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# interfaces.fsl.model

## Cluster

Link to code

Wraps command cluster

Uses FSL cluster to perform clustering on statistical output

### Examples

    >>> cl = Cluster()
    >>> cl.inputs.threshold = 2.3
    >>> cl.inputs.in_file = 'zstat1.nii.gz'
    >>> cl.inputs.out_localmax_txt_file = 'stats.txt'
    >>> cl.cmdline
    'cluster --in=zstat1.nii.gz --olmax=stats.txt --thresh=2.3000000000'

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input volume
    threshold: (a float)
            threshold for input volume

    [Optional]
    args: (a string)
            Additional parameters to the command
    connectivity: (an integer)
            the connectivity of voxels (default 26)
    cope_file: (a file name)
            cope volume
    dlh: (a float)
            smoothness estimate = sqrt(det(Lambda))
    environ: (a dictionary with keys which are a value of type 'str' and with values which
             are a value of type 'str', nipype default value: {})
            Environment variables
    find_min: (a boolean)
            find minima instead of maxima
    fractional: (a boolean)
            interprets the threshold as a fraction of the robust range
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    minclustersize: (a boolean)
            prints out minimum significant cluster size
    no_table: (a boolean)
            suppresses printing of the table info
    num_maxima: (an integer)
            no of local maxima to report
    out_index_file: (a boolean or a file name)
            output of cluster index (in size order)
    out_localmax_txt_file: (a boolean or a file name)
            local maxima text file
    out_localmax_vol_file: (a boolean or a file name)
            output of local maxima volume
    out_max_file: (a boolean or a file name)
            filename for output of max image
    out_mean_file: (a boolean or a file name)
            filename for output of mean image
    out_pval_file: (a boolean or a file name)
            filename for image output of log pvals
    out_size_file: (a boolean or a file name)
            filename for output of size image
    out_threshold_file: (a boolean or a file name)
            thresholded image
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
            FSL output type
    peak_distance: (a float)
            minimum distance between local maxima/minima, in mm (default 0)
    pthreshold: (a float)
            p-threshold for clusters
            requires: dlh, volume
    std_space_file: (a file name)
            filename for standard-space volume
    use_mm: (a boolean)
            use mm, not voxel, coordinates
    volume: (an integer)
            number of voxels in the mask
    warpfield_file: (a file name)
            file contining warpfield
    xfm_file: (a file name)
            filename for Linear: input->standard-space transform. Non-linear: input->highres
            transform

Outputs:

    index_file: (a file name)
            output of cluster index (in size order)
    localmax_txt_file: (a file name)
            local maxima text file
    localmax_vol_file: (a file name)
            output of local maxima volume
    max_file: (a file name)
            filename for output of max image
    mean_file: (a file name)
            filename for output of mean image
    pval_file: (a file name)
            filename for image output of log pvals
    size_file: (a file name)
            filename for output of size image
    threshold_file: (a file name)
            thresholded image

## ContrastMgr

Link to code

Wraps command contrast_mgr

Use FSL contrast_mgr command to evaluate contrasts

In interface mode this file assumes that all the required inputs are in the same location.

Inputs:

    [Mandatory]
    corrections: (an existing file name)
            statistical corrections used within FILM modelling
    dof_file: (an existing file name)
            degrees of freedom
    param_estimates: (an existing file name)
            Parameter estimates for each column of the design matrix
    sigmasquareds: (an existing file name)
            summary of residuals, See Woolrich, et. al., 2001
    tcon_file: (an existing file name)
            contrast file containing T-contrasts

    [Optional]
    args: (a string)
            Additional parameters to the command
    contrast_num: (an integer)
            contrast number to start labeling copes from
    environ: (a dictionary with keys which are a value of type 'str' and with values which
             are a value of type 'str', nipype default value: {})
            Environment variables
    fcon_file: (an existing file name)
            contrast file containing F-contrasts
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
            FSL output type
    suffix: (a string)
            suffix to put on the end of the cope filename before the contrast number, default is
            nothing

Outputs:

    copes: (an existing file name)
            Contrast estimates for each contrast
    fstats: (an existing file name)
            f-stat file for each contrast
    neffs: (an existing file name)
            neff file ?? for each contrast
    tstats: (an existing file name)
            t-stat file for each contrast
    varcopes: (an existing file name)
            Variance estimates for each contrast
    zfstats: (an existing file name)
            z-stat file for each F contrast
    zstats: (an existing file name)
            z-stat file for each contrast

