3 d@sHdZddlZddljZddlmZddlmZddl Z ddl Z ddl mZddlmZmZddlmZdd lmZmZmZmZmZmZmZmZmZdd lmZej d Z!dQd dZ"GdddeZ#GdddeZ$GdddeZ%GdddeZ&GdddeZ'GdddeZ(GdddeZ)GdddeZ*Gdd d eZ+Gd!d"d"e+Z,Gd#d$d$e)Z-Gd%d&d&e*Z.Gd'd(d(e+Z/Gd)d*d*eZ0Gd+d,d,eZ1Gd-d.d.eZ2Gd/d0d0eZ3Gd1d2d2eZ4Gd3d4d4eZ5dRd7d8Z6dSd9d:Z7dTdd\dOdPZ?dS)]zB Algorithms to compute confounds in :abbr:`fMRI (functional MRI)` N) OrderedDict)chain)Legendre)configlogging)BibTeX) traits TraitedSpec BaseInterfaceBaseInterfaceInputSpecFile isdefinedInputMultiPathOutputMultiPathSimpleInterface)normalize_mc_paramsznipype.interfaceTcCsHytjj|||dStjjk r*YnXddlm}||||ddS)N) full_matrices compute_uvr)svdZgesvd)rrZ lapack_driver)nplinalgr LinAlgErrorZ scipy.linalg)arrrr=/tmp/pip-build-7vycvbft/nipype/nipype/algorithms/confounds.py fallback_svd"s rc@seZdZeddddZeddddZejddddZejddddZ ejddd dZ ejddd dZ ejddd dZ ej d d ZejddddZejddddZejej dej ddddZejddddddZej ddddZdS)ComputeDVARSInputSpecTzfunctional data, after HMC)exists mandatorydescz a brain maskz remove voxels with zero variance) usedefaultr zsave standardized DVARSFzsave non-standardized DVARSz"save voxel-wise standardized DVARSzoutput all DVARSzrepetition time in sec.)r zwrite DVARS plotdzoutput dpi for the plotgffffff'@gffffff@zoutput figure sizeZpngZpdfsvgzoutput format for figuresg@@ajDivide value in each voxel at each timepoint by the median calculated across all voxelsand timepoints within the mask (if specified)and then multiply by the value specified bythis parameter. 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Set this to 0 to disable intensitynormalization altogether.N)__name__ __module__ __qualname__r in_filein_maskr Boolremove_zerovariancesave_std save_nstd save_vxstdsave_allFloat series_tr save_plotIntfigdpiTuplefigsizeEnum figformatintensity_normalizationrrrrr/s.   rc@sxeZdZedddZedddZedddZedddZej Z ej Z ej Z edddZ edddZedddZdS)ComputeDVARSOutputSpecTzoutput text file)rr zoutput DVARS plotN)r$r%r&r out_stdout_nstd out_vxstdout_allr r/avg_stdavg_nstd avg_vxstdfig_stdfig_nstd fig_vxstdrrrrr9Ws      r9csbeZdZdZeZeZeddgdeddgdgZ fddZ dd d Z d d Z d dZ ZS) ComputeDVARSz Computes the DVARS. a}@techreport{nichols_notes_2013, address = {Coventry, UK}, title = {Notes on {Creating} a {Standardized} {Version} of {DVARS}}, url = {http://www2.warwick.ac.uk/fac/sci/statistics/staff/academic-research/nichols/scripts/fsl/standardizeddvars.pdf}, urldate = {2016-08-16}, institution = {University of Warwick}, author = {Nichols, Thomas}, year = {2013} }method)entrytagsa@article{power_spurious_2012, title = {Spurious but systematic correlations in functional connectivity {MRI} networks arise from subject motion}, volume = {59}, doi = {10.1016/j.neuroimage.2011.10.018}, number = {3}, urldate = {2016-08-16}, journal = {NeuroImage}, author = {Power, Jonathan D. and Barnes, Kelly A. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.}, year = {2012}, pages = {2142--2154}, } c si|_tt|jf|dS)N)_resultssuperrD__init__)selfinputs) __class__rrrJszComputeDVARS.