3 d@sdZddlZddljZddlZddlZddlm Z m Z m Z ddl m Z mZmZmZmZmZmZmZmZmZmZddlmZdd l mZmZmZmZmZmZGd d d eZ Gd d d eZ!GdddeZ"GdddeZ#GdddeZ$GdddeZ%GdddeZ&Gddde Z'GdddeZ(GdddeZ)GdddeZ*Gd d!d!eZ+Gd"d#d#eZ,Gd$d%d%eZ-Gd&d'd'eZ.Gd(d)d)eZ/Gd*d+d+eZ0Gd,d-d-eZ1Gd.d/d/e Z2Gd0d1d1eZ3Gd2d3d3eZ4Gd4d5d5eZ5Gd6d7d7eZ6Gd8d9d9eZ7Gd:d;d;eZ8Gdd?d?eZ:Gd@dAdAeZ;GdBdCdCeZGdHdIdIe Z?GdJdKdKeZ@GdLdMdMeZAGdNdOdOeZBGdPdQdQeZCGdRdSdSeZDGdTdUdUeZEGdVdWdWeZFGdXdYdYeZGGdZd[d[eZHGd\d]d]eZIGd^d_d_eZJGd`dadaeZKGdbdcdceZLGdddedeeZMGdfdgdgeZNGdhdidie ZOGdjdkdkeZPGdldmdmeZQGdndodoeZRGdpdqdqeZSGdrdsdseZTGdtdudueZUGdvdwdwe ZVGdxdydyeZWGdzd{d{e ZXGd|d}d}eZYGd~ddeZZGdddeZ[GdddeZ\GdddeZ]GdddeZ^GdddeZ_GdddeZ`GdddeZaGdddeZbGdddeZcGdddeZdGdddeZeGdddeZfGdddeZgGdddeZhGdddeZiGdddeZjGdddeZkGdddeZlGddde ZmGdddeZnGdddeZoGdddeZpGdddeZqGdddeZrGdddeZsGdddeZtGdddeZudS)zAFNI utility interfaces.N) load_json save_jsonsplit_filename) CommandLineInputSpec CommandLine Directory TraitedSpectraits isdefinedFileInputMultiObjectInputMultiPath UndefinedStr)BibTeX)AFNICommandBase AFNICommandAFNICommandInputSpecAFNICommandOutputSpecAFNIPythonCommandInputSpecAFNIPythonCommandc@sleZdZedddddddZedddddddZed d dd Zejd ddZ ejdddZ ejdddZ dS)ABoverlapInputSpecz input file Az%srTF)descargstrposition mandatoryexistscopyfilez input file Brzcollect output to a filez |& tee %sr)rrrz consider input datasets as masksz -no_automask)rrz5be as quiet as possible (without being entirely mute)z-quietz+print out some progress reports (to stderr)z-verbN) __name__ __module__ __qualname__r in_file_a in_file_bout_filer BoolZ no_automaskquietverbr-r->/tmp/pip-build-7vycvbft/nipype/nipype/interfaces/afni/utils.pyr#s*  rc@seZdZdZdZeZeZdS) ABoverlapaOutput (to screen) is a count of various things about how the automasks of datasets A and B overlap or don't overlap. For complete details, see the `3dABoverlap Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> aboverlap = afni.ABoverlap() >>> aboverlap.inputs.in_file_a = 'functional.nii' >>> aboverlap.inputs.in_file_b = 'structural.nii' >>> aboverlap.inputs.out_file = 'out.mask_ae_overlap.txt' >>> aboverlap.cmdline '3dABoverlap functional.nii structural.nii |& tee out.mask_ae_overlap.txt' >>> res = aboverlap.run() # doctest: +SKIP Z 3dABoverlapN) r$r%r&__doc___cmdr input_specr output_specr-r-r-r.r/@sr/c@seZdZedddddddZeddd d dd Zejd d dZejdddZ ejdddZ ejdddgdZ ejdddgdZ dS)AFNItoNIFTIInputSpeczinput file to 3dAFNItoNIFTIz%srTF)rrrrrr z%s.niizoutput image file namez -prefix %sin_file) name_templaterr name_source hash_fileszForce the output dataset to be 32-bit floats. This option should be used when the input AFNI dataset has different float scale factors for different sub-bricks, an option that NIfTI-1.1 does not support.z-float)rrzDo NOT write an AFNI extension field into the output file. Only use this option if needed. You can also use the 'nifti_tool' program to strip extensions from a file.z-purezlWhen writing the AFNI extension field, remove text notes that might contain subject identifying information.z-denotez5Give the new dataset the input datasets AFNI ID code.z-oldidnewid)rrxorzRGive the new dataset a new AFNI ID code, to distinguish it from the input dataset.z-newidoldidNr#) r$r%r&r r5r)r r*Zfloat_ZpureZdenoter;r9r-r-r-r.r4Ys< r4cs6eZdZdZdZeZeZdddZ fddZ Z S) AFNItoNIFTIacConverts AFNI format files to NIFTI format. This can also convert 2D or 1D data, which you can numpy.squeeze() to remove extra dimensions. For complete details, see the `3dAFNItoNIFTI Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> a2n = afni.AFNItoNIFTI() >>> a2n.inputs.in_file = 'afni_output.3D' >>> a2n.inputs.out_file = 'afni_output.nii' >>> a2n.cmdline '3dAFNItoNIFTI -prefix afni_output.nii afni_output.3D' >>> res = a2n.run() # doctest: +SKIP Z 3dAFNItoNIFTINcCs4t|\}}}|jdkr"|d7}tjj|||S)N.nii.nii.gz.1d.1D)r=r>r?r@)rlowerospathjoin)selfvaluenamerCbaseextr-r-r._overload_extensions zAFNItoNIFTI._overload_extensioncstjjtt|j|S)N)rBrCabspathsuperr< _gen_filename)rErG) __class__r-r.rMszAFNItoNIFTI._gen_filename)N) r$r%r&r0r1r4r2rr3rJrM __classcell__r-r-)rNr.r<s  r<c@sHeZdZeddddddZejdddZed d d d Zej d ddZ dS)AutoboxInputSpecTz -input %sz input fileF)rrrrr z-npad %dz1Number of extra voxels to pad on each side of box)rrz -prefix %sr5z %s_autobox)rr7r6z-noclustzDon't do any clustering to find box. Any non-zero voxel will be preserved in the cropped volume. The default method uses some clustering to find the cropping box, and will clip off small isolated blobs.N) r$r%r&r r5r Intpaddingr)r*Z no_clusteringr-r-r-r.rPs  rPc@sFeZdZejZejZejZejZejZ ejZ e ddZ dS)AutoboxOutputSpecz output file)rN) r$r%r&r rQZx_minZx_maxZy_minZy_maxZz_minZz_maxr r)r-r-r-r.rSsrScs.eZdZdZdZeZeZdfdd Z Z S)Autoboxa2Computes size of a box that fits around the volume. Also can be used to crop the volume to that box. For complete details, see the `3dAutobox Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> abox = afni.Autobox() >>> abox.inputs.in_file = 'structural.nii' >>> abox.inputs.padding = 5 >>> abox.cmdline '3dAutobox -input structural.nii -prefix structural_autobox -npad 5' >>> res = abox.run() # doctest: +SKIP Z 3dAutoboxNcsftt|j||}d}xJ|jjdD]:}tj||}|r$|j|jffddj Dq$W|S)Nzvx=(?P-?\d+)\.\.(?P-?\d+) y=(?P-?\d+)\.\.(?P-?\d+) z=(?P-?\d+)\.\.(?P-?\d+) csi|]}t||qSr-)int).0k)dr-r. sz-Autobox.aggregate_outputs..) rLrTaggregate_outputsstderrsplitresearch groupdictZ trait_setkeys)rEruntimeneeded_outputsoutputspatternlinem)rN)rYr.r[s "zAutobox.aggregate_outputs)NN) r$r%r&r0r1rPr2rSr3r[rOr-r-)rNr.rTs rTc@seZdZeddddddZeddddd Zejd d dd Zejd ddZ ejdddZ ejdddZ ejdddZ ejdddZ ejejejejdddZdS)BrickStatInputSpeczinput file to 3dmaskavez%srT)rrrrrz7-mask dset = use dset as mask to include/exclude voxelsz-mask %sr)rrrrz"print the minimum value in datasetz-min)rrrz5read the whole dataset to find the min and max valuesz-slow)rrz&print the maximum value in the datasetz-maxz#print the mean value in the datasetz-meanz&print the sum of values in the datasetz-sumz!print the variance in the datasetz-varzxp0 ps p1 write the percentile values starting at p0% and ending at p1% at a step of ps%. only one sub-brick is accepted.z-percentile %.3f %.3f %.3fNr#)r$r%r&r r5maskr r*minZslowmaxmeansumvarTupleFloatZ percentiler-r-r-r.rhs2  rhc@seZdZejddZdS)BrickStatOutputSpecoutput)rN)r$r%r&r rpmin_valr-r-r-r.rqsrqc@s&eZdZdZdZeZeZdddZ dS) BrickStatayComputes maximum and/or minimum voxel values of an input dataset. TODO Add optional arguments. For complete details, see the `3dBrickStat Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> brickstat = afni.BrickStat() >>> brickstat.inputs.in_file = 'functional.nii' >>> brickstat.inputs.mask = 'skeleton_mask.nii.gz' >>> brickstat.inputs.min = True >>> brickstat.cmdline '3dBrickStat -min -mask skeleton_mask.nii.gz functional.nii' >>> res = brickstat.run() # doctest: +SKIP Z 3dBrickStatNc Cs|j}tjjtjd}|dkrPyt|d}Wqtk rL|jjSXng}xV|j j dD]F}|rb|j }t |dkr|j dd|Dqb|j dd|DqbWt |dkr|d}t|t|d ||_|S) Nzstat_result.jsonstatrUrcSsg|] }t|qSr-)float)rWvalr-r-r. Asz/BrickStat.aggregate_outputs..cSsg|] }t|qSr-)rv)rWrwr-r-r.rxCsr)ru)Z_outputsrBrCrDgetcwdrIOErrorrunrdstdoutr]lenappendextendrdictrs)rErbrcrdoutfilersrfvaluesr-r-r.r[0s&  zBrickStat.aggregate_outputs)NN) r$r%r&r0r1rhr2rqr3r[r-r-r-r.rts rtc@sJeZdZejejedddejddfddddd d d Zed d dZ dS)BucketInputSpecTF)rr z'%s')rz%s%s)Zartstrrz%saList of tuples of input datasets and subbrick selection strings as described in more detail in the following afni help string Input dataset specified using one of these forms: ``prefix+view``, ``prefix+view.HEAD``, or ``prefix+view.BRIK``. You can also add a sub-brick selection list after the end of the dataset name. This allows only a subset of the sub-bricks to be included into the output (by default, all of the input dataset is copied into the output). A sub-brick selection list looks like one of the following forms:: fred+orig[5] ==> use only sub-brick #5 fred+orig[5,9,17] ==> use #5, #9, and #17 fred+orig[5..8] or [5-8] ==> use #5, #6, #7, and #8 fred+orig[5..13(2)] or [5-13(2)] ==> use #5, #7, #9, #11, and #13 Sub-brick indexes start at 0. You can use the character '$' to indicate the last sub-brick in a dataset; for example, you can select every third sub-brick by using the selection list ``fred+orig[0..$(3)]`` N.B.: The sub-bricks are output in the order specified, which may not be the order in the original datasets. For example, using ``fred+orig[0..$(2),1..$(2)]`` will cause the sub-bricks in fred+orig to be output into the new dataset in an interleaved fashion. Using ``fred+orig[$..0]`` will reverse the order of the sub-bricks in the output. N.B.: Bucket datasets have multiple sub-bricks, but do NOT have a time dimension. You can input sub-bricks from a 3D+time dataset into a bucket dataset. You can use the '3dinfo' program to see how many sub-bricks a 3D+time or a bucket dataset contains. N.B.: In non-bucket functional datasets (like the 'fico' datasets output by FIM, or the 'fitt' datasets output by 3dttest), sub-brick ``[0]`` is the 'intensity' and sub-brick [1] is the statistical parameter used as a threshold. Thus, to create a bucket dataset using the intensity from dataset A and the threshold from dataset B, and calling the output dataset C, you would type:: 3dbucket -prefix C -fbuc 'A+orig[0]' -fbuc 'B+orig[1] )rrrrz -prefix %sZbuck)rr6Nr#) r$r%r&r Listror rr5r)r-r-r-r.rMs(rcs,eZdZdZdZeZeZfddZ Z S)BucketaConcatenate sub-bricks from input datasets into one big 'bucket' dataset. .. danger:: Using this program, it is possible to create a dataset that has different basic datum types for different sub-bricks (e.g., shorts for brick 0, floats for brick 1). Do NOT do this! Very few AFNI programs will work correctly with such datasets! Examples -------- >>> from nipype.interfaces import afni >>> bucket = afni.Bucket() >>> bucket.inputs.in_file = [('functional.nii',"{2..