3 d@s,dZddlZddljZddlZddlZddl m Z m Z ddl Z ddl Z ddlmZddlmZddlmZmZmZmZmZmZmZmZmZmZdd lmZmZm Z dd lm!Z!ej"d Z#Gd d d eZ$GdddeZ%GdddeZ&GdddeZ'GdddeZ(GdddeZ)GdddeZ*GdddeZ+GdddeZ,GdddeZ-Gd d!d!eZ.Gd"d#d#eZ/Gd$d%d%eZ0Gd&d'd'eZ1Gd(d)d)eZ2d*d+Z3d,d-Z4Gd.d/d/eZ5Gd0d1d1eZ6Gd2d3d3eZ7d4d5Z8d6d7Z9d8d9Z:d:d;Z;Gdd?d?eZ=Gd@dAdAeZ>GdBdCdCeZ?GdDdEdEeZ@GdFdGdGeZAGdHdIdIeeZBGdJdKdKeZCGdLdMdMeZDGdNdOdOeZEGdPdQdQeZFGdRdSdSeZGdTdUZHGdVdWdWeZIGdXdYdYeZJGdZd[d[eZKGd\d]d]eZLGd^d_d_eZMGd`dadaeZNGdbdcdceZOGdddedeeZPGdfdgdgeZQGdhdidieZRGdjdkdkeZSGdldmdmeZTddndoZUddpdqZVddrdsZWGdtdudueZXGdvdwdweZYGdxdydyeZZGdzd{d{ej[Z[Gd|d}d}ej\Z\Gd~ddej]Z]Gddde!j^Z^dS)zMiscellaneous algorithms.N)floorceil)logging)metrics) BaseInterfacetraits TraitedSpecFileInputMultiPathOutputMultiPathBaseInterfaceInputSpec isdefinedDynamicTraitedSpec Undefined)fname_presuffixsplit_filename ensure_list) confoundsznipype.interfacec@sbeZdZeddddZejejejejdddZ ej dddd dd Z ejdd d Z ed dZ dS)PickAtlasInputSpecTz(Location of the atlas that will be used.)existsdesc mandatoryzcLabels of regions that will be included in the mask. 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Saves the volume as a nifti file. cCst|\}}}tjj|dS)Nz_transformed.nii)rr8r:ra)r1namer^r_r(r(r)r0sz!ModifyAffine._gen_output_filenamecCsVxP|jjD]D}tj|}|j}tj|jj|}tjtj |j ||j |j |q W|S)N) r7rUr.r>rIr?dotrfr/rHrArJr0)r1r2r4r\rIr(r(r)r3s zModifyAffine._run_interfacecCs>|jj}g|d<x$|jjD]}|dj|j|qW|S)Nri)rMrNr7rUr`r0)r1rOr4r(r(r)rPs  zModifyAffine._list_outputsN) rrrrQrbrRrgrSr0r3rPr(r(r(r)rjs rjc@s4eZdZeddddZeddddZejddZdS)CreateNiftiInputSpecTzANALYZE img file)rrrzcorresponding ANALYZE hdr filezaffine transformation array)rN) rrrr data_file header_filer rerIr(r(r(r)rms rmc@seZdZeddZdS)CreateNiftiOutputSpecT)rN)rrrr nifti_filer(r(r(r)rpsrpc@s0eZdZdZeZeZddZddZ ddZ dS) CreateNiftizCreates a nifti volumecCs"t|jj\}}}tjj|dS)Nz.nii)rr7rnr8r:ra)r1r^r_r(r(r)_gen_output_file_namesz!CreateNifti._gen_output_file_namecCst|jjd}tjj|}WdQRXt|jjr<|jj}nd}t|jjd}|j |}WdQRXtj |||}tj ||j |S)Nrb) openr7ror.Z AnalyzeHeaderZ from_fileobjrrIrnZdata_from_fileobjrHr/rs)r1r2Zhdr_filehdrrIrnr]r\r(r(r)r3s  zCreateNifti._run_interfacecCs|jj}|j|d<|S)Nrq)rMrNrs)r1rOr(r(r)rP s  zCreateNifti._list_outputsN) rrrrQrmrRrprSrsr3rPr(r(r(r)rrs rrc@seZdZedddZdS)GunzipInputSpecT)rrN)rrrr in_filer(r(r(r)rwsrwc@seZdZeddZdS)GunzipOutputSpecT)rN)rrrr out_filer(r(r(r)rysryc@s0eZdZdZeZeZddZddZ ddZ dS) GunzipaGunzip wrapper >>> from nipype.algorithms.misc import Gunzip >>> gunzip = Gunzip(in_file='tpms_msk.nii.gz') >>> res = gunzip.run() >>> res.outputs.out_file # doctest: +ELLIPSIS '.../tpms_msk.nii' .. testcleanup:: >>> os.unlink('tpms_msk.nii') cCsBt|jj\}}}|ddjdkr2|dd}tjj||S)Nz.gzr})rr7rxlowerr8r:ra)r1r^r_extr(r(r)rs+s zGunzip._gen_output_file_namecCsVddl}ddl}|j|jjd,}t|jd}|j||WdQRXWdQRX|S)Nrrtwb)gzipshutilrur7rxrs copyfileobj)r1r2rrrxrzr(r(r)r31s  zGunzip._run_interfacecCs|jj}|j|d<|S)Nrz)rMrNrs)r1rOr(r(r)rP:s  zGunzip._list_outputsN) rrrrQrwrRryrSrsr3rPr(r(r(r)r{s   r{cCsFt}x:|D]2}ttj|\}}}tj|||}|j|q W|S)N)rEroprajoinr`)in_listrZout_listfilenamer:rkr^Zout_namer(r(r) replaceext@s  rcCsztj|}|rXttj|dkrXtj|tj|dtj|ddf}tjtj|tj|d}tj ||dd|S)Nrrz.csv,) delimiter) r?ZasarraylenrCreshapeifloggerinforrasavetxt)in_arrayrkr output_arrayZ output_namer(r(r) _matlab2csvIs $rc@s*eZdZeddddZejddddZdS)Matlab2CSVInputSpecTzInput MATLAB .mat file)rrrzgThe output of this interface is meant for R, so matrices will be reshaped to vectors by default.)rrN)rrrr rxr Boolreshape_matrixr(r(r(r)rVs rc@seZdZeeddZdS)Matlab2CSVOutputSpeczFOutput CSV files for each variable saved in the input .mat file)rN)rrrr r csv_filesr(r(r(r)r`src@s(eZdZdZeZeZddZddZ dS) Matlab2CSVa Save the components of a MATLAB .mat file as a text file with comma-separated values (CSVs). CSV files are easily loaded in R, for use in statistical processing. For further information, see cran.r-project.org/doc/manuals/R-data.pdf Example ------- >>> from nipype.algorithms import misc >>> mat2csv = misc.Matlab2CSV() >>> mat2csv.inputs.in_file = 'cmatrix.mat' >>> mat2csv.run() # doctest: +SKIP c CsDddlj}|jtj|jj}t}xJt|jD]:}|j ds2t ||dt j r`|j |q2tjd|q2Wt|dkrtjdt|tj|x|D].}tjd|t||t||||jjqWnpt|dkr6t|jj\}}}|d}tjd|t||tjd||jjt||||jjn tjd |S) Nr__z;One of the keys in the input file, %s, is not a Numpy arrayrz%i variables found:z#...Converting %s - type %s - to CSVz"Single variable found %s, type %s:z...Converting %s to CSV from %szNo values in the MATLAB file?!)scipy.ioioloadmatrrar7rxrEkeys startswithrDr?ndarrayr`rrrtyperrrerror) r1r2sioin_dictsaved_variableskeyvariabler^rkr(r(r)r3|s:        zMatlab2CSV._run_interfacec Csddlj}|jj}|jtj|jj}t }xJt |j D]:}|j ds>t ||dt jrl|j|q>tjd|q>Wt|dkrt|d|d<n>> from nipype.algorithms import misc >>> mat2csv = misc.MergeCSVFiles() >>> mat2csv.inputs.in_files = ['degree.mat','clustering.mat'] >>> mat2csv.inputs.column_headings = ['degree','clustering'] >>> mat2csv.run() # doctest: +SKIP cCsd}d}d}t|jjr,tjd|jj}ntjdt|jj}t|jjrt|jjrp|jj}tjd|nd}tjd|j |d}t |jjd krtj d t|jj rtjd d j |jjd }|djtj|d}d}n"tjdddjtj|d}tjdtj|t|jj}t|jj\} } } | dksLd} tj| | } t| d} | j|WdQRXtj|}t||||}t|||||\}}|r|jj }g}x$|D]}d|d}|j |qWtj|dd}||d<t|jjr\g}t |d kr|d}nd }x"td|D]}|j |jjq.Wtjt ||||<tj|tj|t| d} tj| ||ddWdQRX|S)NFrz#Column