__init__NcCsltjtj|jj\}}|dkr6tj|\}}||}|dkrB|}|jdrX|dd}tjdj|||S)Nz.gz.z{}_{}.{})opsplitextbasenamerLr' startswithabspathformat)rKsuffixextfnameZin_extZin_ext2rrr _gen_fnames  zComputeDVARS._gen_fnamecCsxt|jj|jj|jj|jjd}tj|ddjt \|j d<|j d<|j d<d}t |jj rd|jj }|jj r|jddd }tj||d d d ||j d <|jjr|jd|jjd |j d<t|d |jjd|d}|j|j dt |jj|jjdd|j|jjr|jddd }tj||dd d ||j d<|jjr|jd|jjd |j d<t|d|jjd|d}|j|j dt |jj|jjdd|j|jjr4|jddd }tj||dd d ||j d<|jjr4|jd|jjd |j d<t|d|jjd|d}|j|j dt |jj|jjdd|j|jjrt|jddd }tj|tj|jdddd d!||j d"<|S)#N)r*r8rO)axisr>r?r@Z dvars_stdZtsv)rWrs%0.6f)fmtr:rAzStandardized DVARS)r0tight)dpirU bbox_inches dvars_nstdr;rBZDVARSZ dvars_vxstdrr<rCzVoxelwise std DVARSdvarss%0.8f z)std DVARS non-std DVARS vx-wise std DVARS)r[ delimiterheadercommentsr=) compute_dvarsrLr'r(r*r8rmeanastypefloatrHrr0r+rYsavetxtr1r7 plot_confoundr5savefigr3clfr,r-r.ZvstackT)rKruntimer`trout_filefigrrr_run_interfaces ,              zComputeDVARS._run_interfacecCs|jS)N)rH)rKrrr _list_outputsszComputeDVARS._list_outputs)N)r$r%r&__doc__r input_specr9 output_specr _referencesrJrYrsrt __classcell__rr)rMrrDds    \rDc @seZdZeddddZejdddddd dd Zejd dd d Z edddd Z edddd Z ejddZ ej dddd Zej dddd Zejdddd Zejejdejdddd ZdS)FramewiseDisplacementInputSpecTzmotion parameters)rrr ZFSLZAFNIZSPMZFSFASTZNIPYzSource of movement parameters)r r2z`radius in mm to calculate angular FDs, 50mm is the default since it is used in Power et al. 2012)r!r zfd_power_2012.txtzoutput file namezfd_power_2012.pdfzoutput figure namezrepetition time in sec.)r Fz write FD plotzcalculate FD in mm/sr"zoutput dpi for the FD plotgffffff'@gffffff@zoutput figure sizeN)r$r%r&r r'r r6parameter_sourcer/radiusrq out_figurer0r)r1 normalizer2r3r4r5rrrrrzs0 rzc@s,eZdZeddZeddZejddZdS)FramewiseDisplacementOutputSpeczcalculated FD per timestep)r zoutput image filez average FDN) r$r%r&r rqr~r r/ fd_averagerrrrr$s  rc@s:eZdZdZeZeZeddgdgZ ddZ ddZ d S) FramewiseDisplacementa Calculate the :abbr:`FD (framewise displacement)` as in [Power2012]_. This implementation reproduces the calculation in fsl_motion_outliers .. [Power2012] Power et al., Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion, NeuroImage 59(3), 2012. doi:`10.1016/j.neuroimage.2011.10.018 `_. a@article{power_spurious_2012, title = {Spurious but systematic correlations in functional connectivity {MRI} networks arise from subject motion}, volume = {59}, doi = {10.1016/j.neuroimage.2011.10.018}, number = {3}, urldate = {2016-08-16}, journal = {NeuroImage}, author = {Power, Jonathan D. and Barnes, Kelly A. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.