3 d@s$dZddlZddljZddlmZddlZddlZddlZ ddl m Z ddl m Z mZddlmZmZdd lmZdd lmZmZmZmZmZmZmZmZmZd d lmZmZm Z m!Z!m"Z"d d l#m$Z$dZ%e j&dZ'e"j(j)Z*GdddeZ+GdddeZ,GdddeZ-GdddeZ.GdddeZ/GdddeZ0GdddeZ1GdddeZ2Gd d!d!eZ3Gd"d#d#eZ4Gd$d%d%eZ5Gd&d'd'eZ6Gd(d)d)eZ7Gd*d+d+eZ8Gd,d-d-ej9Z:Gd.d/d/eZ;Gd0d1d1eZGd6d7d7eZ?Gd8d9d9eZ@Gd:d;d;eZAGdd?d?eZCGd@dAdAeZDGdBdCdCeZEGdDdEdEeZFGdFdGdGeZGGdHdIdIeZHGdJdKdKeZIGdLdMdMeZJGdNdOdOeZKGdPdQdQeZLGdRdSdSeZMGdTdUdUeZNGdVdWdWeZOGdXdYdYeZPGdZd[d[eZQGd\d]d]eZRGd^d_d_eZSGd`dadaeZTGdbdcdceZUGdddedeeZVGdfdgdgeZWGdhdidieZXGdjdkdkeZYGdldmdmeZZGdndodoe Z[GdpdqdqeZ\Gdrdsdse!Z]Gdtdudue Z^GdvdwdweZ_Gdxdydye!Z`Gdzd{d{e ZaGd|d}d}eZbGd~dde!ZcGdddeZdGdddeZeGdddeZfGdddeZgGdddeZhGdddeZiGdddeZjGdddeZkGdddeZlGdddeZmGdddeZnGdddeZodS)z?Provides interfaces to various commands provided by FreeSurfer N)glob)load)logging LooseVersion)fname_presuffix check_depends)FreeSurferSource) TraitedSpecFiletraits DirectoryInputMultiPathOutputMultiPath CommandLineCommandLineInputSpec isdefined) FSCommand FSTraitedSpecFSTraitedSpecOpenMPFSCommandOpenMPInfo) copy2subjdirZrestructuredtextznipype.interfacec@sHeZdZedddddZedddddZejd d d Z ejd d d Z dS)ParseDICOMDirInputSpecTz--d %szpath to siemens dicom directory)existsargstr mandatorydescz dicominfo.txtz--o %sz!file to which results are written)r usedefaultrz --sortbyrunzassign run numbers)rrz --summarizez#only print out info for run leadersN) __name__ __module__ __qualname__r dicom_dirr dicom_info_filer BoolZ sortbyrunZ summarizer'r'I/tmp/pip-build-7vycvbft/nipype/nipype/interfaces/freesurfer/preprocess.pyr(src@seZdZedddZdS)ParseDICOMDirOutputSpecTz&text file containing dicom information)rrN)r!r"r#r r%r'r'r'r(r);sr)c@s$eZdZdZdZeZeZddZ dS) ParseDICOMDiraUses mri_parse_sdcmdir to get information from dicom directories Examples -------- >>> from nipype.interfaces.freesurfer import ParseDICOMDir >>> dcminfo = ParseDICOMDir() >>> dcminfo.inputs.dicom_dir = '.' >>> dcminfo.inputs.sortbyrun = True >>> dcminfo.inputs.summarize = True >>> dcminfo.cmdline 'mri_parse_sdcmdir --d . --o dicominfo.txt --sortbyrun --summarize' Zmri_parse_sdcmdircCs6|jj}t|jjr2tjjtj|jj|d<|S)Nr%) output_specgetrinputsr%ospathjoingetcwd)selfoutputsr'r'r( _list_outputsSs   zParseDICOMDir._list_outputsN) r!r"r#__doc___cmdr input_specr)r+r4r'r'r'r(r*?s r*c @seZdZedddddZedddZejejej ej ej ddd#d d Z e dddd$ddZ e dddd%ddZ ejdddddZejdddZe ddddZe ddddZejdddZejd d!dZd"S)&UnpackSDICOMDirInputSpecTz-src %szdirectory with the DICOM files)rrrrz-targ %sz3top directory into which the files will be unpacked)rrz-run %d %s %s %srun_infoconfig seq_configz:runno subdir format name : spec unpacking rules on cmdline)rrxorrz-cfg %szspecify unpacking rules in file)rrrr<rz -seqcfg %sz)specify unpacking rules based on sequenceZfsfastZgenericz-%sz(unpack to specified directory structuresz -noinfodumpzdo not create infodump filez -scanonly %sz.only scan the directory and put result in file)rrrz-log %szexplicilty set log filez-nspmzeropad %dz+set frame number zero padding width for SPMz -no-unpackerrz%do not try to unpack runs with errorsN)r9r:r;)r9r:r;)r9r:r;)r!r"r#r source_dir output_dirr TupleIntStrr9r r:r;EnumZ dir_structurer&Z no_info_dumpZ scan_onlyZlog_fileZ spm_zeropadZ no_unpack_errr'r'r'r(r8\sV  r8c@seZdZdZdZeZdS)UnpackSDICOMDira.Use unpacksdcmdir to convert dicom files Call unpacksdcmdir -help from the command line to see more information on using this command. Examples -------- >>> from nipype.interfaces.freesurfer import UnpackSDICOMDir >>> unpack = UnpackSDICOMDir() >>> unpack.inputs.source_dir = '.' >>> unpack.inputs.output_dir = '.' >>> unpack.inputs.run_info = (5, 'mprage', 'nii', 'struct') >>> unpack.inputs.dir_structure = 'generic' >>> unpack.cmdline 'unpacksdcmdir -generic -targ . -run 5 mprage nii struct -src .' Z unpacksdcmdirN)r!r"r#r5r6r8r7r'r'r'r(rCsrCc0@seZdZejdddZejdddZejdddZejdd dZejd d dZ ejd d dZ ejdddZ ejdddZ ej dddZej dddZej dddZejdddZejejejejdddZejejejejdddZejejejejdddZddd d!d"d#d$d%d&d'd(d)d*d+d,d-d.d/d0d1d2d3d4d5d6d7d8d9d:d;dd?d@dAdBdCdDdEdFdGdHdIdJdKdLdMg0ZejedNdOdZejejdPdQdRdSZejdTdUdZej dVdWdZej dXdYdZej dZd[dZejejejejd\d]dZ ej d^d_dZ!ej d`dadZ"ej dbdcdZ#ejejejejddddZ$ejejejejdeddZ%ejejejejdfddZ&ejedgdhdZ'ejejejejdidRdZ(ejdjdkdldmdndodZ)ejdpdqdrdsdtdudvdZ*ejdwdxdZ+ejdydzdZ,ej d{d|dZ-ej d}d~dZ.ej dddZ/e0ddddZ1ejdddZ2e0ddddZ3e0ddddZ4ej5dddZ6ejej ej ej dddZ7ejej ej ej dddZ8ej dddZ9ejej ej dddZ:ejdddZ;ejdddZdddddddgZ?eje>e?dddZ@eje>dddZAejdddZBejej ej ej dddZCejdddZDe0ddddddZEe0dddddōZFejdddZGejdddZHejdddZIejdddZJejdddZKej5dddZLe0ddddZMeje>e?dddZNejdddZOej dddZPejdddZQej dddZRej dddZSejej ej ej dddZTejdddZUejdddZVe0ddddZWejdddZXejdddZYejdddZZe0ddddZ[ejdddZ\e0dddZ]e0ddddZ^ejdddZ_ejdddZ`ejdddZadS)MRIConvertInputSpecz --read_onlyzread the input volume)rrz --no_writezdo not write outputz --in_infozdisplay input infoz --out_infozdisplay output infoz --in_statszdisplay input statsz --out_statszdisplay output statsz --in_matrixzdisplay input matrixz --out_matrixzdisplay output matrixz--in_i_size %dz input i sizez--in_j_size %dz input j sizez--in_k_size %dz input k sizez--force_ras_goodz(use default when orientation info absentz--in_i_direction %f %f %fz) z--in_j_direction %f %f %fz--in_k_direction %f %f %fZLAIZLIAZALIZAILZILAZIALZLASZLSAZALSZASLZSLAZSALZLPIZLIPZPLIZPILZILPZIPLZLPSZLSPZPLSZPSLZSLPZSPLZRAIZRIAZARIZAIRZIRAZIARZRASZRSAZARSZASRZSRAZSARZRPIZRIPZPRIZPIRZIRPZIPRZRPSZRSPZPRSZPSRZSRPZSPRz--in_orientation %szspecify the input orientationrz--in_center %sz, )maxlenrrz--sphinxz!change orientation info to sphinxz--out_i_count %dzsome count ?? in i directionz--out_j_count %dzsome count ?? in j directionz--out_k_count %dzsome count ?? in k directionz-voxsize %f %f %fzX specify the size (mm) - useful for upsampling or downsamplingz--out_i_size %dz output i sizez--out_j_size %dz output j sizez--out_k_size %dz output k sizez--out_i_direction %f %f %fz--out_j_direction %f %f %fz--out_k_direction %f %f %fz--out_orientation %szspecify the output orientationz--out_center %f %f %fZucharshortintfloatz--out_data_type %sz(output data type Z interpolateZweightednearestZsinccubicz--resample_type %szB (default is interpolate)z --no_scale 1zdont rescale values for CORz --nochangez.don't change type of input to that of templatez-tr %dz TR in msecz-te %dz TE in msecz-ti %dz!TI in msec (note upper case flag)Tz--autoalign %sztext file with autoalign matrix)rrrz--unwarp_gradient_nonlinearityzunwarp gradient nonlinearityz--apply_transform %szapply xfm filez--apply_inverse_transform %sz%apply inverse transformation xfm filez--devolvexfm %sz subject idz--crop %d %d %dz/ crop to 256 around center (x, y, z)z--cropsize %d %d %dz( crop to size z --cutends %dz remove ncut slices from the endsz--slice-crop %d %dz,s_start s_end : keep slices s_start to s_endz--slice-reversez'reverse order of slices, update vox2rasz--slice-bias %fzapply half-cosine bias fieldz --fwhm %fzsmooth input volume by fwhm mmcormghmgzmincanalyze analyze4dspmafnibrikbshortbfloatZsdtoutlineZotlZgdfnifti1niiniigzgeZgelxZlxZximgZsiemensZdicomZ siemens_dicomz --in_type %szinput file typez --out_type %szoutput file typez--asciiz(save output as ascii col>row>slice>framez--reorder %d %d %dzolddim1 olddim2 olddim3z--invert_contrast %fz!threshold for inversting contrastr z--input_volume %szFile to read/convert)rrpositionrrz--output_volume %srz'output filename or True to generate one)rr[genfilerz --conformzGconform to 1mm voxel size in coronal slice direction with 256^3 or morez --conform_minzconform to smallest sizez--conform_size %szconform to