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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
            flag: --in=%s
    threshold: (a float)
            threshold for input volume
            flag: --thresh=%.10f

    [Optional]
    args: (a string)
            Additional parameters to the command
            flag: %s
    connectivity: (an integer)
            the connectivity of voxels (default 26)
            flag: --connectivity=%d
    cope_file: (a file name)
            cope volume
            flag: --cope=%s
    dlh: (a float)
            smoothness estimate = sqrt(det(Lambda))
            flag: --dlh=%.10f
    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
            flag: --minclustersize
    no_table: (a boolean)
            suppresses printing of the table info
    num_maxima: (an integer)
            no of local maxima to report
            flag: --num=%d
    out_index_file: (a boolean or a file name)
            output of cluster index (in size order)
            flag: --oindex=%s
    out_localmax_txt_file: (a boolean or a file name)
            local maxima text file
            flag: --olmax=%s
    out_localmax_vol_file: (a boolean or a file name)
            output of local maxima volume
            flag: --olmaxim=%s
    out_max_file: (a boolean or a file name)
            filename for output of max image
            flag: --omax=%s
    out_mean_file: (a boolean or a file name)
            filename for output of mean image
            flag: --omean=%s
    out_pval_file: (a boolean or a file name)
            filename for image output of log pvals
            flag: --opvals=%s
    out_size_file: (a boolean or a file name)
            filename for output of size image
            flag: --osize=%s
    out_threshold_file: (a boolean or a file name)
            thresholded image
            flag: --othresh=%s
    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)
            flag: --peakdist=%.10f
    pthreshold: (a float)
            p-threshold for clusters
            flag: --pthresh=%.10f
            requires: dlh, volume
    std_space_file: (a file name)
            filename for standard-space volume
            flag: --stdvol=%s
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    use_mm: (a boolean)
            use mm, not voxel, coordinates
    volume: (an integer)
            number of voxels in the mask
            flag: --volume=%d
    warpfield_file: (a file name)
            file contining warpfield
            flag: --warpvol=%s
    xfm_file: (a file name)
            filename for Linear: input->standard-space transform. Non-linear:
            input->highres transform
            flag: --xfm=%s

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: (a list of items which are 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
            flag: %s, position: -1

    [Optional]
    args: (a string)
            Additional parameters to the command
            flag: %s
    contrast_num: (an integer >= 1)
            contrast number to start labeling copes from
            flag: -cope
    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
            flag: -f %s
    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
            flag: -suffix %s
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

Outputs:

    copes: (a list of items which are an existing file name)
            Contrast estimates for each contrast
    fstats: (a list of items which are an existing file name)
            f-stat file for each contrast
    neffs: (a list of items which are an existing file name)
            neff file ?? for each contrast
    tstats: (a list of items which are an existing file name)
            t-stat file for each contrast
    varcopes: (a list of items which are an existing file name)
            Variance estimates for each contrast
    zfstats: (a list of items which are an existing file name)
            z-stat file for each F contrast
    zstats: (a list of items which are 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: (an existing file name)
            File specifying the feat design spec file
            flag: %s, position: 0

    [Optional]
    args: (a string)
            Additional parameters to the command
            flag: %s
    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
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

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
            flag: %s, position: 1
    fsf_file: (an existing file name)
            File specifying the feat design spec file
            flag: %s, position: 0

    [Optional]
    args: (a string)
            Additional parameters to the command
            flag: %s
    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
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

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 list of items which are an existing directory name)
            Lower level feat dirs
    reg_image: (an existing 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
            flag: %s, position: -3

    [Optional]
    args: (a string)
            Additional parameters to the command
            flag: %s
    autocorr_estimate_only: (a boolean)
            perform autocorrelation estimatation only
            flag: -ac
            mutually_exclusive: autocorr_estimate_only, fit_armodel,
             tukey_window, multitaper_product, use_pava, autocorr_noestimate
    autocorr_noestimate: (a boolean)
            do not estimate autocorrs
            flag: -noest
            mutually_exclusive: autocorr_estimate_only, fit_armodel,
             tukey_window, multitaper_product, use_pava, autocorr_noestimate
    brightness_threshold: (an integer >= 0)
            susan brightness threshold, otherwise it is estimated
            flag: -epith %d
    design_file: (an existing file name)
            design matrix file
            flag: %s, position: -2
    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)
            flag: -ar
            mutually_exclusive: autocorr_estimate_only, fit_armodel,
             tukey_window, multitaper_product, use_pava, autocorr_noestimate
    full_data: (a boolean)
            output full data
            flag: -v
    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
            flag: -ms %d
    multitaper_product: (an integer)
            multitapering with slepian tapers and num is the time-bandwidth
            product
            flag: -mt %d
            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
            flag: -output_pwdata
    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
            flag: -rn %s
    smooth_autocorr: (a boolean)
            Smooth auto corr estimates
            flag: -sa
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    threshold: (a floating point number >= 0.0, nipype default value:
             0.0)
            threshold
            flag: %f, position: -1
    tukey_window: (an integer)
            tukey window size to estimate autocorr
            flag: -tukey %d
            mutually_exclusive: autocorr_estimate_only, fit_armodel,
             tukey_window, multitaper_product, use_pava, autocorr_noestimate
    use_pava: (a boolean)
            estimates autocorr using PAVA
            flag: -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: (a list of items which are 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
    thresholdac: (an existing file name)
            The FILM autocorrelation parameters

