_fileout_fileZin_maskrEnummethodFloatZreg_termpredicted_signalrrC/tmp/pip-build-7vycvbft/nipype/nipype/interfaces/mrtrix3/reconst.pyr s>r c@s"eZdZedddZeddZdS)FitTensorOutputSpecTzthe output DTI file)r rz$Predicted signal from fitted tensors)rN)rrrrrrrrrrr>s rc@s$eZdZdZdZeZeZddZ dS) FitTensora Convert diffusion-weighted images to tensor images Example ------- >>> import nipype.interfaces.mrtrix3 as mrt >>> tsr = mrt.FitTensor() >>> tsr.inputs.in_file = 'dwi.mif' >>> tsr.inputs.in_mask = 'mask.nii.gz' >>> tsr.inputs.grad_fsl = ('bvecs', 'bvals') >>> tsr.cmdline # doctest: +ELLIPSIS 'dwi2tensor -fslgrad bvecs bvals -mask mask.nii.gz dwi.mif dti.mif' >>> tsr.run() # doctest: +SKIP Z dwi2tensorcCs@|jj}tj|jj|d<t|jjrrrrrrrCs   rCc@s$eZdZdZdZeZeZddZ dS) EstimateFODa Estimate fibre orientation distributions from diffusion data using spherical deconvolution .. warning:: The CSD algorithm does not work as intended, but fixing it in this interface could break existing workflows. This interface has been superseded by :py:class:`.ConstrainedSphericalDecomposition`. Example ------- >>> import nipype.interfaces.mrtrix3 as mrt >>> fod = mrt.EstimateFOD() >>> fod.inputs.algorithm = 'msmt_csd' >>> fod.inputs.in_file = 'dwi.mif' >>> fod.inputs.wm_txt = 'wm.txt' >>> fod.inputs.grad_fsl = ('bvecs', 'bvals') >>> fod.cmdline 'dwi2fod -fslgrad bvecs bvals -lmax 8 msmt_csd dwi.mif wm.txt wm.mif gm.mif csf.mif' >>> fod.run() # doctest: +SKIP Zdwi2fodcCs|jj}tj|jj|d<t|jjrr.zH'predicted_signal' option can only be used with the 'msmt_csd' algorithmr) r!r"r#r$r%r<rr=r>rr; Exception)r&r'rrrr(s     zEstimateFOD._list_outputsN) rrrr)r*r,r+rCr!r(rrrrrDs rDc@s:eZdZedd ddZedd ddZeejddd d Z d S)*ConstrainedSphericalDeconvolutionInputSpecz%sr4z output GM ODF)r rrrzoutput CSF ODFz-lmax %sr5ztmaximum harmonic degree of response function - single value for single-shell response, list for multi-shell response)r r6rNr:r) rrrrr=r>rrrArBrrrrrFsrFc@seZdZdZeZdS)!ConstrainedSphericalDeconvolutionaq Estimate fibre orientation distributions from diffusion data using spherical deconvolution This interface supersedes :py:class:`.EstimateFOD`. The old interface has contained a bug when using the CSD algorithm as opposed to the MSMT CSD algorithm, but fixing it could potentially break existing workflows. The new interface works the same, but does not populate the following inputs by default: * ``gm_odf`` * ``csf_odf`` * ``max_sh`` Example ------- >>> import nipype.interfaces.mrtrix3 as mrt >>> fod = mrt.ConstrainedSphericalDeconvolution() >>> fod.inputs.algorithm = 'csd' >>> fod.inputs.in_file = 'dwi.mif' >>> fod.inputs.wm_txt = 'wm.txt' >>> fod.inputs.grad_fsl = ('bvecs', 'bvals') >>> fod.cmdline 'dwi2fod -fslgrad bvecs bvals csd dwi.mif wm.txt wm.mif' >>> fod.run() # doctest: +SKIP N)rrrr)rFr+rrrrrGsrG)os.pathpathr#baserrrrrr r r rr r,rCrDrFrGrrrrs 3H-