size_in_mmz--cw256zconfrom to dimensions of 256^3z --parse_onlyzparse input onlyz--subject_name %szsubject name ???z--reslice_like %szreslice output to match filez--template_type %sztemplate file typez--splitz/split output frames into separate output files.z --frame %dzkeep only 0-based frame numberz --mid-framezkeep only the middle framez --nskip %dzskip the first n framesz --ndrop %dzdrop the last n framesz--fsubsample %d %d %dz6start delta end : frame subsampling (end = -1 for end)z --scale %fzinput intensity scale factorz--out-scale %dzoutput intensity scale factorz --in_like %szinput looks likez--fill_parcellationzfill parcellationz--smooth_parcellationzsmooth parcellationz--zero_outlinesz zero outlinesz--color_file %sz color filez--no_translatez???z --status %sz status file for DICOM conversionz --sdcmlist %sz"list of DICOM files for conversionz--template_infozdump info about templatez --crop_gdfzapply GDF croppingz--zero_ge_z_offsetzzero ge z offset ???N)br!r"r#r r&Z read_onlyZno_writeZin_infoZout_infoZin_statsZ out_statsZ in_matrixZ out_matrixr@Z in_i_sizeZ in_j_sizeZ in_k_sizeZ force_rasr?FloatZin_i_dirZin_j_dirZin_k_dirZ _orientationsrBZin_orientationListZ in_centerZsphinxZ out_i_countZ out_j_countZ out_k_countZvox_sizeZ out_i_sizeZ out_j_sizeZ out_k_sizeZ out_i_dirZ out_j_dirZ out_k_dirZout_orientationZ out_centerZ out_datatypeZ resample_typeZno_scaleZ no_changetrteZtir Zautoalign_matrixZunwarp_gradientZapply_transformZapply_inv_transformrAZdevolve_transformZ crop_centerZ crop_sizeZcut_endsZ slice_cropZ slice_reverseZ slice_biasZfwhm _filetypesZ _infiletypesin_typeout_typeasciiZreorderZinvert_contrastin_fileout_fileZconformZ conform_minZ conform_sizeZcw256Z parse_onlyZ subject_nameZ reslice_like template_typesplitframeZmidframeZskip_nZdrop_nZframe_subsampleZin_scaleZ out_scaleZin_likeZfill_parcellationZsmooth_parcellationZ zero_outlinesZ color_fileZ no_translateZ status_fileZ sdcm_listZ template_infoZcrop_gdfZzero_ge_z_offsetr'r'r'r(rDs*                      rDc@seZdZeeddddZdS)MRIConvertOutputSpecT)rzconverted output file)rN)r!r"r#rr rhr'r'r'r(rlsrlcsheZdZdZdZeZeZe dddddddd d d d d d d d Z fddZ ddZ ddZ ddZZS) MRIConvertause fs mri_convert to manipulate files .. note:: Adds niigz as an output type option Examples -------- >>> mc = MRIConvert() >>> mc.inputs.in_file = 'structural.nii' >>> mc.inputs.out_file = 'outfile.mgz' >>> mc.inputs.out_type = 'mgz' >>> mc.cmdline 'mri_convert --out_type mgz --input_volume structural.nii --output_volume outfile.mgz' mri_convertrKrLrMZmncrSrTimgrUrXznii.gz)rKrLrMrNrRrSrTrQrOrPrUrWrXrYcs.|dkr|dkr|jdStt|j|||S)NrdrerirYrX)rdreri)rsuperrm _format_arg)r2namespecvalue) __class__r'r(rq s zMRIConvert._format_argcCsX|jj}t|sLt|jjr0d|j|jj}nd}t|jjtj|dd}tj j |S)Nz_out.z _out.nii.gzF)newpathsuffixuse_ext) r-rhrrefilemaprrgr.r1r/abspath)r2outfilerwr'r'r(_get_outfilename&s zMRIConvert._get_outfilenamec Cs||jj}|j}t|jjr|jjrt|jjj}t |dkrHd}n|d}|j drn|jdd}d}nH|j dr|jdd}d}n*dj |jddd}d|jdd}g}x(t d|D]}|j |d||qWt|jjrp|jjdkrpt|jjj}t |dkr"d}n|d}td g}|j}x,t |D] } |j t|d | dd qHW|}||d <|S)Nrrz.mgzrz.nii.gz.z%04drQrOzGNot taking frame manipulations into account- please warn the developersz%03d)rwrhr^r^r^)rQrOr^)r+r,r|rr-rjrrgshapelenendswithr0rangeappendre Exceptionr) r2r3r{sizetpZstemextidxoutfilesir'r'r(r42sB     zMRIConvert._list_outputscCs|dkr|jSdS)Nrh)r|)r2rrr'r'r( _gen_filename[szMRIConvert._gen_filename)r!r"r#r5r6rDr7rlr+dictryrqr|r4r __classcell__r'r')rur(rms.  )rmc@seZdZeddddZedddZejddddZej d d Z ej ej ejejd d Z ejd ejdd dZedddZej ejdgddZejdgddZdS)DICOMConvertInputSpecTz1dicom directory from which to convert dicom files)rrrz2directory in which subject directories are created)rrzS.%04dz#template for subject directory name)r rz*subject identifier to insert into template)rz&defines the output fields of interfacerYz(defines the type of output file producedz:File containing summary information from mri_parse_sdcmdir)rr dicom_infoz-list of pulse sequence names to be converted.)requiresrz(ignore volumes containing a single sliceN)r!r"r#rr$base_output_dirr rAsubject_dir_templateZAny subject_idr`r?Z file_mappingrBrDrcrer rseq_listr&ignore_single_slicer'r'r'r(ras2    rc@sDeZdZdZdZeZddZddZddZ d d Z e d d Z d S) DICOMConverta4use fs mri_convert to convert dicom files Examples -------- >>> from nipype.interfaces.freesurfer import DICOMConvert >>> cvt = DICOMConvert() >>> cvt.inputs.dicom_dir = 'dicomdir' >>> cvt.inputs.file_mapping = [('nifti', '*.nii'), ('info', 'dicom*.txt'), ('dti', '*dti.bv*')] rncCsttjjtjj|jjdS)z?validate fsl bet options if set to None ignore z*-1.dcm)rr.r/rzr0r-r$)r2r'r'r(_get_dicomfilesszDICOMConvert._get_dicomfilescCs|jj}t|s:tjj|jd\}}t|jdd}t|jjrR|jj|}|jj }t|sntjj d}tjj tjj ||}|S)zreturns output directoryr-r}) r-rrr.r/rjrrGrrrzr0)r2Zsubjidr/fnamebasediroutdirr'r'r( _get_outdirs   zDICOMConvert._get_outdircstj|jjtd}g}x|D]|jjr|jjrntddkrtfdd|jjDr|j tdqtfdd|jjDr|j tdq|j tdqW|S)zReturns list of dicom series that should be converted. Requires a dicom info summary file generated by ``DicomDirInfo`` )Zdtypercsg|]}dj|qS) ) startswith).0sn)sr'r( sz*DICOMConvert._get_runs..r csg|]}dj|qS)r)r)rr)rr'r(rs) npZ genfromtxtr-robjectrrrGanyr)r2seqrunsr')rr( _get_runss zDICOMConvert._get_runsc six|jD]x}tjj|\}}tjj|\}}|jd}t|d}tj|jj }tjj |dj d|d|f|f} || f|<qW|jj rfdd|j D} nfddt jD} | S) z%Returns list of files to be convertedrrr}z%s-%02drcsg|] }|qSr'r')rr)ryr'r(rsz.DICOMConvert._get_filelist..csg|] }|qSr'r')rr)ryr'r(rs)rr.r/rjsplitextrGrmryr-rer0rrlistkeys) r2rfheadrrZ filepartsZrunnorer{filesr')ryr( _get_filelists  zDICOMConvert._get_filelistc Cs|j|j}g}tjj|s@dtjtj|f}|j |gtjj |d}tjj|svd|j j |f}|j |g|j |}xF|D]>\}}tjj|sd|j|j|tjj ||f}|j |gqWdj |S)zZ`command` plus any arguments (args) validates arguments and generates command linez$%s -c "import os; os.makedirs('%s')"z shortinfo.txtzdcmdir-info-mgh %s > %sz %s%s %s %sz; )Z_check_mandatory_inputsrr.r/ropbasenamesys executableextendr0r-r$rZ _cmd_prefixcmd) r2rrZcmdstrZinfofilerinfiler{Z single_cmdr'r'r(cmdlines,       zDICOMConvert.cmdlineN) r!r"r#r5r6rr7rrrrpropertyrr'r'r'r(rs rc@sJeZdZeddddddZeddddd Zejejejejd d dd Z d S)ResampleInputSpecTz-i %szfile to resampler )rrrrr[z-o %szoutput filenamer)rrr\r[z-vs %.2f %.2f %.2fztriplet of output voxel sizes)rrrNr]r^) r!r"r#r rgresampled_filer r?r_Z voxel_sizer'r'r'r(rsrc@seZdZedddZdS)ResampleOutputSpecTzoutput filename)rrN)r!r"r#r rr'r'r'r(rsrc@s4eZdZdZdZeZeZddZ ddZ ddZ d S) ResampleaUse FreeSurfer mri_convert to up or down-sample image files Examples -------- >>> from nipype.interfaces import freesurfer >>> resampler = freesurfer.Resample() >>> resampler.inputs.in_file = 'structural.nii' >>> resampler.inputs.resampled_file = 'resampled.nii' >>> resampler.inputs.voxel_size = (2.1, 2.1, 2.1) >>> resampler.cmdline 'mri_convert -vs 2.10 2.10 2.10 -i structural.nii -o resampled.nii' rncCs0t|jjr|jj}nt|jjtjdd}|S)NZ _resample)rvrw)rr-rrrgr.r1)r2r{r'r'r(r|s   zResample._get_outfilenamecCs|jj}|j|d<|S)Nr)r+r,r|)r2r3r'r'r(r4&s  zResample._list_outputscCs|dkr|jSdS)Nr)r|)r2rrr'r'r(r+szResample._gen_filenameN) r!r"r#r5r6rr7rr+r|r4rr'r'r'r(r s rc@s"eZdZejdddddZejdddd d d d d dddddddddddZejdddddZe e ddddd Z e dd!d"d#d$Z e dd%d"d&d$Z ejd'd"d(gd)d*Zejd+d"d,gd-d*Zejd.d/d Zejd0d1d Zejd2d3d4d5Zejd6d7d Zejd8d9d Zejd:d;d Zejdd3d?d5Ze dd@dAdBZejdCdDdEdFdGd ZeddHdIdJddKZe ejdLdMd ZejdNdOgdPZ ejdQdOgdPZ!ejdRdOgdPZ"ejdSdOgdPZ#ejdTdOgdPZ$ejdUdOgdPZ%ejdVdOgdPZ&ejdWdOgdPZ'ejdXdOgdPZ(ejdYdOgdPZ)ejdZdOgdPZ*ejd[dOgdPZ+ejd\dOgdPZ,ejd]dOgdPZ-ejd^dOgdPZ.ejd_dOgdPZ/ejd`dOgdPZ0ejdadOgdPZ1ejdbdOgdPZ2ejdcdOgdPZ3ejdddOgdPZ4ejdedOgdPZ5ejdfdOgdPZ6ejdgdOgdPZ7ejdhdOgdPZ8ejdidOgdPZ9djS)kReconAllInputSpecZ recon_allz -subjid %sz subject nameT)rrr all autorecon1 autorecon2zautorecon2-volonlyzautorecon2-perhemizautorecon2-inflate1z autorecon2-cpz autorecon2-wm autorecon3zautorecon3-T2pialzautorecon-pialzautorecon-hemiZlocalGIZqcachez-%szprocess directiver)rrr r[lhrhzhemisphere to processz-hemi %s)rr)rz-i %s...zname of T1 file to process)rrz-T2 %sz5.3.0z"Convert T2 image to orig directory)rrmin_verrz -FLAIR %sz%Convert FLAIR image to orig directoryz-T2pial use_FLAIRz'Use T2 image to refine the pial surface)rrr<rz -FLAIRpialuse_T2z*Use FLAIR image to refine the pial surfacez -openmp %dz'Number of processors to use in parallelz -parallelzEnable parallel executionz-hiresz6.0.0z0Conform to minimum voxel size (for voxels < 1mm))rrrz-mpragezRAssume scan parameters are MGH MP-RAGE protocol, which produces darker gray matterz-bigventriclesz,For use in subjects with enlarged ventriclesz-brainstem-structureszSegment brainstem structuresz-hippocampal-subfields-T1z1segment hippocampal subfields using input T1 scanz-hippocampal-subfields-T2 %s %szmsegment hippocampal subfields using T2 scan, identified by ID (may be combined with hippocampal_subfields_T1)z -expert %sz Set parameters using expert file)rrrZuseclean overwritez -xopts-%sz5Use, delete or overwrite existing expert options filez-sd %sFzpath to subjects directory)rr hash_filesrr\z%szadditional parametersz#Flags to pass to talairach commandsexpert)rr<z'Flags to pass to mri_normalize commandsz'Flags to pass to mri_watershed commandsz)Flags to pass to mri_em_register commandsz*Flags to pass to mri_ca_normalize commandsz)Flags to pass to mri_ca_register commandsz)Flags to pass to mri_remove_neck commandsz&Flags to pass to mri_ca_label commandsz&Flags to pass to mri_segstats commandsz"Flags to pass to mri_mask commandsz%Flags to pass to mri_segment commandsz/Flags to pass to mri_edit_wm_with_aseg commandsz%Flags to pass to mri_pretess commandsz"Flags to pass to mri_fill commandsz(Flags to pass to mri_tessellate commandsz$Flags to pass to mri_smooth commandsz%Flags to pass to mri_inflate commandsz%Flags to pass to mris_sphere commandsz+Flags to pass to mris_fix_topology commandsz,Flags to pass to mris_make_surfaces commandsz'Flags to pass to mris_surf2vol commandsz'Flags to pass to mris_register commandsz%Flags to pass to mrisp_paint commandsz'Flags to pass to mris_ca_label commandsz/Flags to pass to mris_anatomical_stats commandsz(Flags to pass to mri_aparc2aseg commandsN):r!r"r#r rArrB directivehemirr T1_filesZT2_fileZ FLAIR_filer&rrr@ZopenmpparallelZhiresZmprageZbig_ventriclesZ brainstemhippocampal_subfields_T1r?hippocampal_subfields_T2rxoptsr subjects_dirflags talairach mri_normalize mri_watershedmri_em_registermri_ca_normalizemri_ca_registermri_remove_neck mri_ca_label mri_segstatsmri_mask mri_segmentmri_edit_wm_with_aseg mri_pretessmri_fillmri_tessellate mris_smooth mris_inflate mris_spheremris_fix_topologymris_make_surfaces mris_surf2vol mris_register mrisp_paint mris_ca_labelmris_anatomical_statsmri_aparc2asegr'r'r'r(r1s                          rc@s$eZdZedddZejddZdS)ReconAllOutputSpecTzFreesurfer subjects directory.)rrz&Subject name for whom to retrieve data)rN)r!r"r#rrr rArr'r'r'r(rs rcseZdZdZdZddgZeZeZ dZ dZ ddd ggfd d d d ggfddggfddggfddddggfgZ e jedkr2ddggfddggfddggfdddd ggfd!d"ggfd#d$ggfd%d&d'd(ggfd)d*ggfd+d,ggfd-d.d/d0ggfd1d2ggfg Zd3d4ggfd5d6ggfd7d8ggfd9d:ggfd;dd?d@dAggfdBdCggfdDdEdFdGdHggfdIdJggfg ZdKdLggfdMdNggfdOdPggfdQdRggfdSdTggfdUdVdWdXdYggfdZd[ggfd\d]ggfd^d_ggfd`daggfdbdcggfdddaggfdedfggfg ZdgdhdidjggfdkdlggfdmdndoggfdpdqdrggfdsdtduggfgZne jedvkrddggfddggfddggfd%d&d'd(ggfd)d*ggfd+d,ggfd-d.d/d0ggfd1d2ggfgZd3d4ggfd5d6ggfd7d8ggfd9d:ggfd;dggfd\d]ggfd^d_ggfd`daggfdbd{ggfddd|ggfdZd[ggfg Zdgdhdidjggfd}d~ggfdmdndodggfdd(gdngfdkdlggfdpdqdrggfdsdddfdggfgZnddggfddggfddggfd%d&d'dggfd)d*ggfd+d,ggfd-d.d/d0ggfd1d2ggfgZd3d4ggfd5d6ggfd7d8ggfd9d:ggfd;dEdggfd\d]ggfd^d_ggfd`daggfdbd{ggfddd|ggfdZd[dggfg Zdgdhdidjggfd}d~ggfdmdndodggfdd(gdngfdkdlggfdpdqdrggfdsdddfdddggfgZddeDZddeDZeeZddeDZddeDZeeZeeZeeZe eeZd dddddddddddddddddddddddddgZddZddZddZ ddZ!fddZ"e#fddZ$ddZ%ddZ&e#ddZ'Z(S)ReconAllaUses recon-all to generate surfaces and parcellations of structural data from anatomical images of a subject. Examples -------- >>> from nipype.interfaces.freesurfer import ReconAll >>> reconall = ReconAll() >>> reconall.inputs.subject_id = 'foo' >>> reconall.inputs.directive = 'all' >>> reconall.inputs.subjects_dir = '.' >>> reconall.inputs.T1_files = 'structural.nii' >>> reconall.cmdline 'recon-all -all -i structural.nii -subjid foo -sd .' >>> reconall.inputs.flags = "-qcache" >>> reconall.cmdline 'recon-all -all -i structural.nii -qcache -subjid foo -sd .' >>> reconall.inputs.flags = ["-cw256", "-qcache"] >>> reconall.cmdline 'recon-all -all -i structural.nii -cw256 -qcache -subjid foo -sd .' Hemisphere may be specified regardless of directive: >>> reconall.inputs.flags = [] >>> reconall.inputs.hemi = 'lh' >>> reconall.cmdline 'recon-all -all -i structural.nii -hemi lh -subjid foo -sd .' ``-autorecon-hemi`` uses the ``-hemi`` input to specify the hemisphere to operate upon: >>> reconall.inputs.directive = 'autorecon-hemi' >>> reconall.cmdline 'recon-all -autorecon-hemi lh -i structural.nii -subjid foo -sd .' Hippocampal subfields can accept T1 and T2 images: >>> reconall_subfields = ReconAll() >>> reconall_subfields.inputs.subject_id = 'foo' >>> reconall_subfields.inputs.directive = 'all' >>> reconall_subfields.inputs.subjects_dir = '.' >>> reconall_subfields.inputs.T1_files = 'structural.nii' >>> reconall_subfields.inputs.hippocampal_subfields_T1 = True >>> reconall_subfields.cmdline 'recon-all -all -i structural.nii -hippocampal-subfields-T1 -subjid foo -sd .' >>> reconall_subfields.inputs.hippocampal_subfields_T2 = ( ... 'structural.nii', 'test') >>> reconall_subfields.cmdline 'recon-all -all -i structural.nii -hippocampal-subfields-T1T2 structural.nii test -subjid foo -sd .' >>> reconall_subfields.inputs.hippocampal_subfields_T1 = False >>> reconall_subfields.cmdline 'recon-all -all -i structural.nii -hippocampal-subfields-T2 structural.nii test -subjid foo -sd .' z recon-alllocZaltkeyTFZ motioncorzmri/rawavg.mgzz mri/orig.mgzrzmri/orig_nu.mgzz!mri/transforms/talairach.auto.xfmzmri/transforms/talairach.xfmZnuintensitycorz mri/nu.mgzZ normalizationz mri/T1.mgzZ skullstripz'mri/transforms/talairach_with_skull.ltazmri/brainmask.auto.mgzzmri/brainmask.mgzz6.0.0Zgcaregzmri/transforms/talairach.ltaZcanormz mri/norm.mgzZcaregzmri/transforms/talairach.m3zZcareginvz&mri/transforms/talairach.m3z.inv.x.mgzz&mri/transforms/talairach.m3z.inv.y.mgzz&mri/transforms/talairach.m3z.inv.z.mgzZrmneckzmri/nu_noneck.mgzz skull-ltaz)mri/transforms/talairach_with_skull_2.ltaZcalabelzmri/aseg.auto_noCCseg.mgzzmri/aseg.auto.mgzz mri/aseg.mgzZnormalization2z mri/brain.mgzZmaskbfszmri/brain.finalsurfs.mgz segmentationzmri/wm.seg.mgzzmri/wm.asegedit.mgzz mri/wm.mgzfillzmri/filled.mgzZ tessellatezsurf/lh.orig.nofixZsmooth1zsurf/lh.smoothwm.nofixZinflate1zsurf/lh.inflated.nofixZqspherezsurf/lh.qsphere.nofixZfixz surf/lh.origZwhitez surf/lh.whitez surf/lh.curvz surf/lh.areazlabel/lh.cortex.labelZsmooth2zsurf/lh.smoothwmZinflate2zsurf/lh.inflatedz surf/lh.sulczsurf/lh.inflated.Hzsurf/lh.inflated.KZ curvstatszstats/lh.curv.statsZspherezsurf/lh.sphereZsurfregzsurf/lh.sphere.regZjacobian_whitezsurf/lh.jacobian_whiteZavgcurvzsurf/lh.avg_curvZcortparczlabel/lh.aparc.annotZpialz