## 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
            flag: --copefile=%s
    cov_split_file: (an existing file name)
            ascii matrix specifying the groups the covariance is split into
            flag: --covsplitfile=%s
    design_file: (an existing file name)
            design matrix file
            flag: --designfile=%s
    mask_file: (an existing file name)
            mask file
            flag: --maskfile=%s
    run_mode: ('fe' or 'ols' or 'flame1' or 'flame12')
            inference to perform
            flag: --runmode=%s
    t_con_file: (an existing file name)
            ascii matrix specifying t-contrasts
            flag: --tcontrastsfile=%s

    [Optional]
    args: (a string)
            Additional parameters to the command
            flag: %s
    burnin: (an integer)
            number of jumps at start of mcmc to be discarded
            flag: --burnin=%d
    dof_var_cope_file: (an existing file name)
            dof data file for varcope data
            flag: --dofvarcopefile=%s
    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
            flag: --fcontrastsfile=%s
    fix_mean: (a boolean)
            fix mean for tfit
            flag: --fixmean
    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
            flag: --inferoutliers
    log_dir: (a directory name, nipype default value: stats)
            flag: --ld=%s
    n_jumps: (an integer)
            number of jumps made by mcmc
            flag: --njumps=%d
    no_pe_outputs: (a boolean)
            do not output pe files
            flag: --nopeoutput
    outlier_iter: (an integer)
            Number of max iterations to use when inferring outliers. Default is
            12.
            flag: --ioni=%d
    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
            flag: --sampleevery=%d
    sigma_dofs: (an integer)
            sigma (in mm) to use for Gaussian smoothing the DOFs in FLAME 2.
            Default is 1mm, -1 indicates no smoothing
            flag: --sigma_dofs=%d
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    var_cope_file: (an existing file name)
            varcope weightings data file
            flag: --varcopefile=%s

Outputs:

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

## GLM

Link to code

Wraps command fsl_glm

FSL GLM:

### Example

    >>> import nipype.interfaces.fsl as fsl
    >>> glm = fsl.GLM(in_file='functional.nii', design='maps.nii', output_type='NIFTI')
    >>> glm.cmdline
    'fsl_glm -i functional.nii -d maps.nii -o functional_glm.nii'

Inputs:

    [Mandatory]
    design: (an existing file name)
            file name of the GLM design matrix (text time courses for temporal
            regression or an image file for spatial regression)
            flag: -d %s, position: 2
    in_file: (an existing file name)
            input file name (text matrix or 3D/4D image file)
            flag: -i %s, position: 1

    [Optional]
    args: (a string)
            Additional parameters to the command
            flag: %s
    contrasts: (an existing file name)
            matrix of t-statics contrasts
            flag: -c %s
    dat_norm: (a boolean)
            switch on normalization of the data time series to unit std
            deviation
            flag: --dat_norm
    demean: (a boolean)
            switch on demeaining of design and data
            flag: --demean
    des_norm: (a boolean)
            switch on normalization of the design matrix columns to unit std
            deviation
            flag: --des_norm
    dof: (an integer)
            set degrees of freedom explicitly
            flag: --dof=%d
    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
    mask: (an existing file name)
            mask image file name if input is image
            flag: -m %s
    out_cope: (a file name)
            output file name for COPE (either as txt or image
            flag: --out_cope=%s
    out_data_name: (a file name)
            output file name for pre-processed data
            flag: --out_data=%s
    out_f_name: (a file name)
            output file name for F-value of full model fit
            flag: --out_f=%s
    out_file: (a file name)
            filename for GLM parameter estimates (GLM betas)
            flag: -o %s, position: 3
    out_p_name: (a file name)
            output file name for p-values of Z-stats (either as text file or
            image)
            flag: --out_p=%s
    out_pf_name: (a file name)
            output file name for p-value for full model fit
            flag: --out_pf=%s
    out_res_name: (a file name)
            output file name for residuals
            flag: --out_res=%s
    out_sigsq_name: (a file name)
            output file name for residual noise variance sigma-square
            flag: --out_sigsq=%s
    out_t_name: (a file name)
            output file name for t-stats (either as txt or image
            flag: --out_t=%s
    out_varcb_name: (a file name)
            output file name for variance of COPEs
            flag: --out_varcb=%s
    out_vnscales_name: (a file name)
            output file name for scaling factors for variance normalisation
            flag: --out_vnscales=%s
    out_z_name: (a file name)
            output file name for Z-stats (either as txt or image
            flag: --out_z=%s
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or
             'NIFTI')
            FSL output type
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    var_norm: (a boolean)
            perform MELODIC variance-normalisation on data
            flag: --vn

