3 do@sdZddlZddlZddlZddlZddlZddlmZmZm Z m Z m Z m Z m Z mZmZmZmZddlmZejdedGd d d e ZGd d d eZGd dde ZGddde ZGdddeZGddde ZGdddeZGdddeZGddde ZGdddeZGdddeZGdd d e Z Gd!d"d"eZ!Gd#d$d$eZ"Gd%d&d&e Z#Gd'd(d(e Z$Gd)d*d*eZ%Gd+d,d,e Z&Gd-d.d.eZ'Gd/d0d0eZ(Gd1d2d2e Z)Gd3d4d4eZ*Gd5d6d6eZ+Gd7d8d8e Z,Gd9d:d:eZ-Gd;d<dd>e Z/Gd?d@d@e Z0GdAdBdBeZ1GdCdDdDe Z2GdEdFdFeZ3GdGdHdHeZ4GdIdJdJe Z5GdKdLdLeZ6GdMdNdNeZ7GdOdPdPe Z8GdQdRdReZ9GdSdTdTeZ:GdUdVdVe Z;GdWdXdXeZGd]d^d^eZ?Gd_d`d`eZ@Gdadbdbe ZAGdcddddeZBGdedfdfeZCGdgdhdhe ZDGdidjdjeZEGdkdldleZFGdmdndne ZGGdodpdpeZHGdqdrdreZIGdsdtdte ZJGdudvdveZKGdwdxdxeZLGdydzdze ZMGd{d|d|eZNGd}d~d~eZOGddde ZPGdddeZQGdddeZRGddde ZSGdddeZTGdddeZUGddde ZVGdddeZWGdddeZXGddde ZYGdddeZZGdddeZ[Gddde Z\GdddeZ]GdddeZ^Gddde Z_GdddeZ`GdddeZaGddde ZbGdddeZcGdddeZdGddde ZeGdddeZfGdddeZgGddde ZhdS)aThe minc module provides classes for interfacing with the `MINC `_ command line tools. This module was written to work with MINC version 2.2.00. Author: `Carlo Hamalainen `__ N) TraitedSpecCommandLineInputSpec CommandLineStdOutCommandLineInputSpecStdOutCommandLineFile DirectoryInputMultiPathOutputMultiPathtraits isdefined)aggregate_filenamealways)categoryc @s,eZdZedddddcdZeddddgd d d d ZdeZejddedZ ejddedZ ejddedZ ejddedZ ejddedZ ejd d!edZejd"d#edZdfZejd$d%edZejd&d'edZejejejd(d)d*ZdgZejd-d.edZejd/d0edZejejejd1d2d3Zejd4d5d3Zejd6d7d3Zeejd8d9d:d;Zeejd>> from nipype.interfaces.minc import Extract >>> from nipype.interfaces.minc.testdata import minc2Dfile >>> extract = Extract(input_file=minc2Dfile) >>> extract.run() # doctest: +SKIP >>> extract = Extract(input_file=minc2Dfile, start=[3, 10, 5], count=[4, 4, 4]) # extract a 4x4x4 slab at offset [3, 10, 5] >>> extract.run() # doctest: +SKIP Z mincextractN) r9r:r;__doc__r input_specrJ output_spec_cmdrHrHrHrIrKsrKc@seZdZeddddd/dZedd0dgd d d d Zd1ZejddedZ ejddedZ ejddedZ ejddedZ ejddedZ ejddedZd2Zejd!d"edZejd#d$edZejejejd%d&d'Zd3Zejd*d+edZejd,d-edZd.S)4ToRawInputSpecz input fileTz%sr)rrrrrz output filerrFz%s.raw)rrrrrrrrr r!r"r#zWrite out data as bytes.z-byte)rrr&z!Write out data as short integers.z-shortz"Write out data as 32-bit integers.z-intzSuperseded by write_int.z-longz9Write out data as single precision floating-point values.z-floatz9Write out data as double precision floating-point values.z-doubler$r%zWrite out signed data.z-signedzWrite out unsigned data.z -unsignedz -range %s %szLSpecify the range of output values.Default value: 1.79769e+308 1.79769e+308.)rrr'r(z3Normalize integer pixel values to file max and min.z -normalizezTurn off pixel normalization.z -nonormalizeNr7r8)rrr r!r"r#)r$r%)r'r()r9r:r;rrr<r=r r>rrr r!r"r#r?r$r%r@rArBrCr'r(rHrHrHrIrPs`      rPc@seZdZedddZdS)ToRawOutputSpeczoutput file in raw formatT)rrN)r9r:r;rr<rHrHrHrIrQ[srQc@seZdZdZeZeZdZdS)ToRawaDump a chunk of MINC file data. This program is largely superceded by mincextract (see Extract). Examples -------- >>> from nipype.interfaces.minc import ToRaw >>> from nipype.interfaces.minc.testdata import minc2Dfile >>> toraw = ToRaw(input_file=minc2Dfile) >>> toraw.run() # doctest: +SKIP >>> toraw = ToRaw(input_file=minc2Dfile, write_range=(0, 100)) >>> toraw.run() # doctest: +SKIP Z minctorawN) r9r:r;rLrPrMrQrNrOrHrHrHrIrR_srRc@seZdZeddddd#dZedddd$dgd d d Zejd d dddZejdddZ ejdddZ ej ddddddddddddd Z ej ddd d!Zd"S)%ConvertInputSpeczinput file for convertingTz%sr)rrrrrz output filerrFz%s_convert_output.mnc)rgenfilerrrrrzOverwrite existing file.z-clobber)rr usedefault default_valuezCreate a MINC 2 output file.z-2)rrz}Create a template file. The dimensions, variables, andattributes of the input file are preserved but all data it set to zero.z -templater z -compress %sz1 block size).z -chunk %d)lowrrNr7r8)r9r:r;rrr<r r>clobbertwotemplateEnum compressionRangechunkrHrHrHrIrSusPrSc@seZdZedddZdS)ConvertOutputSpecz output fileT)rrN)r9r:r;rr<rHrHrHrIrfsrfc@seZdZdZeZeZdZdS)ConvertaHconvert between MINC 1 to MINC 2 format. Examples -------- >>> from nipype.interfaces.minc import Convert >>> from nipype.interfaces.minc.testdata import minc2Dfile >>> c = Convert(input_file=minc2Dfile, output_file='/tmp/out.mnc', two=True) # Convert to MINC2 format. >>> c.run() # doctest: +SKIP Z mincconvertN) r9r:r;rLrSrMrfrNrOrHrHrHrIrgs rgc @sZeZdZeddddddZedddddgd d d ZdZejddedZ ejddedZ dS) CopyInputSpeczinput file to copyTz%sr)rrrrrz output filerrFz %s_copy.mnc)rrTrrrrr pixel_values real_valueszCopy pixel values as is.z -pixel_values)rrr&z&Copy real pixel intensities (default).z -real_valuesNr7r8)rirj) r9r:r;rrr<Z _xor_pixelr r>rirjrHrHrHrIrhs" rhc@seZdZedddZdS)CopyOutputSpecz output fileT)rrN)r9r:r;rr<rHrHrHrIrksrkc@seZdZdZeZeZdZdS)Copya Copy image values from one MINC file to another. Both the input and output files must exist, and the images in both files must have an equal number dimensions and equal dimension lengths. NOTE: This program is intended primarily for use with scripts such as mincedit. It does not follow the typical design rules of most MINC command-line tools and therefore should be used only with caution. ZminccopyN) r9r:r;rLrhrMrkrNrOrHrHrHrIrls rlc @seZdZeddddddZedddddgd d d d Zejd d dZejdddZ ejdddZ ejdddZ ejdddZ ejdddZ ejdddZejdddZdS) ToEcatInputSpeczinput file to convertTz%sr)rrrrrz output filerrFz %s_to_ecat.v)rrTrrrrrrz3Ignore informations from the minc patient variable.z-ignore_patient_variable)rrz1Ignore informations from the minc study variable.z-ignore_study_variablez7Ignore informations from the minc acquisition variable.z-ignore_acquisition_variablezZignore_patient_variableZignore_study_variableZignore_acquisition_variableZ ignore_ecat_acquisition_variableZignore_ecat_mainZignore_ecat_subheader_variableZno_decay_corr_fctrZvoxels_as_integersrHrHrHrIrmsL rmc@seZdZedddZdS)ToEcatOutputSpecz output fileT)rrN)r9r:r;rr<rHrHrHrIrn3srnc@seZdZdZeZeZdZdS)ToEcataConvert a 2D image, a 3D volumes or a 4D dynamic volumes written in MINC file format to a 2D, 3D or 4D Ecat7 file. Examples -------- >>> from nipype.interfaces.minc import ToEcat >>> from nipype.interfaces.minc.testdata import minc2Dfile >>> c = ToEcat(input_file=minc2Dfile) >>> c.run() # doctest: +SKIP >>> c = ToEcat(input_file=minc2Dfile, voxels_as_integers=True) >>> c.run() # doctest: +SKIP Z minctoecatN) r9r:r;rLrmrMrnrNrOrHrHrHrIro7sroc@seZdZeddddd)dZedd*dgd d d d Zd+ZejddedZ ejddedZ d,Z ej dddde dZ ej dddde dZeejddddZejd