headings have been provided:z:Column headings not provided! Pulled from input filenames:z!Extra column heading provided: %srz2Extra column heading was not defined. Using "type"TrzOnly one file input!z>Row headings have been provided. Adding "labels"column header.z"{p}",")pz","z" z$Row headings have not been provided."zFinal Headings:z.csvwz|S40)dtyperrar)r) rr7rrrrrrrr`rwarningrformatrr itertoolschainrrrzrraruwriter?rCrrarrayrr)r1r2rrrZheadingsprefixZ csv_headingsrr^rkrrzZ file_handlerCrrr<Zrow_heading_listZrow_heading_list_with_quotesZ row_headingZrow_heading_with_quotesrZextrafieldlistZmxrr(r(r)r3_sz                        zMergeCSVFiles._run_interfacecCsD|jj}t|jj\}}}|dks*d}tj||}||d<|S)Nz.csvr)rSrNrr7rzrra)r1rOr^rkrrzr(r(r)rPs zMergeCSVFiles._list_outputsN) rrrrQrrRrrSr3rPr(r(r(r)rHs Urc@s@eZdZeddddZeddddZejddZejd dZ d S) AddCSVColumnInputSpecTz'Input comma-separated value (CSV) files)rrrzextra_heading.csvz#Output filename for merged CSV file)rrz'New heading to add for the added field.)rzcNew field to add to each row. This is useful for saving the group or subject ID in the file.N) rrrr rxrzr rrrr(r(r(r)rs   rc@seZdZeddZdS)AddCSVColumnOutputSpecz#Output CSV file containing columns )rN)rrrr rr(r(r(r)rsrc@s(eZdZdZeZeZddZddZ dS) AddCSVColumnag Short interface to add an extra column and field to a text file. Example ------- >>> from nipype.algorithms import misc >>> addcol = misc.AddCSVColumn() >>> addcol.inputs.in_file = 'degree.csv' >>> addcol.inputs.extra_column_heading = 'group' >>> addcol.inputs.extra_field = 'male' >>> addcol.run() # doctest: +SKIP c Cst|jjd}t|jj\}}}|dks,d}tj||}t|d}|j}|jdd}|d|jj d}|j |x6|D].} | jdd} | d|jj d} |j | q|W|j |j |S) Nrz.csvr rz,"z" r) rur7rxrrzrrarrrrrclose) r1r2rxr^rkrrz firstlineZ new_firstlinelinenew_liner(r(r)r3s"     zAddCSVColumn._run_interfacecCsD|jj}t|jj\}}}|dks*d}tj||}||d<|S)Nz.csvr)rSrNrr7rzrra)r1rOr^rkrrzr(r(r)rPs zAddCSVColumn._list_outputsN) rrrrQrrRrrSr3rPr(r(r(r)rs  rcs:eZdZedddZejejiddZfddZ Z S)AddCSVRowInputSpecTz'Input comma-separated value (CSV) files)rr)rdrcs\||jkr2t|s&tt|j||||j|<n&||jkrF||j|<tt|j||dS)N)copyable_trait_namesrsuperr __setattr__rM)r1rrd) __class__r(r)rs    zAddCSVRowInputSpec.__setattr__) rrrr rxr DictAnyrMr __classcell__r(r()rr)rs rc@seZdZeddZdS)AddCSVRowOutputSpecz Output CSV file containing rows )rN)rrrr rr(r(r(r)rsrcsNeZdZdZeZeZdfdd ZddZ dd Z fd d Z d d Z Z S) AddCSVRowa3 Simple interface to add an extra row to a CSV file. .. note:: Requires `pandas `_ .. warning:: Multi-platform thread-safe execution is possible with `lockfile `_. Please recall that (1) this module is