$}"), ('functional.nii',"{1}")] >>> bucket.inputs.out_file = 'vr_base' >>> bucket.cmdline "3dbucket -prefix vr_base functional.nii'{2..$}' functional.nii'{1}'" >>> res = bucket.run() # doctest: +SKIP See Also -------- For complete details, see the `3dbucket Documentation. `__. Z3dbucketcs6|dkr"|jdjdd|DStt|j|||S)Nr5 cSs$g|]}|dd|ddqS)r'rr-)rWir-r-r.rxsz&Bucket._format_arg..)rrDrLr _format_arg)rErGspecrF)rNr-r.rszBucket._format_arg) r$r%r&r0r1rr2rr3rrOr-r-)rNr.rs rc@seZdZeddddddZeddddd Zedd d dd Zed d dddZedddddZ e j ddgdZ e j ddgdZ e j ddZe jdddZedd dZd!S)" CalcInputSpeczinput file to 3dcalcz-a %srT)rrrrrzoperand file to 3dcalcz-b %sr)rrrrz-c %srz%s_calczoutput image file namez -prefix %sr')r6rrr7exprz -expr "%s"r)rrrrzstart index for in_file_astop_idx)rrequireszstop index for in_file_a start_idxzvolume index for in_file_a)rzoverwrite outputz -overwrite)rrz other optionsN)r$r%r&r r'r( in_file_cr)rrr rQrr single_idxr* overwriteotherr-r-r-r.rs* rcs:eZdZdZdZeZeZfddZ dfdd Z Z S) Calca+This program does voxel-by-voxel arithmetic on 3D datasets. For complete details, see the `3dcalc Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> calc = afni.Calc() >>> calc.inputs.in_file_a = 'functional.nii' >>> calc.inputs.in_file_b = 'functional2.nii' >>> calc.inputs.expr='a*b' >>> calc.inputs.out_file = 'functional_calc.nii.gz' >>> calc.inputs.outputtype = 'NIFTI' >>> calc.cmdline # doctest: +ELLIPSIS '3dcalc -a functional.nii -b functional2.nii -expr "a*b" -prefix functional_calc.nii.gz' >>> res = calc.run() # doctest: +SKIP >>> from nipype.interfaces import afni >>> calc = afni.Calc() >>> calc.inputs.in_file_a = 'functional.nii' >>> calc.inputs.expr = '1' >>> calc.inputs.out_file = 'rm.epi.all1' >>> calc.inputs.overwrite = True >>> calc.cmdline '3dcalc -a functional.nii -expr "1" -prefix rm.epi.all1 -overwrite' >>> res = calc.run() # doctest: +SKIP Z3dcalccsj|dkrV|j|}t|jjr6|d|jj|jjf7}t|jjrR|d|jj7}|Stt|j|||S)Nr'z[%d..%d]z[%d]) rr inputsrrrrLrr)rErG trait_specrFarg)rNr-r.rs   zCalc._format_argNcstt|jddS)z*Skip the arguments without argstr metadatarrr)skip)rrr)rLr _parse_inputs)rEr)rNr-r.rszCalc._parse_inputs)N) r$r%r&r0r1rr2rr3rrrOr-r-)rNr.rs  rc @seZdZejeddddd.dZeddddd/dd Zejd d d Z ejddd Z ej ddddddddddddgdZ ejddd Z ejdd d Zejd!d"d#ddddgd$Zejd%d&d#ddddgd$Zejd'd(d#ddddgd$Zejd)d*d#ddddgd$Zejd+d#ddddgd,Zd-S)0 CatInputSpecT)rz%sr)rrrz> %sz catout.1dzoutput (concatenated) file namer)rrF usedefaultrrrz7Omit columns that are identically constant from output.z -nonconst)rrzKeep only columns that are marked as 'free' in the 3dAllineate header from '-1Dparam_save'. If there is no such header, all columns are kept.z -nonfixedrVnicedoubleZfintZcintz-form %szspecify data type for output.out_intout_nice out_doubleout_fintout_cint)rrr:z8Stack the columns of the resultant matrix in the output.z-stackzPApply the same column/row selection string to all filenames on the command line.z-sel %sz!specifiy int data type for outputz-i out_format)rrr:z"specifiy nice data type for outputz-nz$specifiy double data type for outputz-dz0specifiy int, rounded down, data type for outputz-fz.specifiy int, rounded up, data type for output)rr:Nr"r#)r$r%r&r rr in_filesr)r*Z omitconstZkeepfreeEnumrstackrselrrrrrr-r-r-r.rs^ rc@seZdZdZdZeZeZdS)Cata1dcat takes as input one or more 1D files, and writes out a 1D file containing the side-by-side concatenation of all or a subset of the columns from the input files. For complete details, see the `1dcat Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> cat1d = afni.Cat() >>> cat1d.inputs.sel = "'[0,2]'" >>> cat1d.inputs.in_files = ['f1.1D', 'f2.1D'] >>> cat1d.inputs.out_file = 'catout.1d' >>> cat1d.cmdline "1dcat -sel '[0,2]' f1.1D f2.1D > catout.1d" >>> res = cat1d.run() # doctest: +SKIP Z1dcatN) r$r%r&r0r1rr2rr3r-r-r-r.r7src @seZdZejejejejdddddZedddd d ddd Z ej d dddgdZ ej ddddgdZ ej ddddgdZ dS)CatMatvecInputSpecz.list of tuples of mfiles and associated opkeysTz%sr)rrrrz > %sz%s_cat.aff12.1Dr5Fz&File to write concattenated matvecs tor)rr6r7keep_extensionrrrzindicates that the resulting matrix willbe written to outfile in the 'MATRIX(...)' format (FORM 3).This feature could be used, with clever scripting, to inputa matrix directly on the command line to program 3dWarp.z-MATRIXoneline fourxfour)rrr:zTindicates that the resulting matrixwill simply be written as 12 numbers on one line.z-ONELINEmatrixzgOutput matrix in augmented form (last row is 0 0 0 1)This option does not work with -MATRIX or -ONELINEz-4x4Nr"r#)r$r%r&r rrorr5r r)r*rrrr-r-r-r.rQs4  rcs,eZdZdZdZeZeZfddZ Z S) CatMatveca#Catenates 3D rotation+shift matrix+vector transformations. For complete details, see the `cat_matvec Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> cmv = afni.CatMatvec() >>> cmv.inputs.in_file = [('structural.BRIK::WARP_DATA','I')] >>> cmv.inputs.out_file = 'warp.anat.Xat.1D' >>> cmv.cmdline 'cat_matvec structural.BRIK::WARP_DATA -I > warp.anat.Xat.1D' >>> res = cmv.run() # doctest: +SKIP Z cat_matveccs0|dkrdjdd|DStt|j|||S)Nr5rcss&|]\}}|rd||fn|VqdS)z%s -%sNr-)rWZmfileZopkeyr-r-r. sz(CatMatvec._format_arg..)rDrLrr)rErGrrF)rNr-r.rszCatMatvec._format_arg) r$r%r&r0r1rr2rr3rrOr-r-)rNr.rxs rc @seZdZedddddddZeddddd d dd Zed dddZejdddZ ej ej ej ej fdddZ ejdddZ ejejdddZejdddZdS)CenterMassInputSpeczinput file to 3dCMz%srT)rrrrrr r5z %s_cm.outFzFile to write center of mass toz> %sr)r7r6r8rrrrzFOnly voxels with nonzero values in the provided mask will be averaged.z-mask %s)rrrzGenerate the mask automaticallyz -automask)rrzAfter computing the center of mass, set the origin fields in the header so that the center of mass will be at (x,y,z) in DICOM coords.z -set %f %f %fz,Output values as (i,j,k) in local orienationz -local_ijkzCompute center of mass for each blob with voxel value of v0, v1, v2, etc. This option is handy for getting ROI centers of mass.z -roi_vals %szkDon't bother listing the values of ROIs you want: The program will find all of them and produce a full listz -all_roisNr"r#)r$r%r&r r5cm_file mask_filer r*automaskrorpZset_cmZ local_ijkrrQZroi_valsZall_roisr-r-r-r.rsB rc@sFeZdZedddZeddZejejej ej ej ddZ dS)CenterMassOutputSpecTz output file)rrz(file with the center of mass coordinates)rzcenter of massN) r$r%r&r r)rr rrorpcmr-r-r-r.rs   rcs,eZdZdZdZeZeZfddZ Z S) CenterMassaComputes center of mass using 3dCM command .. note:: By default, the output is (x,y,z) values in DICOM coordinates. But as of Dec, 2016, there are now command line switches for other options. For complete details, see the `3dCM Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> cm = afni.CenterMass() >>> cm.inputs.in_file = 'structural.nii' >>> cm.inputs.cm_file = 'cm.txt' >>> cm.inputs.roi_vals = [2, 10] >>> cm.cmdline '3dCM -roi_vals 2 10 structural.nii > cm.txt' >>> res = 3dcm.run() # doctest: +SKIP Z3dCMcs^tt|j}tjj|jj|d<tjj|jj|d<t j |ddd}dd|D|d<|S)Nr)rr)ZndmincSsg|] }t|qSr-)tuple)rWsr-r-r.rxsz,CenterMass._list_outputs..r) rLr _list_outputsrBrCrKrr5rnploadtxt)rErdsout)rNr-r.rs zCenterMass._list_outputs) r$r%r&r0r1rr2rr3rrOr-r-)rNr.rs rc @sBeZdZeddddddZeddddd ZejddddddZdS)ConvertDsetInputSpeczinput file to ConvertDsetz -input %srT)rrrrrzoutput file for ConvertDsetz -prefix %sr)rrrrnimlniml_ascniml_bi1D1Dp1Dptgiigii_ascgii_b64 gii_b64gzz output typez-o_%sr)rrrrNr"r#) rrrrrrrrrr) r$r%r&r r5r)r rZout_typer-r-r-r.rs4rc@s$eZdZdZdZeZeZddZ dS) ConvertDsetaConverts a surface dataset from one format to another. For complete details, see the `ConvertDset Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> convertdset = afni.ConvertDset() >>> convertdset.inputs.in_file = 'lh.pial_converted.gii' >>> convertdset.inputs.out_type = 'niml_asc' >>> convertdset.inputs.out_file = 'lh.pial_converted.niml.dset' >>> convertdset.cmdline 'ConvertDset -o_niml_asc -input lh.pial_converted.gii -prefix lh.pial_converted.niml.dset' >>> res = convertdset.run() # doctest: +SKIP cCs"|jj}tj|jj|d<|S)Nr))r3getoprKrr))rErdr-r-r.r2s zConvertDset._list_outputsN) r$r%r&r0r1rr2rr3rr-r-r-r.rs rc@s@eZdZedddddddZeddddd d Zejd d dZdS) CopyInputSpeczinput file to 3dcopyz%srTF)rrrrrr z%s_copyzoutput image file namerr5)r6rrrr7zprint progress reportsz-verb)rrNr"r#) r$r%r&r r5r)r r*verboser-r-r-r.r8src@seZdZdZdZeZeZdS)CopyaoCopies an image of one type to an image of the same or different type using 3dcopy command For complete details, see the `3dcopy Documentation. `__ Examples -------- >>> from nipype.interfaces import afni >>> copy3d = afni.Copy() >>> copy3d.inputs.in_file = 'functional.nii' >>> copy3d.cmdline '3dcopy functional.nii functional_copy' >>> res = copy3d.run() # doctest: +SKIP >>> from copy import deepcopy >>> copy3d_2 = deepcopy(copy3d) >>> copy3d_2.inputs.outputtype = 'NIFTI' >>> copy3d_2.cmdline '3dcopy functional.nii functional_copy.nii' >>> res = copy3d_2.run() # doctest: +SKIP >>> copy3d_3 = deepcopy(copy3d) >>> copy3d_3.inputs.outputtype = 'NIFTI_GZ' >>> copy3d_3.cmdline '3dcopy functional.nii functional_copy.nii.gz' >>> res = copy3d_3.run() # doctest: +SKIP >>> copy3d_4 = deepcopy(copy3d) >>> copy3d_4.inputs.out_file = 'new_func.nii' >>> copy3d_4.cmdline '3dcopy functional.nii new_func.nii' >>> res = copy3d_4.run() # doctest: +SKIP Z3dcopyN) r$r%r&r0r1rr2rr3r-r-r-r.rKs#rc@seZdZejeddd"dZeddd#dZedd d Zej ej ej fd d d Z ej d dd Z ej ddd Zej ddd Zej ddd Zej ddd Zej ddd