d!d"d#Zejd$d%d&Zejejejejejd'dd&Zd(S)- DumpInputSpecz input fileTz%sr)rrrrrz output filerrFz %s_dump.txt)rrrrrrcoordinate_data header_dataz0Coordinate variable data and header information.z-c)rrr&z!Header information only, no data.z-hannotations_briefannotations_fullcfz-b %sz3Brief annotations for C or Fortran indices in data.)rrr&z-f %sz2Full annotations for C or Fortran indices in data.z)Output data for specified variables only.r)z-v %s)rr*rrz1Line length maximum in data section (default 80).z-l %d)r^rrz1Name for netCDF (default derived from file name).z-n %s)rrz1Display floating-point values with less precisionNr7r8)rqrr)rsrt)r9r:r;rrr<Z_xor_coords_or_headerr r>rqrrZ_xor_annotationsrbrsrtr Str variablesrdZ line_lengthZ netcdf_nameEitherrEr@ precisionrHrHrHrIrpNsV   rpc@seZdZedddZdS)DumpOutputSpecz output fileT)rrN)r9r:r;rr<rHrHrHrIr{sr{cs,eZdZdZeZeZdZfddZ Z S)DumpaDump a MINC file. Typically used in conjunction with mincgen (see Gen). Examples -------- >>> from nipype.interfaces.minc import Dump >>> from nipype.interfaces.minc.testdata import minc2Dfile >>> dump = Dump(input_file=minc2Dfile) >>> dump.run() # doctest: +SKIP >>> dump = Dump(input_file=minc2Dfile, output_file='/tmp/out.txt', precision=(3, 4)) >>> dump.run() # doctest: +SKIP Zmincdumpcsx|dkrdt|trd|St|trTt|dtrTt|dtrTd|d|dfStdt|tt|j|||S)Nrzz-p %drrz-p %d,%dzInvalid precision argument: ) isinstanceinttuple ValueErrorstrsuperr| _format_arg)selfnamespecvalue) __class__rHrIrs  zDump._format_arg) r9r:r;rLrprMr{rNrOr __classcell__rHrH)rrIr|s r|c @s@eZdZdiZeedddddddjed Zed d dded Zed dddkdgdddZe j dddZ e j dddddZ dlZ e j dde dZe j dde dZe j dd dZdmZe j d#d$edZe j d%d&edZdnZe j d0d1edZe j d2d3edZe j d4d5edZe j d6d7edZe j d8d9edZe j d:d;edZe j dd?edZe j d@dAedZe jdBdCdDddEdFZdoZ e j dIdJe dZ!e j dKdLe dZ"e j#e j$e j$dMdNdOZ%edPdQdZ&dpZ'e j dTdUe'dZ(e j dVdWe'dZ)e j*dXdYdZ+e j dZd[dZ,e j#e j-e j-d\d]dOZ.e j-d^d_dZ/ee j*d`dadbdcZ0e j dddedqdgZ1dhS)rAverageInputSpec input_filesfilelistT)rz input file(s) z%sr)rrr*rrr&z7Specify the name of a file containing input file names.z -filelist %s)rrrrr&z output filerFz%s_averaged.mnc)rrTrrrrrzCreate a MINC 2 output file.z-2)rrzOverwrite existing file.z-clobber)rrrUrVverbosequietz!Print out log messages (default).z-verbose)rrr&zDo not print out log messages.z-quietzPrint out debugging messages.z-debugcheck_dimensionsno_check_dimensionsz9Check that dimension info matches across files (default).z-check_dimensionszDo not check dimension info.z-nocheck_dimensionsformat_filetype format_byte format_short format_int format_long format_float format_double format_signedformat_unsignedz&Use data type of first file (default).z -filetypezWrite out byte data.z-bytezWrite out short integer data.z-shortzWrite out 32-bit integer data.z-intzSuperseded by -int.z-longz/Write out single-precision floating-point data.z-floatz/Write out double-precision floating-point data.z-doublezWrite signed integer data.z-signedz&Write unsigned integer data (default).z -unsignedrz=Specify the maximum size of the internal buffers (in kbytes).iz-max_buffer_size_in_kb %d)r^rrrUrr'r(z'Normalize data sets for mean intensity.z -normalizez%Do not normalize data sets (default).z -nonormalizez -range %d %dzValid range for output data.)rrz)Specify an output sd file (default=none).z -sdfile %s copy_headerno_copy_headerzBCopy all of the header from the first file (default for one file).z -copy_headerzLDo not copy all of the header from the first file (default for many files)).z-nocopy_headerz3Specify a dimension along which we wish to average.z -avgdim %sz;Binarize the volume by looking for values in a given range.z -binarizez-binrange %s %szLSpecify a range for binarization. Default value: 1.79769e+308 -1.79769e+308.zNSpecify a target value (+/- 0.5) forbinarization. Default value: -1.79769e+308z -binvalue %sz0Specify weights for averaging (",,...").r)z -weights %s)rr*rz0Weight by dimension widths when -avgdim is used.z-width_weightedavgdim)rrrequiresN)rrr7r8)rr)rr) rrrrrrrrr)r'r()rr)r)2r9r:r;_xor_input_filesr rrrr<r r>r`r_ _xor_verboserrdebug_xor_check_dimensionsrr _xor_formatrrrrrrrrrrdmax_buffer_size_in_kbrCr'r(r@rE voxel_rangeZsdfile_xor_copy_headerrrrwrZbinarizerAZbinrangeZbinvalueweightsZwidth_weightedrHrHrHrIrs         rc@seZdZedddZdS)AverageOutputSpecz output fileT)rrN)r9r:r;rr<rHrHrHrIrzsrc@seZdZdZeZeZdZdS)AverageafAverage a number of MINC files. Examples -------- >>> from nipype.interfaces.minc import Average >>> from nipype.interfaces.minc.testdata import nonempty_minc_data >>> files = [nonempty_minc_data(i) for i in range(3)] >>> average = Average(input_files=files, output_file='/tmp/tmp.mnc') >>> average.run() # doctest: +SKIP Z mincaverageN) r9r:r;rLrrMrrNrOrHrHrHrIr~s rc @sneZdZeddddddZedddddgd d d Zejd d dZejdddZ ejdddZ ejdddZ dS) BlobInputSpeczinput file to blobTz%sr)rrrrrz output filerrFz %s_blob.mnc)rrTrrrrrztraceZ determinant translationZ magnituderHrHrHrIrs( rc@seZdZedddZdS)BlobOutputSpecz output fileT)rrN)r9r:r;rr<rHrHrHrIrsrc@seZdZdZeZeZdZdS)Bloba4Calculate blobs from minc deformation grids. Examples -------- >>> from nipype.interfaces.minc import Blob >>> from nipype.interfaces.minc.testdata import minc2Dfile >>> blob = Blob(input_file=minc2Dfile, output_file='/tmp/tmp.mnc', trace=True) >>> blob.run() # doctest: +SKIP ZmincblobN) r9r:r;rLrrMrrNrOrHrHrHrIrs rc @sDeZdZdhZeedddddddid Zed dddjdgd d dZej dddZ ej dddddZ dkZ ej dde dZ ej dde dZej dddZeddded ZdlZej d#d$edZej d%d&edZdmZej d0d1edZej d2d3edZej d4d5edZej d6d7edZej d8d9edZej d:d;edZej dd?edZej d@dAedZejejejdBdCdDZ ej!dEdFdGdHZ"dnZ#ej dKdLe#dZ$ej dMdNe#dZ%ej dOdPdZ&ej dQdRdZ'doZ(ej dVdWe(dZ)ej dXdYe(dZ*ej dZd[e(dZ+dpZ,ej-d^d_e,dd`Z.edadbe,dd`Z/ej0ejej-edcdddDZ1ejdedfdZ2dgS)q CalcInputSpecrrT)rzinput file(s) for calculationrz%sr)rrr*rrz output filerFz %s_calc.mnc)rrTrrrrrzCreate a MINC 2 output file.z-2)rrzOverwrite existing file.z-clobber)rrrUrVrrz!Print out log messages (default).z-verbose)rrr&zDo not print out log messages.z-quietzPrint out debugging messages.z-debugz7Specify the name of a file containing input file names.z -filelist %s)rrrr&rrz+Copy all of the header from the first file.z -copy_headerz2Do not copy all of the header from the first file.z-nocopy_headerrrrrrrrrrz&Use data type of first file (default).z -filetypezWrite out byte data.z-bytezWrite out short integer data.z-shortzWrite