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# interfaces.io

## BIDSDataGrabber

Link to code

BIDS datagrabber module that wraps around pybids to allow arbitrary querying of BIDS datasets.

### Examples

By default, the BIDSDataGrabber fetches anatomical and functional images from a project, and makes BIDS entities (e.g. subject) available for filtering outputs.

    >>> bg = BIDSDataGrabber()
    >>> bg.inputs.base_dir = 'ds005/'
    >>> bg.inputs.subject = '01'
    >>> results = bg.run()

Dynamically created, user-defined output fields can also be defined to return different types of outputs from the same project. All outputs are filtered on common entities, which can be explicitly defined as infields.

    >>> bg = BIDSDataGrabber(infields = ['subject'], outfields = ['dwi'])
    >>> bg.inputs.base_dir = 'ds005/'
    >>> bg.inputs.subject = '01'
    >>> bg.inputs.output_query['dwi'] = dict(modality='dwi')
    >>> results = bg.run()

Inputs:

    [Mandatory]
    base_dir: (an existing directory name)
            Path to BIDS Directory.

    [Optional]
    output_query: (a dictionary with keys which are a unicode string and
             with values which are a dictionary with keys which are any value
             and with values which are any value)
            Queries for outfield outputs
    raise_on_empty: (a boolean, nipype default value: True)
            Generate exception if list is empty for a given field
    return_type: (u'file' or u'namedtuple', nipype default value: file)

Outputs:

    None

## DataFinder

Link to code

Search for paths that match a given regular expression. Allows a less proscriptive approach to gathering input files compared to DataGrabber. Will recursively search any subdirectories by default. This can be limited with the min/max depth options. Matched paths are available in the output ‘out_paths’. Any named groups of captured text from the regular expression are also available as ouputs of the same name.

### Examples

    >>> from nipype.interfaces.io import DataFinder
    >>> df = DataFinder()
    >>> df.inputs.root_paths = '.'
    >>> df.inputs.match_regex = '.+/(?P<series_dir>.+(qT1|ep2d_fid_T1).+)/(?P<basename>.+)\.nii.gz'
    >>> result = df.run()
    >>> result.outputs.out_paths
    ['./027-ep2d_fid_T1_Gd4/acquisition.nii.gz',
     './018-ep2d_fid_T1_Gd2/acquisition.nii.gz',
     './016-ep2d_fid_T1_Gd1/acquisition.nii.gz',
     './013-ep2d_fid_T1_pre/acquisition.nii.gz']
    >>> result.outputs.series_dir
    ['027-ep2d_fid_T1_Gd4',
     '018-ep2d_fid_T1_Gd2',
     '016-ep2d_fid_T1_Gd1',
     '013-ep2d_fid_T1_pre']
    >>> result.outputs.basename
    ['acquisition',
     'acquisition'
     'acquisition',
     'acquisition']

Inputs:

    [Mandatory]
    root_paths: (a list of items which are any value or a unicode string)

    [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
    ignore_regexes: (a list of items which are any value)
            List of regular expressions, if any match the path it will be
            ignored.
    match_regex: (a unicode string, nipype default value: (.+))
            Regular expression for matching paths.
    max_depth: (an integer (int or long))
            The maximum depth to search beneath the root_paths
    min_depth: (an integer (int or long))
            The minimum depth to search beneath the root paths
    unpack_single: (a boolean, nipype default value: False)
            Unpack single results from list

Outputs:

    None

## DataGrabber

Link to code

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

Attention

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]
    sort_filelist: (a boolean)
            Sort the filelist that matches the template
    template: (a unicode 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.
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    raise_on_empty: (a boolean, nipype default value: True)
            Generate exception if list is empty for a given field
    template_args: (a dictionary with keys which are a unicode 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:

    None

## DataSink

Link to code

Generic datasink module to store structured outputs

Primarily for use within a workflow. This interface allows 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]] …

where parts between [] are optional.

An attribute such as contrasts.@con will create a ‘contrasts’ directory to store the results linked to the attribute. If the @ is left out, such as in ‘contrasts.con’, a subdirectory ‘con’ will be created under ‘contrasts’.

the general form of the output is:

    'base_directory/container/parameterization/destloc/filename'

    destloc = string[[.[@]]string[[.[@]]string]] and
    filename comesfrom the input to the connect statement.

