Documentation 1.6.0. > API Reference > data

data
- Module: data.fetcher

`data`

Read test or example data.

`DataError`
`GradientTable`(gradients[, big_delta, ...])	Diffusion gradient information
`HemiSphere`([x, y, z, theta, phi, xyz, ...])	Points on the unit sphere.
`Sphere`([x, y, z, theta, phi, xyz, faces, edges])	Points on the unit sphere.
`as_native_array`(arr)	Return arr as native byteordered array
`dirname`(p)	Returns the directory component of a pathname
`dsi_deconv_voxels`()
`dsi_voxels`()
`exists`(path)	Test whether a path exists.
`fetch_bundle_atlas_hcp842`()	Download atlas tractogram from the hcp842 dataset with 80 bundles
`fetch_bundle_fa_hcp`()	Download map of FA within two bundles in oneof the hcp dataset subjects
`fetch_bundles_2_subjects`()	Download 2 subjects from the SNAIL dataset with their bundles
`fetch_cenir_multib`([with_raw])	Fetch 'HCP-like' data, collected at multiple b-values.
`fetch_cfin_multib`()	Download CFIN multi b-value diffusion data
`fetch_gold_standard_io`()	Downloads the gold standard for streamlines io testing.
`fetch_hbn`(subjects[, path])	Fetch preprocessed data from the Healthy Brain Network POD2 study [1, 2]_.
`fetch_isbi2013_2shell`()	Download a 2-shell software phantom dataset
`fetch_ivim`()	Download IVIM dataset
`fetch_mni_template`()	fetch the MNI 2009a T1 and T2, and 2009c T1 and T1 mask files Notes ----- The templates were downloaded from the MNI (McGill University) website in July 2015.
`fetch_resdnn_weights`()	Download ResDNN model weights for Nath et.
`fetch_scil_b0`()	Download b=0 datasets from multiple MR systems (GE, Philips, Siemens) and different magnetic fields (1.5T and 3T)
`fetch_sherbrooke_3shell`()	Download a 3shell HARDI dataset with 192 gradient direction
`fetch_stanford_hardi`()	Download a HARDI dataset with 160 gradient directions
`fetch_stanford_labels`()	Download reduced freesurfer aparc image from stanford web site
`fetch_stanford_pve_maps`()
`fetch_stanford_t1`()
`fetch_syn_data`()	Download t1 and b0 volumes from the same session
`fetch_taiwan_ntu_dsi`()	Download a DSI dataset with 203 gradient directions
`fetch_target_tractogram_hcp`()	Download tractogram of one of the hcp dataset subjects
`fetch_tissue_data`()	Download images to be used for tissue classification
`get_3shell_gtab`()
`get_bundle_atlas_hcp842`()	Returns:
`get_cmap`(name)	Make a callable, similar to maptlotlib.pyplot.get_cmap.
`get_fnames`([name])	Provide full paths to example or test datasets.
`get_gtab_taiwan_dsi`()
`get_isbi2013_2shell_gtab`()
`get_sim_voxels`([name])	provide some simulated voxel data
`get_skeleton`([name])	Provide skeletons generated from Local Skeleton Clustering (LSC).
`get_sphere`([name])	provide triangulated spheres
`get_target_tractogram_hcp`()	Returns:
`get_two_hcp842_bundles`()	Returns:
`gradient_table`(bvals[, bvecs, big_delta, ...])	A general function for creating diffusion MR gradients.
`load_nifti`(fname[, return_img, ...])	Load data and other information from a nifti file.
`load_npz`(file)	Load a sparse matrix from a file using `.npz` format.
`load_sdp_constraints`(model_name[, order])	Import semidefinite programming constraint matrices for different models, generated as described for example in [1].
`loads_compat`(byte_data)
`matlab_life_results`()
`mrtrix_spherical_functions`()	Spherical functions represented by spherical harmonic coefficients and evaluated on a discrete sphere.
`pjoin`(a, *p)	Join two or more pathname components, inserting '/' as needed.
`read_DiB_217_lte_pte_ste`()	Read q-space trajectory encoding data with 217 between linear, planar, and spherical tensor encoding.
`read_DiB_70_lte_pte_ste`()	Read q-space trajectory encoding data with 70 between linear, planar, and spherical tensor encoding measurements.
`read_bundles_2_subjects`([subj_id, metrics, ...])	Read images and streamlines from 2 subjects of the SNAIL dataset.
`read_cenir_multib`([bvals])	Read CENIR multi b-value data.
`read_cfin_dwi`()	Load CFIN multi b-value DWI data.
`read_cfin_t1`()	Load CFIN T1-weighted data.
`read_five_af_bundles`()	Load 5 small left arcuate fasciculus bundles.
`read_isbi2013_2shell`()	Load ISBI 2013 2-shell synthetic dataset.
`read_ivim`()	Load IVIM dataset.
`read_mni_template`([version, contrast])	Read the MNI template from disk.
`read_qte_lte_pte`()	Read q-space trajectory encoding data with linear and planar tensor encoding.
`read_scil_b0`()	Load GE 3T b0 image form the scil b0 dataset.
`read_sherbrooke_3shell`()	Load Sherbrooke 3-shell HARDI dataset.
`read_stanford_hardi`()	Load Stanford HARDI dataset.
`read_stanford_labels`()	Read stanford hardi data and label map.
`read_stanford_pve_maps`()
`read_stanford_t1`()
`read_syn_data`()	Load t1 and b0 volumes from the same session.
`read_taiwan_ntu_dsi`()	Load Taiwan NTU dataset.
`read_tissue_data`([contrast])	Load images to be used for tissue classification
`relist_streamlines`(points, offsets)	Given a representation of a set of streamlines as a large array and an offsets array return the streamlines as a list of shorter arrays.
`two_cingulum_bundles`()