}, year = {2012}, pages = {2142--2154}, } rE)rFrGcCsVtj|jj}tjtd||jjd}|ddddf|ddddf}|ddddf|jj9<tj|j dd}t j |jj t |jd|_tj|jj |ddd |jjrRd}t|jjr|jj}|jjr|dkrtjd t j |jj|jd <t||jjd d ||jjd}|j|jd t |jj|jjdddd|j|S)NrO)Zfunc1drZZarrsource)rZ)rqrrrb)rdrez-FD plot cannot be normalized if TR is not setr~ZFDmm)unitsr0rr\)r]rUr^)rZloadtxtrLr'apply_along_axisrr|r}abssumrPrTrqrirgrHrjr1rr0rIFLOGGERwarningr~rkr5rlr3rm)rKroZmparsdiffZfd_resrprrrrrrsQsD (       z$FramewiseDisplacement._run_interfacecCs|jS)N)rH)rKrrrrtsz#FramewiseDisplacement._list_outputsN) r$r%r&rurzrvrrwrrxrsrtrrrrr*s  /rc@s\eZdZeddddZeeddddZejddd d gd gd d Z ej ddgd gddZ ej ej ddZej ddddZejdej dddgddZej dddddgdd Zejd!d"d#dd$dZejd%d&d'd(Zej dddd)d*Zej d+dZejd,dd-dZejd.dZejejed#dd/d0Zejejed#dd1d0Zej ddd2d3Zejd4d5dd6dZd7S)8CompCorInputSpecTz"already realigned brain image (4D))rrr )rzOne or more mask files that determines ROI (3D). When more that one file is provided ``merge_method`` or ``merge_index`` must be provided)r union intersectnone mask_index mask_fileszMerge method if multiple masks are present - ``union`` uses voxels included in at least one input mask, ``intersect`` uses only voxels present in all input masks, ``none`` performs CompCor on each mask individually)xorrequiresr r merge_methodzAPosition of mask in ``mask_files`` to use - first is the default.)lowrrr zNames for provided masks (for printing into metadata). If provided, it must be as long as the final mask list (after any merge and indexing operations).zcomponents_file.txtz*Filename to store physiological components)r!r allrO)rvariance_thresholdz~Number of components to return from the decomposition. If ``num_components`` is ``all``, then all components will be retained.)rr gg?num_componentsa[Select the number of components to be returned automatically based on their ability to explain variance in the dataset. ``variance_threshold`` is a fractional value between 0 and 1; the number of components retained will be equal to the minimum number of components necessary to explain the provided fraction of variance in the masked time series.)rhigh exclude_low exclude_highrr polynomialcosineFz1Detrend time series prior to component extractionz0.15.0 pre_filterz2use polynomial regression pre-component extraction)Z deprecatednew_namer zthe degree polynomial to use)rvaluer!r z`the desired header for the output tsv file (one column). 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CompCor provides three pre-filter options, all of which include per-voxel mean removal: - ``'polynomial'``: Legendre polynomial basis - ``'cosine'``: Discrete cosine basis - ``False``: mean-removal only In the case of ``polynomial`` and ``cosine`` filters, a pre-filter file may be saved with a row for each volume/timepoint, and a column for each non-constant regressor. If no non-constant (mean-removal) columns are used, this file may be empty. If ``ignore_initial_volumes`` is set, then the specified number of initial volumes are excluded both from pre-filtering and CompCor component extraction. Each column in the components and pre-filter files are prefixe with zeros for each excluded volume so that the number of rows continues to match the number of volumes in the input file. In addition, for each excluded volume, a column is added to the pre-filter file with a 1 in the corresponding row. Example ------- >>> ccinterface = CompCor() >>> ccinterface.inputs.realigned_file = 'functional.nii' >>> ccinterface.inputs.mask_files = 'mask.nii' >>> ccinterface.inputs.num_components = 1 >>> ccinterface.inputs.pre_filter = 'polynomial' >>> ccinterface.inputs.regress_poly_degree = 2 rEimplementationa@article{compcor_2007, title = {A component based noise correction method (CompCor) for BOLD and perfusion based}, volume = {37}, number = {1}, doi = {10.1016/j.neuroimage.2007.04.042}, urldate = {2016-08-13}, journal = {NeuroImage}, author = {Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T.}, year = {2007}, pages = {90-101} })rGrFcstt|j||d|_dS)z-exactly the same as compcor except the headerrN)rIrrJ_header)rKargskwargs)rMrrrJCszCompCor.__init__csg}t|jjr(t|jj|jj|jj}|jjr8d|j_|jjdkrL|jjnd}t j |jj }t |j dkrtdj|j|jj t |j |j t |dkrt jtj|j ddtd|j|jd}|g}|jj}|r|j|jd|df|j|j}|j||j}d}|jjd krt|jjr,|jj}nhy.|jjd}|jjd d krX|d }Wnttfk rxd}YnX|dkrtd j|jt|jj r|jj }n&t|jj!r|jj!}nd}t"j#dt$|j%tj&d|||jj||jj'||jj(|jj) \} } } |rH| } || j d} tj*| | j d f| j+d} | | |d<t,j-j.t,j/|jj0}|j1| j d }tj2|| dddj.|ddt,j-j.|j3|jj0|j4d<d}|jjd)kr|jj5}|r||j4d<|dkrt,j-j.|j3d|j4d<ddd|jj| j6dkr| j d nd}fddt7|D}|r| }tj*| ||f| j+d} |j6dkrr|| |dd|f<tj8|| d|| df<|j9ddt7|Dtj2|j4d| dddj.|dd|jj:}|r||j4d<|dkrt,j-j.|j3d |j4d<tj;t | d!d"d}tj<| d#}tj|j4dd%V}|j?dj.d&gt@| jAd'x,tB|f| jCD]}|j?d(j|qWWdQRX|S)*NrrzC{} expected a 4-D nifti file. Input {} has {} dimensions (shape {})r)dtype)affinerd.rrOZmsecizK{} cannot detect repetition time from image - Set the repetition_time inputrz`num_components` and `variance_threshold` are not defined. Setting number of components to 6 for backward compatibility. Please set either `num_components` or `variance_threshold`, as this feature may be deprecated in the future.s%.10f rb)r[rcrdrerFrTzpre_filter.tsvrZCosine)rrcsg|]}dj|qS)z{}{:02d})rU).0i)ftyperr sz*CompCor._run_interface..cSsg|]}dj|qS)zNonSteadyStateOutlier{:02d})rU)rrrrrrsrzcomponent_metadata.tsvmaskZobject_retainedcSsg|]}dj|qS)z dropped{})rU)rrrrrrsw component z9{0[0]} {0[1]} {0[2]:.10f} {0[3]:.10f} {0[4]:.10f} {0[5]} )rr)DrrLrcombine_mask_filesrrrrrnbloadrlenshape ValueErrorrUr Nifti1ImageronesboolrrdrrMdataobj_process_masksrZ get_zoomsZget_xyzt_unitsAttributeError IndexErrorrrrrcompute_noise_components get_fdatafloat32rrrzerosrospathjoingetcwdr _make_headersrjcwdrHrsizerangeZeyeextendremptywhereZ logical_notopenwritelistkeyszipvalues)rKro mask_imagesdegree imgseriesimgZ skip_volsZTRcomponents_criterion componentsZ filter_basismetadataZold_compZnrowsrZcomponents_headerrZncolsrdZ old_basisrZcomponents_namesrZ not_retainedfrr)rrrsHs                   "zCompCor._run_interfaceNcCs|S)Nr)rKr timeseriesrrrrszCompCor._process_maskscs4t|jjr|jjn|jfddt|D}|S)Ncsg|]}dj|qS)z{}{:02d})rU)rr)rdrrrsz)CompCor._make_headers..)rrLrrr)rKZnum_colheadersr)rdrrszCompCor._make_headers)N)r$r%r&rurrvrrwrrxrJrsrrryrr)rMrr s"  - rcs eZdZdZfddZZS)ACompCorz Anatomical compcor: for inputs and outputs, see CompCor. When the mask provided is an anatomical mask, then CompCor is equivalent to ACompCor. cstt|j||d|_dS)z-exactly the same as compcor except the headerZaCompCorN)rIrrJr)rKrr)rMrrrJszACompCor.