Warning

This is not a thread-safe node because it can write to a common shared location. It will not complain when it overwrites a file.

Note

If both substitutions and regexp_substitutions are used, then substitutions are applied first followed by regexp_substitutions.

This interface cannot be used in a MapNode as the inputs are defined only when the connect statement is executed.

### 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()

To use DataSink in a MapNode, its inputs have to be defined at the time the interface is created.

    >>> ds = DataSink(infields=['contasts.@con'])
    >>> 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()

Inputs:

    [Mandatory]

    [Optional]
    _outputs: (a dictionary with keys which are a unicode string and with
             values which are any value, nipype default value: {})
    base_directory: (a directory name)
            Path to the base directory for storing data.
    bucket: (any value)
            Boto3 S3 bucket for manual override of bucket
    container: (a unicode string)
            Folder within base directory in which to store output
    creds_path: (a unicode string)
            Filepath to AWS credentials file for S3 bucket access; if not
            specified, the credentials will be taken from the AWS_ACCESS_KEY_ID
            and AWS_SECRET_ACCESS_KEY environment variables
    encrypt_bucket_keys: (a boolean)
            Flag indicating whether to use S3 server-side AES-256 encryption
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    local_copy: (a unicode string)
            Copy files locally as well as to S3 bucket
    parameterization: (a boolean, nipype default value: True)
            store output in parametrized structure
    regexp_substitutions: (a list of items which are a tuple of the form:
             (a unicode string, a unicode string))
            List of 2-tuples reflecting a pair of a Python regexp pattern and a
            replacement string. Invoked after string `substitutions`
    remove_dest_dir: (a boolean, nipype default value: False)
            remove dest directory when copying dirs
    strip_dir: (a directory name)
            path to strip out of filename
    substitutions: (a list of items which are a tuple of the form: (a
             unicode string, a unicode string))
            List of 2-tuples reflecting string to substitute and string to
            replace it with

Outputs:

    out_file: (any value)
            datasink output

## FreeSurferSource

Link to code

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()

    >>> fs.inputs.hemi = 'lh'
    >>> res = fs.run()

Inputs:

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

    [Optional]
    hemi: (u'both' or u'lh' or u'rh', nipype default value: both)
            Selects hemisphere specific outputs
    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:

    BA_stats: (a list of items which are an existing file name)
            Brodmann Area statistics files
    T1: (an existing file name)
            Intensity normalized whole-head volume
    annot: (a list of items which are an existing file name)
            Surface annotation files
    aparc_a2009s_stats: (a list of items which are an existing file name)
            Aparc a2009s parcellation statistics files
    aparc_aseg: (a list of items which are an existing file name)
            Aparc parcellation projected into aseg volume
    aparc_stats: (a list of items which are an existing file name)
            Aparc parcellation statistics files
    area_pial: (a list of items which are an existing file name)
            Mean area of triangles each vertex on the pial surface is associated
            with
    aseg: (an existing file name)
            Volumetric map of regions from automatic segmentation
    aseg_stats: (a list of items which are an existing file name)
            Automated segmentation statistics file
    avg_curv: (a list of items which are an existing file name)
            Average atlas curvature, sampled to subject
    brain: (an existing file name)
            Intensity normalized brain-only volume
    brainmask: (an existing file name)
            Skull-stripped (brain-only) volume
    curv: (a list of items which are an existing file name)
            Maps of surface curvature
    curv_pial: (a list of items which are an existing file name)
            Curvature of pial surface
    curv_stats: (a list of items which are an existing file name)
            Curvature statistics files
    entorhinal_exvivo_stats: (a list of items which are an existing file
             name)
            Entorhinal exvivo statistics files
    filled: (an existing file name)
            Subcortical mass volume
    graymid: (a list of items which are an existing file name)
            Graymid/midthickness surface meshes
    inflated: (a list of items which are an existing file name)
            Inflated surface meshes
    jacobian_white: (a list of items which are an existing file name)
            Distortion required to register to spherical atlas
    label: (a list of items which are an existing file name)
            Volume and surface label files
    norm: (an existing file name)
            Normalized skull-stripped volume
    nu: (an existing file name)
            Non-uniformity corrected whole-head volume
    orig: (an existing file name)
            Base image conformed to Freesurfer space
    pial: (a list of items which are an existing file name)
            Gray matter/pia mater surface meshes
    rawavg: (an existing file name)
            Volume formed by averaging input images
    ribbon: (a list of items which are an existing file name)
            Volumetric maps of cortical ribbons
    smoothwm: (a list of items which are an existing file name)
            Smoothed original surface meshes
    sphere: (a list of items which are an existing file name)
            Spherical surface meshes
    sphere_reg: (a list of items which are an existing file name)
            Spherical registration file
    sulc: (a list of items which are an existing file name)
            Surface maps of sulcal depth
    thickness: (a list of items which are an existing file name)
            Surface maps of cortical thickness
    volume: (a list of items which are an existing file name)
            Surface maps of cortical volume
    white: (a list of items which are an existing file name)
            White/gray matter surface meshes
    wm: (an existing file name)
            Segmented white-matter volume
    wmparc: (an existing file name)
            Aparc parcellation projected into subcortical white matter
    wmparc_stats: (a list of items which are an existing file name)
            White matter parcellation statistics file

