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

nipype.interfaces.io
====================


:class:`DataGrabber`
--------------------


Generic datagrabber module that wraps around glob in an
intelligent way for neuroimaging tasks to grab files


.. note::
   Doesn't support directories currently

Examples
~~~~~~~~

>>> from nipype.interfaces.io import DataGrabber

Pick all files from current directory

>>> dg = DataGrabber()
>>> dg.inputs.template = '*'

Pick file foo/foo.nii from current directory

>>> dg.inputs.template = '%s/%s.dcm'
>>> dg.inputs.template_args['outfiles']=[['dicomdir','123456-1-1.dcm']]

Same thing but with dynamically created fields

>>> dg = DataGrabber(infields=['arg1','arg2'])
>>> dg.inputs.template = '%s/%s.nii'
>>> dg.inputs.arg1 = 'foo'
>>> dg.inputs.arg2 = 'foo'

however this latter form can be used with iterables and iterfield in a
pipeline.

Dynamically created, user-defined input and output fields

>>> dg = DataGrabber(infields=['sid'], outfields=['func','struct','ref'])
>>> dg.inputs.base_directory = '.'
>>> dg.inputs.template = '%s/%s.nii'
>>> dg.inputs.template_args['func'] = [['sid',['f3','f5']]]
>>> dg.inputs.template_args['struct'] = [['sid',['struct']]]
>>> dg.inputs.template_args['ref'] = [['sid','ref']]
>>> dg.inputs.sid = 's1'

Change the template only for output field struct. The rest use the
general template

>>> dg.inputs.field_template = dict(struct='%s/struct.nii')
>>> dg.inputs.template_args['struct'] = [['sid']]

Inputs:: 

	[Mandatory]
	template : (a string)
		Layout used to get files. relative to base directory if defined

	[Optional]
	base_directory : (an existing directory name)
		Path to the base directory consisting of subject data.
	template_args : (a dictionary with keys which are a string and with values which are a list of items which are a list of items which are any value)
		Information to plug into template


Outputs:: 

	outfiles	Unknown

:class:`DataSink`
-----------------


Generic datasink module to store structured outputs

Primarily for use within a workflow. This interface all arbitrary
creation of input attributes. The names of these attributes define the
directory structure to create for storage of the files or directories.

The attributes take the following form:
string[[@|.]string[[@|.]string]] ...

An attribute such as contrasts@con will create a contrasts directory to
store the results linked to the attribute. If the @ is replaced with a
'.', such as 'contrasts.con' a subdirectory 'con' will be created under
contrasts.

Examples
~~~~~~~~

>>> ds = DataSink()
>>> ds.inputs.base_directory = 'results_dir'
>>> ds.inputs.container = 'subject'
>>> ds.inputs.structural = 'structural.nii'
>>> setattr(ds.inputs, 'contrasts@con', ['cont1.nii', 'cont2.nii'])
>>> setattr(ds.inputs, 'contrasts.alt', ['cont1a.nii', 'cont2a.nii'])
>>> ds.run() # doctest: +SKIP

Inputs:: 

	[Optional]
	_outputs : (a dictionary with keys which are a string and with values which are any value)
		Unknown
	base_directory : (a directory name)
		Path to the base directory for storing data.
	container : (a string)
		Folder within base directory in which to store output
	parameterization : (a boolean)
		store output in parameterized structure
	strip_dir : (a directory name)
		path to strip out of filename
	substitutions : (a tuple of the form: (a string, a string))
		List of 2-tuples reflecting stringto substitute and string to replaceit with



:class:`FreeSurferSource`
-------------------------


Generates freesurfer subject info from their directories

Examples
~~~~~~~~

>>> from nipype.interfaces.io import FreeSurferSource
>>> fs = FreeSurferSource()
>>> #fs.inputs.subjects_dir = '.'
>>> fs.inputs.subject_id = 'PWS04'
>>> res = fs.run() # doctest: +SKIP

>>> fs.inputs.hemi = 'lh'
>>> res = fs.run() # doctest: +SKIP

Inputs:: 

	[Mandatory]
	subject_id : (a string)
		Subject name for whom to retrieve data
	subjects_dir : (a directory name)
		Freesurfer subjects directory.

	[Optional]
	hemi : ('both' or 'lh' or 'rh')
		Selects hemisphere specific outputs


Outputs:: 

	T1 : (an existing file name)
		T1 image
	annot : (an existing file name)
		surface annotation files
	aparc_aseg : (an existing file name)
		aparc+aseg file
	aseg : (an existing file name)
		Auto-seg image
	brain : (an existing file name)
		brain only image
	brainmask : (an existing file name)
		brain binary mask
	curv : (an existing file name)
		surface curvature files
	filled : (an existing file name)
		?
	inflated : (an existing file name)
		inflated surface meshes
	label : (an existing file name)
		volume and surface label files
	norm : (an existing file name)
		intensity normalized image
	nu : (an existing file name)
		?
	orig : (an existing file name)
		original image conformed to FS space
	pial : (an existing file name)
		pial surface meshes
	rawavg : (an existing file name)
		averaged input images to recon-all
	ribbon : (an existing file name)
		cortical ribbon
	smoothwm : (an existing file name)
		smooth white-matter surface meshes
	sphere : (an existing file name)
		spherical surface meshes
	sphere_reg : (an existing file name)
		spherical registration file
	sulc : (an existing file name)
		surface sulci files
	thickness : (an existing file name)
		surface thickness files
	volume : (an existing file name)
		surface volume files
	white : (an existing file name)
		white matter surface meshes
	wm : (an existing file name)
		white matter image
	wmparc : (an existing file name)
		white matter parcellation

:class:`IOBase`
---------------


:class:`XNATSource`
-------------------


Generic XNATSource module that wraps around glob in an
intelligent way for neuroimaging tasks to grab files

Examples
~~~~~~~~

>>> from nipype.interfaces.io import XNATSource

Pick all files from current directory

>>> dg = XNATSource()
>>> dg.inputs.template = '*'

>>> dg = XNATSource(infields=['project','subject','experiment','assessor','inout'])
>>> dg.inputs.query_template = '/projects/%s/subjects/%s/experiments/%s'                    '/assessors/%s/%s_resources/files'
>>> dg.inputs.project = 'IMAGEN'
>>> dg.inputs.subject = 'IMAGEN_000000001274'
>>> dg.inputs.experiment = '*SessionA*'
>>> dg.inputs.assessor = '*ADNI_MPRAGE_nii'
>>> dg.inputs.inout = 'out'

>>> dg = XNATSource(infields=['sid'],outfields=['struct','func'])
>>> dg.inputs.query_template = '/projects/IMAGEN/subjects/%s/experiments/*SessionA*'                    '/assessors/*%s_nii/out_resources/files'
>>> dg.inputs.query_template_args['struct'] = [['sid','ADNI_MPRAGE']]
>>> dg.inputs.query_template_args['func'] = [['sid','EPI_faces']]
>>> dg.inputs.sid = 'IMAGEN_000000001274'

Inputs:: 

	[Mandatory]
	config_file : (an existing file name)
		a json config file containing xnat access info: url, username and password
	query_template : (a string)
		Layout used to get files. relative to base directory if defined

	[Optional]
	query_template_args : (a dictionary with keys which are a string and with values which are a list of items which are a list of items which are any value)
		Information to plug into template


Outputs:: 

	outfiles	Unknown