alpha software; and (2) it should be installed for thread-safe writing. If lockfile is not installed, then the interface is not thread-safe. Example ------- >>> from nipype.algorithms import misc >>> addrow = misc.AddCSVRow() >>> addrow.inputs.in_file = 'scores.csv' >>> addrow.inputs.si = 0.74 >>> addrow.inputs.di = 0.93 >>> addrow.inputs.subject_id = 'S400' >>> addrow.inputs.list_of_values = [ 0.4, 0.7, 0.3 ] >>> addrow.run() # doctest: +SKIP NTc stt|jf|i}||_d|_d|_|r^x0|D](}|jj|tj t |jj |<t ||<q2W|jj fddi||r~d|_ dS)NFZtrait_change_notifyT)rr__init__Z _infields _have_lock_lockr7Z add_traitr rrrMZ trait_setZ _always_run)r1ZinfieldsZ force_runkwargsZundefined_traitsr)rr(r)r0s   zAddCSVRow.__init__cCsy ddl}Wn,tk r8}ztd|WYdd}~XnXyddlm}d|_Wn(tk rxddlm}|dYnXi}xnt|jj j D]Z\}}|dkr||jj krqt |trx.t |D]\} } | |d|| f<qWq|||<qW|j|g} |jr|d |jj|_|jjtj|jjrR|j|jjdd } |j| | gdd } t|jjd } | j| WdQRX|jr|jj|S) NrzBThis interface requires pandas (http://pandas.pydata.org/) to run.) SoftFileLockT)warnzdPython module filelock was not found: AddCSVRow will not be thread-safe in multi-processor executionZ trait_addedz%s_%dz%s.lock)Z index_col)Z ignore_indexr)Zpandas ImportErrorZfilelockrrwarningsrrEr7rMitemsrrDrZ DataFramerxracquirerrZread_csvconcatruZto_csvrelease)r1r2pderrZ input_dictrvalivZdfZformerdffr(r(r)r3AsB          zAddCSVRow._run_interfacecCs|jj}|jj|d<|S)Nr)rSrNr7rx)r1rOr(r(r)rPws  zAddCSVRow._list_outputscs|jtt|jS)N)_add_output_traitsrrrM)r1)rr(r)rM|szAddCSVRow._outputscCs|S)Nr()r1r_r(r(r)rszAddCSVRow._add_output_traits)NT)rrrrQrrRrrSrr3rPrMrrr(r()rr)rs6 rc@s(eZdZeddddZejdddZdS)#CalculateNormalizedMomentsInputSpecTzXText file with timeseries in columns and timepoints in rows, whitespace separated)rrrzPDefine which moment should be calculated, 3 for skewness, 4 for kurtosis.)rrN)rrrr timeseries_filer r!momentr(r(r(r)rsrc@seZdZejejddZdS)$CalculateNormalizedMomentsOutputSpecZMoments)rN)rrrr r"rVZmomentsr(r(r(r)rsrc@s(eZdZdZeZeZddZddZ dS)CalculateNormalizedMomentsa Calculates moments of timeseries. Example ------- >>> from nipype.algorithms import misc >>> skew = misc.CalculateNormalizedMoments() >>> skew.inputs.moment = 3 >>> skew.inputs.timeseries_file = 'timeseries.txt' >>> skew.run() # doctest: +SKIP cCst|jj|jj|_|S)N) calc_momentsr7rr_moments)r1r2r(r(r)r3sz)CalculateNormalizedMoments._run_interfacecCs|jj}|j|d<|S)NZskewness)rSrNr )r1rOr(r(r)rPs  z(CalculateNormalizedMoments._list_outputsN) rrrrQrrRrrSr3rPr(r(r(r)rs  rcCsVddlj}tj|}|j|ddd}|j||dd}|dk}tj|d|||dS)zReturns nth moment (3 for skewness, 4 for kurtosis) of timeseries (list of values; one per timeseries). Keyword