.. AUTO-GENERATED FILE -- DO NOT EDIT!

nipype.interfaces.fsl.model
===========================


:class:`Cluster`
----------------


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')
		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)
		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

:class:`ContrastMgr`
--------------------


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')
		Environment variables
	fcon_file : (an existing file name)
		contrast file containing F-contrasts
	ignore_exception : (a boolean)
		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
	zstats : (an existing file name)
		z-stat file for each contrast

:class:`FEAT`
-------------


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')
		Environment variables
	ignore_exception : (a boolean)
		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)
		Unknown

:class:`FEATModel`
------------------


Wraps command **feat_model**

Uses FSL feat_model to generate design.mat files

Inputs:: 

	[Mandatory]
	ev_files : (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')
		Environment variables
	ignore_exception : (a boolean)
		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

:class:`FEATRegister`
---------------------


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)
		Print an error message instead of throwing an exception in case the interface fails to run
	reg_dof : (an integer)
		registration degrees of freedom


Outputs:: 

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

:class:`FILMGLS`
----------------


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') #doctest: +SKIP

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() #doctest: +SKIP

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() #doctest: +SKIP

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
		exclusive: autocorr_estimate_only,fit_armodel,tukey_window,multitaper_product,use_pava,autocorr_noestimate
	autocorr_noestimate : (a boolean)
		do not estimate autocorrs
		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')
		Environment variables
	fit_armodel : (a boolean)
		fits autoregressive model - default is to use tukey with M=sqrt(numvols)
		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)
		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
		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)
		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
		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

:class:`FLAMEO`
---------------


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')
		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)
		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)
		Unknown
	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

:class:`L2Model`
----------------


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)
		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

:class:`Level1Design`
---------------------


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() # doctest: +SKIP

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

:class:`MELODIC`
----------------


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() # doctest: +SKIP

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')
		Environment variables
	epsilon : (a float)
		minimum error change
	epsilonS : (a float)
		minimum error change for rank-1 approximation in TICA
	ignore_exception : (a boolean)
		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 : (an existing 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)
		Unknown
	report_dir : (an existing directory name)
		Unknown

:class:`MultipleRegressDesign`
------------------------------


Generate multiple regression design

.. note::
  FSL does not demean columns for higher level analysis.

Please see `FSL documentation <http://www.fmrib.ox.ac.uk/fsl/feat5/detail.html#higher>`_
for more details on model specification for higher level analysis.

Examples
~~~~~~~~

>>> from nipype.interfaces.fsl import L2Model
>>> model = MultipleRegressDesign()
>>> model.inputs.contrasts = [['group mean','T',['reg1'],[1]]]
>>> model.inputs.regressors = dict(reg1=[1,1,1],reg2=[2.,-4,3])
>>> model.run() # doctest: +SKIP

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)
		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

:class:`Randomise`
------------------


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.dti 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]
	design_mat : (an existing file name)
		design matrix file
	in_file : (an existing file name)
		4D input file
	tcon : (an existing file name)
		t contrasts file

	[Optional]
	args : (a string)
		Additional parameters to the command
	base_name : (a string)
		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
	environ : (a dictionary with keys which are a value of type 'str' and with values which are a value of type 'str')
		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)
		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
	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:: 

	tstat1_file : (an existing file name)
		path/name of tstat image corresponding to the first t contrast

:class:`SMM`
------------


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')
		Environment variables
	ignore_exception : (a boolean)
		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)
		Unknown
	deactivation_p_map : (an existing file name)
		Unknown
	null_p_map : (an existing file name)
		Unknown

:class:`SmoothEstimate`
-----------------------


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
		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')
		Environment variables
	ignore_exception : (a boolean)
		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
		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