## IOBase

Link to code

Inputs:

    [Mandatory]

    [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:

    None

## JSONFileGrabber

Link to code

Datagrabber interface that loads a json file and generates an output for every first-level object

### Example

    >>> tmp = getfixture('tmpdir')
    >>> old = tmp.chdir() # changing to a temporary directory

    >>> import pprint
    >>> from nipype.interfaces.io import JSONFileGrabber
    >>> jsonSource = JSONFileGrabber()
    >>> jsonSource.inputs.defaults = {'param1': 'overrideMe', 'param3': 1.0}
    >>> res = jsonSource.run()
    >>> pprint.pprint(res.outputs.get())
    {'param1': 'overrideMe', 'param3': 1.0}
    >>> jsonSource.inputs.in_file = os.path.join(datadir, 'jsongrabber.txt')
    >>> res = jsonSource.run()
    >>> pprint.pprint(res.outputs.get())
    {'param1': 'exampleStr', 'param2': 4, 'param3': 1.0}

    >>> os.chdir(old.strpath)

Inputs:

    [Mandatory]

    [Optional]
    defaults: (a dictionary with keys which are any value and with values
             which are any value)
            JSON dictionary that sets default outputvalues, overridden by values
            found in in_file
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    in_file: (an existing file name)
            JSON source file

Outputs:

    None

## JSONFileSink

Link to code

Very simple frontend for storing values into a JSON file. Entries already existing in in_dict will be overridden by matching entries dynamically added as inputs.

> Warning

> This is not a thread-safe node because it can write to a common shared location. It will not complain when it overwrites a file.

>     >>> jsonsink = JSONFileSink(input_names=['subject_id',
>     ...                         'some_measurement'])
>     >>> jsonsink.inputs.subject_id = 's1'
>     >>> jsonsink.inputs.some_measurement = 11.4
>     >>> jsonsink.run()

> Using a dictionary as input:

>     >>> dictsink = JSONFileSink()
>     >>> dictsink.inputs.in_dict = {'subject_id': 's1',
>     ...                            'some_measurement': 11.4}
>     >>> dictsink.run()

Inputs:

    [Mandatory]

    [Optional]
    _outputs: (a dictionary with keys which are any value and with values
             which are any value, nipype default value: {})
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    in_dict: (a dictionary with keys which are any value and with values
             which are any value, nipype default value: {})
            input JSON dictionary
    out_file: (a file name)
            JSON sink file

Outputs:

    out_file: (a file name)
            JSON sink file

## MySQLSink

Link to code

Very simple frontend for storing values into MySQL database.

### Examples

    >>> sql = MySQLSink(input_names=['subject_id', 'some_measurement'])
    >>> sql.inputs.database_name = 'my_database'
    >>> sql.inputs.table_name = 'experiment_results'
    >>> sql.inputs.username = 'root'
    >>> sql.inputs.password = 'secret'
    >>> sql.inputs.subject_id = 's1'
    >>> sql.inputs.some_measurement = 11.4
    >>> sql.run()

Inputs:

    [Mandatory]
    config: (a file name)
            MySQL Options File (same format as my.cnf)
            mutually_exclusive: host
    database_name: (a unicode string)
            Otherwise known as the schema name
    host: (a unicode string, nipype default value: localhost)
            mutually_exclusive: config
            requires: username, password
    table_name: (a unicode string)

    [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
    password: (a unicode string)
    username: (a unicode string)

Outputs:

    None

## S3DataGrabber

Link to code

Generic datagrabber module that wraps around glob in an intelligent way for neuroimaging tasks to grab files from Amazon S3

Works exactly like DataGrabber, except, you must specify an S3 “bucket” and “bucket_path” to search for your data and a “local_directory” to store the data. “local_directory” should be a location on HDFS for Spark jobs. Additionally, “template” uses regex style formatting, rather than the glob-style found in the original DataGrabber.