Module: `data.fetcher`

`FetcherError`
`TripWire`(msg)	Class raising error if used
`tqdm`	alias of `tqdm_asyncio`
`check_md5`(filename[, stored_md5])	Computes the md5 of filename and check if it matches with the supplied string md5
`copyfileobj`(fsrc, fdst[, length])	copy data from file-like object fsrc to file-like object fdst
`copyfileobj_withprogress`(fsrc, fdst, ...[, ...])
`extract_example_tracts`(out_dir)	Extract 5 'AF_L','CST_R' and 'CC_ForcepsMajor' trk files in out_dir folder.
`fetch_DiB_217_lte_pte_ste`()	Download QTE data with linear, planar, and spherical tensor encoding.
`fetch_DiB_70_lte_pte_ste`()	Download QTE data with linear, planar, and spherical tensor encoding.
`fetch_bundle_atlas_hcp842`()	Download atlas tractogram from the hcp842 dataset with 80 bundles
`fetch_bundle_fa_hcp`()	Download map of FA within two bundles in oneof the hcp dataset subjects
`fetch_bundles_2_subjects`()	Download 2 subjects from the SNAIL dataset with their bundles
`fetch_cenir_multib`([with_raw])	Fetch 'HCP-like' data, collected at multiple b-values.
`fetch_cfin_multib`()	Download CFIN multi b-value diffusion data
`fetch_data`(files, folder[, data_size])	Downloads files to folder and checks their md5 checksums
`fetch_file_formats`()	Download 5 bundles in various file formats and their reference
`fetch_fury_surface`()	Surface for testing and examples
`fetch_gold_standard_io`()	Downloads the gold standard for streamlines io testing.
`fetch_hbn`(subjects[, path])	Fetch preprocessed data from the Healthy Brain Network POD2 study [1, 2]_.
`fetch_hcp`(subjects[, hcp_bucket, ...])	Fetch HCP diffusion data and arrange it in a manner that resembles the BIDS [1] specification.
`fetch_isbi2013_2shell`()	Download a 2-shell software phantom dataset
`fetch_ivim`()	Download IVIM dataset
`fetch_mni_template`()	fetch the MNI 2009a T1 and T2, and 2009c T1 and T1 mask files Notes ----- The templates were downloaded from the MNI (McGill University) website in July 2015.
`fetch_qtdMRI_test_retest_2subjects`()	Downloads test-retest qt-dMRI acquisitions of two C57Bl6 mice.
`fetch_qte_lte_pte`()	Download QTE data with linear and planar tensor encoding.
`fetch_resdnn_weights`()	Download ResDNN model weights for Nath et.
`fetch_scil_b0`()	Download b=0 datasets from multiple MR systems (GE, Philips, Siemens) and different magnetic fields (1.5T and 3T)
`fetch_sherbrooke_3shell`()	Download a 3shell HARDI dataset with 192 gradient direction
`fetch_stanford_hardi`()	Download a HARDI dataset with 160 gradient directions
`fetch_stanford_labels`()	Download reduced freesurfer aparc image from stanford web site
`fetch_stanford_pve_maps`()
`fetch_stanford_t1`()
`fetch_syn_data`()	Download t1 and b0 volumes from the same session
`fetch_taiwan_ntu_dsi`()	Download a DSI dataset with 203 gradient directions
`fetch_target_tractogram_hcp`()	Download tractogram of one of the hcp dataset subjects
`fetch_tissue_data`()	Download images to be used for tissue classification
`get_bundle_atlas_hcp842`()	Returns:
`get_file_formats`()	Returns:
`get_fnames`([name])	Provide full paths to example or test datasets.
`get_target_tractogram_hcp`()	Returns:
`get_two_hcp842_bundles`()	Returns:
`gradient_table`(bvals[, bvecs, big_delta, ...])	A general function for creating diffusion MR gradients.
`gradient_table_from_gradient_strength_bvecs`(...)	A general function for creating diffusion MR gradients.
`load_nifti`(fname[, return_img, ...])	Load data and other information from a nifti file.