Zej ddd Zej ddd Zej ddd Zej dd d Zd!S)$ DotInputSpecz5list of input files, possibly with subbrick selectorsz%s ...r)rrrzcollect output to a filez |& tee %srzUse this dataset as a maskz-mask %s)rrzyMeans to further restrict the voxels from 'mset' so thatonly those mask values within this range (inclusive) willbe used.z -mrange %s %szCRemove the mean from each volume prior to computing the correlationz-demeanz-Return the correlation coefficient (default).z-docorz"Return the dot product (unscaled).z-dodotz\Return the least square fit coefficients {{a,b}} so that dset2 is approximately a + b\*dset1z-docoefztReturn the 6 numbers xbar= ybar= <(x-xbar)^2> <(y-ybar)^2> <(x-xbar)(y-ybar)> and the correlation coefficient.z-dosumsz6Return the Dice coefficient (the Sorensen-Dice index).z-dodicez,Return eta-squared (Cohen, NeuroImage 2008).z-doeta2zACompute the whole matrix. A waste of time, but handy for parsing.z-fullzPrint sub-brick labels to help identify what is being correlated. This option is useful whenyou have more than 2 sub-bricks at input.z -show_labelszCompute upper triangular matrixz-upperNr"r#)r$r%r&r rr rr)rirorpZmranger*demeanZdocorZdodotZdocoefZdosumsZdodiceZdoeta2fullZ show_labelsupperr-r-r-r.rusD    rc@seZdZdZdZeZeZdS)DotaCorrelation coefficient between sub-brick pairs. All datasets in in_files list will be concatenated. You can use sub-brick selectors in the file specification. .. warning:: This program is not efficient when more than two subbricks are input. For complete details, see the `3ddot Documentation. `_ >>> from nipype.interfaces import afni >>> dot = afni.Dot() >>> dot.inputs.in_files = ['functional.nii[0]', 'structural.nii'] >>> dot.inputs.dodice = True >>> dot.inputs.out_file = 'out.mask_ae_dice.txt' >>> dot.cmdline '3dDot -dodice functional.nii[0] structural.nii |& tee out.mask_ae_dice.txt' >>> res = copy3d.run() # doctest: +SKIP Z3dDotN) r$r%r&r0r1rr2rr3r-r-r-r.rsrc@seZdZedddddddZedd"d d Zejd d d dddZej dddddgdZ ej dddddgdZ ej dddddgdZ ej dddddgdZej ddd Zd!S)#Edge3InputSpeczinput file to 3dedge3z -input %srTF)rrrrrr zoutput image file namerz -prefix %s)rrrbyteshortrvz -datum %szJspecify data type for output. Valid types are 'byte', 'short' and 'float'.)rrz9Force scaling of the output to the maximum integer range.z-fscalegscalenscale scale_floats)rrr:z[Same as '-fscale', but also forces each output sub-brick to to get the same scaling factor.z-gscalefscalez9Don't do any scaling on output to byte or short datasets.z-nscaleaPMultiply input by VAL, but only if the input datum is float. This is needed when the input dataset has a small range, like 0 to 2.0 for instance. With such a range, very few edges are detected due to what I suspect to be truncation problems. Multiplying such a dataset by 10000 fixes the problem and the scaling is undone at the output.z-scale_floats %fz)Print out some information along the way.z-verbose)rrNr#)r$r%r&r r5r)r rdatumr*rrrrprrr-r-r-r.rs@rc@s<eZdZdZdZeZeZe ddgde ddgdgZ dS)Edge3a;Does 3D Edge detection using the library 3DEdge by Gregoire Malandain. For complete details, see the `3dedge3 Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> edge3 = afni.Edge3() >>> edge3.inputs.in_file = 'functional.nii' >>> edge3.inputs.out_file = 'edges.nii' >>> edge3.inputs.datum = 'byte' >>> edge3.cmdline '3dedge3 -input functional.nii -datum byte -prefix edges.nii' >>> res = edge3.run() # doctest: +SKIP Z3dedge3z@article{Deriche1987, author={R. Deriche}, title={Optimal edge detection using recursive filtering}, journal={International Journal of Computer Vision}, volume={2},' pages={167-187}, year={1987}, }method)entrytagsa,@article{MongaDericheMalandainCocquerez1991, author={O. Monga, R. Deriche, G. Malandain, J.P. Cocquerez}, title={Recursive filtering and edge tracking: two primary tools for 3D edge detection}, journal={Image and vision computing}, volume={9},' pages={203-214}, year={1991}, }N) r$r%r&r0r1rr2rr3r _referencesr-r-r-r.rs  rc@seZdZeddddddZeddddd Zedd d dd Zed d dddZej dddZ e dddddZ ej ddgdZej ddgdZej ddZedd dZd!S)" EvalInputSpeczinput file to 1devalz-a %srT)rrrrrzoperand file to 1devalz-b %sr)rrrrz-c %srz%s_calczoutput image file namez -prefix %sr')r6rrr7z output in 1Dz-1D)rrrz -expr "%s"r)rrrrzstart index for in_file_ar)rrzstop index for in_file_arzvolume index for in_file_a)rz other optionsrN)r$r%r&r r'r(rr)r r*Zout1DrrrQrrrrr-r-r-r.r+s* rcs:eZdZdZdZeZeZfddZ dfdd Z Z S) EvalaEvaluates an expression that may include columns of data from one or more text files. For complete details, see the `1deval Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> eval = afni.Eval() >>> eval.inputs.in_file_a = 'seed.1D' >>> eval.inputs.in_file_b = 'resp.1D' >>> eval.inputs.expr = 'a*b' >>> eval.inputs.out1D = True >>> eval.inputs.out_file = 'data_calc.1D' >>> eval.cmdline '1deval -a seed.1D -b resp.1D -expr "a*b" -1D -prefix data_calc.1D' >>> res = eval.run() # doctest: +SKIP Z1devalcsj|dkrV|j|}t|jjr6|d|jj|jjf7}t|jjrR|d|jj7}|Stt|j|||S)Nr'z[%d..%d]z[%d]) rr rrrrrLrr)rErGrrFr)rNr-r.ras   zEval._format_argNcstt|jddS)z*Skip the arguments without argstr metadatarrr)r)rrr)rLrr)rEr)rNr-r.rkszEval._parse_inputs)N) r$r%r&r0r1rr2rr3rrrOr-r-)rNr.rGs  rc @s*eZdZedddddZedddd3d d d Zed dd d ddZeddddZej d ddddZ ej ej ej d ddgdddZ ej d ddgddZej d ddd Zed!dd"d d#dZej d$d%gd&dZej d'd(gd)dZej d*d+d Zej d,d-d Zej ej eejedd.ejd dd/d0d1Zd2S)4FWHMxInputSpecz input datasetz -input %sT)rrrrz> %sr5z %s_fwhmx.outrFz output file)rr7r6rrrz-out %sz%s_subbricks.outz&output file listing the subbricks FWHM)rr7r6rrz(use only voxels that are nonzero in maskz-mask %s)rrrz -automaskz1compute a mask from THIS dataset, a la 3dAutomask)rrrz-detrenddemedzinstead of demed (0th order detrending), detrend to the specified order. If order is not given, the program picks q=NT/30. -detrend disables -demed, and includes -unif.)defaultrr:rrz-demeddetrendzIf the input dataset has more than one sub-brick (e.g., has a time axis), then subtract the median of each voxel's time series before processing FWHM. This will tend to remove intrinsic spatial structure and leave behind the noise.)rr:rz-unifzIf the input dataset has more than one sub-brick, then normalize each voxel's time series to have the same MAD before processing FWHM.)rrz -detprefix %sz %s_detrendz&Save the detrended file into a datasetz-geomarithzif in_file has more than one sub-brick, compute the final estimate as the geometric mean of the individual sub-brick FWHM estimatesz-arithgeomzif in_file has more than one sub-brick, compute the final estimate as the arithmetic mean of the individual sub-brick FWHM estimatesz-combinez.combine the final measurements along each axisz-compatz#be compatible with the older 3dFWHM)rz-acfz$computes the spatial autocorrelation)rrrrNr#)r$r%r&r r5r) out_subbricksrir r*rEitherrQrrZunif out_detrendrrcombinecompatrorpacfr-r-r-r.rps  rc@seZdZedddZedddZeddZejej ej ej ej ej ej ej ej ej ddZ ejej ej ej ej ej ej ej ej ej ddZ edd dZ d S) FWHMxOutputSpecTz output file)rrzoutput file (subbricks)zoutput file, detrended)rzFWHM along each axiszfitted ACF model parameterszoutput acf fileN)r$r%r&r r)rrr rrorpfwhm acf_paramout_acfr-r-r-r.rs   rcs\eZdZdZdZeZeZe ddgdgZ dZ dfdd Z fd d Z fd d ZZS)FWHMxa Unlike the older 3dFWHM, this program computes FWHMs for all sub-bricks in the input dataset, each one separately. The output for each one is written to the file specified by '-out'. The mean (arithmetic or geometric) of all the FWHMs along each axis is written to stdout. (A non-positive output value indicates something bad happened; e.g., FWHM in z is meaningless for a 2D dataset; the estimation method computed incoherent intermediate results.) For complete details, see the `3dFWHMx Documentation. `_ (Classic) METHOD: * Calculate ratio of variance of first differences to data variance. * Should be the same as 3dFWHM for a 1-brick dataset. (But the output format is simpler to use in a script.) .. note:: IMPORTANT NOTE [AFNI > 16] A completely new method for estimating and using noise smoothness values is now available in 3dFWHMx and 3dClustSim. This method is implemented in the '-acf' options to both programs. 'ACF' stands for (spatial) AutoCorrelation Function, and it is estimated by calculating moments of differences out to a larger radius than before. Notably, real FMRI data does not actually have a Gaussian-shaped ACF, so the estimated ACF is then fit (in 3dFWHMx) to a mixed model (Gaussian plus mono-exponential) of the form .. math:: ACF(r) = a * exp(-r*r/(2*b*b)) + (1-a)*exp(-r/c) where :math:`r` is the radius, and :math:`a, b, c` are the fitted parameters. The apparent FWHM from this model is usually somewhat larger in real data than the FWHM estimated from just the nearest-neighbor differences used in the 'classic' analysis. The longer tails provided by the mono-exponential are also significant. 