## FEAT

Link to code

Wraps command feat

Uses FSL feat to calculate first level stats

Inputs:

    [Mandatory]
    fsf_file: (a file name)
            File specifying the feat design spec file

    [Optional]
    args: (a string)
            Additional parameters to the command
    environ: (a dictionary with keys which are a value of type 'str' and with values which
             are a value of type 'str', nipype default value: {})
            Environment variables
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
            FSL output type

Outputs:

    feat_dir: (an existing directory name)

## FEATModel

Link to code

Wraps command feat_model

Uses FSL feat_model to generate design.mat files

Inputs:

    [Mandatory]
    ev_files: (a list of items which are an existing file name)
            Event spec files generated by level1design
    fsf_file: (a file name)
            File specifying the feat design spec file

    [Optional]
    args: (a string)
            Additional parameters to the command
    environ: (a dictionary with keys which are a value of type 'str' and with values which
             are a value of type 'str', nipype default value: {})
            Environment variables
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
            FSL output type

Outputs:

    con_file: (an existing file name)
            Contrast file containing contrast vectors
    design_cov: (an existing file name)
            Graphical representation of design covariance
    design_file: (an existing file name)
            Mat file containing ascii matrix for design
    design_image: (an existing file name)
            Graphical representation of design matrix
    fcon_file: (a file name)
            Contrast file containing contrast vectors

## FEATRegister

Link to code

Register feat directories to a specific standard

Inputs:

    [Mandatory]
    feat_dirs: (a directory name)
            Lower level feat dirs
    reg_image: (a file name)
            image to register to (will be treated as standard)

    [Optional]
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    reg_dof: (an integer, nipype default value: 12)
            registration degrees of freedom

Outputs:

    fsf_file: (an existing file name)
            FSL feat specification file

## FILMGLS

Link to code

Wraps command film_gls

Use FSL film_gls command to fit a design matrix to voxel timeseries

### Examples

Initialize with no options, assigning them when calling run:

    >>> from nipype.interfaces import fsl
    >>> fgls = fsl.FILMGLS()
    >>> res = fgls.run('in_file', 'design_file', 'thresh', rn='stats')

Assign options through the `inputs` attribute:

    >>> fgls = fsl.FILMGLS()
    >>> fgls.inputs.in_file = 'functional.nii'
    >>> fgls.inputs.design_file = 'design.mat'
    >>> fgls.inputs.threshold = 10
    >>> fgls.inputs.results_dir = 'stats'
    >>> res = fgls.run()

Specify options when creating an instance:

    >>> fgls = fsl.FILMGLS(in_file='functional.nii', design_file='design.mat', threshold=10, results_dir='stats')
    >>> res = fgls.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input data file

    [Optional]
    args: (a string)
            Additional parameters to the command
    autocorr_estimate_only: (a boolean)
            perform autocorrelation estimatation only
            mutually_exclusive: autocorr_estimate_only, fit_armodel, tukey_window,
             multitaper_product, use_pava, autocorr_noestimate
    autocorr_noestimate: (a boolean)
            do not estimate autocorrs
            mutually_exclusive: autocorr_estimate_only, fit_armodel, tukey_window,
             multitaper_product, use_pava, autocorr_noestimate
    brightness_threshold: (an integer)
            susan brightness threshold, otherwise it is estimated
    design_file: (an existing file name)
            design matrix file
    environ: (a dictionary with keys which are a value of type 'str' and with values which
             are a value of type 'str', nipype default value: {})
            Environment variables
    fit_armodel: (a boolean)
            fits autoregressive model - default is to use tukey with M=sqrt(numvols)
            mutually_exclusive: autocorr_estimate_only, fit_armodel, tukey_window,
             multitaper_product, use_pava, autocorr_noestimate
    full_data: (a boolean)
            output full data
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    mask_size: (an integer)
            susan mask size
    multitaper_product: (an integer)
            multitapering with slepian tapers and num is the time-bandwidth product
            mutually_exclusive: autocorr_estimate_only, fit_armodel, tukey_window,
             multitaper_product, use_pava, autocorr_noestimate
    output_pwdata: (a boolean)
            output prewhitened data and average design matrix
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
            FSL output type
    results_dir: (a directory name, nipype default value: results)
            directory to store results in
    smooth_autocorr: (a boolean)
            Smooth auto corr estimates
    threshold: (a float)
            threshold
    tukey_window: (an integer)
            tukey window size to estimate autocorr
            mutually_exclusive: autocorr_estimate_only, fit_armodel, tukey_window,
             multitaper_product, use_pava, autocorr_noestimate
    use_pava: (a boolean)
            estimates autocorr using PAVA