out 32-bit integer data.z-intzSuperseded by -int.z-longz/Write out single-precision floating-point data.z-floatz/Write out double-precision floating-point data.z-doublezWrite signed integer data.z-signedz&Write unsigned integer data (default).z -unsignedz -range %d %dzValid range for output data.)rrrz=Specify the maximum size of the internal buffers (in kbytes).z-max_buffer_size_in_kb %d)r^rrrrz4Check that files have matching dimensions (default).z-check_dimensionsz1Do not check that files have matching dimensions.z-nocheck_dimensionsz,Ignore invalid data (NaN) for accumulations.z -ignore_nanz=Invalid data in any file at a voxel produces a NaN (default).z-propagate_nan output_nan output_zerooutput_illegal_valuez7Output NaN when an illegal operation is done (default).z-nanz.Output zero when an illegal operation is done.z-zerozQValue to write out when an illegal operation is done. Default value: 1.79769e+308z-illegal_value expressionexpfilez"Expression to use in calculations.z-expression '%s')rrr&rz#Name of file containing expression.z -expfile %sz-outfile %s %saPList of (symbol, file) tuples indicating that output should be writtento the specified file, taking values from the symbol which should becreated in the expression (see the EXAMPLES section). If this optionis given, then all non-option arguments are taken as input files.This option can be used multiple times for multiple output files.z,Number of voxels to evaluate simultaneously.z-eval_width %sN)rrr7r8)rr)rr) rrrrrrrrr)rr)rrr)rr)3r9r:r;rr rrr<r r>r`r_rrrrrrrrrrrrrrrrrrr@rErrdrrrr ignore_nan propagate_nan_xor_nan_zero_illegalrroutput_illegalZ_xor_expressionrwrrListoutfilesZ eval_widthrHrHrHrIrs         rc@seZdZedddZdS)CalcOutputSpecz output fileT)rrN)r9r:r;rr<rHrHrHrIrsrc@seZdZdZeZeZdZdS)CalcaCompute an expression using MINC files as input. Examples -------- >>> from nipype.interfaces.minc import Calc >>> from nipype.interfaces.minc.testdata import nonempty_minc_data >>> file0 = nonempty_minc_data(0) >>> file1 = nonempty_minc_data(1) >>> calc = Calc(input_files=[file0, file1], output_file='/tmp/calc.mnc', expression='A[0] + A[1]') # add files together >>> calc.run() # doctest: +SKIP ZminccalcN) r9r:r;rLrrMrrNrOrHrHrHrIrs rc@seZdZeddddddZedddgd d d d Zejd d ddZd Z ej dde dZ ej dde dZ ej dddZ ej dddZej dddZdS)! BBoxInputSpecz input fileTz%sr)rrrrrz+output file containing bounding box cornersrrFz %s_bbox.txt)rrrrrrrzrrZformat_mincresampleZformat_mincreshapeZformat_minccroprHrHrHrIrs>rc@seZdZedddZdS)BBoxOutputSpecz+output file containing bounding box cornersT)rrN)r9r:r;rr<rHrHrHrIrsrc@seZdZdZeZeZdZdS)BBoxa+Determine a bounding box of image. Examples -------- >>> from nipype.interfaces.minc import BBox >>> from nipype.interfaces.minc.testdata import nonempty_minc_data >>> file0 = nonempty_minc_data(0) >>> bbox = BBox(input_file=file0) >>> bbox.run() # doctest: +SKIP ZmincbboxN) r9r:r;rLrrMrrNrOrHrHrHrIrs rc@sVeZdZdZejdddZejdddZejdddZejd d dZ ejd d dZ ejd ddZ ejdddddZ e dddZejdddddZejdddddZejdddddZejdddd dZejd!dd"d#dZejd!dd$d%dZejd&dd'd(dZejd)dd*d+dZejd,d-dZed.d9d0dd1Ze d2d:d0dd1Ze d3d;d0d4gd5d6d7Zd8S)<BeastInputSpeca TODO: Command-specific options: -verbose: Enable verbose output. -positive: Specify mask of positive segmentation (inside mask) instead of the default mask. -output_selection: Specify file to output selected files. -count: Specify file to output the patch count. -mask: Specify a segmentation mask instead of the the default mask. -no_mask: Do not apply a segmentation mask. Perform the segmentation over the entire image. -no_positive: Do not apply a positive mask. Generic options for all commands: -help: Print summary of command-line options and abort -version: Print version number of program and exit Copyright (C) 2011 Simon Fristed Eskildsen, Vladimir Fonov, Pierrick Coupe, Jose V. Manjon This program comes with ABSOLUTELY NO WARRANTY; for details type 'cat COPYING'. This is free software, and you are welcome to redistribute it under certain conditions; type 'cat COPYING' for details. Usage: mincbeast [options] mincbeast -help Get this example to work? https://github.com/BIC-MNI/BEaST/blob/master/README.library 2.3 Source the minc-toolkit (if installed): $ source /opt/minc/minc-toolkit-config.sh 2.4 Generate library by running: $ beast_prepareADNIlib -flip Example: $ sudo beast_prepareADNIlib -flip Downloads/ADNI /opt/minc/share/beast-library-1.1 3. Test the setup 3.1 Normalize your data $ beast_normalize -modeldir /opt/minc/share/icbm152_model_09c input.mnc normal.mnc normal.xfm 3.2 Run BEaST $ mincbeast /opt/minc/share/beast-library-1.1 normal.mnc brainmask.mnc -conf /opt/minc/share/beast-library-1.1/default.2mm.conf -same_res z1Output the probability map instead of crisp mask.z -probability)rrzBFlip images around the mid-sagittal plane to increase patch count.z-flipz^Do not calculate moments instead use precalculatedlibrary moments. (for optimization purposes)z -load_momentsz Fill holes in the binary output.z-fillz-Apply a median filter on the probability map.z-medianz:Apply an NLM filter on the probability map (experimental).z -nlm_filterzOverwrite existing file.z-clobberT)rrrUrVzSpecify configuration file.z-configuration %srXzSpecify voxel size for calculations (4, 2, or 1).Default value: 4. Assumes no multiscale. Use configurationfile for multiscale.z-voxel_size %s)rUrrzMFile paths in the library are absolute (default is relative to library root).z-abspathrz?Specify patch size for single scale approach. Default value: 1.z-patch_size %srzHSpecify size of search area for single scale approach. Default value: 2.z-search_area %sg?z2Specify confidence level Alpha. Default value: 0.5z -alpha %sz3Specify smoothness factor Beta. Default value: 0.25z-beta %sgffffff?z:Specify threshold for patch selection. Default value: 0.95z -threshold %sz4Specify number of selected images. Default value: 20z-selection_num %sz9Output final mask with the same resolution as input file.z-same_resolutionzlibrary directoryrWz%s)rrrrz input filez output filerFz%s_beast_mask.mnc)rrrrrrNr7r8)r9r:r;rLr r>Zprobability_mapZ flip_imagesZ load_momentsZ fill_holesZ median_filterZ nlm_filterr_rZconfiguration_filerEZ voxel_sizeabspathZ patch_sizeZ search_arearAZconfidence_level_alphaZsmoothness_factor_betaZthreshold_patch_selectionZnumber_selected_imagesZsame_resolutionr