3dClustSim has also been modified to use the ACF model given above to generate noise random fields. .. note:: TL;DR or summary The take-awaymessage is that the 'classic' 3dFWHMx and 3dClustSim analysis, using a pure Gaussian ACF, is not very correct for FMRI data -- I cannot speak for PET or MEG data. .. warning:: Do NOT use 3dFWHMx on the statistical results (e.g., '-bucket') from 3dDeconvolve or 3dREMLfit!!! The function of 3dFWHMx is to estimate the smoothness of the time series NOISE, not of the statistics. This proscription is especially true if you plan to use 3dClustSim next!! .. note:: Recommendations * For FMRI statistical purposes, you DO NOT want the FWHM to reflect the spatial structure of the underlying anatomy. Rather, you want the FWHM to reflect the spatial structure of the noise. This means that the input dataset should not have anatomical (spatial) structure. * One good form of input is the output of '3dDeconvolve -errts', which is the dataset of residuals left over after the GLM fitted signal model is subtracted out from each voxel's time series. * If you don't want to go to that much trouble, use '-detrend' to approximately subtract out the anatomical spatial structure, OR use the output of 3dDetrend for the same purpose. * If you do not use '-detrend', the program attempts to find non-zero spatial structure in the input, and will print a warning message if it is detected. .. note:: Notes on -demend * I recommend this option, and it is not the default only for historical compatibility reasons. It may become the default someday. * It is already the default in program 3dBlurToFWHM. This is the same detrending as done in 3dDespike; using 2*q+3 basis functions for q > 0. * If you don't use '-detrend', the program now [Aug 2010] checks if a large number of voxels are have significant nonzero means. If so, the program will print a warning message suggesting the use of '-detrend', since inherent spatial structure in the image will bias the estimation of the FWHM of the image time series NOISE (which is usually the point of using 3dFWHMx). Examples -------- >>> from nipype.interfaces import afni >>> fwhm = afni.FWHMx() >>> fwhm.inputs.in_file = 'functional.nii' >>> fwhm.cmdline '3dFWHMx -input functional.nii -out functional_subbricks.out > functional_fwhmx.out' >>> res = fwhm.run() # doctest: +SKIP Z3dFWHMxz@article{CoxReynoldsTaylor2016,author={R.W. Cox, R.C. Reynolds, and P.A. Taylor},title={AFNI and clustering: false positive rates redux},journal={bioRxiv},year={2016},}r)rrTNcs0|jjs|dkrg}|dg7}tt|j|dS)Nr)r)rrrLrr)rEr)rNr-r.rOs  zFWHMx._parse_inputscs|dkr:|dkr|jS|dkr"dSt|tr:|jd|S|dkr|dkrP|jS|dkrbd|_dSt|trz|jd|St|ttfr|jd|Stt|j |||S)NrTFz %drz %s %fr) r isinstancerV_acfrstrbytesrLrr)rErGrrF)rNr-r.rVs$  zFWHMx._format_argcstt|j}|jjrXtj|jj\}}d|krFtj|\}}||}|d|7<nt|d<t j |d}|j dkrt |dddf|d<n t ||d<|j r|j dkstdt|t |d|d <tjd |d <t|jjttfrtj|jj|d <|S) Nz.gzrr)rrzWrong number of elements in %srrz 3dFWHMx.1Dr)rLrrrrrsplitextr5rrrsizerrAssertionErrorrrKrrr)rErdfnamerI_ext2r)rNr-r.rks&  zFWHMx._list_outputs)N)r$r%r&r0r1rr2rr3rrrrrrrOr-r-)rNr.rs^  rc@seZdZedddd1ddZedddd2ddZejejej dd d ej ejej d ejej ej ej dd d dZ ddddddddddddddgZ e ej e ddddZeddd d!Zejd"d#d$gd%Zedd&d'd(gd)Zed*d+d,d-dd.d/Zd0S)3LocalBistatInputSpecTz%srzFilename of the first image)rrrrrrzFilename of the second imageSPHERERHDDTOHDRECTa The region around each voxel that will be extracted for the statistics calculation. Possible regions are: 'SPHERE', 'RHDD' (rhombic dodecahedron), 'TOHD' (truncated octahedron) with a given radius in mm or 'RECT' (rectangular block) with dimensions to specify in mm.z-nbhd '%s(%s)')rrrZpearsonZspearmanZquadrantZmutinfoZnormutiZjointentZ hellingerZcrUZcrMZcrAZL2slopeZL1slopenumALLajStatistics to compute. Possible names are: * pearson = Pearson correlation coefficient * spearman = Spearman correlation coefficient * quadrant = Quadrant correlation coefficient * mutinfo = Mutual Information * normuti = Normalized Mutual Information * jointent = Joint entropy * hellinger= Hellinger metric * crU = Correlation ratio (Unsymmetric) * crM = Correlation ratio (symmetrized by Multiplication) * crA = Correlation ratio (symmetrized by Addition) * L2slope = slope of least-squares (L2) linear regression of the data from dataset1 vs. the dataset2 (i.e., d2 = a + b*d1 ==> this is 'b') * L1slope = slope of least-absolute-sum (L1) linear regression of the data from dataset1 vs. the dataset2 * num = number of the values in the region: with the use of -mask or -automask, the size of the region around any given voxel will vary; this option lets you map that size. * ALL = all of the above, in that order More than one option can be used.z -stat %s...zmask image file name. Voxels NOT in the mask will not be used in the neighborhood of any voxel. Also, a voxel NOT in the mask will have its statistic(s) computed as zero (0).z-mask %s)rrrz*Compute the mask as in program 3dAutomask.z -automask weight_file)rrr:zPFile name of an image to use as a weight. Only applies to 'pearson' statistics.z -weight %sr)rrrr:zOutput dataset.z -prefix %sin_file1z %s_bistatr)rrr7r6rrNr"r#)r$r%r&r rZin_file2r rrorrp neighborhood _stat_namesrrurr*rrr)r-r-r-r.rsv  rcs,eZdZdZdZeZeZfddZ Z S) LocalBistata 3dLocalBistat - computes statistics between 2 datasets, at each voxel, based on a local neighborhood of that voxel. For complete details, see the `3dLocalBistat Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> bistat = afni.LocalBistat() >>> bistat.inputs.in_file1 = 'functional.nii' >>> bistat.inputs.in_file2 = 'structural.nii' >>> bistat.inputs.neighborhood = ('SPHERE', 1.2) >>> bistat.inputs.stat = 'pearson' >>> bistat.inputs.outputtype = 'NIFTI' >>> bistat.cmdline "3dLocalBistat -prefix functional_bistat.nii -nbhd 'SPHERE(1.2)' -stat pearson functional.nii structural.nii" >>> res = automask.run() # doctest: +SKIP Z 3dLocalBistatcs8|dkr$|ddkr$dd|df}tt|j|||S)Nrrrz%s,%s,%sr)rLr r)rErGrrF)rNr-r.rszLocalBistat._format_arg) r$r%r&r0r1rr2rr3rrOr-r-)rNr.r s r c@seZdZeddddHddZejejejdddej ejejd ejej ej ej dd d d Z d ddddddddddddddddddgZ e ejeje ejejd ejej ej ej dd!d"d Z edd#d$d%Zejd&d'd(Zejd)d*d(Zejej ejej ej ej d+d,d-gd.d/Zejej ejej ej ej d0d-d1gd2d/Zej d3d,d1gd4d/Zejd5d6d7d8d9d,gd:d;ZejdZejd?d@d(ZedAdBdCdDddEdFZdGS)ILocalstatInputSpecTz%srz input dataset)rrrrrrrrra The region around each voxel that will be extracted for the statistics calculation. Possible regions are: 'SPHERE', 'RHDD' (rhombic dodecahedron), 'TOHD' (truncated octahedron) with a given radius in mm or 'RECT' (rectangular block) with dimensions to specify in mm.z-nbhd '%s(%s)')rrrrlZstdevrnZcvarZmedianZMADrjrkZabsmaxrrmZFWHMZFWHMbarZrankZfrankZP2skewrZmMP2sZmmMP2spercastatistics to compute. Possible names are: * mean = average of the values * stdev = standard deviation * var = variance (stdev\*stdev) * cvar = coefficient of variation = stdev/fabs(mean) * median = median of the values * MAD = median absolute deviation * min = minimum * max = maximum * absmax = maximum of the absolute values * num = number of the values in the region: with the use of -mask or -automask, the size of the region around any given voxel will vary; this option lets you map that size. It may be useful if you plan to compute a t-statistic (say) from the mean and stdev outputs. * sum = sum of the values in the region * FWHM = compute (like 3dFWHM) image smoothness inside each voxel's neighborhood. Results are in 3 sub-bricks: FWHMx, FHWMy, and FWHMz. Places where an output is -1 are locations where the FWHM value could not be computed (e.g., outside the mask). * FWHMbar= Compute just the average of the 3 FWHM values (normally would NOT do this with FWHM also). * perc:P0:P1:Pstep = Compute percentiles between P0 and P1 with a step of Pstep. Default P1 is equal to P0 and default P2 = 1 * rank = rank of the voxel's intensity * frank = rank / number of voxels in neighborhood * P2skew = Pearson's second skewness coefficient 3 \* (mean - median) / stdev * ALL = all of the above, in that order (except for FWHMbar and perc). * mMP2s = Exactly the same output as: median, MAD, P2skew, but a little faster * mmMP2s = Exactly the same output as: mean, median, MAD, P2skew More than one option can be used.z -stat %s...zMask image file name. Voxels NOT in the mask will not be used in the neighborhood of any voxel. Also, a voxel NOT in the mask will have its statistic(s) computed as zero (0) unless the parameter 'nonmask' is set to true.z-mask %s)rrrz*Compute the mask as in program 3dAutomask.z -automask)rrzVoxels not in the mask WILL have their local statistics computed from all voxels in their neighborhood that ARE in the mask. For instance, this option can be used to compute the average local white matter time series, even at non-WM voxels.z -use_nonmaskz-reduce_grid %sreduce_restore_gridreduce_max_voxaCompute output on a grid that is reduced by the specified factors. If a single value is passed, output is resampled to the specified isotropic grid. Otherwise, the 3 inputs describe the reduction in the X, Y, and Z directions. This option speeds up computations at the expense of resolution. It should only be used when the nbhd is quite large with respect to the input's resolution, and the resultant stats are expected to be smooth.)rr:rz-reduce_restore_grid %s reduce_gridz=Like reduce_grid, but also resample output back to inputgrid.z-reduce_max_vox %sz~Like reduce_restore_grid, but automatically set Rx Ry Rz sothat the computation grid is at a resolution of nbhd/MAX_VOXvoxels.NNLiCuBkz-grid_rmode %saInterpolant to use when resampling the output with thereduce_restore_grid option. The resampling method string RESAM should come from the set {'NN', 'Li', 'Cu', 'Bk'}. These stand for 'Nearest Neighbor', 'Linear', 'Cubic', and 'Blocky' interpolation, respectively.)rrrz-quietz-Stop the highly informative progress reports.)rrz*overwrite output file if it already existsz -overwritezOutput dataset.z -prefix %sr5z %s_localstatr)rrr7r6rrNr#)r$r%r&r r5r rrorrprr rrurr*rZnonmaskrr rZ grid_rmoder+rr)r-r-r-r.r s-    r cs,eZdZdZdZeZeZfddZ Z S) LocalstataY3dLocalstat - computes statistics at each voxel, based on a local neighborhood of that voxel. For complete details, see the `3dLocalstat Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> localstat = afni.Localstat() >>> localstat.inputs.in_file = 'functional.nii' >>> localstat.inputs.mask_file = 'skeleton_mask.nii.gz' >>> localstat.inputs.neighborhood = ('SPHERE', 45) >>> localstat.inputs.stat = 'mean' >>> localstat.inputs.nonmask = True >>> localstat.inputs.outputtype = 'NIFTI_GZ' >>> localstat.cmdline "3dLocalstat -prefix functional_localstat.nii -mask skeleton_mask.nii.gz -nbhd 'SPHERE(45.0)' -use_nonmask -stat mean functional.nii" >>> res = localstat.run() # doctest: +SKIP Z 3dLocalstatcsr|dkr$|ddkr$dd|df}|dkr:dd|D}|d ksJ|d kr^t|d kr^d |}tt|j|||S) Nrrrz%s,%s,%srrucSs(g|] }t|dkr d|dn|qS)rz perc:%s:%s:%sr)r})rWvr-r-r.rxsz)Localstat._format_arg..rr rz%s %s %s)r}rLrr)rErGrrF)rNr-r.rs zLocalstat._format_arg) r$r%r&r0r1r r2rr3rrOr-r-)rNr.rs rc@seZdZeeddddd+dddZedd d d d Zejd dddZ ej ddddddZ e dddZ e dddZejdddZejdddZejd d!dZejd"d#dZe d$d%d&gd'Zejd(d)dZd*S),MaskToolInputSpecT)rz"input file or files to 3dmask_toolz -input %srF)rrrrr z%s_maskzoutput image file namez -prefix %sr5)r6rrr7zInstead of created a binary 0/1 mask dataset, create one with counts of voxel overlap, i.e., each voxel will contain the number of masks that it is set in.z-countr)rrrrrrvz -datum %szspecify data type for output.)rrzhUse this option to dilate and/or erode datasets as they are read. ex. '5 -5' to dilate and erode 5 timesz-dilate_inputs %s)rrz6dilate and/or erode combined mask at the given levels.z-dilate_results %szWhen combining masks (across datasets and sub-bricks), use this option to restrict the result to a certain fraction of the set of volumesz-frac %sz"intersection, this means -frac 1.0z-interzunion, this means -frac 0z-unionzkThis option can be used to fill holes in the resulting mask, i.e. after all other processing has been done.z -fill_holeszfill holes only in the given directions. This option is for use with -fill holes. should be a single string that specifies 1-3 of the axes using {x,y,z} labels (i.e. dataset axis order), or using the labels in {R,L,A,P,I,S}.z -fill_dirs %s fill_holes)rrrz#specify verbosity level, for 0 to 3z-verb %sNr#)r$r%r&rr r5r)r r*countrrrZ dilate_inputsZdilate_resultsrpfracZinterunionrZ fill_dirsrQrr-r-r-r.rsR rc@seZdZedddZdS)MaskToolOutputSpecz mask fileT)rrN)r$r%r&r r)r-r-r-r.r&src@seZdZdZdZeZeZdS)MaskToola'3dmask_tool - for combining/dilating/eroding/filling masks For complete details, see the `3dmask_tool Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> masktool = afni.MaskTool() >>> masktool.inputs.in_file = 'functional.nii' >>> masktool.inputs.outputtype = 'NIFTI' >>> masktool.cmdline '3dmask_tool -prefix functional_mask.nii -input functional.nii' >>> res = automask.run() # doctest: +SKIP Z 3dmask_toolN) r$r%r&r0r1rr2rr3r-r-r-r.r*src@sTeZdZeeddddddddZedd d d d Zejd ddZ ej ddddZ dS)MergeInputSpeczinput file to 3dmergeT)rrz%srF)rrrr z%s_mergezoutput image file namez -prefix %sr)r6rrr7z*apply options to all sub-bricks in datasetz-doall)rrzFWHM blur value (mm)z-1blur_fwhm %dmm)rrZunitsNr#) r$r%r&rr rr)r r*ZdoallrQZblurfwhmr-r-r-r.rAs  rc@seZdZdZdZeZeZdS)MergeacMerge or edit volumes using AFNI 3dmerge command For complete details, see the `3dmerge Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> merge = afni.Merge() >>> merge.inputs.in_files = ['functional.nii', 'functional2.nii'] >>> merge.inputs.blurfwhm = 4 >>> merge.inputs.doall = True >>> merge.inputs.out_file = 'e7.nii' >>> merge.cmdline '3dmerge -1blur_fwhm 4 -doall -prefix e7.nii functional.nii functional2.nii' >>> res = merge.run() # doctest: +SKIP Z3dmergeN) r$r%r&r0r1rr2rr3r-r-r-r.rWsrc@steZdZedddddddZeddd Zed d d gd Zedddgd Ze j ddd Z e j ddd Z eddd ZdS)NotesInputSpeczinput file to 3dNotesz%srTF)rrrrrr z note to addz-a "%s")rrznote to add to historyz-h "%s" rep_history)rrr:z"note with which to replace historyz-HH "%s" add_historyzdelete note number numz-d %dz"print to stdout the expanded notesz-seszoutput image file nameNr#)r$r%r&r r5raddr"r!r rQdeleter*Zsesr)r-r-r-r.r ps"  r c@s$eZdZdZdZeZeZddZ dS)NotesakA program to add, delete, and show notes for AFNI datasets. For complete details, see the `3dNotes Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> notes = afni.Notes() >>> notes.inputs.in_file = 'functional.HEAD' >>> notes.inputs.add = 'This note is added.' >>> notes.inputs.add_history = 'This note is added to history.' >>> notes.cmdline '3dNotes -a "This note is added." -h "This note is added to history." functional.HEAD' >>> res = notes.run() # doctest: +SKIP Z3dNotescCs$|jj}tjj|jj|d<|S)Nr))r3rrBrCrKrr5)rErdr-r-r.rs zNotes._list_outputsN) r$r%r&r0r1r r2rr3rr-r-r-r.r%s r%c@sPeZdZeedddddddZeedddddd Zed d d d dd gdZdS)NwarpAdjustInputSpecT)rz -nwarp %szList of input 3D warp datasets)minlenrrrz -source %szList of input 3D datasets to be warped by the adjusted warp datasets. There must be exactly as many of these datasets as there are input warps.)r(rrzOutput mean dataset, only needed if in_files are also given. The output dataset will be on the common grid shared by the source datasets.z -prefix %srz%s_NwarpAdjust)rrr7r6rrN)r$r%r&rr Zwarpsrr)r-r-r-r.r&s$r&cs6eZdZdZdZeZeZdfdd Z ddZ Z S) NwarpAdjustaThis program takes as input a bunch of 3D warps, averages them, and computes the inverse of this average warp. It then composes each input warp with this inverse average to 'adjust' the set of warps. Optionally, it can also read in a set of 1-brick datasets corresponding to the input warps, and warp each of them, and average those. For complete details, see the `3dNwarpAdjust Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> adjust = afni.NwarpAdjust() >>> adjust.inputs.warps = ['func2anat_InverseWarp.nii.gz', 'func2anat_InverseWarp.nii.gz', 'func2anat_InverseWarp.nii.gz', 'func2anat_InverseWarp.nii.gz', 'func2anat_InverseWarp.nii.gz'] >>> adjust.cmdline '3dNwarpAdjust -nwarp func2anat_InverseWarp.nii.gz func2anat_InverseWarp.nii.gz func2anat_InverseWarp.nii.gz func2anat_InverseWarp.nii.gz func2anat_InverseWarp.nii.gz' >>> res = adjust.run() # doctest: +SKIP Z 3dNwarpAdjustNcs0|jjs|dkrg}|dg7}tt|j|dS)Nr))r)rrrLr)r)rEr)rNr-r.rs  zNwarpAdjust._parse_inputscCs|jj}|jjr|jjr2tjj|jj|d<nXtjj|jjd}t j |\}}d|krrt j |\}}||}tjj|d||d<|S)Nr)rz.gzZ _NwarpAdjust) r3rrrr)rBrCrKbasenamerr)rErdr*Zbasename_noextrIrr-r-r.rs zNwarpAdjust._list_outputs)N) r$r%r&r0r1r&r2rr3rrrOr-r-)rNr.r)s r)c@seZdZejeddejeddddddZejddddZ ej d d d Z edd d dZ ej dddddddddddddd Zej ddddddddddddd Zeddd d!d"Zej d#d$d Zej d%d&d'gd(Zej d)d*d+gd(Zd,S)-NwarpApplyInputSpecT)rz -source %sz=the name of the dataset to be warped can be multiple datasets)rrrzWthe name of the warp dataset. multiple warps can be concatenated (make sure they exist)z -nwarp %s)rrrz;After the warp specified in '-nwarp' is computed, invert itz-iwarp)rrz=the name of the master dataset, which defines the output gridz -master %s)rrrwsinc5rZnearestneighbourZnearestneighborlinearZ trilinearZcubicZtricubicquinticZ triquinticz/defines interpolation method to use during warpz -interp %s)rrrzHspecify a different interpolation method than might be used for the warpz -ainterp %sz%s_Nwarpzoutput image file namez -prefix %sr5)r6rrr7zVWrite output dataset using 16-bit short integers, rather than the usual 32-bit floats.z-shortzdon't be verbose :(z-quietr,)rrr:zbe extra verbose :)z-verbr+N)r$r%r&r rr rr5StringZwarpr*inv_warpmasterrinterpZainterpr)rr+r,r-r-r-r.r+slr+c@seZdZdZdZeZeZdS) NwarpApplyaProgram to apply a nonlinear 3D warp saved from 3dQwarp (or 3dNwarpCat, etc.) to a 3D dataset, to produce a warped version of the source dataset. For complete details, see the `3dNwarpApply Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> nwarp = afni.NwarpApply() >>> nwarp.inputs.in_file = 'Fred+orig' >>> nwarp.inputs.master = 'NWARP' >>> nwarp.inputs.warp = "'Fred_WARP+tlrc Fred.Xaff12.1D'" >>> nwarp.cmdline "3dNwarpApply -source Fred+orig -interp wsinc5 -master NWARP -prefix Fred+orig_Nwarp -nwarp 'Fred_WARP+tlrc Fred.Xaff12.1D'" >>> res = nwarp.run() # doctest: +SKIP Z 3dNwarpApplyN) r$r%r&r0r1r+r2rr3r-r-r-r.r37sr3c @seZdZejejeejejddddeddddd Z ej d d d Z ej d dd Z ejdddddddZejddd ZedddddZej ddd ZdS) NwarpCatInputSpecIDENTZINVZSQRTZSQRTINVz3list of tuples of 3D warps and associated functionsTz%sr)rrrrzAstring to attach to the output dataset as its atlas space marker.z -space %s)rrz#invert the final warp before outputz-iwarpr,r-r.zHspecify a different interpolation method than might be used for the warpz -interp %s)rrrzPad the nonlinear warps by the given number of voxels voxels in all directions. The warp displacements are extended by linear extrapolation from the faces of the input grid..z -expad %dz %s_NwarpCatzoutput image file namez -prefix %sr)r6rrr7z be verbosez-verbNr#)r$r%r&r rrr rorrr/spacer*r0r2rQZexpadr)r,r-r-r-r.r4Qs6r4cs<eZdZdZdZeZeZfddZ ddZ ddZ Z S) NwarpCata Catenates (composes) 3D warps defined on a grid, OR via a matrix. .. note:: * All transformations are from DICOM xyz (in mm) to DICOM xyz. * Matrix warps are in files that end in '.1D' or in '.txt'. A matrix warp file should have 12 numbers in it, as output (for example), by '3dAllineate -1Dmatrix_save'. * Nonlinear warps are in dataset files (AFNI .HEAD/.BRIK or NIfTI .nii) with 3 sub-bricks giving the DICOM order xyz grid displacements in mm. * If all the input warps are matrices, then the output is a matrix and will be written to the file 'prefix.aff12.1D'. Unless the prefix already contains the string '.1D', in which case the filename is just the prefix. * If 'prefix' is just 'stdout', then the output matrix is written to standard output. In any of these cases, the output format is 12 numbers in one row. * If any of the input warps are datasets, they must all be defined on the same 3D grid! And of course, then the output will be a dataset on the same grid. However, you can expand the grid using the '-expad' option. * The order of operations in the final (output) warp is, for the case of 3 input warps: OUTPUT(x) = warp3( warp2( warp1(x) ) ) That is, warp1 is applied first, then warp2, et cetera. The 3D x coordinates are taken from each grid location in the first dataset defined on a grid. For complete details, see the `3dNwarpCat Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> nwarpcat = afni.NwarpCat() >>> nwarpcat.inputs.in_files = ['Q25_warp+tlrc.HEAD', ('IDENT', 'structural.nii')] >>> nwarpcat.inputs.out_file = 'Fred_total_WARP' >>> nwarpcat.cmdline "3dNwarpCat -interp wsinc5 -prefix Fred_total_WARP Q25_warp+tlrc.HEAD 'IDENT(structural.nii)'" >>> res = nwarpcat.run() # doctest: +SKIP Z 3dNwarpCatcs6|dkr"|jdjdd|DStt|j|||S)NrrcSs6g|].