dipy logo

Site Navigation

NIPY Community

Previous topic

dipy.boots.resampling

Next topic

dipy external

dipy data

Read test or example data

dipy.data.get_data(name='small_64D')

provides filenames of some test datasets or other useful parametrisations

Parameters:

name : str

the filename/s of which dataset to return, one of: ‘small_64D’ small region of interest nifti,bvecs,bvals 64 directions ‘small_101D’ small region of interest nifti,bvecs,bvals 101 directions ‘aniso_vox’ volume with anisotropic voxel size as Nifti ‘fornix’ 300 tracks in Trackvis format (from Pittsburgh Brain Competition) ‘gqi_vectors’ the scanner wave vectors needed for a GQI acquisitions of 101 directions tested on Siemens 3T Trio ‘small_25’ small ROI (10x8x2) DTI data (b value 2000, 25 directions)

Returns:

fnames : tuple

filenames for dataset

Examples

>>> import numpy as np
>>> from dipy.data import get_data
>>> fimg,fbvals,fbvecs=get_data('small_101D')
>>> bvals=np.loadtxt(fbvals)
>>> bvecs=np.loadtxt(fbvecs).T
>>> import nibabel as nib
>>> img=nib.load(fimg)
>>> data=img.get_data()
>>> data.shape
(6, 10, 10, 102)
>>> bvals.shape
(102,)
>>> bvecs.shape
(102, 3)
dipy.data.get_sim_voxels(name='fib1')

provide some simulated voxel data

Parameters:

name : str, which file?

‘fib0’, ‘fib1’ or ‘fib2’

Returns:

dix : dictionary, where dix[‘data’] returns a 2d array

where every row is a simulated voxel with different orientation

Notes

These sim voxels were provided by M.M. Correia using Rician noise.

Examples

>>> from dipy.data import get_sim_voxels
>>> sv=get_sim_voxels('fib1')
>>> sv['data'].shape
(100, 102)
>>> sv['fibres']
'1'
>>> sv['gradients'].shape
(102, 3)
>>> sv['bvals'].shape
(102,)
>>> sv['snr']
'60'
>>> sv2=get_sim_voxels('fib2')
>>> sv2['fibres']
'2'
>>> sv2['snr']
'80'
dipy.data.get_skeleton(name='C1')

provide skeletons generated from Local Skeleton Clustering (LSC)

Parameters:name : str, ‘C1’ or ‘C3’
Returns:dix : dictionary

Examples

>>> from dipy.data import get_skeleton
>>> C=get_skeleton('C1')
>>> len(C.keys())
117
>>> for c in C: break
>>> sorted(C[c].keys())
['N', 'hidden', 'indices', 'most']
dipy.data.get_sphere(name='symmetric362')

provide triangulated spheres

Parameters:

name : str

which sphere - one of: * ‘symmetric362’ * ‘symmetric642’ * ‘symmetric724’

Returns:

sphere : a dipy.core.sphere.Sphere class instance

Examples

>>> import numpy as np
>>> from dipy.data import get_sphere
>>> sphere = get_sphere('symmetric362')
>>> verts, faces = sphere.vertices, sphere.faces
>>> verts.shape
(362, 3)
>>> faces.shape
(720, 3)
>>> verts, faces = get_sphere('not a sphere name') 
Traceback (most recent call last):
    ...
DataError: No sphere called "not a sphere name"
dipy.data.mrtrix_spherical_functions()

Spherical functions represented by spherical harmonic coefficients and evaluated on a discrete sphere.

Returns:

func_coef : array (2, 3, 4, 45)

Functions represented by the coefficients associated with the mxtrix spherical harmonic basis of order 8.

func_discrete : array (2, 3, 4, 81)

Functions evaluated on sphere.

sphere : Sphere

The discrete sphere, points on the surface of a unit sphere, used to evaluate the functions.

Notes

These coefficients were obtained by using the dwi2SH command of mrtrix.