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

nipype.interfaces.spm.preprocess
================================


:class:`Coregister`
-------------------


Use spm_coreg for estimating cross-modality rigid body alignment

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=39

Examples
~~~~~~~~

>>> import nipype.interfaces.spm as spm
>>> coreg = spm.Coregister()
>>> coreg.inputs.target = 'functional.nii'
>>> coreg.inputs.source = 'structural.nii'
>>> coreg.run() # doctest: +SKIP

Inputs:: 

	[Mandatory]
	source : (an existing file name)
		file to register to target
	target : (an existing file name)
		reference file to register to

	[Optional]
	apply_to_files : (an existing file name)
		files to apply transformation to
	cost_function : ('mi' or 'nmi' or 'ecc' or 'ncc')
		cost function, one of: 'mi' - Mutual Information,
                'nmi' - Normalised Mutual Information,
                'ecc' - Entropy Correlation Coefficient,
                'ncc' - Normalised Cross Correlation
	fwhm : (a float)
		gaussian smoothing kernel width (mm)
	ignore_exception : (a boolean)
		Print an error message instead of throwing an exception in case the interface fails to run
	jobtype : ('estwrite' or 'estimate' or 'write')
		one of: estimate, write, estwrite
	matlab_cmd : (a string)
		matlab command to use
	mfile : (a boolean)
		Run m-code using m-file
	paths : (a directory name)
		Paths to add to matlabpath
	separation : (a list of items which are a float)
		sampling separation in mm
	tolerance : (a list of items which are a float)
		acceptable tolerance for each of 12 params
	use_mcr : (a boolean)
		Run m-code using SPM MCR
	write_interp : (an integer >= 0)
		degree of b-spline used for interpolation
	write_mask : (a boolean)
		True/False mask output image
	write_wrap : (a list of from 3 to 3 items which are a boolean)
		Check if interpolation should wrap in [x,y,z]


Outputs:: 

	coregistered_files : (an existing file name)
		Coregistered other files
	coregistered_source : (an existing file name)
		Coregistered source files

:class:`DARTEL`
---------------


Use spm DARTEL to create a template and flow fields

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=197

Examples
~~~~~~~~
>>> import nipype.interfaces.spm as spm
>>> dartel = spm.DARTEL()
>>> dartel.inputs.image_files = [['rc1s1.nii','rc1s2.nii'],['rc2s1.nii', 'rc2s2.nii']]
>>> dartel.run() # doctest: +SKIP

Inputs:: 

	[Mandatory]
	image_files : (a list of items which are a list of items which are an existing file name)
		A list of files to be segmented

	[Optional]
	ignore_exception : (a boolean)
		Print an error message instead of throwing an exception in case the interface fails to run
	iteration_parameters : (a list of from 6 to 6 items which are a tuple of the form: (1 <= an integer <= 10, a tuple of the form: (a float, a float, a float), 1 or 2 or 4 or 8 or 16 or 32 or 64 or 128 or 256 or 512, 0 or 0.5 or 1 or 2 or 4 or 8 or 16 or 32))
		List of tuples for each iteration
                                       - Inner iterations
                                       - Regularization parameters
                                       - Time points for deformation model
                                       - smoothing parameter
                                       
	matlab_cmd : (a string)
		matlab command to use
	mfile : (a boolean)
		Run m-code using m-file
	optimization_parameters : (a tuple of the form: (a float, 1 <= an integer <= 8, 1 <= an integer <= 8))
		Optimization settings a tuple
                                           - LM regularization
                                           - cycles of multigrid solver
                                           - relaxation iterations
                                           
	paths : (a directory name)
		Paths to add to matlabpath
	regularization_form : ('Linear' or 'Membrane' or 'Bending')
		Form of regularization energy term
	template_prefix : (a string)
		Prefix for template
	use_mcr : (a boolean)
		Run m-code using SPM MCR


Outputs:: 

	dartel_flow_fields : (a list of items which are an existing file name)
		DARTEL flow fields
	final_template_file : (an existing file name)
		final DARTEL template
	template_files : (a list of items which are an existing file name)
		Templates from different stages of iteration

