Tutorial : Interfaces

Specifying options

The nipype interface modules provide a Python interface to external packages like FSL and SPM. Within the module are a series of Python classes which wrap specific package functionality. For example, in the fsl module, the class nipype.interfaces.fsl.Bet wraps the bet command-line tool. Using the command-line tool, one would specify options using flags like -o, -m, -f <f>, etc... However, in nipype, options are assigned to Python attributes and can be specified in the following ways:

Options can be assigned when you first create an interface object:

import nipype.interfaces.fsl as fsl
mybet = fsl.BET(in_file='foo.nii', out_file='bar.nii')
result = mybet.run()

Options can be assigned through the inputs attribute:

import nipype.interfaces.fsl as fsl
mybet = fsl.BET()
mybet.inputs.in_file = 'foo.nii'
mybet.inputs.out_file = 'bar.nii'
result = mybet.run()

Options can be assigned when calling the run method:

import nipype.interfaces.fsl as fsl
mybet = fsl.BET()
result = mybet.run(in_file='foo.nii', out_file='bar.nii', frac=0.5)

Getting Help

In IPython you can view the docstrings which provide some basic documentation and examples.

In [2]: fsl.FAST?
Type:               type
Base Class: <type 'type'>
String Form:        <class 'nipype.interfaces.fsl.preprocess.FAST'>
Namespace:  Interactive
File:               /Users/satra/sp/nipype/interfaces/fsl/preprocess.py
    Use FSL FAST for segmenting and bias correction.

    For complete details, see the `FAST Documentation.

    >>> from nipype.interfaces import fsl
    >>> from nipype.testing import anatfile

    Assign options through the ``inputs`` attribute:

    >>> fastr = fsl.FAST()
    >>> fastr.inputs.in_files = anatfile
    >>> out = fastr.run() #doctest: +SKIP

Constructor information:
Definition: fsl.FAST(self, **inputs)
In [5]: spm.Realign?
Type:                type
Base Class:  <type 'type'>
String Form: <class 'nipype.interfaces.spm.preprocess.Realign'>
Namespace:   Interactive
File:                /Users/satra/sp/nipype/interfaces/spm/preprocess.py
    Use spm_realign for estimating within modality rigid body alignment



    >>> 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

Constructor information:
Definition:  spm.Realign(self, **inputs)

All of the nipype.interfaces classes have an help method which provides information on each of the options one can assign.

In [6]: fsl.BET.help()

 in_file: input file to skull strip

 args: Additional parameters to the command
 center: center of gravity in voxels
 environ: Environment variables (default={})
 frac: fractional intensity threshold
 functional: apply to 4D fMRI data
  mutually exclusive: functional, reduce_bias
 mask: create binary mask image
 mesh: generate a vtk mesh brain surface
 no_output: Don't generate segmented output
 out_file: name of output skull stripped image
 outline: create surface outline image
 output_type: FSL output type
 radius: head radius
 reduce_bias: bias field and neck cleanup
  mutually exclusive: functional, reduce_bias
 skull: create skull image
 threshold: apply thresholding to segmented brain image and mask
 vertical_gradient: vertical gradient in fractional intensity threshold (-1, 1)

mask_file: path/name of binary brain mask (if generated)
meshfile: path/name of vtk mesh file (if generated)
out_file: path/name of skullstripped file
outline_file: path/name of outline file (if generated)
In [7]: spm.Realign.help()

 in_files: list of filenames to realign

 fwhm: gaussian smoothing kernel width
 interp: degree of b-spline used for interpolation
 jobtype: one of: estimate, write, estwrite (default=estwrite)
 matlab_cmd: None
 mfile: Run m-code using m-file (default=True)
 paths: Paths to add to matlabpath
 quality: 0.1 = fast, 1.0 = precise
 register_to_mean: Indicate whether realignment is done to the mean image
 separation: sampling separation in mm
 weight_img: filename of weighting image
 wrap: Check if interpolation should wrap in [x,y,z]
 write_interp: degree of b-spline used for interpolation
 write_mask: True/False mask output image
 write_which: determines which images to reslice
 write_wrap: Check if interpolation should wrap in [x,y,z]

mean_image: Mean image file from the realignment
realigned_files: Realigned files
realignment_parameters: Estimated translation and rotation parameters

Our Interfaces and Algorithms documentation provides html versions of our docstrings and includes links to the specific package documentation. For instance, the nipype.interfaces.fsl.Bet docstring has a direct link to the online BET Documentation.

FSL interface example

Using FSL to realign a time_series:

import nipype.interfaces.fsl as fsl
realigner = fsl.McFlirt()
result = realigner.run()

SPM interface example

Using SPM to realign a time-series:

import nipype.interfaces.spm as spm
from glob import glob
allepi = glob('epi*.nii') # this will return an unsorted list
realigner = spm.Realign()
realigner.inputs.in_files = allepi
result = realigner.run()