}t|tr.d|dd|ddn|qS)rr(rz)')rr)rWrr-r-r.rxsz(NwarpCat._format_arg..)rrDrLr7r)rErGrrF)rNr-r.rs  zNwarpCat._format_argcCs&|dkr"|j|jjddddSdS)Nr)rZ _NwarpCat)suffix) _gen_fnamerr)rErGr-r-r.rMszNwarpCat._gen_filenamecCsV|jj}t|jjr.tjj|jj|d<n$tjj|j|jj dddd|d<|S)Nr)rz_NwarpCat+tlrcz.HEAD)r9rI) r3rr rr)rBrCrKr:r)rErdr-r-r.rs  zNwarpCat._list_outputs) r$r%r&r0r1r4r2rr3rrMrrOr-r-)rNr.r7xs2 r7c@seZdZedddddZejdddZejdd dZ ejd d dZ ed d dgdZ ejdddZ ej ejefdddZejdddZejdddddddZeddd&dgd Zejd!d"dZejd#d$dZd%S)'OneDToolPyInputSpeczinput file to OneDToolz -infile %sT)rrrrz'treat the input data as if it has nrunsz -set_nruns %d)rrzOtake the temporal derivative of each vector (done as first backward difference)z -derivativez,demean each run (new mean of each run = 0.0)z-demeanz!write the current 1D data to FILEz -write %sshow_cormat_warnings)rrr:zdisplay the total number of censored TRs Note : if input is a valid xmat.1D dataset, then the count will come from the header. Otherwise the input is assumed to be a binary censorfile, and zeros are simply counted.z-show_censor_countzWTuple of motion limit and outfile prefix. need to also set set_nruns -r set_run_lengthsz-censor_motion %f %sz*for each censored TR, also censor previousz-censor_prev_TRcommar6encodedrzDdisplay a list of TRs which were not censored in the specified stylez-show_trs_uncensored %szWrite cormat warnings to a filez-show_cormat_warnings |& tee %srr))rrrr:z+display column indices for regs of interestz-show_indices_interestz8restrict -show_trs_[un]censored to the given 1-based runz-show_trs_run %dNr#)r$r%r&r r5r rQZ set_nrunsr*Z derivativerr)Zshow_censor_countrorp censor_motionZcensor_prev_TRrZshow_trs_uncensoredr<Zshow_indices_interestZ show_trs_runr-r-r-r.r;sP      r;c@seZdZeddZdS)OneDToolPyOutputSpeczoutput of 1D_tool.py)rN)r$r%r&r r)r-r-r-r.r@ sr@c@s$eZdZdZdZeZeZddZ dS) OneDToolPyaThis program is meant to read/manipulate/write/diagnose 1D datasets. Input can be specified using AFNI sub-brick[]/time{} selectors. >>> from nipype.interfaces import afni >>> odt = afni.OneDToolPy() >>> odt.inputs.in_file = 'f1.1D' >>> odt.inputs.set_nruns = 3 >>> odt.inputs.demean = True >>> odt.inputs.out_file = 'motion_dmean.1D' >>> odt.cmdline # doctest: +ELLIPSIS 'python2 ...1d_tool.py -demean -infile f1.1D -write motion_dmean.1D -set_nruns 3' >>> res = odt.run() # doctest: +SKIPz 1d_tool.pycCs|jj}t|jjr2tjjtj|jj|d<t|jj rXtjjtj|jj |d<t|jj rtjjtj|jj dd|d<|S)Nr)rz _censor.1D) r3rr rr)rBrCrDryr<r?)rErdr-r-r.r s    zOneDToolPy._list_outputsN) r$r%r&r0r1r;r2r@r3rr-r-r-r.rA s  rAc@s2eZdZeddd.ddddZejdddZed d dZ ed d dZ ed ddZ eddddZ ej dddZej dddZej dddZej dddZejddddddZejedd ejd!d"dZejejejd#d$dZejejejd%d&dZejejejd'd(dZejd)d*dZejd+d,dZd-S)/RefitInputSpeczinput file to 3drefitz%srT)rrrrrr z:replace current transformation matrix with cardinal matrixz -deoblique)rrz x distance for edge voxel offsetz -xorigin %sz y distance for edge voxel offsetz -yorigin %sz z distance for edge voxel offsetz -zorigin %sz -duporigin %szTCopies the xorigin, yorigin, and zorigin values from the header of the given dataset)rrrznew x voxel dimension in mmz-xdel %fznew y voxel dimension in mmz-ydel %fznew z voxel dimension in mmz-zdel %fz8Scale the size of the dataset voxels by the given factorz -xyzscale %fZTLRCZMNIZORIGz -space %szJAssociates the dataset with a specific template type, e.g. TLRC, MNI, ORIG)rr)rz-atrcopy %s %sa[Copy AFNI header attribute from the given file into the header of the dataset(s) being modified. For more information on AFNI header attributes, see documentation file README.attributes. More than one '-atrcopy' option can be used. For AFNI advanced users only. Do NOT use -atrcopy or -atrstring with other modification options. See also -copyaux.z-atrstring %s %szCopy the last given string into the dataset(s) being modified, giving it the attribute name given by the last string.To be safe, the last string should be in quotes.z-atrfloat %s %szCreate or modify floating point attributes. The input values may be specified as a single string in quotes or as a 1D filename or string, example '1 0.2 0 0 -0.2 1 0 0 0 0 1 0' or flipZ.1D or '1D:1,0.2,2@0,-0.2,1,2@0,2@0,1,0'z -atrint %s %szCreate or modify integer attributes. The input values may be specified as a single string in quotes or as a 1D filename or string, example '1 0 0 0 0 1 0 0 0 0 1 0' or flipZ.1D or '1D:1,0,2@0,-0,1,2@0,2@0,1,0'z-saveatrz(default) Copy the attributes that are known to AFNI into the dset->dblk structure thereby forcing changes to known attributes to be present in the output. This option only makes sense with -atrcopy.z -nosaveatrzOpposite of -saveatrNr#)r$r%r&r r5r r*Z deobliquerZxoriginZyoriginZzoriginZduporigin_filerpZxdelZydelZzdelZxyzscalerr6roZatrcopyZ atrstringZatrfloatZatrintZsaveatrZ nosaveatrr-r-r-r.rB- sj   rBc@s$eZdZdZdZeZeZddZ dS)Refita3Changes some of the information inside a 3D dataset's header For complete details, see the `3drefit Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> refit = afni.Refit() >>> refit.inputs.in_file = 'structural.nii' >>> refit.inputs.deoblique = True >>> refit.cmdline '3drefit -deoblique structural.nii' >>> res = refit.run() # doctest: +SKIP >>> refit_2 = afni.Refit() >>> refit_2.inputs.in_file = 'structural.nii' >>> refit_2.inputs.atrfloat = ("IJK_TO_DICOM_REAL", "'1 0.2 0 0 -0.2 1 0 0 0 0 1 0'") >>> refit_2.cmdline "3drefit -atrfloat IJK_TO_DICOM_REAL '1 0.2 0 0 -0.2 1 0 0 0 0 1 0' structural.nii" >>> res = refit_2.run() # doctest: +SKIP Z3drefitcCs$|jj}tjj|jj|d<|S)Nr))r3rrBrCrKrr5)rErdr-r-r.r s zRefit._list_outputsN) r$r%r&r0r1rBr2rr3rr-r-r-r.rC s rCc@seZdZeddddddZedddd dd d Zejd d dZedddZ ej dddddgdddZ ej dddgddZ ejej ej ej ddgdddZedd d!d"Zejd#d$dZd%S)& ReHoInputSpecz input datasetz -inset %srT)rrrrrzOutput dataset.z -prefix %sr5z%s_rehor)rrr7r6rrz-chi_sqzOutput the Friedman chi-squared value in addition to the Kendall's W. This option is currently compatible only with the AFNI (BRIK/HEAD) output type; the chi-squared value will be the second sub-brick of the output dataset.)rrz5Mask within which ReHo should be calculated voxelwisez-mask %s)rrfacesedgesverticessphere ellipsoidz -nneigh %sz voxels in neighborhood. can be: ``faces`` (for voxel and 6 facewise neighbors, only), ``edges`` (for voxel and 18 face- and edge-wise neighbors), ``vertices`` (for voxel and 26 face-, edge-, and node-wise neighbors).)r:rrz -neigh_RAD %sra\ For additional voxelwise neighborhood control, the radius R of a desired neighborhood can be put in; R is a floating point number, and must be >1. Examples of the numbers of voxels in a given radius are as follows (you can roughly approximate with the ol' :math:`4\pi\,R^3/3` thing): * R=2.0 -> V=33 * R=2.3 -> V=57, * R=2.9 -> V=93, * R=3.1 -> V=123, * R=3.9 -> V=251, * R=4.5 -> V=389, * R=6.1 -> V=949, but you can choose most any value.)rr:rz#-neigh_X %s -neigh_Y %s -neigh_Z %saX\ Tuple indicating the x, y, and z radius of an ellipsoid defining the neighbourhood of each voxel. The 'hood is then made according to the following relation: :math:`(i/A)^2 + (j/B)^2 + (k/C)^2 \le 1.` which will have approx. :math:`V=4 \pi \, A B C/3`. The impetus for this freedom was for use with data having anisotropic voxel edge lengths.z -in_rois %szZa set of ROIs, each labelled with distinct integers. ReHo will then be calculated per ROI.)rrrz*overwrite output file if it already existsz -overwriteN)r$r%r&r r5r)r r*Zchi_sqrrrrprHrorI label_setrr-r-r-r.rD sT rDc@s"eZdZedddZeddZdS)ReHoOutputSpecTz"Voxelwise regional homogeneity map)rrz-Table of labelwise regional homogenity values)rN)r$r%r&r r)out_valsr-r-r-r.rK s rKcs8eZdZdZdZeZeZfddZ fddZ Z S)ReHoa^Compute regional homogenity for a given neighbourhood.l, based on a local neighborhood of that voxel. For complete details, see the `3dReHo Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> reho = afni.ReHo() >>> reho.inputs.in_file = 'functional.nii' >>> reho.inputs.out_file = 'reho.nii.gz' >>> reho.inputs.neighborhood = 'vertices' >>> reho.cmdline '3dReHo -prefix reho.nii.gz -inset functional.nii -nneigh 27' >>> res = reho.run() # doctest: +SKIP Z3dReHocs*tt|j}|jjr&|dd|d<|S)Nr)z_ROI_reho.valsrL)rLrMrrrJ)rErd)rNr-r.r szReHo._list_outputscs0dddd}|dkr||}tt|j|||S)N)rErFrGr)rLrMr)rErGrrFZ _neigh_dict)rNr-r.r s zReHo._format_arg) r$r%r&r0r1rDr2rKr3rrrOr-r-)rNr.rM s  rMc@szeZdZedddddddZeddd d d Zed d dZej dddddddZ ej ej gddddZ edddZdS)ResampleInputSpeczinput file to 3dresamplez -inset %srTF)rrrrrr z %s_resamplezoutput image file namez -prefix %sr5)r6rrr7znew orientation codez -orient %s)rrrrrrz -rmode %szresampling