surf/lh.pialzsurf/lh.curv.pialzsurf/lh.area.pialzsurf/lh.thicknessZ pctsurfconzsurf/lh.w-g.pct.mghZ parcstatszstats/lh.aparc.statsZ cortparc2zlabel/lh.aparc.a2009s.annotZ parcstats2zstats/lh.aparc.a2009s.statsZ cortparc3zlabel/lh.aparc.DKTatlas40.annotZ parcstats3zlabel-exvivo-ecz label/lh.entorhinal_exvivo.labelZ cortribbonzmri/lh.ribbon.mgzzmri/rh.ribbon.mgzzmri/ribbon.mgzZsegstatszstats/aseg.statsZ aparc2asegzmri/aparc+aseg.mgzzmri/aparc.a2009s+aseg.mgzZwmparczmri/wmparc.mgzzstats/wmparc.statsZbalabelsz label/BA.ctabzlabel/BA.thresh.ctabz7.0.0zsurf/lh.white.preaparcZcurvHKzsurf/lh.white.Hzsurf/lh.white.Kzlabel/lh.aparc.DKTatlas.annotzstats/lh.aparc.DKTatlas.statsZ hyporelabelzmri/aseg.presurf.hypos.mgzzmri/aparc.DKTatlas+aseg.mgzZ apas2asegzlabel/BA_exvivo.ctabzlabel/BA_exvivo.thresh.ctabz label/rh.entorhinal_exvivo.labelzmri/aseg.presurf.mgzzstats/lh.w-g.pct.statsz label/lh.perirhinal_exvivo.labelz label/rh.perirhinal_exvivo.labelcCs&g|]\}}}|dd|D|fqS)cSsg|]}|jddqS)rr)replace)routr'r'r(rszReconAll..r')rstepoutsinsr'r'r(rszReconAll.cCs&g|]\}}}|dd|D|fqS)cSs&g|]}||jddfD]}|qqS)rr)r)rrofr'r'r(rszReconAll..r')rrrrr'r'r(rscCs&g|]\}}}|dd|D|fqS)cSsg|]}|jddqS)rr)r)rrr'r'r(rszReconAll..r')rrrrr'r'r(rscCs&g|]\}}}|dd|D|fqS)cSs&g|]}||jddfD]}|qqS)rr)r)rrrr'r'r(rszReconAll..r')rrrrr'r'r(rsrrrrrrrrrrrrrrrrrrrrrrrrrcCstjS)N)r.r1)r2r'r'r(_gen_subjects_dirszReconAll._gen_subjects_dircCs|dkr|jSdS)Nr)r)r2rrr'r'r(rszReconAll._gen_filenamecCsxt|jjr|jj}n|j}t|jjr4|jj}nd}|jj}|jt|jj ||dj |jj |d<||d<|S)zR See io.FreeSurferSource.outputs for the list of outputs returned Zboth)rrrrr) rr-rrrZ_outputsr,updater rr4)r2rrr3r'r'r(r4s      zReconAll._list_outputscCs<|jj}t|s|j}tjjtjj||jjdr8dSdS)NmriTF) r-rrrr.r/isdirr0r)r2rr'r'r( _is_resumings zReconAll._is_resumingcs|dkr|jrdS|dkr,t|jjr,dSt|dkt|jjoF|jjfrb|jjdd}||S|dkr|dkrt|jjst d|d |jj7}t|d kt|jj o|jj dkfrdSt t |j |||S) NrrrZT2ZT1T2rzautorecon-hemiz8Directive 'autorecon-hemi' requires hemi input to be set r)rrr-rrrrrr ValueErrorrrprrq)r2rrZ trait_specrtr)rur'r(rqs.      zReconAll._format_argc stt|j}||j7}|js$|S|jj}t|s<|j}|jj }t|sRg}n|dkrb|j }n|dkrr|j }n|dkr|j }n|j drt|jjr|jjdkr|j |j}q|j |j}n|j}n<|dkr|jjdkr|j}n|j}n|dkr|j}n|j}d}g}x|D]\}}} d j|} d j|} | |kr@qn| |krRd }qtjj||jjtfd d |Dfdd | Dr|j| nd }qW|r|j rtjddS|ddj|7}tjd||S)Nrzautorecon2-volonlyzautorecon2-perhemirrzautorecon-hemirTz-{}z-no{}Fcsg|]}tjj|qSr')r.r/r0)rr)subj_dirr'r(r_sz$ReconAll.cmdline..csg|]}tjj|qSr')r.r/r0)rr)rr'r(r`sz recon-all complete : Not runningzecho recon-all: nothing to dorzresume recon-all : %s) rprr_prep_expert_filerr-rrrr_autorecon1_steps_autorecon2_volonly_steps_autorecon2_perhemi_stepsrr_autorecon2_lh_steps_autorecon2_rh_steps_autorecon2_steps_autorecon_lh_steps_autorecon_rh_steps_autorecon3_steps_stepsformatr.r/r0rrr force_runifloggerinfo) r2rrrZstepsZno_runrrrinfilesflagZnoflag)ru)rr(r(sf               zReconAll.cmdlinec Cst|jjrdSg}x4|jD]*}t|j|}t|r|jdj||qW|gkrVdSdj|}t|jj r~|j |kr~dSt j j d}t |d}|j|WdQRXdj|S)Nz{} {} z -xopts-usez expert.optswz -expert {})rr-r _binariesgetattrrr r0r_get_expert_filer.r/rzopenwrite)r2linesbinaryargscontentsZ expert_fnamefobjr'r'r(rns       zReconAll._prep_expert_filec Csbt|jjr|jj}n|j}tjj||jjdd}tjj|sDdSt |d }|j SQRXdS)Nscriptszexpert-optionsrr) rr-rrr.r/r0rrrread)r2rZ xopts_filerr'r'r(rs    zReconAll._get_expert_filecCstj}|tdkr|jSdS)Nz0.0.0)r looseversionrvstring)r2verr'r'r(versions zReconAll.version))r!r"r#r5r6Z_additional_metadatarr7rr+Z _can_resumerrrr!rrrZ_autorecon3_lh_stepsZ_autorecon3_added_stepsrrrZ_autorecon3_rh_stepsZ_autorecon3_perhemi_stepsr r r r rrrr4rrqrrrrr$rr'r')rur(rs.5                                                                                     !Frc @sBeZdZejddddZedddddZejd d d d dd gddZ eddddgddZ ejddddddddZ eddddZ ej ddgdd Zejd!d"gd#d Zed$d%dd&Zejd'd(d)Zejd*d+d)Zejd,d-d.d/d0d)Zej d1d2d)Zejejed3d4d)Zejejed5d6d7d8Zejejed9d:d)Zejejed;dBBRegisterInputSpecz--s %szfreesurfer subject idT)rrrz--mov %szsource file to be registeredF)rrrcopyfilerQfslheaderz --init-%s init_reg_filez(initialize registration spm, fsl, header)rrr<rz --init-reg %szexisting registration fileinit)rrrr<rt1t2ZboldZdtiz--%szcontrast type of imagez--int %sz/Intermediate image, e.g. in case of partial FOV)rrrz --frame %dreg_middle_framez&0-based frame index for 4D source file)rr<rz --mid-frame reg_framez'Register middle frame of 4D source filez--reg %szoutput registration file)rrr\z --spm-niiz/force use of nifti rather than analyze with SPM)rrz --epi-maskz%mask out B0 regions in stages 1 and 2 rz--%dz&number of transform degrees of freedomz --fsl-dof %dz1degrees of freedom for initial registration (FSL)z --fslmat %sz3write the transformation matrix in FSL FLIRT formatz--lta %sz5.2.0z-write the transformation matrix in LTA format)rrrz--o %sz0output warped sourcefile either True or filenamez --initcost %sz%output initial registration cost fileN)r!r"r#r rArr source_filerBr*r)Z contrast_typeZintermediate_filer@r.r&r- out_reg_fileZ spm_niftiZepi_maskZdofZfsldofEither out_fsl_file out_lta_fileregistered_fileinit_cost_filer'r'r'r(r%s     r%c @s<eZdZejddddddddgd d Zed d d dgdZdS)BBRegisterInputSpec6ZcoregZrrrQr'r(bestz --init-%sr)z;initialize registration with mri_coreg, spm, fsl, or header)rr<rTz --init-reg %szexisting registration filer*)rrrr<N)r!r"r#r rBr*r r)r'r'r'r(r8sr8c@sTeZdZedddZedddZedddZedddZedddZedddZ d S) BBRegisterOutputSpecTzOutput registration file)rrz$Output FLIRT-style registration filez"Output LTA-style registration filez%Output registration minimum cost filez%Output initial registration cost filez$Registered and resampled source fileN) r!r"r#r r2r4r5 min_cost_filer7r6r'r'r'r(r: s      r:csfeZdZdZdZedejko.edknr:eZ ne Z e Z ddZ fddZd d ZZS) BBRegisteraqUse FreeSurfer bbregister to register a volume to the Freesurfer anatomical. This program performs within-subject, cross-modal registration using a boundary-based cost function. It is required that you have an anatomical scan of the subject that has already been recon-all-ed using freesurfer. Examples -------- >>> from nipype.interfaces.freesurfer import BBRegister >>> bbreg = BBRegister(subject_id='me', source_file='structural.nii', init='header', contrast_type='t2') >>> bbreg.cmdline 'bbregister --t2 --init-header --reg structural_bbreg_me.dat --mov structural.nii --s me' Z bbregisterz0.0.0z6.0.0cCst|jj}|j}t|jr.tj|j|d<n$|jrRd|j}t |j|dd|d<t|j rt |j t r|t |jdd|d<ntj|j |d<t|j rt |j t rd|j}t |j|dd}||d <ntj|j |d <t|jr$t |jt rd |j}t |j|dd}||d <ntj|j|d <t|jr`t |jt rP|dd |d <ntj|j|d <|dd|d<|S)Nr2z _bbreg_%s.datF)rwrxZ_bbreg)rwr6z _bbreg_%s.ltar5z _bbreg_%s.matr4z .initcostr7z.mincostr;)r+r,r-rr2rrzr1rrr6 isinstanceboolr5r4r7)r2r3Z_inrwr5r4r'r'r(r4+sB             zBBRegister._list_outputscs2|dkrt|tr|j|}tt|j|||S)Nr6r4r5r7)r6r4r5r7)r=r>r4rpr<rq)r2rrrsrt)rur'r(rq]s  zBBRegister._format_argcCs|dkr|j|SdS)Nr2)r4)r2rrr'r'r(rgs zBBRegister._gen_filename)r!r"r#r5r6rrr!r%r7r8r:r+r4rqrrr'r')rur(r<s$2 r<c@s~eZdZeddddddZeddddZdDZedd ed ddZej dedddZ ej dddZ ej deddgddZ dEZeded dd!d"Zeded#dd$d"Zeded%dd&d"Zeded dd!d"Zeded'dd(d"Zeded)dd*d"Zej ed+dd,d-Zej ed+dd.d-Zejed/dd0d-Zej d1d2d3Zejd4d5d6d7d8dZej d9d:d3Zed;dgd?d@Zej dAd>gdBd@ZdCS)FApplyVolTransformInputSpecTz--mov %sFz"Input volume you wish to transform)rrr&rrz Output volumez--o %s)rrr\ target_filetal fs_targetz --targ %szOutput template volume)rrr<rrz--talz,map to a sub FOV of MNI305 (with --reg only))rr<rrz--talres %.10fz#Resolution to sample when using tal)rrz--fstargreg_filez.use orig.mgz from subject in regfile as target)rr<rrrlta_file lta_inv_file fsl_reg_file xfm_reg_file reg_header mni_152_regsubjectz--reg %sz,tkRAS-to-tkRAS matrix (tkregister2 format))rr<rrrz--lta %szLinear Transform Array filez --lta-inv %sz LTA, invertz--fsl %sz$fslRAS-to-fslRAS matrix (FSL format)z--xfm %sz,ScannerRAS-to-ScannerRAS matrix (MNI format)z --regheaderz*ScannerRAS-to-ScannerRAS matrix = identity)r<rrrztarget MNI152 spacez--s %sz7set matrix = identity and use subject for any templateszsample from target to sourcez--inv)rrZtrilinrIrJz --interp %sz*Interpolation method ( or nearest)z+Do not resample; just change vox2ras matrixz --no-resamplez--m3z %szThis is the morph to be applied to the volume. Unless the morph is in mri/transforms (eg.: for talairach.m3z computed by reconall), you will need to specify the full path to this morph and use the --noDefM3zPath flag.z--noDefM3zPathm3z_filezTo be used with the m3z flag. Instructs the code not to look for them3z morph in the default location (SUBJECTS_DIR/subj/mri/transforms), but instead just use the path indicated in --m3z.)rrrz --inv-morphzfCompute and use the inverse of the non-linear morph to resample the input volume. To be used by --m3z.N)r@rArB)rCrDrErFrGrHrIrJ)r!r"r#r r1transformed_fileZ _targ_xorr@r r&rAr_Ztal_resolutionrBZ_reg_xorrCrDrErFrGrHrIrArJinverserBZinterpZ no_resamplerKZno_ded_m3z_pathZ invert_morphr'r'r'r(r?ns   r?c@seZdZedddZdS)ApplyVolTransformOutputSpecTz$Path to output file if used normally)rrN)r!r"r#r rLr'r'r'r(rNsrNc@s4eZdZdZdZeZeZddZ ddZ ddZ d S) ApplyVolTransformaUse FreeSurfer mri_vol2vol to apply a transform. Examples -------- >>> from nipype.interfaces.freesurfer import ApplyVolTransform >>> applyreg = ApplyVolTransform() >>> applyreg.inputs.source_file = 'structural.nii' >>> applyreg.inputs.reg_file = 'register.dat' >>> applyreg.inputs.transformed_file = 'struct_warped.nii' >>> applyreg.inputs.fs_target = True >>> applyreg.cmdline 'mri_vol2vol --fstarg --reg register.dat --mov structural.nii --o struct_warped.nii' Z mri_vol2volcCsV|jj}t|sR|jjdkr8|jjdkr.d}q@|jj}n|jj}t|tj dd}|S)NTzorig.mgzZ_warped)rvrw) r-rLrrMrBr@r1rr.r1)r2r{srcr'r'r( _get_outfiles   zApplyVolTransform._get_outfilecCs$|jj}tjj|j|d<|S)NrL)r+r,r.r/rzrQ)r2r3r'r'r(r4%s zApplyVolTransform._list_outputscCs|dkr|jSdS)NrL)rQ)r2rrr'r'r(r*szApplyVolTransform._gen_filenameN) r!r"r#r5r6r?r7rNr+rQr4rr'r'r'r(rOs rOc@seZdZedddddZedddddZedd dd Zejej ej ej d gd d dZ ej ddgddZ ej ddgddgdddZ ej ddgddddZej dddd Zd!S)"SmoothInputSpecTz source volumez--i %s)rrrrz'registers volume to surface anatomical z--reg %s)rrrrz output volumez--o %s)rrr\ proj_fracz#average a long normal min max deltaz--projfrac-avg %.2f %.2f %.2f)r<rrz/project frac of thickness a long surface normal proj_frac_avgz --projfrac %s)rr<rgrC num_iterszsurface FWHM in mmz --fwhm %f)lowrrr<rrr surface_fwhmz --niters %dz$number of iterations instead of fwhm)rVr<rrrz --vol-fwhm %fz#volume smoothing outside of surface)rVrrN)r!r"r#r rgrC smoothed_filer r?r_rTrSZRangerWrUZvol_fwhmr'r'r'r(rR0sBrRc@seZdZedddZdS)SmoothOutputSpecTzsmoothed input volume)rrN)r!r"r#r rXr'r'r'r(rYZsrYc@s,eZdZdZdZeZeZddZ ddZ dS)Smootha2Use FreeSurfer mris_volsmooth to smooth a volume This function smoothes cortical regions on a surface and non-cortical regions in volume. .. note:: Cortical voxels are mapped to the surface (3D->2D) and then the smoothed values from the surface are put back into the volume to fill the cortical ribbon. If data is smoothed with this algorithm, one has to be careful about how further processing is interpreted. Examples -------- >>> from nipype.interfaces.freesurfer import Smooth >>> smoothvol = Smooth(in_file='functional.nii', smoothed_file = 'foo_out.nii', reg_file='register.dat', surface_fwhm=10, vol_fwhm=6) >>> smoothvol.cmdline 'mris_volsmooth --i functional.nii --reg register.dat --o foo_out.nii --fwhm 10.000000 --vol-fwhm 6.000000' Zmris_volsmoothcCs:|jj}|jj}t|s.|j|jjdd}||d<|S)NZ_smooth)rwrX)r+r,r-rXr _gen_fnamerg)r2r3r{r'r'r(r4xs  zSmooth._list_outputscCs|dkr|j|SdS)NrX)r4)r2rrr'r'r(rs zSmooth._gen_filenameN) r!r"r#r5r6rRr7rYr+r4rr'r'r'r(rZ^s rZc@seZdZedddddZedddddZejdeddddd Zejej ed d d Z ejej ed dd Z ej ddd Z ej ddd Z eddddZejej eddd Zejej eddd Zejej eddd Zejej eddd Zejej eddd Zej d d!gdd"d#Zejd$d%gdd&d#Zej d'd(d Zej d)d*d Zej d+d,d Zejd-d.d Zejd/d0d Zejd1d2d Zejd3d4d Zejd5d6d Zej d7d8d Z ej d9d:d Z!edd;ddZ#ej d?d@d Z$ej dAdBd Z%dCS)DRobustRegisterInputSpecTz--mov %szvolume to be registered)rrrrz--dst %sz"target volume for the registrationz--lta %sz*registration file; either True or filename)defaultr rrz --warp %sz)registered image; either True or filename)rrz --weights %sz/weights image to write; either True or filenamez--iscalez>estimate intensity scale (recommended for unnormalized images)z --transonlyz!find 3 parameter translation onlyz --transformzuse initial transform on source)rrrz --halfmov %sz+write source volume mapped to halfway spacez --halfdst %sz+write target volume mapped to halfway spacez--halfweights %sz,write weights volume mapped to halfway spacez--halfmovlta %sz,write transform from source to halfway spacez--halfdstlta %sz,write transform from target to halfway spacez--satit outlier_senszauto-detect good sensitivity)rr<rrz --sat %.4f auto_sensz"set outlier sensitivity explicitlyz--leastsquaresz-use least squares instead of robust estimatorz--noinitzskip transform initz --initorientz@use moments for initial orient (recommended for stripped brains)z --maxit %dz%maximum # of times on each resolutionz --highit %dz$max # of times on highest resolutionz --epsit %.3fz$stop iterations when below thresholdz--subsample %dz.subsample if dimension is above threshold sizez --wlimit %.3fz"set maximal outlier limit in satitz --vox2voxz*output vox2vox matrix (default is RAS2RAS)z --nomultizwork on highest resolutionz --maskmov %sz image to mask source volume withz --maskdst %sz image to mask target volume withz --doubleprecz use double-precision intensitiesz --floattypezuse float intensitiesN)&r!r"r#r r1r@r r3r2r&r6 weights_fileZ est_int_scaleZ trans_onlyZ in_xfm_file half_source half_targ half_weightshalf_source_xfm half_targ_xfmr_r_r^Z least_squaresZno_initZ init_orientr@Zmax_iterationsZhigh_iterationsZiteration_threshZsubsample_threshZ outlier_limitZ write_vo2voxZno_multiZ mask_sourceZ mask_targetZ force_doubleZ force_floatr'r'r'r(r\s            r\c@sleZdZedddZedddZedddZedddZedddZedddZ edd dZ edd dZ d S) RobustRegisterOutputSpecTzoutput registration file)rrz&output image with registration appliedzimage of weights usedz$source image mapped to halfway spacez$target image mapped to halfway spacez%weights image mapped to halfway spacez3transform file to map source image to halfway spacez3transform file to map target image to halfway spaceN) r!r"r#r r2r6r`rarbrcrdrer'r'r'r(rf s       rfcs4eZdZdZdZeZeZfddZ ddZ Z S)RobustRegisteraPerform intramodal linear registration (translation and rotation) using robust statistics. Examples -------- >>> from nipype.interfaces.freesurfer import RobustRegister >>> reg = RobustRegister() >>> reg.inputs.source_file = 'structural.nii' >>> reg.inputs.target_file = 'T1.nii' >>> reg.inputs.auto_sens = True >>> reg.inputs.init_orient = True >>> reg.cmdline # doctest: +ELLIPSIS 'mri_robust_register --satit --initorient --lta .../structural_robustreg.lta --mov structural.nii --dst T1.nii' References ---------- Reuter, M, Rosas, HD, and Fischl, B, (2010). Highly Accurate Inverse Consistent Registration: A Robust Approach. Neuroimage 53(4) 1181-96. Zmri_robust_registercs6d }||kr"t|tr"|j|}tt|j|||S) Nr2r6r`rarbrcrdre)r2r6r`rarbrcrdre)r=r>r4rprgrq)r2rrrsrtoptions)rur'r(rq* s