:class:`DARTELNorm2MNI`
-----------------------


Use spm DARTEL to normalize data to MNI space

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=200

Examples
~~~~~~~~
>>> import nipype.interfaces.spm as spm
>>> nm = spm.DARTELNorm2MNI()
>>> nm.inputs.template_file = 'Template_6.nii'
>>> nm.inputs.flowfield_files = ['u_rc1s1_Template.nii', 'u_rc1s3_Template.nii']
>>> nm.inputs.apply_to_files = ['c1s1.nii', 'c1s3.nii']
>>> nm.inputs.modulate = True
>>> nm.run() # doctest: +SKIP

Inputs:: 

	[Mandatory]
	apply_to_files : (an existing file name)
		Files to apply the transform to
	flowfield_files : (an existing file name)
		DARTEL flow fields u_rc1*
	template_file : (an existing file name)
		DARTEL template

	[Optional]
	bounding_box : (a tuple of the form: (a float, a float, a float, a float, a float, a float))
		Voxel sizes for output file
	fwhm : (a tuple of the form: (a float, a float, a float) or a float)
		3-list of fwhm for each dimension
	ignore_exception : (a boolean)
		Print an error message instead of throwing an exception in case the interface fails to run
	matlab_cmd : (a string)
		matlab command to use
	mfile : (a boolean)
		Run m-code using m-file
	modulate : (a boolean)
		Modulate out images - no modulation preserves concentrations
	paths : (a directory name)
		Paths to add to matlabpath
	use_mcr : (a boolean)
		Run m-code using SPM MCR
	voxel_size : (a tuple of the form: (a float, a float, a float))
		Voxel sizes for output file


Outputs:: 

	normalization_parameter_file : (an existing file name)
		Transform parameters to MNI space
	normalized_files : (an existing file name)
		Normalized files in MNI space

:class:`NewSegment`
-------------------


Use spm_preproc8 (New Segment) to separate structural images into different
tissue classes. Supports multiple modalities.

NOTE: This interface currently supports single channel input only

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=185

Examples
~~~~~~~~
>>> import nipype.interfaces.spm as spm
>>> seg = spm.NewSegment()
>>> seg.inputs.channel_files = 'structural.nii'
>>> seg.inputs.channel_info = (0.0001, 60, (True, True))
>>> seg.run() # doctest: +SKIP

For VBM pre-processing [http://www.fil.ion.ucl.ac.uk/~john/misc/VBMclass10.pdf],
TPM.nii should be replaced by /path/to/spm8/toolbox/Seg/TPM.nii

>>> seg = NewSegment()
>>> seg.inputs.channel_files = 'structural.nii'
>>> tissue1 = (('TPM.nii', 1), 2, (True,True), (False, False))
>>> tissue2 = (('TPM.nii', 2), 2, (True,True), (False, False))
>>> tissue3 = (('TPM.nii', 3), 2, (True,False), (False, False))
>>> tissue4 = (('TPM.nii', 4), 2, (False,False), (False, False))
>>> tissue5 = (('TPM.nii', 5), 2, (False,False), (False, False))
>>> seg.inputs.tissues = [tissue1, tissue2, tissue3, tissue4, tissue5]
>>> seg.run() # doctest: +SKIP

Inputs:: 

	[Mandatory]
	channel_files : (an existing file name)
		A list of files to be segmented

	[Optional]
	affine_regularization : ('mni' or 'eastern' or 'subj' or 'none')
		mni, eastern, subj, none 
	channel_info : (a tuple of the form: (a float, a float, a tuple of the form: (a boolean, a boolean)))
		A tuple with the following fields:
            - bias reguralisation (0-10)
            - FWHM of Gaussian smoothness of bias
            - which maps to save (Corrected, Field) - a tuple of two boolean values
	ignore_exception : (a boolean)
		Print an error message instead of throwing an exception in case the interface fails to run
	matlab_cmd : (a string)
		matlab command to use
	mfile : (a boolean)
		Run m-code using m-file
	paths : (a directory name)
		Paths to add to matlabpath
	sampling_distance : (a float)
		Sampling distance on data for parameter estimation
	tissues : (a list of items which are a tuple of the form: (a tuple of the form: (an existing file name, an integer), an integer, a tuple of the form: (a boolean, a boolean), a tuple of the form: (a boolean, a boolean)))
		A list of tuples (one per tissue) with the following fields:
            - tissue probability map (4D), 1-based index to frame
            - number of gaussians
            - which maps to save [Native, DARTEL] - a tuple of two boolean values
            - which maps to save [Modulated, Unmodualted] - a tuple of two boolean values
	use_mcr : (a boolean)
		Run m-code using SPM MCR
	warping_regularization : (a float)
		Aproximate distance between sampling points.
	write_deformation_fields : (a list of from 2 to 2 items which are a boolean)
		Which deformation fields to write:[Inverse, Forward]