__init__)r$r%r&rurJryrr)rMrrsrc @s$eZdZejddddddddZdS)TCompCorInputSpecgg?g{Gz?Tzthe percentile used to select highest-variance voxels, represented by a number between 0 and 1, exclusive. By default, this value is set to .02. That is, the 2% of voxels with the highest variance are used.)rrrrrr!r N)r$r%r&r rpercentile_thresholdrrrrrsrc@seZdZeeddddZdS)TCompCorOutputSpecT)rz'voxels exceeding the variance threshold)r N)r$r%r&rr high_variance_masksrrrrr!srcs>eZdZdZeZeZfddZd ddZ fddZ Z S) TCompCora Interface for tCompCor. Computes a ROI mask based on variance of voxels. Example ------- >>> ccinterface = TCompCor() >>> ccinterface.inputs.realigned_file = 'functional.nii' >>> ccinterface.inputs.mask_files = 'mask.nii' >>> ccinterface.inputs.num_components = 1 >>> ccinterface.inputs.pre_filter = 'polynomial' >>> ccinterface.inputs.regress_poly_degree = 2 >>> ccinterface.inputs.percentile_threshold = .03 cs"tt|j||d|_g|_dS)z-exactly the same as compcor except the headerZtCompCorN)rIrrJr _mask_files)rKrr)rMrrrJ;szTCompCor.__init__Nc Csg}g|_tj|}xt|D]\}}tj|jjt}||ddf}td|d}t|dd d}tj |tj dd|j j jt } tj|} || k| |dk<tj| |j|jd} tjjdj|} | j| tjd t|j| |jj| |j| qW|S) NrrrO)rZgY@g?)rrdzmask_{:03d}.nii.gzz>> tsnr = TSNR() >>> tsnr.inputs.in_file = 'functional.nii' >>> res = tsnr.run() # doctest: +SKIP c Csttj|jjd}|jj}dd|jjD}tjdd|Ddd}tj|}|j j dkrt|j tj |j tj }t|jjrt|jj|dd d}tj||j|}tj|tj|jjtj|dd}tj|dd}tj|}|d k} || || || <tj||j|}tj|tj|jjtj||j|}tj|tj|jjtj||j|}tj|tj|jj|S) NrcSsg|]}tj|qSr)rr)rfilenamerrrrsz'TSNR._run_interface..cSs.g|]&}|jtjdj|jdddqS))rNrrOr)r)rrrreshaper)rZvolrrrrsr)rZrF) remove_meangMbP?)rrrLr'rdcopyrZ concatenateZ nan_to_numrkindZset_data_dtyperrhrrrrsaverPrTrrgstdrrrr) rKrorrdZvollistdataZmeanimgZ stddevimgZtsnrZstddevimg_nonzerorrrrss6       zTSNR._run_interfacecCsR|jj}x"dD]}tjt|j|||<qWt|jjrNtj|jj|d<|S)Nrrrr)rrr) Z_outputsgetrPrTgetattrrLrrr)rKr krrrrts    zTSNR._list_outputsN) r$r%r&rur rvrrwrsrtrrrrrs  %rc@seZdZeddddZdS)NonSteadyStateDetectorInputSpecTz4D NIFTI EPI file)rrr N)r$r%r&r r'rrrrrsrc@seZdZejddZdS) NonSteadyStateDetectorOutputSpeczHNumber of non-steady state volumesdetected in the beginning of the scan.)r N)r$r%r&r r2n_volumes_to_discardrrrrr sr c@s(eZdZdZeZeZddZddZ dS)NonSteadyStateDetectorzc Returns the number of non-steady state volumes detected at the beginning of the scan. cCsZtj|jj}|jddddddddfjddjddjdd}dt|i|_|S)Nr{r)rZr!)rrrLr'rrg is_outlierrH)rKroZin_niiZ global_signalrrrrss:z%NonSteadyStateDetector._run_interfacecCs|jS)N)rH)rKrrrrtsz$NonSteadyStateDetector._list_outputsN) r$r%r&rurrvr rwrsrtrrrrr"s  r"Fc Csddl}ddl}ddlm}ddl}|j|j|jd}|j|j|j j |j } t |j dkrhtd|j| dk} || d| d| dddf} |dkr| |j| |} |j| d dd d |j| d dd d d } |r| | dkddf} | | dk} |j|dtd| dddj |jddddf} |j|jd| dj| }|j}|j| dd}|j|j|jdd}||}|jD|jd|j||j|g|j