Generic datagrabber module that wraps around glob in an intelligent way for neuroimaging tasks to grab files
Note
Doesn’t support directories currently
>>> 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.
field_template : (a dictionary with keys which are 'outfiles' and with values which are any value)
arguments that fit into template
ignore_exception : (a boolean)
Print an error message instead of throwing an exception in case the interface fails to run
raise_on_empty : (a boolean)
Generate exception if list is empty for a given field
sort_filelist : (a boolean)
Sort the filelist that matches the template
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
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’.
Unlike most nipype-nodes 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.
>>> 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()
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
ignore_exception : (a boolean)
Print an error message instead of throwing an exception in case the interface fails to run
parameterization : (a boolean)
store output in parametrized structure
regexp_substitutions : (a tuple of the form: (a string, a 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)
remove dest directory when copying dirs
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 string to substitute and string to replace it with
Generates freesurfer subject info from their directories
>>> 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 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
ignore_exception : (a boolean)
Print an error message instead of throwing an exception in case the interface fails to run
Outputs:
T1 : (an existing file name)
Intensity normalized whole-head volume
annot : (an existing file name)
Surface annotation files
aparc_aseg : (an existing file name)
Aparc parcellation projected into aseg volume
aseg : (an existing file name)
Volumetric map of regions from automatic segmentation
brain : (an existing file name)
Intensity normalized brain-only volume
brainmask : (an existing file name)
Skull-stripped (brain-only) volume
curv : (an existing file name)
Maps of surface curvature
filled : (an existing file name)
Subcortical mass volume
inflated : (an existing file name)
Inflated surface meshes
label : (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 : (an existing file name)
Gray matter/pia mater surface meshes
rawavg : (an existing file name)
Volume formed by averaging input images
ribbon : (an existing file name)
Volumetric maps of cortical ribbons
smoothwm : (an existing file name)
Smoothed original 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 maps of sulcal depth
thickness : (an existing file name)
Surface maps of cortical thickness
volume : (an existing file name)
Surface maps of cortical volume
white : (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
Inputs:
[Optional]
ignore_exception : (a boolean)
Print an error message instead of throwing an exception in case the interface fails to run
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)
Unknown
exclusive: server
experiment_id : (a string)
Set to workflow name
project_id : (a string)
Project in which to store the outputs
server : (a string)
Unknown
exclusive: config
requires: user,pwd
subject_id : (a string)
Set to subject id
[Optional]
_outputs : (a dictionary with keys which are a string and with values which are any value)
Unknown
assessor_id : (a string)
Option to customize ouputs representation in XNAT - assessor level will be used with specified id
exclusive: reconstruction_id
cache_dir : (a directory name)
ignore_exception : (a boolean)
Print an error message instead of throwing an exception in case the interface fails to run
pwd : (a string)
Unknown
reconstruction_id : (a string)
Option to customize ouputs representation in XNAT - reconstruction level will be used with specified id
exclusive: assessor_id
share : (a boolean)
Option to share the subjects from the original projectinstead of creating new ones when possible - the created experiments are then shared backk to the original project
user : (a string)
Unknown
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.
>>> 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)
Unknown
exclusive: server
query_template : (a string)
Layout used to get files. Relative to base directory if defined
server : (a string)
Unknown
exclusive: config
requires: user,pwd
[Optional]
cache_dir : (a directory name)
Cache directory
ignore_exception : (a boolean)
Print an error message instead of throwing an exception in case the interface fails to run
pwd : (a string)
Unknown
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
user : (a string)
Unknown
Outputs:
outfiles Unknown