arguments: timeseries_file -- text file with white space separated timepoints in rows rNr)axisg@)Z scipy.statsstatsr?Z genfromtxtrwhere)rrr Z timeseriesm2Zm3Zzeror(r(r)r s   r c@sheZdZeddddZedddZejddddZej d d ddd d Z ej d d dddd Z eddZ dS)AddNoiseInputSpecTz-input image that will be corrupted with noise)rrrzBinput mask, voxels outside this mask will be considered background)rrg$@zdesired output SNR in dB)rrnormalricianzdesired noise distribution)rrrrayleighz@desired noise distribution, currently only normal is implementedzdesired output filename)rN) rrrr rxin_maskr rVsnrr$distbg_distrzr(r(r(r)rs*rc@seZdZedddZdS)AddNoiseOutputSpecTzcorrupted image)rrN)rrrr rzr(r(r(r)rsrc@s:eZdZdZeZeZddZddZ ddZ d d d Z dS)AddNoisea( Corrupts with noise the input image. Example ------- >>> from nipype.algorithms.misc import AddNoise >>> noise = AddNoise() >>> noise.inputs.in_file = 'T1.nii' >>> noise.inputs.in_mask = 'mask.nii' >>> noise.snr = 30.0 >>> noise.run() # doctest: +SKIP cCstj|jj}|j}|jj}t|jjrBtj tj|jjj }n tj |}|j ||||jj |jjd}tj||j|j}|j|j|S)N)masksnr_dbrr)r.r>r7rxr[rrrr?r@rA ones_like gen_noiserrrHrIrJ to_filenamer0)r1r2Zin_imageZin_datarrresultZres_imr(r(r)r3s   zAddNoise._run_interfacecCsFt|jjs:t|jj\}}}tjjd||jj|f}n|jj}|S)Nz%s_SNR%03.2f%s) rr7rzrrxr8r:rar)r1r^r_rrzr(r(r)r0 s  zAddNoise._gen_output_filenamecCs|jj}|j|d<|S)Nrz)rSrNr0)r1rOr(r(r)rPs  zAddNoise._list_outputsN$@rcCsddlm}|tjd|d}|dkr4tj|}nDd||dk<d||dk<|j|jkrxtjtj|g|jddd}||dkj d}|dkr ||j }||j |} tj j |j| d } tj|dko|d krtj j|j| d } | |dk| |dk<|| } nv|d krx|j |} tj j |j| d } tj j |j| d }| |d }||d }tj||d |d } ntd| S)z Generates a copy of an image with a certain amount of added gaussian noise (rayleigh for background in mask) r)sqrtg$@Nrr|rcr)sizeZscalerrg@rz2Only normal and rician distributions are supported)mathr r?powerrndimZrollaxisrrCrZmeanvarrandomranyrNotImplementedError)r1imagerrrrr rsignalZsigma_nZnoiseZbg_noiseZim_noiseZn_1Zn_2Zstde_1Zstde_2r(r(r)rs6             zAddNoise.gen_noise)Nrrr) rrrrQrrRrrSr3r0rPrr(r(r(r)rs rc@s*eZdZeeddddZedddZdS)#NormalizeProbabilityMapSetInputSpecTzThe tpms to be normalized)rrrz-Masked voxels must sum up 1.0, 0.0 otherwise.)rrN)rrrr r rrr(r(r(r)r,Gsr,c@seZdZeeddddZdS)$NormalizeProbabilityMapSetOutputSpecT)rznormalized maps)rN)rrrr r out_filesr(r(r(r)r-Nsr-c@s(eZdZdZeZeZddZddZ dS)NormalizeProbabilityMapSeta- Returns the input tissue probability maps (tpms, aka volume fractions). The tissue probability maps are normalized to sum up 1.0 at each voxel within the mask. .. note:: Please recall this is not a spatial normalization algorithm Example ------- >>> from