zRobustRegister._format_argc Cs|jj}tj}t|jj|jjd}tddddddddd }xft|j D]V\}}t |j|}|rN|dkrt ||d |d||dd||<qNtj j |||<qNW|S)N)rPtrgrP_robustreg.ltaF _robustregT_robustweights_halfwayri _halfweights_robustxfm.lta)r2r6r`rarbrcrdrerrr )rwrvrx)rPrjF)rPrkT)rPrlT)rPrmT)rirmT)rPrnT)rProF)riroF)r+r,r.r1rr-r1r@ritemsrrr/rz)r2r3cwdZpreficesZsufficesrrZsufftuprtr'r'r(r49 s.   zRobustRegister._list_outputs) r!r"r#r5r6r\r7rfr+rqr4rr'r')rur(rg s  rgc@szeZdZejedddddddZejejddZejej d dZ ejejd dZ ejeddd dZ e dddd dZdS)FitMSParamsInputSpecT)rz%sr z,list of FLASH images (must be in mgh format))rr[rrz(list of TRs of the input files (in msec))rz(list of TEs of the input files (in msec)z&list of flip angles of the input filesz4list of transform files to apply to each FLASH imagerzdirectory to store output in)rr[r\rNr]r^)r!r"r#r r`r in_filesr@tr_listr_te_list flip_listxfm_listrout_dirr'r'r'r(rrV srrc@s0eZdZedddZedddZedddZdS)FitMSParamsOutputSpecTz'image of estimated T1 relaxation values)rrz(image of estimated proton density valueszimage of estimated T2* valuesN)r!r"r#r t1_imagepd_image t2star_imager'r'r'r(ryj s  rycs<eZdZdZdZeZeZfddZ ddZ ddZ Z S) FitMSParamsa}Estimate tissue paramaters from a set of FLASH images. Examples -------- >>> from nipype.interfaces.freesurfer import FitMSParams >>> msfit = FitMSParams() >>> msfit.inputs.in_files = ['flash_05.mgz', 'flash_30.mgz'] >>> msfit.inputs.out_dir = 'flash_parameters' >>> msfit.cmdline 'mri_ms_fitparms flash_05.mgz flash_30.mgz flash_parameters' Zmri_ms_fitparmscs|dkrd}xt|D]\}}t|jjrDdj|d|jj|f}t|jjrjdj|d|jj|f}t|jjrdj|d|jj|f}t|jjrdj|d|jj|f}dj||f}qW|Stt |j |||S)Nrsrrz-tr %.1fz-te %.3fz-fa %.1fz-at %s) enumeraterr-rtr0rurvrwrpr}rq)r2rrrsrtrrfile)rur'r(rq s    zFitMSParams._format_argcCsf|jj}t|jjs$|jd}n|jj}tjj|d|d<tjj|d|d<tjj|d|d<|S)NrxzT1.mgzrzzPD.mgzr{z T2star.mgzr|) r+r,rr-rxrr.r/r0)r2r3rxr'r'r(r4 s   zFitMSParams._list_outputscCs|dkrtjSdS)Nrx)r.r1)r2rrr'r'r(r szFitMSParams._gen_filename) r!r"r#r5r6rrr7ryr+rqr4rrr'r')rur(r}q s   r}c@seZdZejddddZejddddd Zejdd dd d Zejdd d dd Z e ddddddZ e ddddddZ e ddddZ dS)SynthesizeFLASHInputSpecrz-wz=use a fixed weighting to generate optimal gray/white contrast)r[rrTr z%.2fzrepetition time (in msec))rr[rrrzflip angle (in degrees)z%.3fzecho time (in msec)z%szimage of T1 values)rrr[rrr/zimage of proton density valueszimage to write)r\rrN)r!r"r#r r&Zfixed_weightingr_ra flip_anglerbr rzr{rhr'r'r'r(r s&rc@seZdZedddZdS)SynthesizeFLASHOutputSpecTzsynthesized FLASH acquisition)rrN)r!r"r#r rhr'r'r'r(r src@s,eZdZdZdZeZeZddZ ddZ dS)SynthesizeFLASHaSynthesize a FLASH acquisition from T1 and proton density maps. Examples -------- >>> from nipype.interfaces.freesurfer import SynthesizeFLASH >>> syn = SynthesizeFLASH(tr=20, te=3, flip_angle=30) >>> syn.inputs.t1_image = 'T1.mgz' >>> syn.inputs.pd_image = 'PD.mgz' >>> syn.inputs.out_file = 'flash_30syn.mgz' >>> syn.cmdline 'mri_synthesize 20.00 30.00 3.000 T1.mgz PD.mgz flash_30syn.mgz' Zmri_synthesizecCsD|jj}t|jjr&|jj|d<n|jd|jjdd|d<|S)Nrhzsynth-flash_%02d.mgzr)rw)r+r,rr-rhr[r)r2r3r'r'r(r4 s   zSynthesizeFLASH._list_outputscCs|dkr|jdSdS)Nrh)r4)r2rrr'r'r(r s zSynthesizeFLASH._gen_filenameN) r!r"r#r5r6rr7rr+r4rr'r'r'r(r s   rc@seZdZedddddZeddgdddd$d Zejd dd d&dZejdd'dZ ejdddZ ej dddZ eddddZ eddddZejdd dZejd!d"dZd#S)(MNIBiasCorrectionInputSpecTz--i %sz>input volume. Input can be any format accepted by mri_convert.)rrrrz--o %srgz %s_outputFzAoutput volume. Output can be any format accepted by mri_convert. z;If the output format is COR, then the directory must exist.)r name_source name_templaterkeep_extensionrrz--n %dzRNumber of iterations to run nu_correct. Default is 4. This is the number of times zYthat nu_correct is repeated (ie, using the output from the previous run as the input for zGthe next). This is different than the -iterations option to nu_correct.)r rrz--proto-iters %dzOPasses Np as argument of the -iterations flag of nu_correct. This is different zPthan the --n flag above. Default is not to pass nu_correct the -iterations flag.)rrz --distance %dzN3 -distance optionz --no-rescalezAdo not rescale so that global mean of output == input global meanz --mask %szBbrainmask volume. Input can be any format accepted by mri_convert.)rrrz --uchar %sz1tal.xfm. Use mri_make_uchar instead of conformingz --stop %fzJConvergence threshold below which iteration stops (suggest 0.01 to 0.0001)z --shrink %dz2Shrink parameter for finer sampling (default is 4)Nz|output volume. Output can be any format accepted by mri_convert. If the output format is COR, then the directory must exist.zNumber of iterations to run nu_correct. Default is 4. This is the number of times that nu_correct is repeated (ie, using the output from the previous run as the input for zNumber of iterations to run nu_correct. Default is 4. This is the number of times that nu_correct is repeated (ie, using the output from the previous run as the input for the next). This is different than the -iterations option to nu_correct.zPasses Np as argument of the -iterations flag of nu_correct. This is different than the --n flag above. Default is not to pass nu_correct the -iterations flag.)r!r"r#r rgrhr r@Z iterationsZprotocol_iterationsZdistancer&Z no_rescalemask transformr_stopshrinkr'r'r'r(r sNrc@seZdZedddZdS)MNIBiasCorrectionOutputSpecTz output volume)rrN)r!r"r#r rhr'r'r'r(r src@seZdZdZdZeZeZdS)MNIBiasCorrectionaLWrapper for nu_correct, a program from the Montreal Neurological Insitute (MNI) used for correcting intensity non-uniformity (ie, bias fields). You must have the MNI software installed on your system to run this. See [www.bic.mni.mcgill.ca/software/N3] for more info. mri_nu_correct.mni uses float internally instead of uchar. It also rescales the output so that the global mean is the same as that of the input. These two changes are linked and can be turned off with --no-float Examples -------- >>> from nipype.interfaces.freesurfer import MNIBiasCorrection >>> correct = MNIBiasCorrection() >>> correct.inputs.in_file = "norm.mgz" >>> correct.inputs.iterations = 6 >>> correct.inputs.protocol_iterations = 1000 >>> correct.inputs.distance = 50 >>> correct.cmdline 'mri_nu_correct.mni --distance 50 --i norm.mgz --n 6 --o norm_output.mgz --proto-iters 1000' References ---------- [http://freesurfer.net/fswiki/mri_nu_correct.mni] [http://www.bic.mni.mcgill.ca/software/N3] [https://github.com/BIC-MNI/N3] zmri_nu_correct.mniN) r!r"r#r5r6rr7rr+r'r'r'r(r$ src @sbeZdZeddddddZeddddddd d Zejd d d ZedddddZ edddddZ dS)WatershedSkullStripInputSpecz%sTr z input volume)rrrr[rzbrainmask.auto.mgzFrz output volume)rrrr[r rz-T1z-specify T1 input volume (T1 grey value = 110))rrz-brain_atlas %srr)rrr[rrZ undocumentedNr]r^) r!r"r#r rgrhr r&r+Z brain_atlasrr'r'r'r(rF src@seZdZedddZdS)WatershedSkullStripOutputSpecFzskull stripped brain volume)rrN)r!r"r#r rhr'r'r'r(rZ src@s$eZdZdZdZeZeZddZ dS)WatershedSkullStripaThis program strips skull and other outer non-brain tissue and produces the brain volume from T1 volume or the scanned volume. The "watershed" segmentation algorithm was used to dertermine the intensity values for white matter, grey matter, and CSF. A force field was then used to fit a spherical surface to the brain. The shape of the surface fit was then evaluated against a previously derived template. The default parameters are: -w 0.82 -b 0.32 -h 10 -seedpt -ta -wta (Segonne 2004) Examples ======== >>> from nipype.interfaces.freesurfer import WatershedSkullStrip >>> skullstrip = WatershedSkullStrip() >>> skullstrip.inputs.in_file = "T1.mgz" >>> skullstrip.inputs.t1 = True >>> skullstrip.inputs.transform = "transforms/talairach_with_skull.lta" >>> skullstrip.inputs.out_file = "brainmask.auto.mgz" >>> skullstrip.cmdline 'mri_watershed -T1 transforms/talairach_with_skull.lta T1.mgz brainmask.auto.mgz' rcCs$|jj}tjj|jj|d<|S)Nrh)r+r,r.r/rzr-rh)r2r3r'r'r(r4| s z!WatershedSkullStrip._list_outputsN) r!r"r#r5r6rr7rr+r4r'r'r'r(r^ s rc @sleZdZeddddddZedddgdd dd d Zejd d dZeddddZ eddddZ edddZ dS)NormalizeInputSpecz%sTr zThe input file for Normalize)rrrr[rrrgz%s_normFzThe output file for Normalize)rr[rrrrrz-g %dz+use max intensity/mm gradient g (default=1))rrz-mask %sz!The input mask file for Normalize)rrrz-aseg %sz$The input segmentation for Normalizez/Tranform file from the header of the input file)rrNr]r^) r!r"r#r rgrhr r@Zgradientrrrr'r'r'r(r s,   rc@seZdZedddZdS)NormalizeOutputSpecFzThe output file for Normalize)rrN)r!r"r#r rhr'r'r'r(r src@s$eZdZdZdZeZeZddZ dS) Normalizea Normalize the white-matter, optionally based on control points. The input volume is converted into a new volume where white matter image values all range around 110. Examples ======== >>> from nipype.interfaces import freesurfer >>> normalize = freesurfer.Normalize() >>> normalize.inputs.in_file = "T1.mgz" >>> normalize.inputs.gradient = 1 >>> normalize.cmdline 'mri_normalize -g 1 T1.mgz T1_norm.mgz' rcCs$|jj}tjj|jj|d<|S)Nrh)r+r,r.r/rzr-rh)r2r3r'r'r(r4 s zNormalize._list_outputsN) r!r"r#r5r6rr7rr+r4r'r'r'r(r s rc @seZdZeddddddZedddgdd dd d Zeddddd dZeddddddZeddddZedddZ edddZ dS)CANormalizeInputSpecz%sTrzThe input file for CANormalize)rrrr[rrrgz%s_normFzThe output file for CANormalize)rr[rrrrrrzThe atlas file in gca formatr zThe tranform file in lta formatz-mask %szSpecifies volume to use as mask)rrrz-c %sz'File name for the output control points)rrz-long %sz1undocumented flag used in longitudinal processingNrr^rr]) r!r"r#r rgrhZatlasrrcontrol_pointsZ long_filer'r'r'r(r s> rc@s$eZdZedddZedddZdS)CANormalizeOutputSpecFzThe output file for Normalize)rrz'The output control points for NormalizeN)r!r"r#r rhrr'r'r'r(r s rc@s$eZdZdZdZeZeZddZ dS) CANormalizeaThis program creates a normalized volume using the brain volume and an input gca file. See Also -------- For complete details, see the `FS Documentation `__. Examples -------- >>> from nipype.interfaces import freesurfer >>> ca_normalize = freesurfer.CANormalize() >>> ca_normalize.inputs.in_file = "T1.mgz" >>> ca_normalize.inputs.atlas = "atlas.nii.gz" # in practice use .gca atlases >>> ca_normalize.inputs.transform = "trans.mat" # in practice use .lta transforms >>> ca_normalize.cmdline 'mri_ca_normalize T1.mgz atlas.nii.gz trans.mat T1_norm.mgz' rcCs8|jj}tjj|jj|d<tjj|jj|d<|S)Nrhr)r+r,r.r/rzr-rhr)r2r3r'r'r(r4 s zCANormalize._list_outputsN) r!r"r#r5r6rr7rr+r4r'r'r'r(r s rc@seZdZedddd"ddZedd#dddZeddd$d d Zed dd dZej dd%ddZ ej dddZ eddddZ ej dddZejdddZejdddZeeddd ddZd!S)&CARegisterInputSpecz%sTrzThe input volume for CARegister)rrrr[rrz The output volume for CARegister)rr[r\rr zThe template file in gca format)rrr[rz-mask %szSpecifies volume to use as mask)rrrz-invert-and-saverzXInvert and save the .m3z multi-dimensional talaraich transform to x, y, and z .mgz files)rr[rz-nobigventricleszNo big ventricles)rrz-T %sz!Specifies transform in lta formatz -align-%sz#Specifies when to perform alignmentz -levels %dzPdefines how many surrounding voxels will be used in interpolations, default is 6z-A %dz1undocumented flag used in longitudinal processingF)rz-l %sNrr^r]r)r!r"r#r rgrhtemplaterr r&Zinvert_and_saveno_big_ventriclesrStringalignr@ZlevelsArl_filesr'r'r'r(r s:   rc@seZdZedddZdS)CARegisterOutputSpecFzThe output file for CARegister)rrN)r!r"r#r rhr'r'r'r(r: srcs<eZdZdZdZeZeZfddZ ddZ ddZ Z S) CARegisteraGenerates a multi-dimensional talairach transform from a gca file and talairach.lta file See Also -------- For complete details, see the `FS Documentation `__ Examples -------- >>> from nipype.interfaces import freesurfer >>> ca_register = freesurfer.CARegister() >>> ca_register.inputs.in_file = "norm.mgz" >>> ca_register.inputs.out_file = "talairach.m3z" >>> ca_register.cmdline 'mri_ca_register norm.mgz talairach.m3z' rcs2|dkrt|dkr|jdtt|j|||S)Nrrzidentity.nofile)rrrprrq)r2rrrsrt)rur'r(rqU s zCARegister._format_argcCs|dkrtjjdSdS)Nrhz talairach.m3z)r.r/rz)r2rrr'r'r(r[Z s zCARegister._gen_fnamecCs$|jj}tjj|jj|d<|S)Nrh)r+r,r.r/rzr-rh)r2r3r'r'r(r4_ s zCARegister._list_outputs) r!r"r#r5r6rr7rr+rqr[r4rr'r')rur(r> s rc@seZdZeddddddZedd ddddZedd!ddd dZedd"ddd dZed dddZed dddZ e j dddZ e j dddZ e jdddZe je je jdddZeddddZeddddZdS)#CALabelInputSpecz%srTzInput volume for CALabel)rr[rrrrFzOutput file for CALabelrzInput transform for CALabelr zInput template for CALabelz-r %szset input volume)rrrz=input label intensities file(used in longitudinal processing)z-nobigventricleszNo big ventricles)rrz-alignz Align CALabelz -prior %.1fzPrior for CALabelz-relabel_unlikely %d %.1fzVReclassify voxels at least some std devs from the mean using some size Gaussian windowz-l %szSUndocumented flag. Autorecon3 uses ../label/{hemisphere}.cortex.label as input filez-aseg %szHUndocumented flag. Autorecon3 uses ../mri/aseg.presurf.mgz as input fileNrr^rr])r!r"r#r rgrhrrZin_volZ intensitiesr r&rrr_Zpriorr?r@Zrelabel_unlikelylabelasegr'r'r'r(re sZrc@seZdZedddZdS)CALabelOutputSpecFzOutput volume from CALabel)rrN)r!r"r#r rhr'r'r'r(r src@s$eZdZdZdZeZeZddZ dS)CALabelamLabel subcortical structures based in GCA model. See Also -------- For complete details, see the `FS Documentation `__ Examples -------- >>> from nipype.interfaces import freesurfer >>> ca_label = freesurfer.CALabel() >>> ca_label.inputs.in_file = "norm.mgz" >>> ca_label.inputs.out_file = "out.mgz" >>> ca_label.inputs.transform = "trans.mat" >>> ca_label.inputs.template = "Template_6.nii" # in practice use .gcs extension >>> ca_label.cmdline 'mri_ca_label norm.mgz trans.mat Template_6.nii out.mgz' rcCs$|jj}tjj|jj|d<|S)Nrh)r+r,r.r/rzr-rh)r2r3r'r'r(r4 s zCALabel._list_outputsN) r!r"r#r5r6rr7rr+r4r'r'r'r(r s rc @seZdZejddd(ddddZejdddd)dd d Zedd*ddd dZ edd+ddddZ eddddZ eddddZ eddddZ edd,ddgdddddZeddddZeddddZejd d!d"Zejd.d&Zd'S)/MRIsCALabelInputSpecrz%srTzSubject name or ID)rr[r rrrrrzHemisphere ('lh' or 'rh'))rr[rrrzInput canonical surface file)rr[rrrr zClassifier array input filez$implicit input {hemisphere}.smoothwm)rrrz implicit input {hemisphere}.curvz implicit input {hemisphere}.sulcrF hemispherez%s.aparc.annotzAnnotated surface output file)rr[rrrrrrz-l %szSUndocumented flag. Autorecon3 uses ../label/{hemisphere}.cortex.label as input file)rrrz-aseg %szHUndocumented flag. Autorecon3 uses ../mri/aseg.presurf.mgz as input filez-seed %dr)rrz)Copies implicit inputs to node directory z'and creates a temp subjects_directory. zUse this when running as a node)rNrrr]r^zPCopies implicit inputs to node directory and creates a temp subjects_directory. zoCopies implicit inputs to node directory and creates a temp subjects_directory. Use this when running as a node)r!r"r#r rrrBrr canonsurf classifiersmoothwmcurvsulcrhrrr@seedr& copy_inputsr'r'r'r(r sd rc@seZdZedddZdS)MRIsCALabelOutputSpecFzOutput volume from MRIsCALabel)rrN)r!r"r#r rhr'r'r'r(r srcs4eZdZdZdZeZeZfddZ ddZ Z S) MRIsCALabelaV For a single subject, produces an annotation file, in which each cortical surface vertex is assigned a neuroanatomical label.This automatic procedure employs data from a previously-prepared atlas file. An atlas file is created from a training set, capturing region data manually drawn by neuroanatomists combined with statistics on