method from set {"NN", "Li", "Cu", "Bk"}. These are for "Nearest Neighbor", "Linear", "Cubic" and "Blocky"interpolation, respectively. Default is NN.)rrrz-dxyz %f %f %fzresample to new dx, dy and dzz -master %sz&align dataset grid to a reference fileNr#)r$r%r&r r5r)r orientationr rZ resample_moderorpZ voxel_sizer1r-r-r-r.rQ% s2   rQc@seZdZdZdZeZeZdS)ResampleaZResample or reorient an image using AFNI 3dresample command For complete details, see the `3dresample Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> resample = afni.Resample() >>> resample.inputs.in_file = 'functional.nii' >>> resample.inputs.orientation= 'RPI' >>> resample.inputs.outputtype = 'NIFTI' >>> resample.cmdline '3dresample -orient RPI -prefix functional_resample.nii -inset functional.nii' >>> res = resample.run() # doctest: +SKIP Z 3dresampleN) r$r%r&r0r1rQr2rr3r-r-r-r.rSH srSc@sZeZdZeeddddddddZedd d d d Zejd ddddddZ ej dddZ dS) TCatInputSpecT)rzinput file to 3dTcatz %srF)rrrrr z%s_tcatzoutput image file namez -prefix %sr)r6rrr7r+z++z-rlt%saRemove linear trends in each voxel time series loaded from each input dataset, SEPARATELY. Option -rlt removes the least squares fit of 'a+b*t' to each voxel time series. Option -rlt+ adds dataset mean back in. Option -rlt++ adds overall mean of all dataset timeseries back in.)rrrz,Print out some verbose output as the programz-verb)rrNr#) r$r%r&rr rr)r rrltr*rr-r-r-r.rT` s*rTc@seZdZdZdZeZeZdS)TCataConcatenate sub-bricks from input datasets into one big 3D+time dataset. TODO Replace InputMultiPath in_files with Traits.List, if possible. Current version adds extra whitespace. For complete details, see the `3dTcat Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> tcat = afni.TCat() >>> tcat.inputs.in_files = ['functional.nii', 'functional2.nii'] >>> tcat.inputs.out_file= 'functional_tcat.nii' >>> tcat.inputs.rlt = '+' >>> tcat.cmdline '3dTcat -rlt+ -prefix functional_tcat.nii functional.nii functional2.nii' >>> res = tcat.run() # doctest: +SKIP 3dTcatN) r$r%r&r0r1rTr2rr3r-r-r-r.rW srWc@sVeZdZejejeddeddddddZedd dd Z ej d d d ddddZ dS)TCatSBInputSpecT)rzList of tuples of file names and subbrick selectors as strings.Don't forget to protect the single quotes in the subbrick selectorso the contents are protected from the command line interpreter.z%s%s ...rF)rrrrr zoutput image file namez -prefix %s)rrZgenfilerrUz++z-rlt%saRemove linear trends in each voxel time series loaded from each input dataset, SEPARATELY. Option -rlt removes the least squares fit of 'a+b*t' to each voxel time series. Option -rlt+ adds dataset mean back in. Option -rlt++ adds overall mean of all dataset timeseries back in.)rrrNr#) r$r%r&r rror rrr)rrVr-r-r-r.rY srYc@s$eZdZdZdZeZeZddZ dS) TCatSubBrickaHopefully a temporary function to allow sub-brick selection until afni file managment is improved. For complete details, see the `3dTcat Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> tcsb = afni.TCatSubBrick() >>> tcsb.inputs.in_files = [('functional.nii', "'{2..$}'"), ('functional2.nii', "'{2..$}'")] >>> tcsb.inputs.out_file= 'functional_tcat.nii' >>> tcsb.inputs.rlt = '+' >>> tcsb.cmdline "3dTcat -rlt+ -prefix functional_tcat.nii functional.nii'{2..$}' functional2.nii'{2..$}' " >>> res = tcsb.run() # doctest: +SKIP rXcCs&|dkr"|j|jjddddSdS)Nr)rZ_tcat)r9)r:rr)rErGr-r-r.rM szTCatSubBrick._gen_filenameN) r$r%r&r0r1rYr2rr3rMr-r-r-r.rZ s rZc@sJeZdZedddddddZeddd d d Zed d ddZedddZdS)TStatInputSpeczinput file to 3dTstatz%srTF)rrrrrr z%s_tstatzoutput image file namez -prefix %sr5)r6rrr7z mask filez-mask %s)rrrzselected statistical output)rrNr#) r$r%r&r r5r)riroptionsr-r-r-r.r[ sr[c@seZdZdZdZeZeZdS)TStataCompute voxel-wise statistics using AFNI 3dTstat command For complete details, see the `3dTstat Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> tstat = afni.TStat() >>> tstat.inputs.in_file = 'functional.nii' >>> tstat.inputs.args = '-mean' >>> tstat.inputs.out_file = 'stats' >>> tstat.cmdline '3dTstat -mean -prefix stats functional.nii' >>> res = tstat.run() # doctest: +SKIP Z3dTstatN) r$r%r&r0r1r[r2rr3r-r-r-r.r] sr]c@seZdZeddddgdZeddd4d d d Zejd d d ddddddddddddddddddd d!d"d#d$d%Z ej d&d'd(Z ej d)d*d(Z ejd+d,d-d.d/d0d(Z ed1d2d(Zd3S)5 To3DInputSpecz%szoutput image file namez -prefix %s in_folder)r6rrr7z#folder with DICOM images to convertz%s/*.dcmrT)rrrrrZspgrZfseZepanZanatctZspctZpetZmraZbmapZdiffZomriZabucZfimZfithZficoZfittZfiftZfiztZfictZfibtZfibnZfigtZfiptZfbucz-%sz type of datafile being converted)rrzskip the outliers checkz-skip_outliers)rrz#assume that Siemens image is mosaicz-assume_dicom_mosaicrrvrcomplexzset output file datatypez -datum %szparameters for functional dataz-time:zt %s alt+z2Nr#)r$r%r&r r)r r_r rZfiletyper*Z skipoutliersZ assumemosaicdatatyperZ funcparamsr-r-r-r.r^ sb  r^c@seZdZdZdZeZeZdS)To3DaSCreate a 3D dataset from 2D image files using AFNI to3d command For complete details, see the `to3d Documentation `_ Examples -------- >>> from nipype.interfaces import afni >>> to3d = afni.To3D() >>> to3d.inputs.datatype = 'float' >>> to3d.inputs.in_folder = '.' >>> to3d.inputs.out_file = 'dicomdir.nii' >>> to3d.inputs.filetype = 'anat' >>> to3d.cmdline # doctest: +ELLIPSIS 'to3d -datum float -anat -prefix dicomdir.nii ./*.dcm' >>> res = to3d.run() # doctest: +SKIP Zto3dN) r$r%r&r0r1r^r2rr3r-r-r-r.rc6 srcc@seZdZeddd(ddddZeddd d Zed d d Zejdddddd Z ej ddd Z ej ddd Z ejddddd Z ej ddd Zejejddejdd ejd!d"d#d$d Zejd%d&d Zd'S))UndumpInputSpeczOinput file to 3dUndump, whose geometry will determinethe geometry of the outputz -master %srTF)rrrrrr zoutput image file namez -prefix %sr5)rrr7zJmask image file name. Only voxels that are nonzero in the mask can be set.z-mask %s)rrrrvrzset output file datatypez -datum %sz^default value stored in each input voxel that does not have a value supplied in the input filez-dval %fzUvalue, used for each voxel in the output dataset that is NOT listed in the input filez-fval %fZijkZxyzz_Coordinates in the input file as index triples (i, j, k) or spatial coordinates (x, y, z) in mmz-%szradius in mm of the sphere that will be filled about each input (x,y,z) or (i,j,k) voxel. If the radius is not given, or is 0, then each input data line sets the value in only one voxel.z-srad %fRLAPISaSpecifies the coordinate order used by -xyz. The code must be 3 letters, one each from the pairs {R,L} {A,P} {I,S}. The first letter gives the orientation of the x-axis, the second the orientation of the y-axis, the third the z-axis: R = right-to-left L = left-to-right A = anterior-to-posterior P = posterior-to-anterior I = inferior-to-superior S = superior-to-inferior If -orient isn't used, then the coordinate order of the -master (in_file) dataset is used to interpret (x,y,z) inputs.z -orient %sz^create only the .HEAD file which gets exploited by the AFNI matlab library function New_HEAD.mz -head_onlyNr#)r$r%r&r r5r)rr rrbrp default_valueZ fill_valueZcoordinates_specificationZsradroZorientr*Z head_onlyr-r-r-r.rdO sJ     rdc@seZdZedddZdS)UndumpOutputSpeczassembled fileT)rrN)r$r%r&r r)r-r-r-r.rl srlc@seZdZdZdZeZeZdS)Undumpa(3dUndump - Assembles a 3D dataset from an ASCII list of coordinates and (optionally) values. The input file(s) are ASCII files, with one voxel specification per line. A voxel specification is 3 numbers (-ijk or -xyz coordinates), with an optional 4th number giving the voxel value. For example: 1 2 3 3 2 1 5 5.3 6.2 3.7 // this line illustrates a comment The first line puts a voxel (with value given by '-dval') at point (1,2,3). The second line puts a voxel (with value 5) at point (3,2,1). The third line puts a voxel (with value given by '-dval') at point (5.3,6.2,3.7). If -ijk is in effect, and fractional coordinates are given, they will be rounded to the nearest integers; for example, the third line would be equivalent to (i,j,k) = (5,6,4). For complete details, see the `3dUndump Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> unndump = afni.Undump() >>> unndump.inputs.in_file = 'structural.nii' >>> unndump.inputs.out_file = 'structural_undumped.nii' >>> unndump.cmdline '3dUndump -prefix structural_undumped.nii -master structural.nii' >>> res = unndump.run() # doctest: +SKIP Z3dUndumpN) r$r%r&r0r1rdr2rlr3r-r-r-r.rm s"rmc@seZdZeddd&ddddZeddd d d Zejd d dZejdddZ ej dddZ edddZ ejdddZ ejddddgdgdZejej ej ej dddZej dd dZej d!d"dZejd#d$dZd%S)'UnifizeInputSpeczinput file to 3dUnifizez -input %srTF)rrrrrr z %s_unifizedzoutput image file namez -prefix %sr5)r6rrr7a5Treat the input as if it were T2-weighted, rather than T1-weighted. This processing is done simply by inverting the image contrast, processing it as if that result were T1-weighted, and then re-inverting the results counts of voxel overlap, i.e., each voxel will contain the number of masks that it is set in.z-T2)rrztAlso scale to unifize 'gray matter' = lower intensity voxels (to aid in registering images from different scanners).z-GMzSets the radius (in voxels) of the ball used for the sneaky trick. Default value is 18.3, and should be changed proportionally if the dataset voxel size differs significantly from 1 mm.z-Urad %sz=output file name to save the scale factor used at each voxel z -ssave %szSDo NOT use the 'duplo down' step; this can be useful for lower resolution datasets.z-noduploa9Assume the input dataset is a T2 (or T2\*) weighted EPI time series. After computing the scaling, apply it to ALL volumes (TRs) in the input dataset. That is, a given voxel will be scaled by the same factor at each TR. This option also implies '-noduplo' and '-T2'.This option turns off '-GM' if you turned