nipype.algorithms import misc >>> normalize = misc.NormalizeProbabilityMapSet() >>> normalize.inputs.in_files = [ 'tpm_00.nii.gz', 'tpm_01.nii.gz', 'tpm_02.nii.gz' ] >>> normalize.inputs.in_mask = 'tpms_msk.nii.gz' >>> normalize.run() # doctest: +SKIP cCs,d}t|jjr|jj}t|jj||_|S)N)rr7rnormalize_tpmsr_out_filenames)r1r2rr(r(r)r3is  z)NormalizeProbabilityMapSet._run_interfacecCs|jj}|j|d<|S)Nr.)rSrNr1)r1rOr(r(r)rPrs  z(NormalizeProbabilityMapSet._list_outputsN) rrrrQr,rRr-rSr3rPr(r(r(r)r/Rs  r/c@s>eZdZeddddZedddZejejejejddZ dS) SplitROIsInputSpecTzfile to be splitted)rrrzonly process files inside mask)rrzdesired ROI size)rN) rrrr rxrr Tupler!roi_sizer(r(r(r)r2xs r2c@sBeZdZeeddddZeeddddZeeddddZdS)SplitROIsOutputSpecT)rzthe resulting ROIs)rza mask indicating valid valuesz!arrays keeping original locationsN)rrrr r r. out_masks out_indexr(r(r(r)r5~s r5c@s(eZdZdZeZeZddZddZ dS) SplitROIsa] Splits a 3D image in small chunks to enable parallel processing. ROIs keep time series structure in 4D images. Example ------- >>> from nipype.algorithms import misc >>> rois = misc.SplitROIs() >>> rois.inputs.in_file = 'diffusion.nii' >>> rois.inputs.in_mask = 'mask.nii' >>> rois.run() # doctest: +SKIP cCstd}d}i|_t|jjr"|jj}t|jjr6|jj}t|jj||}|d|jd<|d|jd<|d|jd<|S)Nrr.rr6rr7) _outnamesrr7rr4 split_roisrx)r1r2rroisizeresr(r(r)r3s  zSplitROIs._run_interfacecCs4|jj}x"t|jjD]\}}|||<qW|S)N)rSrNrEr9r)r1rOkrr(r(r)rPs  zSplitROIs._list_outputsN) rrrrQr2rRr5rSr3rPr(r(r(r)r8s r8c@s>eZdZeeddddZeedddddZedddZd S) MergeROIsInputSpecTzfiles to be re-merged)rrr)rrz array keeping original locations)rzreference file)rrN)rrrr r rin_index in_referencer(r(r(r)r>s r>c@seZdZedddZdS)MergeROIsOutputSpecTzthe recomposed file)rrN)rrrr merged_filer(r(r(r)rAsrAc@s(eZdZdZeZeZddZddZ dS) MergeROIsa Splits a 3D image in small chunks to enable parallel processing. ROIs keep time series structure in 4D images. Example ------- >>> from nipype.algorithms import misc >>> rois = misc.MergeROIs() >>> rois.inputs.in_files = ['roi%02d.nii' % i for i in range(1, 6)] >>> rois.inputs.in_reference = 'mask.nii' >>> rois.inputs.in_index = ['roi%02d_idx.npz' % i for i in range(1, 6)] >>> rois.run() # doctest: +SKIP cCs"t|jj|jj|jj}||_|S)N) merge_roisr7rr?r@_merged)r1r2r<r(r(r)r3szMergeROIs._run_interfacecCs|jj}|j|d<|S)NrB)rSrNrE)r1rOr(r(r)rPs  zMergeROIs._list_outputsN) rrrrQr>rRrArSr3rPr(r(r(r)rCs rCcs,ddlddlddlj}j|j}|dkr4g}t|t|krxdt|D]X\}}|j|j |\}}|dkr|j|\}}||}|j d|||f} || g7}qNWfdd|D} t|dkr*| dj j d} d | | d k<| dj j} | jj jj| | dj| |d|dSjfd d| D} d | | d k<j| dd } j| dj}d|| dk<|dk rjj|j}d||dk<d||dk<jj|d}xlt|D]`\}} jj| |d}|| }| |j