Inputs:

    [Mandatory]
    bucket: (a unicode string)
            Amazon S3 bucket where your data is stored
    sort_filelist: (a boolean)
            Sort the filelist that matches the template
    template: (a unicode string)
            Layout used to get files. Relative to bucket_path if defined.Uses
            regex rather than glob style formatting.

    [Optional]
    anon: (a boolean, nipype default value: False)
            Use anonymous connection to s3. If this is set to True, boto may
            print a urlopen error, but this does not prevent data from being
            downloaded.
    bucket_path: (a unicode string, nipype default value: )
            Location within your bucket for subject data.
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    local_directory: (an existing directory name)
            Path to the local directory for subject data to be downloaded and
            accessed. Should be on HDFS for Spark jobs.
    raise_on_empty: (a boolean, nipype default value: True)
            Generate exception if list is empty for a given field
    region: (a unicode string, nipype default value: us-east-1)
            Region of s3 bucket
    template_args: (a dictionary with keys which are a unicode 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:

    None

## SQLiteSink

Link to code

Very simple frontend for storing values into SQLite database.

Warning

This is not a thread-safe node because it can write to a common shared location. It will not complain when it overwrites a file.

### Examples

    >>> sql = SQLiteSink(input_names=['subject_id', 'some_measurement'])
    >>> sql.inputs.database_file = 'my_database.db'
    >>> sql.inputs.table_name = 'experiment_results'
    >>> sql.inputs.subject_id = 's1'
    >>> sql.inputs.some_measurement = 11.4
    >>> sql.run()

Inputs:

    [Mandatory]
    database_file: (an existing file name)
    table_name: (a unicode string)

    [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:

    None

## SSHDataGrabber

Link to code

Extension of DataGrabber module that downloads the file list and optionally the files from a SSH server. The SSH operation must not need user and password so an SSH agent must be active in where this module is being run.

Attention

Doesn’t support directories currently

### Examples

    >>> from nipype.interfaces.io import SSHDataGrabber
    >>> dg = SSHDataGrabber()
    >>> dg.inputs.hostname = 'test.rebex.net'
    >>> dg.inputs.user = 'demo'
    >>> dg.inputs.password = 'password'
    >>> dg.inputs.base_directory = 'pub/example'

Pick all files from the base directory

    >>> dg.inputs.template = '*'

Pick all files starting with “s” and a number from current directory

    >>> dg.inputs.template_expression = 'regexp'
    >>> dg.inputs.template = 'pop[0-9].*'

Same thing but with dynamically created fields

    >>> dg = SSHDataGrabber(infields=['arg1','arg2'])
    >>> dg.inputs.hostname = 'test.rebex.net'
    >>> dg.inputs.user = 'demo'
    >>> dg.inputs.password = 'password'
    >>> dg.inputs.base_directory = 'pub'
    >>> dg.inputs.template = '%s/%s.txt'
    >>> dg.inputs.arg1 = 'example'
    >>> 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 = SSHDataGrabber(infields=['sid'], outfields=['func','struct','ref'])
    >>> dg.inputs.hostname = 'myhost.com'
    >>> dg.inputs.base_directory = '/main_folder/my_remote_dir'
    >>> 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]
    base_directory: (a unicode string)
            Path to the base directory consisting of subject data.
    hostname: (a unicode string)
            Server hostname.
    sort_filelist: (a boolean)
            Sort the filelist that matches the template
    template: (a unicode string)
            Layout used to get files. relative to base directory if defined

    [Optional]
    download_files: (a boolean, nipype default value: True)
            If false it will return the file names without downloading them
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    password: (a string)
            Server password.
    raise_on_empty: (a boolean, nipype default value: True)
            Generate exception if list is empty for a given field
    ssh_log_to_file: (a unicode string, nipype default value: )
            If set SSH commands will be logged to the given file
    template_args: (a dictionary with keys which are a unicode 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
    template_expression: (u'fnmatch' or u'regexp', nipype default value:
             fnmatch)
            Use either fnmatch or regexp to express templates
    username: (a unicode string)
            Server username.