Z library_dirrr<rHrHrHrIrs,   rc@seZdZedddZdS)BeastOutputSpeczoutput mask fileT)rrN)r9r:r;rr<rHrHrHrIrsrc@seZdZdZeZeZdZdS)BeastalExtract brain image using BEaST (Brain Extraction using non-local Segmentation Technique). Examples -------- >>> from nipype.interfaces.minc import Beast >>> from nipype.interfaces.minc.testdata import nonempty_minc_data >>> file0 = nonempty_minc_data(0) >>> beast = Beast(input_file=file0) >>> beast .run() # doctest: +SKIP Z mincbeastN) r9r:r;rLrrMrrNrOrHrHrHrIrs rc @seZdZedddddTdZdUZejded Zejd ed Z ed dddVd gddddZ ejdddddZ ej dddddZ ej dddZejdddddZdWZejejd d!ejd"d!dd#Zej d$d%d&gd'Zejd(d)dZejejejd*d+dZdXZejejejd.d/ed0Zejd1d2ed0Zej d3d4dZdYZejd8d9ed0Zejd:d;ed0Z ejdd?dZ"ej d@dAdZ#dZZ$ej dDdEdZ%ej&dFdGdHdIdJZ'd[Z(ejdMdNe(d0Z)ejdOdPe(d0Z*ejdQdRdZ+dSS)\ PikInputSpecz input fileTz%sr)rrrrrjpgpngzOutput a jpg file.)rr&zOutput a png file (default).z output filerrFz%s.png)rrrTrrrrrzOverwrite existing file.z-clobber)rrrUrVzcScaling factor for resulting image. By default images areoutput at twice their original resolution.z --scale %s)rUrrzFAutoscale the resulting image to have a fixed image width (in pixels).z --width %s)rrr\z9Bitdepth for resulting image 8 or 16 (MSB machines only!)z --depth %s title_stringtitle_with_filenamez/Use input filename as title in resulting image.)rz#Add a title to the resulting image.)rzFont point size for the title.z--title_size %stitle)rrrzKcreate an annotated bar to match the image (use height of the output image)z --anot_barz$Valid range of values for MINC file.z --range %s %srD auto_rangez1Range of image values to use for pixel intensity.z--image_range %s %s)rrr&zFAutomatically determine image range using a 5 and 95% PcT. (histogram)z --auto_rangez:Slice number to get. (note this is in voxel co-ordinates).z --slice %sslice_zslice_yslice_xz"Get an axial/transverse (z) slice.z-zzGet a coronal (y) slice.z-yzGet a sagittal (x) slice.z-xz*Create a triplanar view of the input file.z --triplanarz)Pixel size for each image in a triplanar.z --tilesize %ssagittal_offsetsagittal_offset_percz*Offset the sagittal slice from the centre.z--sagittal_offset %srdz:Offset the sagittal slice by a percentage from the centre.z--sagittal_offset_perc %d)r^highrrvertical_triplanar_viewhorizontal_triplanar_viewz+Create a vertical triplanar view (Default).z --verticalz#Create a horizontal triplanar view.z --horizontalzArguments to pass to minclookupz --lookup %sNr7)rrr8)rr)rDr)rrr)rr)rr),r9r:r;rrZ_xor_image_typer r>rrr<r_rEscalewidthrbdepthZ _xor_titleryrwrZ title_sizeZ annotated_barr@rAZ minc_rangeZ_xor_image_rangerDrrFZ _xor_slicerrrZ triplanarZ tile_sizeZ_xor_sagittal_offsetrrdrZ_xor_vertical_horizontalrrlookuprHrHrHrIrs        rc@seZdZedddZdS) PikOutputSpecz output imageT)rrN)r9r:r;rr<rHrHrHrIrEsrcs,eZdZdZeZeZdZfddZ Z S)PikazGenerate images from minc files. Mincpik uses Imagemagick to generate images from Minc files. Examples -------- >>> from nipype.interfaces.minc import Pik >>> from nipype.interfaces.minc.testdata import nonempty_minc_data >>> file0 = nonempty_minc_data(0) >>> pik = Pik(input_file=file0, title='foo') >>> pik .run() # doctest: +SKIP ZmincpikcsR|dkr>t|tr|rdSt|tr.d|fStdt|tt|j|||S)Nrz--titlez--title --title_text %sz$Unknown value for "title" argument: )r}boolrrrrr)rrrr)rrHrIr_s  zPik._format_arg) r9r:r;rLrrMrrNrOrrrHrH)rrIrIs rc @seZdZeddddd*dZeddd+dZejd d ddd Zd,Z ejdde dZ ejdde dZ ejdddZ ejdddZ ejdddZd-Zejdddedd Zejdd!d"edd Zejejejejd#d$edd%Zejd&ddd'd(dZd)S). BlurInputSpecz input fileTz%sr)rrrrrzoutput file baser)rrrzOverwrite existing file.z-clobber)rrrUrVgaussianrectz*Use a gaussian smoothing kernel (default).z -gaussian)rrr&z"Use a rect (box) smoothing kernel.z-rectz-Create the gradient magnitude volume as well.z -gradient)rrzECreate the partial derivative and gradient magnitude volumes as well.z-partialz(Do not apodize the data before blurring.z -no_apodizefwhmfwhm3d standard_devrz=Full-width-half-maximum of gaussian kernel. Default value: 0.z-fwhm %s)rrr&rz8Standard deviation of gaussian kernel. Default value: 0.z-standarddev %sz-3dfwhm %s %s %szdFull-width-half-maximum of gaussian kernel.Default value: -1.79769e+308 -1.79769e+308 -1.79769e+308.)rrr&rrWzANumber of dimensions to blur (either 1,2 or 3). Default value: 3.z-dimensions %sNr7r8)rr)rrr)r9r:r;rroutput_file_baser r>r_Z _xor_kernelrrgradientpartialZ no_apodizeZ_xor_main_optionsrArrr@rrbZ dimensionsrHrHrHrIrjsb   rc@sJeZdZedddZeddZeddZeddZeddZed dZ d S) BlurOutputSpeczBlurred output file.T)rrzGradient dxyz.)rzPartial gradient dx.zPartial gradient dy.zPartial gradient dz.zPartial gradient dxyz.N) r9r:r;rr< gradient_dxyz partial_dx partial_dy partial_dz partial_dxyzrHrHrHrIrs      rcs@eZdZdZeZeZdZddZ ddZ e fddZ Z S) Blura Convolve an input volume with a Gaussian blurring kernel of user-defined width. Optionally, the first partial derivatives and the gradient magnitude volume can be calculated. Examples -------- >>> from nipype.interfaces.minc import Blur >>> from nipype.interfaces.minc.testdata import minc3Dfile (1) Blur an input volume with a 6mm fwhm isotropic Gaussian blurring kernel: >>> blur = Blur(input_file=minc3Dfile, fwhm=6, output_file_base='/tmp/out_6') >>> blur.run() # doctest: +SKIP mincblur will create /tmp/out_6_blur.mnc. (2) Calculate the blurred and gradient magnitude data: >>> blur = Blur(input_file=minc3Dfile, fwhm=6, gradient=True, output_file_base='/tmp/out_6') >>> blur.run() # doctest: +SKIP will create /tmp/out_6_blur.mnc and /tmp/out_6_dxyz.mnc. (3) Calculate the blurred data, the partial derivative volumes and the gradient magnitude for the same data: >>> blur = Blur(input_file=minc3Dfile, fwhm=6, partial=True, output_file_base='/tmp/out_6') >>> blur.run() # doctest: +SKIP will create /tmp/out_6_blur.mnc, /tmp/out_6_dx.mnc, /tmp/out_6_dy.mnc, /tmp/out_6_dz.mnc and /tmp/out_6_dxyz.mnc. ZmincblurcCsV|jj}t|r|Stjj|jjd}tjj|d}tjjtj |d}|SdS)NrrZ _bluroutput) inputsrr ospathsplitrsplitextjoingetcwd)rrZbase_file_nameZbase_file_name_no_extZ output_baserHrHrI_gen_output_baseszBlur._gen_output_basecCsx|jj}|j}|d|d<t|jjr8|d|d<t|jjrt|d|d<|d|d<|d |d <|d|d <|S) Nz _blur.mncr<z _dxyz.mncrz_dx.mncrz_dy.mncrz_dz.mncrr)rNgetrr rrr)routputsrrHrHrI _list_outputss         zBlur._list_outputscs4|jj}tt|j}t|r |Sd||jfSdS)Nz%s %s)rrrrcmdliner r)rrZ orig_cmdline)rrHrIr s  z