dj}|j|jdd}WdQRX|||fS)a Compute the :abbr:`DVARS (D referring to temporal derivative of timecourses, VARS referring to RMS variance over voxels)` [Power2012]_. Particularly, the *standardized* :abbr:`DVARS (D referring to temporal derivative of timecourses, VARS referring to RMS variance over voxels)` [Nichols2013]_ are computed. .. [Nichols2013] Nichols T, `Notes on creating a standardized version of DVARS `_, 2013. .. note:: Implementation details Uses the implementation of the `Yule-Walker equations from nitime `_ for the :abbr:`AR (auto-regressive)` filtering of the fMRI signal. :param numpy.ndarray func: functional data, after head-motion-correction. :param numpy.ndarray mask: a 3D mask of the brain :param bool output_all: write out all dvars :param str out_file: a path to which the standardized dvars should be saved. :return: the standardized DVARS rN) AR_est_YW)rrz*Input fMRI dataset should be 4-dimensionalrOrKlower)rZ interpolationg/$?T)r)rZrr)numpynibabelZnitime.algorithmsr%warningsrrrrrrhZuint8rr RuntimeErrorrmedianrrrZsqueezesqrttolistrgrZsquarecatch_warningsfilterwarningsarrayrn)r'r(r*r8rrr%r,funcridxZmfuncZfunc_sdZar1Z diff_sdhatZ diff_sd_meanZ func_diffr_Z dvars_stdzZ diff_vx_stdzZ dvars_vx_stdzrrrrfs@   &   rfcCsZddl}|jtjddddlj}ddlm}ddlm } ddl } |j |d} | | |dd d dgd d } | j d dddd| j | dddf} |r|dk r||}| j|| jdt|f|}|dk r||rdndj|7}| j|d}|dk rdj|}| j|| j}| j | d} | j|d| d| jd| j|| jg| S)zM A helper function to plot :abbr:`fMRI (functional MRI)` confounds. rNZ executionZmatplotlib_backend)GridSpec)FigureCanvasPdf)r5rOrrg?)Z width_ratiosZwspaceg?gffffff?g?g?)ZhspacerightleftZbottomz speed [{}/s]z [{}]zFrame #zFrame # ({} sec TR)T)ZverticalaxZFramesrr)rr) matplotlibZuserrZmatplotlib.pyplotZpyplotZmatplotlib.gridspecr6Zmatplotlib.backends.backend_pdfr7ZseabornZFigureupdateZ add_subplotZplotZset_xlimrrUZ set_ylabelZ set_xlabelZget_ylimZdistplotZset_ylimZset_yticklabels)Ztseriesr5namerr0rr;Zpltr6Z FigureCanvasZsnsrrZgridr:ZylabelZxlabelZylimrrrrk>s<             rk @cCst|jdkr|dddf}tj|dd}tj||ddd}tj|}tj|}d||}d}x,tt|D]}|||krPqt|d7}qtW|S)a Returns a boolean array with True if points are outliers and False otherwise. :param nparray points: an numobservations by numdimensions numpy array of observations :param float thresh: the modified z-score to use as a threshold. Observations with a modified z-score (based on the median absolute deviation) greater than this value will be classified as outliers. :return: A bolean mask, of size numobservations-length array. .. note:: References Boris Iglewicz and David Hoaglin (1993), "Volume 16: How to Detect and Handle Outliers", The ASQC Basic References in Quality Control: Statistical Techniques, Edward F. Mykytka, Ph.D., Editor. rONr)rZrg/$?r)rrrr.rr/r)ZpointsZthreshr.rZmed_abs_deviationZmodified_z_scoreZtimepoints_to_discardrrrrr#is     r#rOrc Cs|j}||}|ddkr0|dkr0|tjgfS|jd|f}|tj|}tt||d} | jddkr| ddddfntjg} tjj| |j d} |s| ddddf} | dd} || j | j } | j|| fS)NrrrOrrrr) rrr3rarange _full_rank _cosine_driftrZlstsqrndot) rZtimestep period_cutrrZr datashape timepoints frametimesXnon_constant_regressorsbetasZ residualsrrr cosine_filters, rJcCs>tjdt|j|j}||}|ddkrB|dkrB|tjgfS|jd|f}tj|df}xLt|D]@}t j |d} tj dd|} tj || | ddtj ff}qhW|jddkr|dddd fntjg} tjj|j|j} |r|j| j} n(|ddddfj| dddfj} || }|j|| fS) z Returns data with degree polynomial regressed out. :param bool remove_mean: whether or not demean data (i.e. degree 0), :param int axis: numpy array axes along which regression is performed z4Performing polynomial regression on data of shape %srrrON.rrr)rr r rrr3rrrrbasisZlinspacehstackZnewaxisrZpinvrBrn)rrrrZrrDrErGrZpolynomial_funcZ value_arrayrHrIZdatahatZregressed_datarrrrs&$,(rcCs~t|st| rht|s4t|dkr,d}ntd|t|krTtj||}|gStdj|t|g}|dkrx|D]}|jtj|qzW|S|dkrd}xF|D]>}tj|}tj|j j ddk}|dkr|}tj |||qWtj ||j |jd }|gS|d krzd}xJ|D]B}tj|}tj|j j ddk}|dkrL|}tj|||qWtj ||j |jd }|gSdS) aCombines input mask files into a single nibabel image A helper function for CompCor Parameters ---------- mask_files: a list one or more binary mask files mask_method: enum ('union', 'intersect', 'none') determines how to combine masks mask_index: an integer determines which file to return (mutually exclusive with mask_method) Returns ------- masks: a list of nibabel images rOrzWWhen more than one mask file is provided, one of merge_method or mask_index must be setz9mask_index {0} must be less than number of mask files {1}rrNZint32)rdr)rrrrrrUr rrrrh logical_orrrrd logical_and)rZ mask_methodrrmasksrrZ img_as_maskrrrrsJ          r?rc sXtjg} |dkrd}|p$tt|}g} g} g} g} g}g}xzt||D]j\}}tjtj|jj t }|j dd|j krt dj |j dd|j ||ddf}d|tjtj|ddddf<|dkr|dkrt d t||||d \}} n|dkrt|||d \}} |j}|t|d }yt|d d\}}}Wntjjt fk r|dkrhtj|j dtjtjd}|dkrtj|j d|ftjtjd}ntj|j ddftjtjd}YnX|dtj|d}tj|}t|d|ko dknr&tj||dn|dkr8t|tdkrLP| j|dddf| j|gt|| j|| j||j||jfddtt|DqLWt| dkrtj| } n0|dkrt dtj|j dftjtjd} tdt t!| fdtj| fdtj| fdtj|fdt t!|fg}| | |fS)ak Compute the noise components from the image series for each mask. Parameters ---------- imgseries: nibabel image Time series data to be decomposed. mask_images: list List of nibabel images. Time series data from ``img_series`` is subset according to the spatial extent of each mask, and the subset data is then decomposed using principal component analysis. Masks should be coextensive with either anatomical or spatial noise ROIs. components_criterion: float Number of noise components to return. If this is a decimal value between 0 and 1, then ``create_noise_components`` will instead return the smallest number of components necessary to explain the indicated fraction of variance. If ``components_criterion`` is ``all``, then all components will be returned. filter_type: str Type of filter to apply to time series before computing noise components. - 'polynomial' - Legendre polynomial basis - 'cosine' - Discrete cosine (DCT) basis - False - None (mean-removal only) failure_mode: str Action to be taken in the event that any decomposition fails to identify