Outputs:

    out_cope: (a list of items which are an existing file name)
            output file name for COPEs (either as text file or image)
    out_data: (a list of items which are an existing file name)
            output file for preprocessed data
    out_f: (a list of items which are an existing file name)
            output file name for F-value of full model fit
    out_file: (an existing file name)
            file name of GLM parameters (if generated)
    out_p: (a list of items which are an existing file name)
            output file name for p-values of Z-stats (either as text file or
            image)
    out_pf: (a list of items which are an existing file name)
            output file name for p-value for full model fit
    out_res: (a list of items which are an existing file name)
            output file name for residuals
    out_sigsq: (a list of items which are an existing file name)
            output file name for residual noise variance sigma-square
    out_t: (a list of items which are an existing file name)
            output file name for t-stats (either as text file or image)
    out_varcb: (a list of items which are an existing file name)
            output file name for variance of COPEs
    out_vnscales: (a list of items which are an existing file name)
            output file name for scaling factors for variance normalisation
    out_z: (a list of items which are an existing file name)
            output file name for COPEs (either as text file or image)

## 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 >= 1)
            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: (any value)
            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 a list of items which are an
             existing file name)
            condition information files
    fsf_files: (a list of items which are 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.cmdline
    'melodic -i functional.nii,functional2.nii,functional3.nii -a tica --bgthreshold=10.000000 --mmthresh=0.500000 --nobet -o groupICA.out --Ostats --Scon=subjectDesign.con --Sdes=subjectDesign.mat --Tcon=timeDesign.con --Tdes=timeDesign.mat --tr=1.500000'
    >>> melodic_setup.run()

Inputs:

    [Mandatory]
    in_files: (a list of items which are an existing file name)
            input file names (either single file name or a list)
            flag: -i %s, position: 0

    [Optional]
    ICs: (an existing file name)
            filename of the IC components file for mixture modelling
            flag: --ICs=%s
    approach: (a string)
            approach for decomposition, 2D: defl, symm (default), 3D: tica
            (default), concat
            flag: -a %s
    args: (a string)
            Additional parameters to the command
            flag: %s
    bg_image: (an existing file name)
            specify background image for report (default: mean image)
            flag: --bgimage=%s
    bg_threshold: (a float)
            brain/non-brain threshold used to mask non-brain voxels, as a
            percentage (only if --nobet selected)
            flag: --bgthreshold=%f
    cov_weight: (a float)
            voxel-wise weights for the covariance matrix (e.g. segmentation
            information)
            flag: --covarweight=%f
    dim: (an integer)
            dimensionality reduction into #num dimensions(default: automatic
            estimation)
            flag: -d %d
    dim_est: (a string)
            use specific dim. estimation technique: lap, bic, mdl, aic, mean
            (default: lap)
            flag: --dimest=%s
    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
            flag: --eps=%f
    epsilonS: (a float)
            minimum error change for rank-1 approximation in TICA
            flag: --epsS=%f
    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
            flag: --logPower
    mask: (an existing file name)
            file name of mask for thresholding
            flag: -m %s
    max_restart: (an integer)
            maximum number of restarts
            flag: --maxrestart=%d
    maxit: (an integer)
            maximum number of iterations before restart
            flag: --maxit=%d
    mix: (an existing file name)
            mixing matrix for mixture modelling / filtering
            flag: --mix=%s
    mm_thresh: (a float)
            threshold for Mixture Model based inference
            flag: --mmthresh=%f
    no_bet: (a boolean)
            switch off BET
            flag: --nobet
    no_mask: (a boolean)
            switch off masking
            flag: --nomask
    no_mm: (a boolean)
            switch off mixture modelling on IC maps
            flag: --no_mm
    non_linearity: (a string)
            nonlinearity: gauss, tanh, pow3, pow4
            flag: --nl=%s
    num_ICs: (an integer)
            number of IC's to extract (for deflation approach)
            flag: -n %d
    out_all: (a boolean)
            output everything
            flag: --Oall
    out_dir: (a directory name)
            output directory name
            flag: -o %s
    out_mean: (a boolean)
            output mean volume
            flag: --Omean
    out_orig: (a boolean)
            output the original ICs
            flag: --Oorig
    out_pca: (a boolean)
            output PCA results
            flag: --Opca
    out_stats: (a boolean)
            output thresholded maps and probability maps
            flag: --Ostats
    out_unmix: (a boolean)
            output unmixing matrix
            flag: --Ounmix
    out_white: (a boolean)
            output whitening/dewhitening matrices
            flag: --Owhite
    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
            flag: --pbsc
    rem_cmp: (a list of items which are an integer)
            component numbers to remove
            flag: -f %d
    remove_deriv: (a boolean)
            removes every second entry in paradigm file (EV derivatives)
            flag: --remove_deriv
    report: (a boolean)
            generate Melodic web report
            flag: --report
    report_maps: (a string)
            control string for spatial map images (see slicer)
            flag: --report_maps=%s
    s_con: (an existing file name)
            t-contrast matrix across subject-domain
            flag: --Scon=%s
    s_des: (an existing file name)
            design matrix across subject-domain
            flag: --Sdes=%s
    sep_vn: (a boolean)
            switch off joined variance normalization
            flag: --sep_vn
    sep_whiten: (a boolean)
            switch on separate whitening
            flag: --sep_whiten
    smode: (an existing file name)
            matrix of session modes for report generation
            flag: --smode=%s
    t_con: (an existing file name)
            t-contrast matrix across time-domain
            flag: --Tcon=%s
    t_des: (an existing file name)
            design matrix across time-domain
            flag: --Tdes=%s
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    tr_sec: (a float)
            TR in seconds
            flag: --tr=%f
    update_mask: (a boolean)
            switch off mask updating
            flag: --update_mask
    var_norm: (a boolean)
            switch off variance normalization
            flag: --vn