variability correlated to geometric information derived from the cortical model (sulcus and curvature). Besides the atlases provided with FreeSurfer, new ones can be prepared using mris_ca_train). Examples ======== >>> from nipype.interfaces import freesurfer >>> ca_label = freesurfer.MRIsCALabel() >>> ca_label.inputs.subject_id = "test" >>> ca_label.inputs.hemisphere = "lh" >>> ca_label.inputs.canonsurf = "lh.pial" >>> ca_label.inputs.curv = "lh.pial" >>> ca_label.inputs.sulc = "lh.pial" >>> ca_label.inputs.classifier = "im1.nii" # in pracice, use .gcs extension >>> ca_label.inputs.smoothwm = "lh.pial" >>> ca_label.cmdline 'mris_ca_label test lh lh.pial im1.nii lh.aparc.annot' rc s|jjrtj|j_d|kr(|jj|d<t||jjddt||jjddj|jj dt||jj ddj|jj dt||jj ddj|jj dtj j |jj|jjd}tj j|stj|tt|jf|S) NrZsurf)folderz {0}.smoothwm)rrz{0}.curvz{0}.sulcr)r-rr.r1rrrrr rrrr/r0rrmakedirsrprrun)r2r-Z label_dir)rur'r(r+ s2    zMRIsCALabel.runcCs>|jj}tjj|jj}tjj|jj|jj d||d<|S)Nrrh) r+r,r.r/rr-rhr0rr)r2r3Z out_basenamer'r'r(r4M s  zMRIsCALabel._list_outputs) r!r"r#r5r6rr7rr+rr4rr'r')rur(r s  "rc @steZdZedddddZeddddZeddd gd ddd d Zed ddddZej dddddddZ ej ddZ dS)SegmentCCInputSpecz-aseg %sTz/Input aseg file to read from subjects directory)rrrrz2Required undocumented input {subject}/mri/norm.mgz)rrrz-o %sFrgz %s.auto.mgzz#Filename to write aseg including CC)rrrrrrrz-lta %sz(Global filepath for writing rotation ltarz%srz Subject name)rrr[r rz'If running as a node, set this to True.z+This will copy the input files to the node z directory.)rNr^zRIf running as a node, set this to True.This will copy the input files to the node z\If running as a node, set this to True.This will copy the input files to the node directory.) r!r"r#r rgin_normrh out_rotationr rrr&rr'r'r'r(rV s>rc@s$eZdZedddZedddZdS)SegmentCCOutputSpecFz-Output segmentation uncluding corpus collosum)rrzOutput lta rotation fileN)r!r"r#r rhrr'r'r'r(r s rcsNeZdZdZdZeZeZfddZ ddZ fddZ d fd d Z Z S) SegmentCCa This program segments the corpus callosum into five separate labels in the subcortical segmentation volume 'aseg.mgz'. The divisions of the cc are equally spaced in terms of distance along the primary eigendirection (pretty much the long axis) of the cc. The lateral extent can be changed with the -T parameter, where is the distance off the midline (so -T 1 would result in the who CC being 3mm thick). The default is 2 so it's 5mm thick. The aseg.stats values should be volume. Examples ======== >>> from nipype.interfaces import freesurfer >>> SegmentCC_node = freesurfer.SegmentCC() >>> SegmentCC_node.inputs.in_file = "aseg.mgz" >>> SegmentCC_node.inputs.in_norm = "norm.mgz" >>> SegmentCC_node.inputs.out_rotation = "cc.lta" >>> SegmentCC_node.inputs.subject_id = "test" >>> SegmentCC_node.cmdline 'mri_cc -aseg aseg.mgz -o aseg.auto.mgz -lta cc.lta test' Zmri_cccs2|dkrtjj|}|j|Stt|j|||S)Nrgrrh)rgrrh)r.r/rrrprrq)r2rrrsrtr)rur'r(rq s  zSegmentCC._format_argcCs8|jj}tjj|jj|d<tjj|jj|d<|S)Nrhr)r+r,r.r/rzr-rhr)r2r3r'r'r(r4 s zSegmentCC._list_outputsc sb|jjrPtj|j_d|kr(|jj|d<x&|jj|jjgD]}t||ddq:Wtt |j f|S)Nrr)r) r-rr.r1rrgrrrprr)r2r-Z originalfile)rur'r(r s  z SegmentCC.runNc s|j}xdD]}||}tjj|stjj|}t|jjrVtjj|jj|jj }ntjjtj |jj }|dkrtjj|d|}n |dkrtjj|dd|}nd}|rtjj|rtjj tjj |stj tjj |tj||qWtt|j||S)NrhrrZ transforms)rhr)r4r.r/isfilerrr-rr0rr1rdirnamershutilmoverpraggregate_outputs) r2ZruntimeZneeded_outputsZpredicted_outputsrrrhZout_baserZout_tmp)rur'r(r s&    zSegmentCC.aggregate_outputs)NN)r!r"r#r5r6rr7rr+rqr4rrrr'r')rur(r s  rc@s0eZdZedddd ddZedddd ddZd S) SegmentWMInputSpecz%sTr zInput file for SegmentWM)rrrr[rFrz*File to be written as output for SegmentWMNr]r^)r!r"r#r rgrhr'r'r'r(r src@seZdZedddZdS)SegmentWMOutputSpecFz Output white matter segmentation)rrN)r!r"r#r rhr'r'r'r(r src@s$eZdZdZdZeZeZddZ dS) SegmentWMa This program segments white matter from the input volume. The input volume should be normalized such that white matter voxels are ~110-valued, and the volume is conformed to 256^3. Examples ======== >>> from nipype.interfaces import freesurfer >>> SegmentWM_node = freesurfer.SegmentWM() >>> SegmentWM_node.inputs.in_file = "norm.mgz" >>> SegmentWM_node.inputs.out_file = "wm.seg.mgz" >>> SegmentWM_node.cmdline 'mri_segment norm.mgz wm.seg.mgz' rcCs$|jj}tjj|jj|d<|S)Nrh)r+r,r.r/rzr-rh)r2r3r'r'r(r4 s zSegmentWM._list_outputsN) r!r"r#r5r6rr7rr+r4r'r'r'r(r s rc@sbeZdZeddddddZeddddddZeddddd dZedddd d dZej d ddZ dS)EditWMwithAsegInputSpecz%srTz$Input white matter segmentation file)rr[rrrrzInput brain/T1 filer zInput presurf segmentation filerFzFile to be written as outputz-keep-inz#Keep edits as found in input volume)rrNrrr]r^) r!r"r#r rgZ brain_fileZseg_filerhr r&Zkeep_inr'r'r'r(r s2rc@seZdZedddZdS)EditWMwithAsegOutputSpecFzOutput edited WM file)rrN)r!r"r#r rhr'r'r'r(r' src@s$eZdZdZdZeZeZddZ dS)EditWMwithAsega Edits a wm file using a segmentation Examples ======== >>> from nipype.interfaces.freesurfer import EditWMwithAseg >>> editwm = EditWMwithAseg() >>> editwm.inputs.in_file = "T1.mgz" >>> editwm.inputs.brain_file = "norm.mgz" >>> editwm.inputs.seg_file = "aseg.mgz" >>> editwm.inputs.out_file = "wm.asegedit.mgz" >>> editwm.inputs.keep_in = True >>> editwm.cmdline 'mri_edit_wm_with_aseg -keep-in T1.mgz norm.mgz aseg.mgz wm.asegedit.mgz' rcCs$|jj}tjj|jj|d<|S)Nrh)r+r,r.r/rzr-rh)r2r3r'r'r(r4@ s zEditWMwithAseg._list_outputsN) r!r"r#r5r6rr7rr+r4r'r'r'r(r+ s rc @seZdZedddd'ddZejeddddd(dd d Zed)dd d gddddZej dddZ ej dddZ ej dddZ ej dddddZeddd*dgdd Zeddd+d"gd#d Zejd$d%dZd&S),ConcatenateLTAInputSpecTz%srzmaps some src1 to dst1)rrrr[r)rzidentity.nofiler zmaps dst1(src2) to dst2)rr[rrrFin_lta1z %s_concatz/the combined LTA maps: src1 to dst2 = LTA2*LTA1)r[rrrrrrz-invert1z!invert in_lta1 before applying it)rrz-invert2z!invert in_lta2 before applying itz -invertoutzinvert output LTAVOX2VOXRAS2RASz -out_type %dzset final LTA typez-tal %srtal_template_filez9if in_lta2 is talairach.xfm, specify source for talairach)rrr[rrrtal_source_filez;if in_lta2 is talairach.xfm, specify template for talairachz -subject %szset subject in output LTANrr]r^rr)r!r"r#r rr r3Zin_lta2rhr&Zinvert_1Zinvert_2Z invert_outrBrerrrArJr'r'r'r(rF sNrc@seZdZedddZdS)ConcatenateLTAOutputSpecFz/the combined LTA maps: src1 to dst2 = LTA2*LTA1)rrN)r!r"r#r rhr'r'r'r(r~ srcs,eZdZdZdZeZeZfddZ Z S)ConcatenateLTAaConcatenates two consecutive LTA transformations into one overall transformation Out = LTA2*LTA1 Examples -------- >>> from nipype.interfaces.freesurfer import ConcatenateLTA >>> conc_lta = ConcatenateLTA() >>> conc_lta.inputs.in_lta1 = 'lta1.lta' >>> conc_lta.inputs.in_lta2 = 'lta2.lta' >>> conc_lta.cmdline 'mri_concatenate_lta lta1.lta lta2.lta lta1_concat.lta' You can use 'identity.nofile' as the filename for in_lta2, e.g.: >>> conc_lta.inputs.in_lta2 = 'identity.nofile' >>> conc_lta.inputs.invert_1 = True >>> conc_lta.inputs.out_file = 'inv1.lta' >>> conc_lta.cmdline 'mri_concatenate_lta -invert1 lta1.lta identity.nofile inv1.lta' To create a RAS2RAS transform: >>> conc_lta.inputs.out_type = 'RAS2RAS' >>> conc_lta.cmdline 'mri_concatenate_lta -invert1 -out_type 1 lta1.lta identity.nofile inv1.lta' Zmri_concatenate_ltacs*|dkrddd|}tt|j|||S)Nrerr)rr)rprrq)r2rrrsrt)rur'r(rq szConcatenateLTA._format_arg) r!r"r#r5r6rr7rr+rqrr'r')rur(r s r)pr5r.os.pathr/rrrrnumpyrZnibabelrrrrZutils.filemaniprrior baser r r rrrrrrrrrrrutilsrZ __docformat__ getLoggerrr!r"Z FSVersionrr)r*r8rCrDrlrmrrrrrrr+rrr%r8r:r<r?rNrOrRrYrZr\rfrgrrryr}rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr'r'r'r(s    ,    7Mh$k(=-\ [-*(zF3#7"$!) +'>BJ*Q!8