Blur.cmdline)r9r:r;rLrrMrrNrOrrpropertyrrrHrH)rrIrs#rc @sheZdZdZeeddddddded Zed ddddgd d dZeddddedZe j dddddZ e j dddZ dZ e j dde dZe j dde dZdZe j d(d)edZe j d*d+edZe j d,d-edZe j d.d/edZe j d0d1edZe j d2d3edZe j d4d5edZe j d6d7edZe j d8d9edZe je je jd:d;d<Ze jd=d>d?dd@dAZdZe j dDdEedZ e j dFdGedZ!e j"dHdIdZ#e j dJdKdZ$e j dLdMdZ%dZ&e j dQdRe&dZ'e j dSdTe&dZ(e j dUdVe&dZ)dWdXdYdZd[d\d]d^d_d`g Z*e j+e j e j,dadbdZ-e j+e j e j,dcdddZ.e j+e j e j,dedfdZ/e j+e j e j,dgdhdZ0e j+e j e j,didjdZ1e j+e j e j,dkdldZ2e j+e j e j,dmdndZ3e j+e j e j,dodpdZ4e j+e j e j,dqdrdZ5e j+e j e j,dsdtdZ6dudvdwdxdydzd{d|d}d~dddg Z7e j+e j,dddZ8e j dddZ9e j dddZ:e j dddZ;e j dddZe je j,e j,ddd<Z?e je j,e j,ddd<Z@e je j,e j,ddd<ZAe je j,e j,ddd<ZBe j dddZCe j dddZDdgZEe j,dddZFdddd]dgZGe j dddZHe j dddZIe j dddZJe j dddZKe j dddZLdS) MathInputSpecrrT)rzinput file(s) for calculationrz%sr)rrr*rrr&z output filerFz%s_mincmath.mnc)rrrTrrrrz7Specify the name of a file containing input file names.z -filelist %s)rrrrr&zOverwrite existing file.z-clobber)rrrUrVzCreate a MINC 2 output file.z-2)rrrrzBCopy all of the header from the first file (default for one file).z -copy_header)rrr&zLDo not copy all of the header from the first file (default for many files)).z-nocopy_headerrrrrrrrrrz&Use data type of first file (default).z -filetypezWrite out byte data.z-bytezWrite out short integer data.z-shortzWrite out 32-bit integer data.z-intzSuperseded by -int.z-longz/Write out single-precision floating-point data.z-floatz/Write out double-precision floating-point data.z-doublezWrite signed integer data.z-signedz&Write unsigned integer data (default).z -unsignedz -range %d %dzValid range for output data.)rrrz=Specify the maximum size of the internal buffers (in kbytes).iz-max_buffer_size_in_kb %d)r^rrrUrrrz9Check that dimension info matches across files (default).z-check_dimensionszDo not check dimension info.z-nocheck_dimensionszASpecify a dimension along which we wish to perform a calculation.z -dimension %sz,Ignore invalid data (NaN) for accumulations.z -ignore_nanz=Invalid data in any file at a voxel produces a NaN (default).z-propagate_nanrrrz7Output NaN when an illegal operation is done (default).z-nanz.Output zero when an illegal operation is done.z-zerozPValue to write out when an illegal operationis done. Default value: 1.79769e+308z-illegal_valuetest_gttest_lttest_eqtest_netest_getest_lecalc_addcalc_subcalc_mulcalc_divz(Test for vol1 > vol2 or vol1 > constant.z-gtz(Test for vol1 < vol2 or vol1 < constant.z-ltz2Test for integer vol1 == vol2 or vol1 == constant.z-eqz/Test for integer vol1 != vol2 or vol1 != const.z-nez'Test for vol1 >= vol2 or vol1 >= const.z-gez'Test for vol1 <= vol2 or vol1 <= const.z-lez#Add N volumes or volume + constant.z-addz(Subtract 2 volumes or volume - constant.z-subz(Multiply N volumes or volume * constant.z-multz&Divide 2 volumes or volume / constant.z-divinvertcalc_notsqrtsquareabsexplogrclampsegmentnsegmentisnanzCalculate 1/c.z-invert -const %szCalculate !vol1.z-notzTake square root of a volume.z-sqrtzTake square of a volume.z-squarez Take absolute value of a volume.z-absz-exp -const2 %s %sz9Calculate c2*exp(c1*x). Both constants must be specified.z-log -const2 %s %sz=Calculate log(x/c2)/c1. The constants c1 and c2 default to 1.z-scale -const2 %s %sz!Scale a volume: volume * c1 + c2.z-clamp -const2 %s %sz)Clamp a volume to lie between two values.z-segment -const2 %s %szMSegment a volume using range of -const2: within range = 1, outside range = 0.z-nsegment -const2 %s %sz:Opposite of -segment: within range = 0, outside range = 1.zTest for NaN values in vol1.z-isnanzNegation of -isnan.z-nisnan percentdiffzBPercent difference between 2 volumes, thresholded (const def=0.0).z -percentdiff count_validmaximumminimumcalc_orz.Count the number of valid values in N volumes.z -count_validzFind maximum of N volumes.z-maximumzFind minimum of N volumes.z-minimumz Calculate vol1 && vol2 (&& ...).z-andz Calculate vol1 || vol2 (|| ...).z-orN)rrr7r8)rr) rrrrrrrrr)rr)rrr)Mr9r:r;rr rrr<rr r>r_r`rrrrrrrrrrrrrr@rErrdrrrrrwZ dimensionrrrrrrbool_or_const_traitsryrArrrrrrrrrrsingle_volume_traitsr r r r r rrrrrrrZnisnantwo_volume_traitsrn_volume_traitsrrrZcalc_andrrHrHrHrIrs        rc@seZdZedddZdS)MathOutputSpecz output fileT)rrN)r9r:r;rr<rHrHrHrIrysrcs8eZdZdZeZeZdZfddZ fddZ Z S)Matha Various mathematical operations supplied by mincmath. Examples -------- >>> from nipype.interfaces.minc import Math >>> from nipype.interfaces.minc.testdata import minc2Dfile Scale: volume*3.0 + 2: >>> scale = Math(input_files=[minc2Dfile], scale=(3.0, 2)) >>> scale.run() # doctest: +SKIP Test if >= 1.5: >>> gt = Math(input_files=[minc2Dfile], test_gt=1.5) >>> gt.run() # doctest: +SKIP Zmincmathcs|dk s t||jjkrtt|tr,|r,|jSt|trL| rLtd|fn(t|trdd|j|fStd||ftt |j |||S)Nz'Does not make sense to specify %s=Falsez %s -const %szInvalid %s argument: %s) AssertionErrorrMrr}rrrfloatrrr)rrrr)rrHrIrs   zMath._format_argcsTt|jj}xx|jjD]l}|jj|}t|rt|trR|dkrt d||fqt|t rv|dkrt d||fqt d|qWx<|jj D]0}|jj|}t|r|dkrt d||fqWx<|jj D]0}|jj|}t|r|dkrt d||fqWxB|jj D]6}|jj|}t|r |dks t d||fq Wtt|jS)zGA number of the command line options expect precisely one or two files.rzHDue to the %s option we expected 2 files but input_files is of length %drzGDue to the %s option we expected 1 file but input_files is of length %dz/Argument should be a bool or const, but got: %szRDue to the %s option we expected at least one file but input_files is of length %d)lenrrrMr__getattribute__r r}rrr rrrrr _parse_inputs)rZnr_input_filesnt)rrHrIr#sL         zMath._parse_inputs) r9r:r;rLrrMrrNrOrr#rrHrH)rrIr}s  rc@s eZdZdZeddddddZeddddd gd d d Zeed dZe j dddZ e j dddddZ dZ e j dde dZe j dde dZe j dde dZe j d d!e dZe jd"ddd#d$d%d&d'd(d)d*d+dgd, ZdZe j d/d0edgd1Ze j d2d3edgd1Zed4dd5d6Ze j d7d8dZdZe j d;dedZed?d@ddAZdZe j dJdKedZe j dLdMedZe j dNdOedZ e j dPdQedZ!e j dRdSedZ"e j dTdUedZ#e j dVdWedZ$e j dXdYedZ%e j&e j'e j'dZd[d\Z(dZ)e j d`dae)dZ*e j dbdce)dZ+e j dddee)dZ,dZ-e j dhdie-dZ.e j djdke-dZ/e j'dldmdggd,Z0dZ1e j dpdqe1dZ2e j drdse1dZ3dZ4e j5dvdwdZ6e j dxdydZ7e j&e j'e j'e j'dzd{dZ8e j d|d}dZ9e j5d~ddZ:dZ;e j&e je jVIO_Transform the input sampling with the transform (default).z-tfm_input_samplingzr?)r@rA)rBrA)rBr@)rFrC)rDrE)rFrE)rFrD)rGrH)rIrJ)rKrJ)rKrI)Pr9r:r;rLrrr<r input_grid_filesr