Outputs:: 

	bias_corrected_images : (an existing file name)
		bias corrected images
	bias_field_images : (an existing file name)
		bias field images
	dartel_input_images : (a list of items which are a list of items which are an existing file name)
		dartel imported class images
	modulated_class_images : (a list of items which are a list of items which are an existing file name)
		modulated+normalized class images
	native_class_images : (a list of items which are a list of items which are an existing file name)
		native space probability maps
	normalized_class_images : (a list of items which are a list of items which are an existing file name)
		normalized class images
	transformation_mat : (an existing file name)
		Normalization transformation

:class:`Normalize`
------------------


use spm_normalise for warping an image to a template

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=51

Examples
~~~~~~~~
>>> import nipype.interfaces.spm as spm
>>> norm = spm.Normalize()
>>> norm.inputs.source = 'functional.nii'
>>> norm.run() # doctest: +SKIP

Inputs:: 

	[Mandatory]
	parameter_file : (a file name)
		normalization parameter file*_sn.mat
		exclusive: source,template
	source : (an existing file name)
		file to normalize to template
		exclusive: parameter_file
	template : (an existing file name)
		template file to normalize to
		exclusive: parameter_file

	[Optional]
	DCT_period_cutoff : (a float)
		Cutoff of for DCT bases (opt)
	affine_regularization_type : ('mni' or 'size' or 'none')
		mni, size, none (opt)
	apply_to_files : (an existing file name)
		files to apply transformation to (opt)
	ignore_exception : (a boolean)
		Print an error message instead of throwing an exception in case the interface fails to run
	jobtype : ('estwrite' or 'estimate' or 'write')
		one of: estimate, write, estwrite (opt, estwrite)
	matlab_cmd : (a string)
		matlab command to use
	mfile : (a boolean)
		Run m-code using m-file
	nonlinear_iterations : (an integer)
		Number of iterations of nonlinear warping (opt)
	nonlinear_regularization : (a float)
		the amount of the regularization for the nonlinear part of the normalization (opt)
	paths : (a directory name)
		Paths to add to matlabpath
	source_image_smoothing : (a float)
		source smoothing (opt)
	source_weight : (a file name)
		name of weighting image for source (opt)
	template_image_smoothing : (a float)
		template smoothing (opt)
	template_weight : (a file name)
		name of weighting image for template (opt)
	use_mcr : (a boolean)
		Run m-code using SPM MCR
	write_bounding_box : (a list of from 6 to 6 items which are a float)
		6-element list (opt)
	write_interp : (an integer >= 0)
		degree of b-spline used for interpolation
	write_preserve : (a boolean)
		True/False warped images are modulated (opt,)
	write_voxel_sizes : (a list of from 3 to 3 items which are a float)
		3-element list (opt)
	write_wrap : (a list of items which are a boolean)
		Check if interpolation should wrap in [x,y,z] - list of bools (opt)


Outputs:: 

	normalization_parameters : (an existing file name)
		MAT files containing the normalization parameters
	normalized_files : (an existing file name)
		Normalized other files
	normalized_source : (an existing file name)
		Normalized source files

:class:`Realign`
----------------


Use spm_realign for estimating within modality rigid body alignment

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=25

Examples
~~~~~~~~

>>> import nipype.interfaces.spm as spm
>>> realign = spm.Realign()
>>> realign.inputs.in_files = 'functional.nii'
>>> realign.inputs.register_to_mean = True
>>> realign.run() # doctest: +SKIP

Inputs:: 

	[Mandatory]
	in_files : (a list of items which are an existing file name or an existing file name)
		list of filenames to realign