Outputs:

    corrections: (an existing file name)
            statistical corrections used within FILM modelling
    dof_file: (an existing file name)
            degrees of freedom
    logfile: (an existing file name)
            FILM run logfile
    param_estimates: (an existing file name)
            Parameter estimates for each column of the design matrix
    residual4d: (an existing file name)
            Model fit residual mean-squared error for each time point
    results_dir: (an existing directory name)
            directory storing model estimation output
    sigmasquareds: (an existing file name)
            summary of residuals, See Woolrich, et. al., 2001

## FLAMEO

Link to code

Wraps command flameo

Use FSL flameo command to perform higher level model fits

### Examples

Initialize FLAMEO with no options, assigning them when calling run:

    >>> from nipype.interfaces import fsl
    >>> import os
    >>> flameo = fsl.FLAMEO(cope_file='cope.nii.gz',                             var_cope_file='varcope.nii.gz',                             cov_split_file='cov_split.mat',                             design_file='design.mat',                             t_con_file='design.con',                             mask_file='mask.nii',                             run_mode='fe')
    >>> flameo.cmdline
    'flameo --copefile=cope.nii.gz --covsplitfile=cov_split.mat --designfile=design.mat --ld=stats --maskfile=mask.nii --runmode=fe --tcontrastsfile=design.con --varcopefile=varcope.nii.gz'

Inputs:

    [Mandatory]
    cope_file: (an existing file name)
            cope regressor data file
    cov_split_file: (an existing file name)
            ascii matrix specifying the groups the covariance is split into
    design_file: (an existing file name)
            design matrix file
    mask_file: (an existing file name)
            mask file
    run_mode: ('fe' or 'ols' or 'flame1' or 'flame12')
            inference to perform
    t_con_file: (an existing file name)
            ascii matrix specifying t-contrasts

    [Optional]
    args: (a string)
            Additional parameters to the command
    burnin: (an integer)
            number of jumps at start of mcmc to be discarded
    dof_var_cope_file: (an existing file name)
            dof data file for varcope data
    environ: (a dictionary with keys which are a value of type 'str' and with values which
             are a value of type 'str', nipype default value: {})
            Environment variables
    f_con_file: (an existing file name)
            ascii matrix specifying f-contrasts
    fix_mean: (a boolean)
            fix mean for tfit
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    infer_outliers: (a boolean)
            infer outliers - not for fe
    log_dir: (a directory name, nipype default value: stats)
    n_jumps: (an integer)
            number of jumps made by mcmc
    no_pe_outputs: (a boolean)
            do not output pe files
    outlier_iter: (an integer)
            Number of max iterations to use when inferring outliers. Default is 12.
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
            FSL output type
    sample_every: (an integer)
            number of jumps for each sample
    sigma_dofs: (an integer)
            sigma (in mm) to use for Gaussian smoothing the DOFs in FLAME 2. Default is 1mm, -1
            indicates no smoothing
    var_cope_file: (an existing file name)
            varcope weightings data file

Outputs:

    copes
            Contrast estimates for each contrast
    mrefvars
            mean random effect variances for each contrast
    pes
            Parameter estimates for each column of the design matrixfor each voxel
    res4d
            Model fit residual mean-squared error for each time point
    stats_dir: (an existing directory name)
            directory storing model estimation output
    tdof
            temporal dof file for each contrast
    tstats
            t-stat file for each contrast
    var_copes
            Variance estimates for each contrast
    weights
            weights file for each contrast
    zstats
            z-stat file for each contrast

## L2Model

Link to code

Generate subject specific second level model

### Examples

    >>> from nipype.interfaces.fsl import L2Model
    >>> model = L2Model(num_copes=3) # 3 sessions

Inputs:

    [Mandatory]
    num_copes: (an integer)
            number of copes to be combined

    [Optional]
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run

Outputs:

    design_con: (an existing file name)
            design contrast file
    design_grp: (an existing file name)
            design group file
    design_mat: (an existing file name)
            design matrix file

## Level1Design

Link to code

Generate FEAT specific files

### Examples

    >>> level1design = Level1Design()
    >>> level1design.inputs.interscan_interval = 2.5
    >>> level1design.inputs.bases = {'dgamma':{'derivs': False}}
    >>> level1design.inputs.session_info = 'session_info.npz'
    >>> level1design.run()

Inputs:

    [Mandatory]
    bases: (a dictionary with keys which are 'dgamma' and with values which are a dictionary
             with keys which are 'derivs' and with values which are a boolean or a dictionary with
             keys which are 'gamma' and with values which are a dictionary with keys which are
             'derivs' and with values which are a boolean or a dictionary with keys which are 'none'
             and with values which are None)
            name of basis function and options e.g., {'dgamma': {'derivs': True}}
    interscan_interval: (a float)
            Interscan  interval (in secs)
    model_serial_correlations: (a boolean)
            Option to model serial correlations using an autoregressive estimator (order 1). Setting
            this option is only useful in the context of the fsf file. If you set this to False, you
            need to repeat this option for FILMGLS by setting autocorr_noestimate to True
    session_info
            Session specific information generated by ``modelgen.SpecifyModel``

    [Optional]
    contrasts: (a list of items which are a tuple of the form: (a string, 'T', a list of
             items which are a string, a list of items which are a float) or a tuple of the form: (a
             string, 'T', a list of items which are a string, a list of items which are a float, a
             list of items which are a float) or a tuple of the form: (a string, 'F', a list of
             items which are a tuple of the form: (a string, 'T', a list of items which are a
             string, a list of items which are a float) or a tuple of the form: (a string, 'T', a
             list of items which are a string, a list of items which are a float, a list of items
             which are a float)))
            List of contrasts with each contrast being a list of the form - [('name', 'stat',
            [condition list], [weight list], [session list])]. if session list is None or not
            provided, all sessions are used. For F contrasts, the condition list should contain
            previously defined T-contrasts.
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run

Outputs:

    ev_files: (a list of items which are an existing file name)
            condition information files
    fsf_files: (an existing file name)
            FSL feat specification files

## MELODIC

Link to code

Wraps command melodic

Multivariate Exploratory Linear Optimised Decomposition into Independent Components

### Examples

    >>> melodic_setup = MELODIC()
    >>> melodic_setup.inputs.approach = 'tica'
    >>> melodic_setup.inputs.in_files = ['functional.nii', 'functional2.nii', 'functional3.nii']
    >>> melodic_setup.inputs.no_bet = True
    >>> melodic_setup.inputs.bg_threshold = 10
    >>> melodic_setup.inputs.tr_sec = 1.5
    >>> melodic_setup.inputs.mm_thresh = 0.5
    >>> melodic_setup.inputs.out_stats = True
    >>> melodic_setup.inputs.t_des = 'timeDesign.mat'
    >>> melodic_setup.inputs.t_con = 'timeDesign.con'
    >>> melodic_setup.inputs.s_des = 'subjectDesign.mat'
    >>> melodic_setup.inputs.s_con = 'subjectDesign.con'
    >>> melodic_setup.inputs.out_dir = 'groupICA.out'
    >>> melodic_setup.run()

Inputs:

    [Mandatory]
    in_files: (an existing file name)
            input file names (either single file name or a list)

    [Optional]
    ICs: (an existing file name)
            filename of the IC components file for mixture modelling
    approach: (a string)
            approach for decomposition, 2D: defl, symm (default),  3D: tica (default), concat
    args: (a string)
            Additional parameters to the command
    bg_image: (an existing file name)
            specify background image for report (default: mean image)
    bg_threshold: (a float)
            brain/non-brain threshold used to mask non-brain voxels, as a percentage (only if
            --nobet selected)
    cov_weight: (a float)
            voxel-wise weights for the covariance matrix (e.g. segmentation information)
    dim: (an integer)
            dimensionality reduction into #num dimensions(default: automatic estimation)
    dim_est: (a string)
            use specific dim. estimation technique: lap, bic, mdl, aic, mean (default: lap)
    environ: (a dictionary with keys which are a value of type 'str' and with values which
             are a value of type 'str', nipype default value: {})
            Environment variables
    epsilon: (a float)
            minimum error change
    epsilonS: (a float)
            minimum error change for rank-1 approximation in TICA
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    log_power: (a boolean)
            calculate log of power for frequency spectrum
    mask: (an existing file name)
            file name of mask for thresholding
    max_restart: (an integer)
            maximum number of restarts
    maxit: (an integer)
            maximum number of iterations before restart
    mix: (an existing file name)
            mixing matrix for mixture modelling / filtering
    mm_thresh: (a float)
            threshold for Mixture Model based inference
    no_bet: (a boolean)
            switch off BET
    no_mask: (a boolean)
            switch off masking
    no_mm: (a boolean)
            switch off mixture modelling on IC maps
    non_linearity: (a string)
            nonlinearity: gauss, tanh, pow3, pow4
    num_ICs: (an integer)
            number of IC's to extract (for deflation approach)
    out_all: (a boolean)
            output everything
    out_dir: (a directory name)
            output directory name
    out_mean: (a boolean)
            output mean volume
    out_orig: (a boolean)
            output the original ICs
    out_pca: (a boolean)
            output PCA results
    out_stats: (a boolean)
            output thresholded maps and probability maps
    out_unmix: (a boolean)
            output unmixing matrix
    out_white: (a boolean)
            output whitening/dewhitening matrices
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
            FSL output type
    pbsc: (a boolean)
            switch off conversion to percent BOLD signal change
    rem_cmp: (a list of items which are an integer)
            component numbers to remove
    remove_deriv: (a boolean)
            removes every second entry in paradigm file (EV derivatives)
    report: (a boolean)
            generate Melodic web report
    report_maps: (a string)
            control string for spatial map images (see slicer)
    s_con: (an existing file name)
            t-contrast matrix across subject-domain
    s_des: (an existing file name)
            design matrix across subject-domain
    sep_vn: (a boolean)
            switch off joined variance normalization
    sep_whiten: (a boolean)
            switch on separate whitening
    smode: (an existing file name)
            matrix of session modes for report generation
    t_con: (an existing file name)
            t-contrast matrix across time-domain
    t_des: (an existing file name)
            design matrix across time-domain
    tr_sec: (a float)
            TR in seconds
    update_mask: (a boolean)
            switch off mask updating
    var_norm: (a boolean)
            switch off variance normalization