r>r`r_Z_xor_interpolationr'r(r)r*rbZhalf_width_sinc_windowZ_xor_sinc_window_typer,r-ZtransformationZinvert_transformationZ_xor_input_samplingr.r/likerrrrrrrrrr@rAZ output_rangeZ _xor_slicesZtransverse_slicesZsagittal_slicesZcoronal_slicesZ _xor_fillZno_fillr4Z fill_valueZ _xor_scaler5r6Z_xor_spacetyperwr7r8originZstandard_samplingZunitsZ_xor_nelementsrEr9r=r;r<Z _xor_stepr>rBr@rAZ _xor_startrFrFrDrEZ _xor_dircosrGrKrIrJrHrHrHrIr&s                 r&c@seZdZedddZdS)ResampleOutputSpecz output fileT)rrN)r9r:r;rr<rHrHrHrIrO} srOc@seZdZdZeZeZdZdS)Resamplea. Resample a minc file.' Examples -------- >>> from nipype.interfaces.minc import Resample >>> from nipype.interfaces.minc.testdata import minc2Dfile >>> r = Resample(input_file=minc2Dfile, output_file='/tmp/out.mnc') # Resample the file. >>> r.run() # doctest: +SKIP Z mincresampleN) r9r:r;rLr&rMrOrNrOrHrHrHrIrP s rPc @seZdZdZeddddd0dZedddd1d gd d d Zed dd gd ddZej dddddZ eddddZ ej dddddZ ej dddddZejddd Zejd!d"d Zejd#d$d Zejd%d&d Zej d'd(d Zej ddd)d*dZej d+d,d Zejd-d.d Zd/S)2 NormInputSpecaf Not implemented: -version print version and exit -verbose be verbose -noverbose opposite of -verbose [default] -quiet be quiet -noquiet opposite of -quiet [default] -fake do a dry run, (echo cmds only) -nofake opposite of -fake [default] zinput file to normaliseTz%sr)rrrrrz output filerrFz %s_norm.mnc)rrTrrrrrz*File in which to store the threshold mask.z-threshold_mask %sz%s_norm_threshold_mask.mnc)rrrrrzOverwrite existing file.z-clobber)rrrUrVz0Calculate the image normalisation within a mask.z-mask %s)rrrz/Force the ouput range between limits [default].z-clampggY@zTCutoff value to use to calculate thresholds by a histogram PcT in %. [default: 0.01]z -cutoff %s)r^rrrzLower real value to use.z -lower %s)rrzUpper real value to use.z -upper %sz!Output files maximum [default: 0]z -out_floor %sz#Output files minimum [default: 100]z -out_ceil %szDThreshold the image (set values below threshold_perc to -out_floor).z -thresholdzJThreshold percentage (0.1 == lower 10% of intensity range) [default: 0.1].z-threshold_perc %sz2Use the resulting image BiModalT as the threshold.z-threshold_bmtz=Blur FWHM for intensity edges then thresholding [default: 2].z-threshold_blur %sNr7r8)r9r:r;rLrrr<output_threshold_maskr r>r_maskrrdcutoffrAlowerupperZ out_floorZout_ceilrZthreshold_percZ threshold_bmtZthreshold_blurrHrHrHrIrQ sx   rQc@s"eZdZedddZeddZdS)NormOutputSpecz output fileT)rrzthreshold mask file)rN)r9r:r;rr<rRrHrHrHrIrW s rWc@seZdZdZeZeZdZdS)NormabNormalise a file between a max and minimum (possibly) using two histogram pct's. Examples -------- >>> from nipype.interfaces.minc import Norm >>> from nipype.interfaces.minc.testdata import minc2Dfile >>> n = Norm(input_file=minc2Dfile, output_file='/tmp/out.mnc') # Normalise the file. >>> n.run() # doctest: +SKIP ZmincnormN) r9r:r;rLrQrMrWrNrOrHrHrHrIrX s rXc @seZdZdZeddddddZeddddd gd d d Zejd ddZ ejdddddZ ejdddZ ej ej ej ej dddZejdddZdS)VolcentreInputSpecz{ Not implemented: -fake do a dry run, (echo cmds only) -nofake opposite of -fake [default] zinput file to centreTz%sr)rrrrrz output filerrFz%s_volcentre.mnc)rrTrrrrrz'Print out log messages. Default: False.z-verbose)rrzOverwrite existing file.z-clobber)rrrUrVzLUse the CoM of the volume for the new centre (via mincstats). Default: Falsez-comz-centre %s %s %sz'Centre to use (x,y,z) [default: 0 0 0].)rrz0Set the direction cosines to identity [default].z -zero_dircosNr7r8)r9r:r;rLrrr<r r>rr_comr@rAZcentreZ zero_dircosrHrHrHrIrY, sB rYc@seZdZedddZdS)VolcentreOutputSpecz output fileT)rrN)r9r:r;rr<rHrHrHrIr[c sr[c@seZdZdZeZeZdZdS) Volcentrea$Centre a MINC image's sampling about a point, typically (0,0,0). Example -------- >>> from nipype.interfaces.minc import Volcentre >>> from nipype.interfaces.minc.testdata import minc2Dfile >>> vc = Volcentre(input_file=minc2Dfile) >>> vc.run() # doctest: +SKIP Z volcentreN) r9r:r;rLrYrMr[rNrOrHrHrHrIr\g s r\c @seZdZdZeddddddZeddddd gd d d Zejd ddZ ejdddddZ ejdddZ ej dddZ ejdddZejdddZejdddZdS) VolpadInputSpeca Not implemented: -fake do a dry run, (echo cmds only) -nofake opposite of -fake [default] | volpad pads a MINC volume | | Problems or comments should be sent to: a.janke@gmail.com Summary of options: -- General Options ------------------------------------------------------------- -verbose be verbose -noverbose opposite of -verbose [default] -clobber clobber existing files -noclobber opposite of -clobber [default] -fake do a dry run, (echo cmds only) -nofake opposite of -fake [default] zinput file to centreTz%sr)rrrrrz output filerrFz %s_volpad.mnc)rrTrrrrrz'Print out log messages. Default: False.z-verbose)rrzOverwrite existing file.z-clobber)rrrUrVzRAutomatically determine padding distances (uses -distance as max). Default: False.z-autozFFrequency of voxels over bimodalt threshold to stop at [default: 500].z -auto_freq %sz*Padding distance (in voxels) [default: 4].z -distance %sz3Smooth (blur) edges before padding. Default: False.z-smoothz,Smoothing distance (in voxels) [default: 4].z-smooth_distance %sNr7r8)r9r:r;rLrrr<r r>rr_autorAZ auto_freqrEZdistanceZsmoothZsmooth_distancerHrHrHrIr]x sF   r]c@seZdZedddZdS)VolpadOutputSpecz output fileT)rrN)r9r:r;rr<rHrHrHrIr_ sr_c@seZdZdZeZeZdZdS)Volpada?Centre a MINC image's sampling about a point, typically (0,0,0). Examples -------- >>> from nipype.interfaces.minc import Volpad >>> from nipype.interfaces.minc.testdata import minc2Dfile >>> vp = Volpad(input_file=minc2Dfile, smooth=True, smooth_distance=4) >>> vp.run() # doctest: +SKIP ZvolpadN) r9r:r;rLr]rMr_rNrOrHrHrHrIr` s r`c @seZdZeddddddZedddddgd d d Zejd d dZejdddddZ ej dddZ ej dddZ ejdddZ dS)VolisoInputSpecz+input file to convert to isotropic samplingTz%sr)rrrrrz output filerrFz %s_voliso.mnc)rrTrrrrrz'Print out log messages. Default: False.z --verbose)rrzOverwrite existing file.z --clobber)rrrUrVz5The target maximum step desired in the output volume.z --maxstep %sz5The target minimum step desired in the output volume.z --minstep %szFCalculate the maximum step from the average steps of the input volume.z --avgstepNr7r8)r9r:r;rrr<r r>rr_rAZmaxstepZminstepZavgsteprHrHrHrIra s< rac@seZdZedddZdS)VolisoOutputSpecz output fileT)rrN)r9r:r;rr<rHrHrHrIrb srbc@seZdZdZeZeZdZdS)VolisoaVChanges the steps and starts in order that the output volume has isotropic sampling. Examples -------- >>> from nipype.interfaces.minc