	[Optional]
	fwhm : (a floating point number >= 0.0)
		gaussian smoothing kernel width
	ignore_exception : (a boolean)
		Print an error message instead of throwing an exception in case the interface fails to run
	interp : (0 <= an integer <= 7)
		degree of b-spline used for interpolation
	jobtype : ('estwrite' or 'estimate' or 'write')
		one of: estimate, write, estwrite
	matlab_cmd : (a string)
		matlab command to use
	mfile : (a boolean)
		Run m-code using m-file
	paths : (a directory name)
		Paths to add to matlabpath
	quality : (0.0 <= a floating point number <= 1.0)
		0.1 = fast, 1.0 = precise
	register_to_mean : (a boolean)
		Indicate whether realignment is done to the mean image
	separation : (a floating point number >= 0.0)
		sampling separation in mm
	use_mcr : (a boolean)
		Run m-code using SPM MCR
	weight_img : (an existing file name)
		filename of weighting image
	wrap : (a tuple of the form: (an integer, an integer, an integer))
		Check if interpolation should wrap in [x,y,z]
	write_interp : (0 <= an integer <= 7)
		degree of b-spline used for interpolation
	write_mask : (a boolean)
		True/False mask output image
	write_which : (a tuple of the form: (an integer, an integer))
		determines which images to reslice
	write_wrap : (a tuple of the form: (an integer, an integer, an integer))
		Check if interpolation should wrap in [x,y,z]


Outputs:: 

	mean_image : (an existing file name)
		Mean image file from the realignment
	realigned_files : (a list of items which are an existing file name or an existing file name)
		Realigned files
	realignment_parameters : (an existing file name)
		Estimated translation and rotation parameters

:class:`Segment`
----------------


use spm_segment to separate structural images into different
tissue classes.

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=43

Examples
~~~~~~~~
>>> import nipype.interfaces.spm as spm
>>> seg = spm.Segment()
>>> seg.inputs.data = 'structural.nii'
>>> seg.run() # doctest: +SKIP

Inputs:: 

	[Mandatory]
	data : (an existing file name)
		one scan per subject

	[Optional]
	affine_regularization : ('mni' or 'eastern' or 'subj' or 'none' or '')
		Possible options: "mni", "eastern", "subj", "none" (no reguralisation), "" (no affine registration)
	bias_fwhm : (30 or 40 or 50 or 60 or 70 or 80 or 90 or 100 or 110 or 120 or 130 or 'Inf')
		FWHM of Gaussian smoothness of bias
	bias_regularization : (0 or 1e-05 or 0.0001 or 0.001 or 0.01 or 0.1 or 1 or 10)
		no(0) - extremely heavy (10)
	clean_masks : ('no' or 'light' or 'thorough')
		clean using estimated brain mask ('no','light','thorough')
	csf_output_type : (a list of from 3 to 3 items which are a boolean)
		Options to produce CSF images: c3*.img, wc3*.img and mwc3*.img.             
            None: [False,False,False], 
            Native Space: [False,False,True], 
            Unmodulated Normalised: [False,True,False], 
            Modulated Normalised: [True,False,False], 
            Native + Unmodulated Normalised: [False,True,True], 
            Native + Modulated Normalised: [True,False,True], 
            Native + Modulated + Unmodulated: [True,True,True], 
            Modulated + Unmodulated Normalised: [True,True,False]
	gaussians_per_class : (a list of items which are an integer)
		num Gaussians capture intensity distribution
	gm_output_type : (a list of from 3 to 3 items which are a boolean)
		Options to produce grey matter images: c1*.img, wc1*.img and mwc1*.img. 
            None: [False,False,False], 
            Native Space: [False,False,True], 
            Unmodulated Normalised: [False,True,False], 
            Modulated Normalised: [True,False,False], 
            Native + Unmodulated Normalised: [False,True,True], 
            Native + Modulated Normalised: [True,False,True], 
            Native + Modulated + Unmodulated: [True,True,True], 
            Modulated + Unmodulated Normalised: [True,True,False]
	ignore_exception : (a boolean)
		Print an error message instead of throwing an exception in case the interface fails to run
	mask_image : (an existing file name)
		Binary image to restrict parameter estimation 
	matlab_cmd : (a string)
		matlab command to use
	mfile : (a boolean)
		Run m-code using m-file
	paths : (a directory name)
		Paths to add to matlabpath
	sampling_distance : (a float)
		Sampling distance on data for parameter estimation
	save_bias_corrected : (a boolean)
		True/False produce a bias corrected image
	tissue_prob_maps : (a list of items which are an existing file name)
		list of gray, white & csf prob. (opt,)
	use_mcr : (a boolean)
		Run m-code using SPM MCR
	warp_frequency_cutoff : (a float)
		Cutoff of DCT bases
	warping_regularization : (a float)
		Controls balance between parameters and data
	wm_output_type : (a list of from 3 to 3 items which are a boolean)
		Options to produce white matter images: c2*.img, wc2*.img and mwc2*.img.             
            None: [False,False,False], 
            Native Space: [False,False,True], 
            Unmodulated Normalised: [False,True,False], 
            Modulated Normalised: [True,False,False], 
            Native + Unmodulated Normalised: [False,True,True], 
            Native + Modulated Normalised: [True,False,True], 
            Native + Modulated + Unmodulated: [True,True,True], 
            Modulated + Unmodulated Normalised: [True,True,False]


Outputs:: 

	inverse_transformation_mat : (an existing file name)
		Inverse normalization info
	modulated_csf_image : (an existing file name)
		modulated, normalized csf probability map
	modulated_gm_image : (an existing file name)
		modulated, normalized grey probability map
	modulated_input_image : (an existing file name)
		modulated version of input image
	modulated_wm_image : (an existing file name)
		modulated, normalized white probability map
	native_csf_image : (an existing file name)
		native space csf probability map
	native_gm_image : (an existing file name)
		native space grey probability map
	native_wm_image : (an existing file name)
		native space white probability map
	normalized_csf_image : (an existing file name)
		normalized csf probability map
	normalized_gm_image : (an existing file name)
		normalized grey probability map
	normalized_wm_image : (an existing file name)
		normalized white probability map
	transformation_mat : (an existing file name)
		Normalization transformation

:class:`SliceTiming`
--------------------


Use spm to perform slice timing correction.

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=19

Examples
~~~~~~~~