Outputs:

    out_dir: (an existing directory name)
    report_dir: (an existing directory name)

## MultipleRegressDesign

Link to code

Generate multiple regression design

Note

FSL does not demean columns for higher level analysis.

Please see FSL documentation for more details on model specification for higher level analysis.

### Examples

    >>> from nipype.interfaces.fsl import MultipleRegressDesign
    >>> model = MultipleRegressDesign()
    >>> model.inputs.contrasts = [['group mean', 'T',['reg1'],[1]]]
    >>> model.inputs.regressors = dict(reg1=[1, 1, 1], reg2=[2.,-4, 3])
    >>> model.run()

Inputs:

    [Mandatory]
    contrasts: (a list of items which are a tuple of the form: (a string, 'T', a list of
             items which are a string, a list of items which are a float) or a tuple of the form: (a
             string, 'F', a list of items which are a tuple of the form: (a string, 'T', a list of
             items which are a string, a list of items which are a float)))
            List of contrasts with each contrast being a list of the form - [('name', 'stat',
            [condition list], [weight list])]. if session list is None or not provided, all sessions
            are used. For F contrasts, the condition list should contain previously defined
            T-contrasts without any weight list.
    regressors: (a dictionary with keys which are a string and with values which are a list
             of items which are a float)
            dictionary containing named lists of regressors

    [Optional]
    groups: (a list of items which are an integer)
            list of group identifiers (defaults to single group)
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run

Outputs:

    design_con: (an existing file name)
            design t-contrast file
    design_fts: (an existing file name)
            design f-contrast file
    design_grp: (an existing file name)
            design group file
    design_mat: (an existing file name)
            design matrix file

## Randomise

Link to code

Wraps command randomise

XXX UNSTABLE DO NOT USE

FSL Randomise: feeds the 4D projected FA data into GLM modelling and thresholding in order to find voxels which correlate with your model

### Example

    >>> import nipype.interfaces.fsl as fsl
    >>> rand = fsl.Randomise(in_file='allFA.nii',     mask = 'mask.nii',     tcon='design.con',     design_mat='design.mat')
    >>> rand.cmdline
    'randomise -i allFA.nii -o tbss_ -d design.mat -t design.con -m mask.nii'