import Voliso >>> from nipype.interfaces.minc.testdata import minc2Dfile >>> viso = Voliso(input_file=minc2Dfile, minstep=0.1, avgstep=True) >>> viso.run() # doctest: +SKIP ZvolisoN) r9r:r;rLrarMrbrNrOrHrHrHrIrc s rcc @sneZdZedddddgdddZejd d d Zejd d dddZejddd Z ej ddd Z eddddZ dS)GennlxfmInputSpecz output fileTz%srrMFz%s_gennlxfm.xfm)rrTrrrrrz'Print out log messages. Default: False.z-verbose)rrzOverwrite existing file.z-clobber)rrrUrVz)Generate an identity xfm. Default: False.z-identz#Output ident xfm step [default: 1].z-step %sz Generate a nlxfm like this file.z-like %s)rrrNr8) r9r:r;rr<r r>rr_identrEr>rMrHrHrHrIrd s&  rdc@s$eZdZedddZedddZdS)GennlxfmOutputSpecz output fileT)rrz output gridN)r9r:r;rr< output_gridrHrHrHrIrf: s rfcs,eZdZdZeZeZdZfddZ Z S)GennlxfmaGenerate nonlinear xfms. Currently only identity xfms are supported! This tool is part of minc-widgets: https://github.com/BIC-MNI/minc-widgets/blob/master/gennlxfm/gennlxfm Examples -------- >>> from nipype.interfaces.minc import Gennlxfm >>> from nipype.interfaces.minc.testdata import minc2Dfile >>> gennlxfm = Gennlxfm(step=1, like=minc2Dfile) >>> gennlxfm.run() # doctest: +SKIP Zgennlxfmcs(tt|j}tjdd|d|d<|S)Nz .(nlxfm|xfm)$z _grid_0.mncr<rg)rrhrresub)rr)rrHrIrU szGennlxfm._list_outputs) r9r:r;rLrdrMrfrNrOrrrHrH)rrIrh? s rhc @sjeZdZeeddddddddZeedd Zed dddd gd ddZej dddZ ej dddddZ dS)XfmConcatInputSpecT)rz input file(s)rz%sr)rrr*rrzinput grid file(s))rz output filerrFz%s_xfmconcat.xfm)rrTrrrrrz'Print out log messages. Default: False.z-verbose)rrzOverwrite existing file.z-clobber)rrrUrVNr7r8) r9r:r;r rrrLr<r r>rr_rHrHrHrIrk] s.  rkc@s*eZdZedddZeeddddZdS)XfmConcatOutputSpecz output fileT)rr)rz output grids)rN)r9r:r;rr<r output_gridsrHrHrHrIrl s rlcs,eZdZdZeZeZdZfddZ Z S) XfmConcataConcatenate transforms together. The output transformation is equivalent to applying input1.xfm, then input2.xfm, ..., in that order. Examples -------- >>> from nipype.interfaces.minc import XfmConcat >>> from nipype.interfaces.minc.testdata import minc2Dfile >>> conc = XfmConcat(input_files=['input1.xfm', 'input1.xfm']) >>> conc.run() # doctest: +SKIP Z xfmconcatcsTtt|j}tjj|drPdt|ddjkrPtjt j dd|d|d<|S)Nr<gridrz .(nlxfm|xfm)$z _grid_*.mncrm) rrnrrrropenreadglobrirj)rr)rrHrIr s zXfmConcat._list_outputs) r9r:r;rLrkrMrlrNrOrrrHrH)rrIrn s  rnc @seZdZeddddddZeddddddZeddddd gd d d d Zeddddd gd dd d Zej dddZ ej dddddZ dS)BestLinRegInputSpeczsource Minc fileTz%srX)rrrrrztarget Minc filerWzoutput xfm filersourceFz%s_bestlinreg.xfm)rrTrrrrrrzoutput mnc filerz%s_bestlinreg.mncz'Print out log messages. Default: False.z-verbose)rrzOverwrite existing file.z-clobber)rrrUrVNrr7r8) r9r:r;rrutarget output_xfm output_mncr r>rr_rHrHrHrIrt s<  rtc@s$eZdZedddZedddZdS)BestLinRegOutputSpeczoutput xfm fileT)rrzoutput mnc fileN)r9r:r;rrxryrHrHrHrIrz s rzc@seZdZdZeZeZdZdS) BestLinRegaHierachial linear fitting between two files. The bestlinreg script is part of the EZminc package: https://github.com/BIC-MNI/EZminc/blob/master/scripts/bestlinreg.pl Examples -------- >>> from nipype.interfaces.minc import BestLinReg >>> from nipype.interfaces.minc.testdata import nonempty_minc_data >>> input_file = nonempty_minc_data(0) >>> target_file = nonempty_minc_data(1) >>> linreg = BestLinReg(source=input_file, target=target_file) >>> linreg.run() # doctest: +SKIP Z bestlinregN) r9r:r;rLrtrMrzrNrOrHrHrHrIr{ sr{c@seZdZeddddddZeddddddZeddddd Zeed d Zed ddddZ edddddZ edddddZ e j dddZe j dddddZdS)NlpFitInputSpeczsource Minc fileTz%srW)rrrrrztarget Minc filerzoutput xfm filer)rrTrrzinput grid file(s))rz.File containing the fitting configuration use.z-config_file %s)rrrrz*Initial transformation (default identity).z -init_xfm %sz"Source mask to use during fitting.z-source_mask %sz'Print out log messages. Default: False.z-verbose)rrzOverwrite existing file.z-clobber)rrrUrVNrr7r8)r9r:r;rrurwrxr rL config_fileZinit_xfmZ source_maskr r>rr_rHrHrHrIr| s8  r|c@s$eZdZedddZedddZdS)NlpFitOutputSpeczoutput xfm fileT)rrzoutput grid fileN)r9r:r;rrxrgrHrHrHrIr~! s r~c@s,eZdZdZeZeZdZddZ ddZ dS)NlpFitaHierarchial non-linear fitting with bluring. This tool is part of the minc-widgets package: https://github.com/BIC-MNI/minc-widgets/blob/master/nlpfit/nlpfit Examples -------- >>> from nipype.interfaces.minc import NlpFit >>> from nipype.interfaces.minc.testdata import nonempty_minc_data, nlp_config >>> from nipype.testing import example_data >>> source = nonempty_minc_data(0) >>> target = nonempty_minc_data(1) >>> source_mask = nonempty_minc_data(2) >>> config = nlp_config >>> initial = example_data('minc_initial.xfm') >>> nlpfit = NlpFit(config_file=config, init_xfm=initial, source_mask=source_mask, source=source, target=target) >>> nlpfit.run() # doctest: +SKIP ZnlpfitcCsH|dkr@|jj}t|r$tjj|St|jj|jjgddSnt dS)NrxZnlpfit_xfm_outputz.xfm) rrxr rrrrrurwNotImplemented)rrrxrHrHrI _gen_filenameA s zNlpFit._gen_filenamecCsf|jj}tjj|jd|d<tjj|ds6tdt|ddj krbt j dd|d|d<|S)Nrxrorpz .(nlxfm|xfm)$z _grid_0.mncrg) rNrrrrrrrrqrrrirj)rrrHrHrIrQ s zNlpFit._list_outputsN) r9r:r;rLr|rMr~rNrOrrrHrHrHrIr& s rc@seZdZeeddddddddZeedd Zed dddd Zej d ddZ ej dddddZ ej dddZ ej dddZ ej dddZej dddZdS)XfmAvgInputSpecT)rz input file(s)rz%sr)rrr*rrzinput grid file(s))rz output filer)rrTrrz'Print out log messages. Default: False.z-verbose)rrzOverwrite existing file.z-clobber)rrrUrVz"average the linear part [default].z -avg_linearz&average the non-linear part [default].z-avg_nonlinearzopposite of -avg_linear.z-ignore_linearzopposite of -avg_nonlinear.z-ignore_nonlineNr7r8)r9r:r;r rrrLr<r r>rr_Z avg_linearZ avg_nonlinearZ ignore_linearZignore_nonlinearrHrHrHrIr^ s0     rc@s$eZdZedddZedddZdS)XfmAvgOutputSpecz output fileT)rrzoutput grid fileN)r9r:r;rr<rgrHrHrHrIr s rc@s4eZdZdZeZeZdZddZ ddZ ddZ d S) XfmAvgaAverage a number of xfm transforms using matrix logs and exponents. The program xfmavg calls Octave for numerical work. This tool is part of the minc-widgets package: https://github.com/BIC-MNI/minc-widgets/tree/master/xfmavg Examples -------- >>> from nipype.interfaces.minc import XfmAvg >>> from nipype.interfaces.minc.testdata import nonempty_minc_data, nlp_config >>> from nipype.testing import example_data >>> xfm1 = example_data('minc_initial.xfm') >>> xfm2 = example_data('minc_initial.xfm') # cheating for doctest >>> xfmavg = XfmAvg(input_files=[xfm1, xfm2]) >>> xfmavg.run() # doctest: +SKIP ZxfmavgcCs@|dkr8|jj}t|r$tjj|St|jjddSntdS)Nr<Z xfmavg_outputz.xfm) rr<r rrrrrr)rrr<rHrHrIr s  zXfmAvg._gen_filenamecCs |jdS)Nr<)r)rrHrHrI_gen_outfilename szXfmAvg._gen_outfilenamecCsd|jj}tjj|j|d<tjj|ds4tdt|ddj kr`t j dd|d|d<|S)Nr<rorpz .