j} | jd jj|jj | |j| | qW|S)z Returns the input tissue probability maps (tpms, aka volume fractions) normalized to sum up 1.0 at each voxel within the mask. rNz.gzz%s_norm_%02d%scsg|]}j|qSr()r>).0Zfim)r.r(r) sz"normalize_tpms..r)rg?gcsg|]}|jdjdqS)Z unchanged)Zcachingr)r[float32)rFim)r?r(r)rGs)r rH)nibabelnumpyrr:Z atleast_1dtolistrrsplitextbasenamerar[rHrJcopyset_data_dtyper/rHrIstacksumonesrCr@r>rAmaZ masked_equalastype)rrr.rrZfinnamer4ZfextZfext2rzimgsZimg_datarvweightsZmskr]Zprobmapr()r.r?r)r0sR          &r0cCsddl}ddl}ddlm}m}ddlj}|dkr6d}|j|}|j} | dd} | d} |d|d|d} |d|d|d| f} |dk r|j |j|j }d||dk<d||dk<n |j | }|j dj |j}|j|}|j|}t||t| }|j |j j |jdf}|j|j|dd}|jd} |jd }|j|j ||jd ddj|g}g}g}xHt|D]:}|| }|d| }d}||kr||}|}|||d f}|jd |}|j|d |j||d||f|dkrx|j||jt|t| f|jd f}|j | f|jd }d|t| d<|jd}|j|j |ddj||j|n |j||jd|}|j|j | ddj||j|qzW|||fS)z9 Splits an image in ROIs for parallel processing rN)r r rr|r)r zonesmask.nii.gz)r.z roi%010d_idxz.npzzpartialmask.nii.gzzroi%010d.nii.gz)rXrXrr"r"r"r")rJrKr#r rrr:r>rCr@rArSrrUZuint8ZnonzerorRrFrGr!rZtakerarHrrr`ZsavezZvstackrBrH)rxrZroishaper.r?r rrrIZimshapeZdshapeZnvolsr;Z droishapeZnzelsZelsZnroisr]Z roidefnamer.Zout_maskZout_idxsrrlastfillZdroiinameZ partialmskZpartnamer4r(r(r)r: sn            "   r:csPddlddl}ddlj}ddl}|dkr4|jd}|dkrB|j}|j|ddkry4tj d||j d|g|j dd |j|d}Wn YnXj |}|j } |jj} |j} ~| d| d| d } j |d} t| jd kr| jd}nd}| d| d| d |f}| j|| jd d |dkr|j| |f|d}xt||D]\}}|j |}|j|d}|jj |jjd|}t|}|f}y|d|df||df<Wn*tdt|jt|jfYnXqRWj|j|| | j|nF| j| ddg}xLt|D]@}|jd|}j|j| dd| | j||j |q&Wxt||D]\}}|j |}|j|d}xt!|D]|\}}|jj |jjd}j |jd|fjd}t|}|f}|d|||<j|j| dd| | j|qWqvWfdd|D}j"|}|j||S)zE Re-builds an image resulting from a parallelized processing rNz merged.nii.gzrz.gzz uncompress %sZgunzipT)stdoutshellrrcmmseci,)rZarr_0.zBConsistency between indexes and chunks was lost: data=%s, chunk=%sr|z vol%06d.niicsg|]}j|qSr()r>)rFrI)r.r(r)rGszmerge_rois..r"r"r"r")#rJrKrr: subprocessrarHrMrr check_callPIPEr>rIrJrOrCrrPZset_xyzt_unitsrBziprr@rArprintrrHrZset_data_shaperr`rZ concat_images)rZin_idxsZin_refrrzr?rsprefZaffrvZrshZnpixZfcimgZndirsZnewshaper]cnamer[rZidxscdataZnelsZidatarKdr4rVZallimr()r.r)rDns          " .  rDc@s:eZdZeeddddZejddZej ddddZ d S) CalculateMedianInputSpecTz)One or more realigned Nifti 4D timeseries)rrrz&Filename prefix to store median images)rFz&Calculate a median file for each Nifti)rrN) rrrr r rr r median_filermedian_per_filer(r(r(r)rjs  rjc@seZdZeeddddZdS)CalculateMedianOutputSpecT)rzOne or more median images)rN)rrrr r median_filesr(r(r(r)rmsrmcsLeZdZdZeZeZfddZdddZ ddZ d d Z dd d Z Z S)CalculateMediana Computes an average of the median across one or more 4D Nifti timeseries Example ------- >>> from nipype.algorithms.misc import CalculateMedian >>> mean = CalculateMedian() >>> mean.inputs.in_files = 'functional.nii' >>> mean.run() # doctest: +SKIP cstt|j||g|_dS)N)rror _median_files)r1argsr)rr(r)rszCalculateMedian.