it on.z-EPIno_duplot2gm)rrrr:aOption for AFNI experts only.Specify the 3 parameters for the algorithm: R = radius; same as given by option '-Urad', [default=18.3] b = bottom percentile of normalizing data range, [default=70.0] r = top percentile of normalizing data range, [default=80.0] z -rbt %f %f %fzOption for AFNI experts only.Set the upper percentile point used for T2-T1 inversion. Allowed to be anything between 90 and 100 (inclusive), with default to 98.5 (for no good reason).z-T2up %fzOption for AFNI experts only.Set the automask 'clip level fraction'. Must be between 0.1 and 0.9. A small fraction means to make the initial threshold for clipping (a la 3dClipLevel) smaller, which will tend to make the mask larger. [default=0.1]z -clfrac %fz"Don't print the progress messages.z-quietNr#)r$r%r&r r5r)r r*rprqrpZurad scale_fileroZepiroZrbtZt2_upZcl_fracr+r-r-r-r.rn sZ rnc@s"eZdZeddZedddZdS)UnifizeOutputSpeczscale factor file)rz unifized fileT)rrN)r$r%r&r rrr)r-r-r-r.rs s rsc@seZdZdZdZeZeZdS)Unifizea3dUnifize - for uniformizing image intensity * The input dataset is supposed to be a T1-weighted volume, possibly already skull-stripped (e.g., via 3dSkullStrip). However, this program can be a useful step to take BEFORE 3dSkullStrip, since the latter program can fail if the input volume is strongly shaded -- 3dUnifize will (mostly) remove such shading artifacts. * The output dataset has the white matter (WM) intensity approximately uniformized across space, and scaled to peak at about 1000. * The output dataset is always stored in float format! * If the input dataset has more than 1 sub-brick, only sub-brick #0 will be processed! * Want to correct EPI datasets for nonuniformity? You can try the new and experimental [Mar 2017] '-EPI' option. * The principal motive for this program is for use in an image registration script, and it may or may not be useful otherwise. * This program replaces the older (and very different) 3dUniformize, which is no longer maintained and may sublimate at any moment. (In other words, we do not recommend the use of 3dUniformize.) For complete details, see the `3dUnifize Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> unifize = afni.Unifize() >>> unifize.inputs.in_file = 'structural.nii' >>> unifize.inputs.out_file = 'structural_unifized.nii' >>> unifize.cmdline '3dUnifize -prefix structural_unifized.nii -input structural.nii' >>> res = unifize.run() # doctest: +SKIP Z 3dUnifizeN) r$r%r&r0r1rnr2rsr3r-r-r-r.rt s)rtc@s<eZdZedddddddZeddd d d Zed d dZdS)ZCutUpInputSpeczinput file to 3dZcutupz%srTF)rrrrrr z %s_zcutupzoutput image file namez -prefix %sr5)r6rrr7zslice range to keep in outputz-keep %s)rrNr#)r$r%r&r r5r)rZkeepr-r-r-r.ruI sruc@seZdZdZdZeZeZdS)ZCutUpa?Cut z-slices from a volume using AFNI 3dZcutup command For complete details, see the `3dZcutup Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> zcutup = afni.ZCutUp() >>> zcutup.inputs.in_file = 'functional.nii' >>> zcutup.inputs.out_file = 'functional_zcutup.nii' >>> zcutup.inputs.keep= '0 10' >>> zcutup.cmdline '3dZcutup -keep 0 10 -prefix functional_zcutup.nii functional.nii' >>> res = zcutup.run() # doctest: +SKIP Z3dZcutupN) r$r%r&r0r1rur2rr3r-r-r-r.rv[ srvc@sPeZdZedddddddZeddddd Zejd d d d Zej dddd Z dS) GCORInputSpecz&input dataset to compute the GCOR overz -input %srTF)rrrrrr z-mask dataset, for restricting the computationz-mask %s)rrrr rz -nfirst %dz'specify number of initial TRs to ignore)rrz -no_demeanz%do not (need to) demean as first stepNr#) r$r%r&r r5rir rQZnfirstr*Z no_demeanr-r-r-r.rws s  rwc@seZdZejddZdS)GCOROutputSpeczglobal correlation value)rN)r$r%r&r rpoutr-r-r-r.rx srxcs4eZdZdZdZeZeZfddZ ddZ Z S)GCORa! Computes the average correlation between every voxel and ever other voxel, over any give mask. For complete details, see the `@compute_gcor Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> gcor = afni.GCOR() >>> gcor.inputs.in_file = 'structural.nii' >>> gcor.inputs.nfirst = 4 >>> gcor.cmdline '@compute_gcor -nfirst 4 -input structural.nii' >>> res = gcor.run() # doctest: +SKIP z @compute_gcorcsJtt|j|}dd|jjdDd}t|dt|tdd|S)NcSs"g|]}|jjdr|jqS)zGCOR = )strip startswith)rWrfr-r-r.rx sz'GCOR._run_interface..rUr_gcorzGCOR = r#)rLrz_run_interfacer|r]setattrrvr})rErbZ gcor_line)rNr-r.r~ s zGCOR._run_interfacecCsdt|diS)Nryr})getattr)rEr-r-r.r szGCOR._list_outputs) r$r%r&r0r1rwr2rxr3r~rrOr-r-)rNr.rz s  rzc@seZdZedddddddZeddd d d Zejd d dZejddddgdZ ejddddgdZ ejddddgdZ e dddZ dS)AxializeInputSpeczinput file to 3daxializez%srTF)rrrrrr z %s_axializezoutput image file namez -prefix %sr5)r6rrr7zPrint out a progerss reportz-verb)rrz%Do sagittal slice order [-orient ASL]z -sagittalcoronalaxial)rrr:z%Do coronal slice order [-orient RSA]z-coronalsagittalzDo axial slice order [-orient RAI]This is the default AFNI axial order, andis the one currently required by thevolume rendering plugin; this is alsothe default orientation output by thisprogram (hence the program's name).z-axialznew orientation codez -orient %sNr")r$r%r&r r5r)r r*r,rrrrrRr-r-r-r.r s4   rc@seZdZdZdZeZeZdS)Axializea?Read in a dataset and write it out as a new dataset with the data brick oriented as axial slices. For complete details, see the `3dcopy Documentation. `__ Examples -------- >>> from nipype.interfaces import afni >>> axial3d = afni.Axialize() >>> axial3d.inputs.in_file = 'functional.nii' >>> axial3d.inputs.out_file = 'axialized.nii' >>> axial3d.cmdline '3daxialize -prefix axialized.nii functional.nii' >>> res = axial3d.run() # doctest: +SKIP Z 3daxializeN) r$r%r&r0r1rr2rr3r-r-r-r.r src@s|eZdZeeddddddddZedd d d d Zejd dddddZ ej dddZ ej dddgdZ ej dddgdZ dS) ZcatInputSpeczinput files to 3dZcatT)rrz%srF)rrrr z%s_zcatz+output dataset prefix name (default 'zcat')z -prefix %sr)r6rrr7rrrvz -datum %szJspecify data type for output. Valid types are 'byte', 'short' and 'float'.)rrz5print out some verbositiness as the program proceeds.z-verb)rrzForce scaling of the output to the maximum integer range. This only has effect if the output datum is byte or short (either forced or defaulted). This option is sometimes necessary to eliminate unpleasant truncation artifacts.z-fscaler)rrr:zDon't do any scaling on output to byte or short datasets. This may be especially useful when operating on mask datasets whose output values are only 0's and 1's.z-nscalerNr#)r$r%r&rr rr)r rrr*r,rrr-r-r-r.r s6   rc@seZdZdZdZeZeZdS)ZcataICopies an image of one type to an image of the same or different type using 3dZcat command For complete details, see the `3dZcat Documentation. `_ Examples -------- >>> from nipype.interfaces import afni >>> zcat = afni.Zcat() >>> zcat.inputs.in_files = ['functional2.nii', 'functional3.nii'] >>> zcat.inputs.out_file = 'cat_functional.nii' >>> zcat.cmdline '3dZcat -prefix cat_functional.nii functional2.nii functional3.nii' >>> res = zcat.run() # doctest: +SKIP Z3dZcatN) r$r%r&r0r1rr2rr3r-r-r-r.r# src@seZdZeddd1ddddZeddd d Zejd d d gdZejddd gdZ ejddd gdZ ejddd gdZ ejddd gdZ ejddd gdZ ejddd gdZejddd gdZejddd gdZejdd d gdZejd!d"d gdZed#d$d%d&d'd(d)d*d+d,d-d.d/g dZd0S)2ZeropadInputSpecz input datasetz%srTF)rrrrrr Zzeropadz.output dataset prefix name (default 'zeropad')z -prefix %s)r6rrz,adds 'n' planes of zero at the Inferior edgez-I %ir1)rrr:z,adds 'n' planes of zero at the Superior edgez-S %iz,adds 'n' planes of zero at the Anterior edgez-A %iz-adds 'n' planes of zero at the Posterior edgez-P %iz(adds 'n' planes of zero at the Left edgez-L %iz)adds 'n' planes of zero at the Right edgez-R %izMadds 'n' planes of zero on EACH of the dataset z-axis (slice-direction) facesz-z %iz~specify that planes should be added or cut symmetrically to make the resulting volume haveN slices in the right-left directionz-RL %izspecify that planes should be added or cut symmetrically to make the resulting volume haveN slices in the anterior-posterior directionz-AP %izspecify that planes should be added or cut symmetrically to make the resulting volume haveN slices in the inferior-superior directionz-IS %izpad counts 'n' are in mm instead of slices, where each 'n' is an integer and at least 'n' mm of slices will be added/removed; e.g., n = 3 and slice thickness = 2.5 mm ==> 2 slices addedz-mma>match the volume described in dataset 'mset', where mset must have the same orientation and grid spacing as dataset to be padded. the goal of -master is to make the output dataset from 3dZeropad match the spatial 'extents' of mset by adding or subtracting slices as needed. You can't use -I,-S,..., or -mm with -masterz -master %srirjrgrhrfrezRLAPISrNr#)r$r%r&r rr)r rQrirjrgrhrfrerrrrr*rr1r-r-r-r.r; sn         rc@seZdZdZdZeZeZdS)ZeropadaAdds planes of zeros to a dataset (i.e., pads it out). For complete details, see the `3dZeropad Documentation. `__ Examples -------- >>> from nipype.interfaces import afni >>> zeropad = afni.Zeropad() >>> zeropad.inputs.in_files = 'functional.nii' >>> zeropad.inputs.out_file = 'pad_functional.nii' >>> zeropad.inputs.I = 10 >>> zeropad.inputs.S = 10 >>> zeropad.inputs.A = 10 >>> zeropad.inputs.P = 10 >>> zeropad.inputs.R = 10 >>> zeropad.inputs.L = 10 >>> zeropad.cmdline '3dZeropad -A 10 -I 10 -L 10 -P 10 -R 10 -S 10 -prefix pad_functional.nii functional.nii' >>> res = zeropad.run() # doctest: +SKIP Z 3dZeropadN) r$r%r&r0r1rr2rr3r-r-r-r.r sr)vr0rBos.pathrCrr^numpyrZutils.filemaniprrrrHrrr r r r r rrrrZ external.duerrrrrrrrr/r4r<rPrSrTrhrqrtrrrrrrrrrrrrrrrrrrrrrrrrrr r rrrrrrr r%r&r)r+r3r4r7r;r@rArBrCrDrKrMrQrSrTrWrYrZr[r]r^rcrdrlrmrnrsrtrurvrwrxrzrrrrrrr-r-r-r.s 4  /! &#55'2@'1 &$*127)[/j!-'>3D'U9#V#U%# 9C)U0(',Y