>>> from nipype.interfaces.spm import SliceTiming
>>> st = SliceTiming()
>>> st.inputs.in_files = 'functional.nii'
>>> st.inputs.num_slices = 32
>>> st.inputs.time_repetition = 6.0
>>> st.inputs.time_acquisition = 6. - 6./32.
>>> st.inputs.slice_order = range(32,0,-1)
>>> st.inputs.ref_slice = 1
>>> st.run() # doctest: +SKIP

Inputs:: 

	[Mandatory]
	in_files : (a list of items which are an existing file name or an existing file name)
		list of filenames to apply slice timing
	num_slices : (an integer)
		number of slices in a volume
	ref_slice : (an integer)
		1-based Number of the reference slice
	slice_order : (a list of items which are an integer)
		1-based order in which slices are acquired
	time_acquisition : (a float)
		time of volume acquisition. usually calculated as TR-(TR/num_slices)
	time_repetition : (a float)
		time between volume acquisitions (start to start time)

	[Optional]
	ignore_exception : (a boolean)
		Print an error message instead of throwing an exception in case the interface fails to run
	matlab_cmd : (a string)
		matlab command to use
	mfile : (a boolean)
		Run m-code using m-file
	paths : (a directory name)
		Paths to add to matlabpath
	use_mcr : (a boolean)
		Run m-code using SPM MCR


Outputs:: 

	timecorrected_files : (a file name)
		Unknown

:class:`Smooth`
---------------


Use spm_smooth for 3D Gaussian smoothing of image volumes.

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=57

Examples
~~~~~~~~
>>> import nipype.interfaces.spm as spm
>>> smooth = spm.Smooth()
>>> smooth.inputs.in_files = 'functional.nii'
>>> smooth.inputs.fwhm = [4, 4, 4]
>>> smooth.run() # doctest: +SKIP

Inputs:: 

	[Mandatory]
	in_files : (an existing file name)
		list of files to smooth

	[Optional]
	data_type : (an integer)
		Data type of the output images (opt)
	fwhm : (a list of from 3 to 3 items which are a float or a float)
		3-list of fwhm for each dimension (opt)
	ignore_exception : (a boolean)
		Print an error message instead of throwing an exception in case the interface fails to run
	implicit_masking : (a boolean)
		A mask implied by a particular voxel value
	matlab_cmd : (a string)
		matlab command to use
	mfile : (a boolean)
		Run m-code using m-file
	paths : (a directory name)
		Paths to add to matlabpath
	use_mcr : (a boolean)
		Run m-code using SPM MCR


Outputs:: 

	smoothed_files : (an existing file name)
		smoothed files