__init__Nc Cs|r|jj|}n$t|jjtr.|jjd}n|jj}tjtj|\}}|dkrhtj|\}}||}|dkrt|}|jdr|dd}|jjr|jj}n dj ||}|r|t |7}tj dj ||S)Nrz.gz.rz{}_{}z{}.{}) r7rrDrErrMrNrrkrrra) r1r5rrrxr4Zin_extZin_ext2outnamer(r(r) _gen_fnames&     zCalculateMedian._gen_fnamecCsd}g|_xntt|jjD]Z\}}tj|}tj|j dd}|jj r`|jj |j |||q|dkrn|}q||7}qW|jj s|jj |j ||||S)Nr|)r ) rprrr7rr.r>r?medianr[rlr` _write_nifti)r1r2totalrr4r\r]r(r(r)r3s  zCalculateMedian._run_interfacecCs|jj}|j|d<|S)Nrn)rMrNrp)r1rOr(r(r)rPs  zCalculateMedian._list_outputsrucCs\|jjr*tj||j|j}|j||d}n$tj||d|j|j}|j|}|j||S)N)rr)r7rlr.rHrIrJrtr)r1r\r]rr5Z median_imgrr(r(r)rvs  zCalculateMedian._write_nifti)NN)ru)rrrrQrjrRrmrSrrtr3rPrvrr(r()rr)ros   rocs eZdZdZfddZZS)DistancezCalculates distance between two volumes. .. deprecated:: 0.10.0 Use :py:class:`nipype.algorithms.metrics.Distance` instead. c s$ttj|jf|tjdtdS)Nz^This interface has been deprecated since 0.10.0, please use nipype.algorithms.metrics.Distance)rnamrxrrrDeprecationWarning)r1r7)rr(r)r1szDistance.__init__)rrrrQrrr(r()rr)rx*srxcs eZdZdZfddZZS)OverlapzCalculates various overlap measures between two maps. .. deprecated:: 0.10.0 Use :py:class:`nipype.algorithms.metrics.Overlap` instead. c s$ttj|jf|tjdtdS)Nz]This interface has been deprecated since 0.10.0, please use nipype.algorithms.metrics.Overlap)rryr{rrrrz)r1r7)rr(r)rCszOverlap.__init__)rrrrQrrr(r()rr)r{<sr{cs eZdZdZfddZZS) FuzzyOverlapzCalculates various overlap measures between two maps, using a fuzzy definition. .. deprecated:: 0.10.0 Use :py:class:`nipype.algorithms.metrics.FuzzyOverlap` instead. c s$ttj|jf|tjdtdS)NzbThis interface has been deprecated since 0.10.0, please use nipype.algorithms.metrics.FuzzyOverlap)rryr|rrrrz)r1r7)rr(r)rVszFuzzyOverlap.__init__)rrrrQrrr(r()rr)r|Nsr|cs eZdZdZfddZZS)TSNRz` .. deprecated:: 0.12.1 Use :py:class:`nipype.algorithms.confounds.TSNR` instead c s$ttj|jf|tjdtdS)NzWThis interface has been moved since 0.12.0, please use nipype.algorithms.confounds.TSNR)rrr}rrr UserWarning)r1r7)rr(r)rgsz TSNR.__init__)rrrrQrrr(r()rr)r}asr})NN)NN)NN)_rQr8rr:rrJr.rKr?r#rrrrrrrryZinterfaces.baserr r r r r rrrrrrrrr getLoggerrrr*r,rTrXrZrbrgrjrmrprrrwryr{rrrrrrrrrrrrrrrrrrrrrr rrrr,r-r/r2r5r8r>rArCr0r:rDrjrmrorxr{r|r}r(r(r(r)s   0   > !"!&    \%!v0pd& * ! ? 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