(nlxfm|xfm)$z _grid_0.mncrg) rNrrrrrrrrqrrrirj)rrrHrHrIr s zXfmAvg._list_outputsN) r9r:r;rLrrMrrNrOrrrrHrHrHrIr src@sNeZdZeddddddZedddddZejd d d Zejd d dddZ dS)XfmInvertInputSpecz input fileTz%sr)rrrrrz output filer)rrTrrz'Print out log messages. Default: False.z-verbose)rrzOverwrite existing file.z-clobber)rrrUrVNr7r8) r9r:r;rrr<r r>rr_rHrHrHrIr s rc@s$eZdZedddZedddZdS)XfmInvertOutputSpecz output fileT)rrzoutput grid fileN)r9r:r;rr<rgrHrHrHrIr s rc@s4eZdZdZeZeZdZddZ ddZ ddZ d S) XfmInverta!Invert an xfm transform file. Examples -------- >>> from nipype.interfaces.minc import XfmAvg >>> from nipype.testing import example_data >>> xfm = example_data('minc_initial.xfm') >>> invert = XfmInvert(input_file=xfm) >>> invert.run() # doctest: +SKIP Z xfminvertcCsB|dkr:|jj}t|r$tjj|St|jjgddSntdS)Nr<Zxfminvert_outputz.xfm) rr<r rrrrrr)rrr<rHrHrIr s zXfmInvert._gen_filenamecCs |jdS)Nr<)r)rrHrHrIr szXfmInvert._gen_outfilenamecCsd|jj}tjj|j|d<tjj|ds4tdt|ddj kr`t j dd|d|d<|S)Nr<rorpz .(nlxfm|xfm)$z _grid_0.mncrg) rNrrrrrrrrqrrrirj)rrrHrHrIr s zXfmInvert._list_outputsN) r9r:r;rLrrMrrNrOrrrrHrHrHrIr s rc @seZdZeeddddddddZedddd d gd d d ZejdddZ ejdddddZ ejdddZ ejdddZ e dddZeddd gd ddZdS)!BigAverageInputSpecT)rz input file(s)rz%sr)rrr*rrz output filerrFz%s_bigaverage.mnc)rrTrrrrrz'Print out log messages. Default: False.z --verbose)rrzOverwrite existing file.z --clobber)rrrUrVz"Output files with float precision.z--floatzPerform robust averaging, features that are outside 1 standarddeviation from the mean are downweighted. Works well for noisydata with artifacts. see the --tmpdir option if you have alarge number of input files.z-robustztemporary files directoryz -tmpdir %sz1Place standard deviation image in specified file.z --sdfile %sz%s_bigaverage_stdev.mnc)rrrrrNr7r8)r9r:r;r rrr<r r>rr_Z output_floatZrobustr Ztmpdirsd_filerHrHrHrIr sD   rc@s$eZdZedddZedddZdS)BigAverageOutputSpecz output fileT)rrzstandard deviation imageN)r9r:r;rr<rrHrHrHrIrDs rc@seZdZdZeZeZdZdS) BigAveragea=Average 1000's of MINC files in linear time. mincbigaverage is designed to discretise the problem of averaging either a large number of input files or averaging a smaller number of large files. (>1GB each). There is also some code included to perform "robust" averaging in which only the most common features are kept via down-weighting outliers beyond a standard deviation. One advantage of mincbigaverage is that it avoids issues around the number of possible open files in HDF/netCDF. In short if you have more than 100 files open at once while averaging things will slow down significantly. mincbigaverage does this via a iterative approach to averaging files and is a direct drop in replacement for mincaverage. That said not all the arguments of mincaverage are supported in mincbigaverage but they should be. This tool is part of the minc-widgets package: https://github.com/BIC-MNI/minc-widgets/blob/master/mincbigaverage/mincbigaverage Examples -------- >>> from nipype.interfaces.minc import BigAverage >>> from nipype.interfaces.minc.testdata import nonempty_minc_data >>> files = [nonempty_minc_data(i) for i in range(3)] >>> average = BigAverage(input_files=files, output_float=True, robust=True) >>> average.run() # doctest: +SKIP ZmincbigaverageN) r9r:r;rLrrMrrNrOrHrHrHrIrIsrc @sdeZdZeddddddZedddddgd d d Zejd d dZejdddddZ ejdddZ dS)ReshapeInputSpecz input fileTz%sr)rrrrrz output filerrFz%s_reshape.mnc)rrTrrrrrz'Print out log messages. Default: False.z-verbose)rrzOverwrite existing file.z-clobber)rrrUrVzConvert to short integer data.z-shortNr7r8) r9r:r;rrr<r r>rr_rrHrHrHrIros$ rc@seZdZedddZdS)ReshapeOutputSpecz output fileT)rrN)r9r:r;rr<rHrHrHrIrsrc@seZdZdZeZeZdZdS)ReshapeaCut a hyperslab out of a minc file, with dimension reordering. This is also useful for rewriting with a different format, for example converting to short (see example below). Examples -------- >>> from nipype.interfaces.minc import Reshape >>> from nipype.interfaces.minc.testdata import nonempty_minc_data >>> input_file = nonempty_minc_data(0) >>> reshape_to_short = Reshape(input_file=input_file, write_short=True) >>> reshape_to_short.run() # doctest: +SKIP Z mincreshapeN) r9r:r;rLrrMrrNrOrHrHrHrIrsrc @seZdZeddddd(dZedddd)dgd d d d Zed ddd*dgd ddZeeddZe j dddZ e j dddddZ e j dddZ e j dddZe j dddZedddd Ze j d!d"dZe j d#d$dZe j d%d&dZd'S)+VolSymmInputSpecz input fileTz%srW)rrrrrzoutput xfm trans filerrFz%s_vol_symm.xfm)rrTrrrrrrz output filerz%s_vol_symm.mnc)rrTrrrrrzinput grid file(s))rz'Print out log messages. Default: False.z-verbose)rrzOverwrite existing file.z-clobber)rrrUrVzFit using a linear xfm.z-linearzFit using a non-linear xfm.z -nonlinearz7Use the input transformation instead of generating one.z-nofitzBFile containing the fitting configuration (nlpfit -help for info).z-config_file %s)rrrz!Flip volume in x-plane (default).z-xzFlip volume in y-plane.z-yzFlip volume in z-plane.z-zNrr7r8)r9r:r;rr trans_filer<r rLr r>rr_Z fit_linearZ fit_nonlinearZnofitr}xyzrHrHrHrIrsL   rc@s0eZdZedddZedddZedddZdS)VolSymmOutputSpecz output fileT)rrzxfm trans filezoutput grid fileN)r9r:r;rr<rrgrHrHrHrIrs  rcs,eZdZdZeZeZdZfddZ Z S)VolSymma1Make a volume symmetric about an axis either linearly and/or nonlinearly. This is done by registering a volume to a flipped image of itself. This tool is part of the minc-widgets package: https://github.com/BIC-MNI/minc-widgets/blob/master/volsymm/volsymm Examples -------- >>> from nipype.interfaces.minc import VolSymm >>> from nipype.interfaces.minc.testdata import nonempty_minc_data >>> input_file = nonempty_minc_data(0) >>> volsymm = VolSymm(input_file=input_file) >>> volsymm.run() # doctest: +SKIP ZvolsymmcsNtt|j}tjj|drJdt|ddjkrJtj dd|d|d<|S)Nrrorpz .(nlxfm|xfm)$z _grid_0.mncrg) rrrrrrrqrrrirj)rr)rrHrIrs zVolSymm._list_outputs) r9r:r;rLrrMrrNrOrrrHrH)rrIrs r)irLrsros.pathriwarningsbaserrrrrrr r r r r rfilterwarnings UserWarningrrJrKrPrQrRrSrfrgrhrkrlrmrnrorpr{r|rrrrrrrrrrrrrrrrrrrrrrrrr&rOrPrQrWrXrYr[r\r]r_r`rarbrcrdrfrhrkrlrnrtrzr{r|r~rrrrrrrrrrrrrrrrrHrHrHrI s4 MT9?C$BK6!M Xbfe.7L."4.8*709&=