Nifti objects data type

Another option for plotting and importing/exporting data is using the Nifti objects. Nifti objects are a subclass of the neuroimaging format Nibabel Nifti1Image, which is a file that generally has the extension “.nii” or “.nii.gz”. This allows the user to use our methods with the Nifti class but also use other functionality such as Nibabel and Nilearn methods.

Load in the required libraries

import warnings
warnings.simplefilter("ignore")
%matplotlib inline
import supereeg as se

First, let’s load in an example nifti file, example_nifti:

nii = se.load('example_nifti')

Initialize nifti objects

Nifti objects can be initialized by passing any of the following to the Nifti class instance initialization function: - A path to a nifti file (ending in .nii or .nii.gz) - An existing Nifti object (this makes a copy of the object) - A path to any other toolbox-supported datatype, or an instance of another supported datatype (Brain or Model objects)

You may also initialize a Nifti object using the load function by specifying return_type='nii'.

For example:

bo_nii = se.Nifti('example_data')

Or:

bo_nii = se.load('example_data', return_type='nii')

Spatial resampling

Any Nifti object may be quickly resampled to an arbitrary voxel size using the vox_size argument. The voxel sizes may be specified either as a scalar (for cubic voxels) or as a 3D tuple (for rectangular prism or parallelopiped voxels):

bo_nii = se.Nifti('example_data', vox_size=6)

Nifti object methods

Some useful methods on a nifti object:

nifti.info()

This method will give you a summary of the nifti object:

nii.info()

Header: <class 'nibabel.nifti1.Nifti1Header'> object, endian='<'
sizeof_hdr      : 348
data_type       :
db_name         :
extents         : 0
session_error   : 0
regular         :
dim_info        : 0
dim             : [  4  30  36  30 500   1   1   1]
intent_p1       : 0.0
intent_p2       : 0.0
intent_p3       : 0.0
intent_code     : none
datatype        : float64
bitpix          : 64
slice_start     : 0
pixdim          : [1. 6. 6. 6. 1. 1. 1. 1.]
vox_offset      : 0.0
scl_slope       : nan
scl_inter       : nan
slice_end       : 0
slice_code      : unknown
xyzt_units      : 0
cal_max         : 0.0
cal_min         : 0.0
slice_duration  : 0.0
toffset         : 0.0
glmax           : 0
glmin           : 0
descrip         :
aux_file        :
qform_code      : unknown
sform_code      : aligned
quatern_b       : 0.0
quatern_c       : 0.0
quatern_d       : 0.0
qoffset_x       : -88.0
qoffset_y       : -124.0
qoffset_z       : -70.0
srow_x          : [  6.   0.   0. -88.]
srow_y          : [   0.    6.    0. -124.]
srow_z          : [  0.   0.   6. -70.]
intent_name     :
magic           : n+1

nifti.get_slice()

This method allows you to slice out images from your nifti object, and returns the indexed nifti.

nii_sliced = bo_nii.get_slice(index=[0,1,2])

nifti.plot_glass_brain()

This method will plot your nifti object.

This method wraps nilearn.plot_glass_brain to plot the nifti object, so any arguments that nilearn.plot_glass_brain accepts are supported by nifti.plot_glass_brain.

nii_sliced.plot_glass_brain()
../_images/nifti_objects_17_0.png

nifti.plot_anat()

This method will plot your nifti object.

This method wraps nilearn.plot_anat to plot the nifti object, so any arguments that nilearn.plot_anat accepts are supported by nifti.anat. For example, you can plot the example nifti:

nii.plot_anat()
../_images/nifti_objects_19_0.png

nifti.make_gif()

This method will plot 4D nifti data as nilearn.plot_glass_brain, save as png files, and compile the files as gif.

This method wraps nilearn.plot_glass_brain to plot the nifti object, so any arguments that nilearn.plot_glass_brain accepts are supported by nifti.plot_glass_brain.

#nii.make_gif(gifpath='/path/to/save/gif', index=range(0, 10), name=None, **kwargs)

nifti.save()

This method will save your nifti object to the specified filepath location as a ‘nii’ file.

#nii.save(filepath='/path/to/save/nifti')