Outputs:

    None

## SelectFiles

Link to code

Flexibly collect data from disk to feed into workflows.

This interface uses the {}-based string formatting syntax to plug values (possibly known only at workflow execution time) into string templates and collect files from persistant storage. These templates can also be combined with glob wildcards. The field names in the formatting template (i.e. the terms in braces) will become inputs fields on the interface, and the keys in the templates dictionary will form the output fields.

### Examples

    >>> import pprint
    >>> from nipype import SelectFiles, Node
    >>> templates={"T1": "{subject_id}/struct/T1.nii",
    ...            "epi": "{subject_id}/func/f[0, 1].nii"}
    >>> dg = Node(SelectFiles(templates), "selectfiles")
    >>> dg.inputs.subject_id = "subj1"
    >>> pprint.pprint(dg.outputs.get())  # doctest:
    {'T1': <undefined>, 'epi': <undefined>}

The same thing with dynamic grabbing of specific files:

    >>> templates["epi"] = "{subject_id}/func/f{run!s}.nii"
    >>> dg = Node(SelectFiles(templates), "selectfiles")
    >>> dg.inputs.subject_id = "subj1"
    >>> dg.inputs.run = [2, 4]

Inputs:

    [Mandatory]

    [Optional]
    base_directory: (an existing directory name)
            Root path common to templates.
    force_lists: (a boolean or a list of items which are a unicode
             string, nipype default value: False)
            Whether to return outputs as a list even when only one file matches
            the template. Either a boolean that applies to all output fields or
            a list of output field names to coerce to a list
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    raise_on_empty: (a boolean, nipype default value: True)
            Raise an exception if a template pattern matches no files.
    sort_filelist: (a boolean, nipype default value: True)
            When matching mutliple files, return them in sorted order.

Outputs:

    None

## XNATSink

Link to code

Generic datasink module that takes a directory containing a list of nifti files and provides a set of structured output fields.

Inputs:

    [Mandatory]
    config: (a file name)
            mutually_exclusive: server
    experiment_id: (a unicode string)
            Set to workflow name
    project_id: (a unicode string)
            Project in which to store the outputs
    server: (a unicode string)
            mutually_exclusive: config
            requires: user, pwd
    subject_id: (a unicode string)
            Set to subject id

    [Optional]
    _outputs: (a dictionary with keys which are a unicode string and with
             values which are any value, nipype default value: {})
    assessor_id: (a unicode string)
            Option to customize ouputs representation in XNAT - assessor level
            will be used with specified id
            mutually_exclusive: reconstruction_id
    cache_dir: (a directory name)
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    pwd: (a string)
    reconstruction_id: (a unicode string)
            Option to customize ouputs representation in XNAT - reconstruction
            level will be used with specified id
            mutually_exclusive: assessor_id
    share: (a boolean, nipype default value: False)
            Option to share the subjects from the original projectinstead of
            creating new ones when possible - the created experiments are then
            shared back to the original project
    user: (a unicode string)

Outputs:

    None

## XNATSource

Link to code

Generic XNATSource module that wraps around the pyxnat module in an intelligent way for neuroimaging tasks to grab files and data from an XNAT server.

### 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: (a file name)
            mutually_exclusive: server
    query_template: (a unicode string)
            Layout used to get files. Relative to base directory if defined
    server: (a unicode string)
            mutually_exclusive: config
            requires: user, pwd

    [Optional]
    cache_dir: (a directory name)
            Cache directory
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    pwd: (a string)
    query_template_args: (a dictionary with keys which are a unicode
             string and with values which are a list of items which are a list
             of items which are any value, nipype default value: {'outfiles':
             []})
            Information to plug into template
    user: (a unicode string)

Outputs:

    None

## `add_traits()`

Link to code

Add traits to a traited class.

All traits are set to Undefined by default

## `copytree()`

Link to code

Recursively copy a directory tree using nipype.utils.filemanip.copyfile()

This is not a thread-safe routine. However, in the case of creating new directories, it checks to see if a particular directory has already been created by another process.

## `push_file()`

Link to code

## `quote_id()`

Link to code

## `unquote_id()`

Link to code