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
            flag: -i %s, position: 0

    [Optional]
    args: (a string)
            Additional parameters to the command
            flag: %s
    base_name: (a string, nipype default value: tbss_)
            the rootname that all generated files will have
            flag: -o "%s", position: 1
    c_thresh: (a float)
            carry out cluster-based thresholding
            flag: -c %.2f
    cm_thresh: (a float)
            carry out cluster-mass-based thresholding
            flag: -C %.2f
    demean: (a boolean)
            demean data temporally before model fitting
            flag: -D
    design_mat: (an existing file name)
            design matrix file
            flag: -d %s, position: 2
    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
            flag: -F %.2f
    f_cm_thresh: (a float)
            carry out f cluster-mass thresholding
            flag: -S %.2f
    f_only: (a boolean)
            calculate f-statistics only
            flag: --f_only
    fcon: (an existing file name)
            f contrasts file
            flag: -f %s
    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
            flag: -m %s
    num_perm: (an integer)
            number of permutations (default 5000, set to 0 for exhaustive)
            flag: -n %d
    one_sample_group_mean: (a boolean)
            perform 1-sample group-mean test instead of generic permutation test
            flag: -1
    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
            flag: -P
    raw_stats_imgs: (a boolean)
            output raw ( unpermuted ) statistic images
            flag: -R
    seed: (an integer)
            specific integer seed for random number generator
            flag: --seed=%d
    show_info_parallel_mode: (a boolean)
            print out information required for parallel mode and exit
            flag: -Q
    show_total_perms: (a boolean)
            print out how many unique permutations would be generated and exit
            flag: -q
    tcon: (an existing file name)
            t contrasts file
            flag: -t %s, position: 3
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    tfce: (a boolean)
            carry out Threshold-Free Cluster Enhancement
            flag: -T
    tfce2D: (a boolean)
            carry out Threshold-Free Cluster Enhancement with 2D optimisation
            flag: --T2
    tfce_C: (a float)
            TFCE connectivity (6 or 26; default=6)
            flag: --tfce_C=%.2f
    tfce_E: (a float)
            TFCE extent parameter (default=0.5)
            flag: --tfce_E=%.2f
    tfce_H: (a float)
            TFCE height parameter (default=2)
            flag: --tfce_H=%.2f
    var_smooth: (an integer)
            use variance smoothing (std is in mm)
            flag: -v %d
    vox_p_values: (a boolean)
            output voxelwise (corrected and uncorrected) p-value images
            flag: -x
    x_block_labels: (an existing file name)
            exchangeability block labels file
            flag: -e %s

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: (an existing file name)
            mask file
            flag: --mask="%s", position: 1
    spatial_data_file: (an existing file name)
            statistics spatial map
            flag: --sdf="%s", position: 0

    [Optional]
    args: (a string)
            Additional parameters to the command
            flag: %s
    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
            flag: --zfstatmode, position: 2
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or
             'NIFTI')
            FSL output type
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

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
            flag: --dof=%d
            mutually_exclusive: zstat_file
    mask_file: (an existing file name)
            brain mask volume
            flag: --mask=%s

    [Optional]
    args: (a string)
            Additional parameters to the command
            flag: %s
    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
            flag: --res=%s
            requires: dof
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    zstat_file: (an existing file name)
            zstat image file
            flag: --zstat=%s
            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