any components. ``error`` indicates that the routine should raise an exception and exit, while any other value indicates that the routine should return a matrix of NaN values equal in size to the requested decomposition matrix. mask_names: list or None List of names for each image in ``mask_images``. This should be equal in length to ``mask_images``, with the ith element of ``mask_names`` naming the ith element of ``mask_images``. degree: int Order of polynomial used to remove trends from the timeseries period_cut: float Minimum period (in sec) for DCT high-pass filter repetition_time: float Time (in sec) between volume acquisitions. This must be defined if the ``filter_type`` is ``cosine``. Returns ------- components: numpy array Numpy array containing the requested set of noise components basis: numpy array Numpy array containing the (non-constant) filter regressors metadata: OrderedDict{str: numpy array} Dictionary of eigenvalues, fractional explained variances, and cumulative explained variances. rrONrzjInputs for CompCor, timeseries and mask, do not have matching spatial dimensions ({} and {}, respectively)r)rZrz2Repetition time must be provided for cosine filter)rrFg?)rr)rrcsg|] }|kqSrr)rr)rrrrsz,compute_noise_components..zNo components foundrZsingular_valuevariance_explainedcumulative_variance_explainedrr)rFr)"rr3rrrrrZ squeeze_imagerrhrrrrUisnanrrJrrnrrrrfullnanrZcumsumrZ searchsortedr rLrrr)rrrZ filter_typerrCrrrrKrZmd_maskZmd_svZmd_varZ md_cumvarZ md_retainedr=rrZvoxel_timecoursesMus_rQrRrr)rrrsA     $      $     rcCs,tj||d}|||dk<||tj|<|S)N)rZr)rrrS)rVxrZZstdMrrrrs rc Cst|}tj|}d|}|d|d}tttjd|||d}tj||f}tjd|}xBtd|D]4} |tj tj ||d| |dd| df<qrWd|dd|df<|S)aCreate a cosine drift matrix with periods greater or equals to period_cut Parameters ---------- period_cut: float Cut period of the low-pass filter (in sec) frametimes: array of shape(nscans) The sampling times (in sec) Returns ------- cdrift: array of shape(n_scans, n_drifts) cosin drifts plus a constant regressor at cdrift[:,0] Ref: http://en.wikipedia.org/wiki/Discrete_cosine_transform DCT-II g?rOrrg@g?N) rrr?maxrfloorrr/rcospi) rCrFZlen_timZn_timesZhfcutdtorderZcdriftZnfctrrrrrAs  4rA4&k Cc Cst|dd\}}}|j|j}}||}||kr<||fStjd||||d}||}tj|tjtj||}||fS)a This function possibly adds a scalar matrix to X to guarantee that the condition number is smaller than a given threshold. Parameters ---------- X: array of shape(nrows, ncols) cmax=1.e-15, float tolerance for condition number Returns ------- X: array of shape(nrows, ncols) after regularization cmax=1.e-15, float tolerance for condition number F)rz8Matrix is singular at working precision, regularizing...rO)rr[minrrrrBZdiag) rGZcmaxUrXVZsmaxZsmincZldarrrr@s r@)TT)Fr$)NNF)r>r)Trrr)Trr)NN)rPFrrNrN)r)ra)@ruros.pathrrP collectionsr itertoolsrr+rr*rZnumpy.polynomialrrbrrZ external.duerZinterfaces.baser r r r r rrrrZ utils.miscr getLoggerrrrr9rDrzrrrrrrrrrr rrrr r"rfrkr#rJrrrrrAr@rrrrsf     ,   ( "Z}y <@ X + '  + H ( $