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            4D input file

    [Optional]
    args: (a string)
            Additional parameters to the command
    base_name: (a string, nipype default value: tbss_)
            the rootname that all generated files will have
    c_thresh: (a float)
            carry out cluster-based thresholding
    cm_thresh: (a float)
            carry out cluster-mass-based thresholding
    demean: (a boolean)
            demean data temporally before model fitting
    design_mat: (an existing file name)
            design matrix file
    environ: (a dictionary with keys which are a value of type 'str' and with values which
             are a value of type 'str', nipype default value: {})
            Environment variables
    f_c_thresh: (a float)
            carry out f cluster thresholding
    f_cm_thresh: (a float)
            carry out f cluster-mass thresholding
    f_only: (a boolean)
            calculate f-statistics only
    fcon: (an existing file name)
            f contrasts file
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    mask: (an existing file name)
            mask image
    num_perm: (an integer)
            number of permutations (default 5000, set to 0 for exhaustive)
    one_sample_group_mean: (a boolean)
            perform 1-sample group-mean test instead of generic permutation test
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
            FSL output type
    p_vec_n_dist_files: (a boolean)
            output permutation vector and null distribution text files
    raw_stats_imgs: (a boolean)
            output raw ( unpermuted ) statistic images
    seed: (an integer)
            specific integer seed for random number generator
    show_info_parallel_mode: (a boolean)
            print out information required for parallel mode and exit
    show_total_perms: (a boolean)
            print out how many unique permutations would be generated and exit
    tcon: (an existing file name)
            t contrasts file
    tfce: (a boolean)
            carry out Threshold-Free Cluster Enhancement
    tfce2D: (a boolean)
            carry out Threshold-Free Cluster Enhancement with 2D optimisation
    tfce_C: (a float)
            TFCE connectivity (6 or 26; default=6)
    tfce_E: (a float)
            TFCE extent parameter (default=0.5)
    tfce_H: (a float)
            TFCE height parameter (default=2)
    var_smooth: (an integer)
            use variance smoothing (std is in mm)
    vox_p_values: (a boolean)
            output voxelwise (corrected and uncorrected) p-value images
    vxf: (a list of items which are an integer)
            list of 4D images containing voxelwise EVs(list order corresponds to numbers in vxl
            option)
    vxl: (a list of items which are an integer)
            list of numbers indicating voxelwise EVsposition in the design matrix (list order
            corresponds to files in vxf option)
    x_block_labels: (an existing file name)
            exchangeability block labels file

Outputs:

    f_corrected_p_files: (a list of items which are an existing file name)
            f contrast FWE (Family-wise error) corrected p values files
    f_p_files: (a list of items which are an existing file name)
            f contrast uncorrected p values files
    fstat_files: (a list of items which are an existing file name)
            f contrast raw statistic
    t_corrected_p_files: (a list of items which are an existing file name)
            t contrast FWE (Family-wise error) corrected p values files
    t_p_files: (a list of items which are an existing file name)
            f contrast uncorrected p values files
    tstat_files: (a list of items which are an existing file name)
            t contrast raw statistic

## SMM

Link to code

Wraps command mm –ld=logdir

Spatial Mixture Modelling. For more detail on the spatial mixture modelling see Mixture Models with Adaptive Spatial Regularisation for Segmentation with an Application to FMRI Data; Woolrich, M., Behrens, T., Beckmann, C., and Smith, S.; IEEE Trans. Medical Imaging, 24(1):1-11, 2005.

Inputs:

    [Mandatory]
    mask: (a file name)
            mask file
    spatial_data_file: (an existing file name)
            statistics spatial map

    [Optional]
    args: (a string)
            Additional parameters to the command
    environ: (a dictionary with keys which are a value of type 'str' and with values which
             are a value of type 'str', nipype default value: {})
            Environment variables
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    no_deactivation_class: (a boolean)
            enforces no deactivation class
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
            FSL output type

Outputs:

    activation_p_map: (an existing file name)
    deactivation_p_map: (an existing file name)
    null_p_map: (an existing file name)

## SmoothEstimate

Link to code

Wraps command smoothest

Estimates the smoothness of an image

### Examples

    >>> est = SmoothEstimate()
    >>> est.inputs.zstat_file = 'zstat1.nii.gz'
    >>> est.inputs.mask_file = 'mask.nii'
    >>> est.cmdline
    'smoothest --mask=mask.nii --zstat=zstat1.nii.gz'

Inputs:

    [Mandatory]
    dof: (an integer)
            number of degrees of freedom
            mutually_exclusive: zstat_file
    mask_file: (an existing file name)
            brain mask volume

    [Optional]
    args: (a string)
            Additional parameters to the command
    environ: (a dictionary with keys which are a value of type 'str' and with values which
             are a value of type 'str', nipype default value: {})
            Environment variables
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
            FSL output type
    residual_fit_file: (an existing file name)
            residual-fit image file
            requires: dof
    zstat_file: (an existing file name)
            zstat image file
            mutually_exclusive: dof

Outputs:

    dlh: (a float)
            smoothness estimate sqrt(det(Lambda))
    resels: (a float)
            number of resels
    volume: (an integer)
            number of voxels in mask