`load_nifti_data`(fname[, as_ndarray])	Load only the data array from a nifti file.
`load_trk`(filename, reference[, to_space, ...])	Load the stateful tractogram of the .trk format
`md5`(/[, string, usedforsecurity])	Returns a md5 hash object; optionally initialized with a string
`optional_package`(name[, trip_msg])	Return package-like thing and module setup for package name
`pjoin`(a, *p)	Join two or more pathname components, inserting '/' as needed.
`read_DiB_217_lte_pte_ste`()	Read q-space trajectory encoding data with 217 between linear, planar, and spherical tensor encoding.
`read_DiB_70_lte_pte_ste`()	Read q-space trajectory encoding data with 70 between linear, planar, and spherical tensor encoding measurements.
`read_bundles_2_subjects`([subj_id, metrics, ...])	Read images and streamlines from 2 subjects of the SNAIL dataset.
`read_bvals_bvecs`(fbvals, fbvecs)	Read b-values and b-vectors from disk.
`read_cenir_multib`([bvals])	Read CENIR multi b-value data.
`read_cfin_dwi`()	Load CFIN multi b-value DWI data.
`read_cfin_t1`()	Load CFIN T1-weighted data.
`read_five_af_bundles`()	Load 5 small left arcuate fasciculus bundles.
`read_isbi2013_2shell`()	Load ISBI 2013 2-shell synthetic dataset.
`read_ivim`()	Load IVIM dataset.
`read_mni_template`([version, contrast])	Read the MNI template from disk.
`read_qtdMRI_test_retest_2subjects`()	Load test-retest qt-dMRI acquisitions of two C57Bl6 mice.
`read_qte_lte_pte`()	Read q-space trajectory encoding data with linear and planar tensor encoding.
`read_scil_b0`()	Load GE 3T b0 image form the scil b0 dataset.
`read_sherbrooke_3shell`()	Load Sherbrooke 3-shell HARDI dataset.
`read_siemens_scil_b0`()	Load Siemens 1.5T b0 image from the scil b0 dataset.
`read_stanford_hardi`()	Load Stanford HARDI dataset.
`read_stanford_labels`()	Read stanford hardi data and label map.
`read_stanford_pve_maps`()
`read_stanford_t1`()
`read_syn_data`()	Load t1 and b0 volumes from the same session.
`read_taiwan_ntu_dsi`()	Load Taiwan NTU dataset.
`read_tissue_data`([contrast])	Load images to be used for tissue classification
`save_nifti`(fname, data, affine[, hdr, dtype])	Save a data array into a nifti file.
`to_bids_description`(path[, fname, BIDSVersion])	Dumps a dict into a bids description at the given location
`urlopen`(url[, data, timeout, cafile, ...])	Open the URL url, which can be either a string or a Request object.

`DataError`

class dipy.data.DataError

Bases: Exception

Attributes:

args

Methods

with_traceback

Exception.with_traceback(tb) -- set self.__traceback__ to tb and return self.

__init__(*args, **kwargs)

`GradientTable`

class dipy.data.GradientTable(gradients, big_delta=None, small_delta=None, b0_threshold=50, btens=None)

Bases: object

Diffusion gradient information

Parameters:

gradientsarray_like (N, 3): Diffusion gradients. The direction of each of these vectors corresponds to the b-vector, and the length corresponds to the b-value.
b0_thresholdfloat: Gradients with b-value less than or equal to b0_threshold are considered as b0s i.e. without diffusion weighting.

See also

gradient_table

Notes

The GradientTable object is immutable. Do NOT assign attributes. If you have your gradient table in a bval & bvec format, we recommend using the factory function gradient_table

Attributes:

gradients(N,3) ndarray: diffusion gradients
bvals(N,) ndarray: The b-value, or magnitude, of each gradient direction.
qvals: (N,) ndarray: The q-value for each gradient direction. Needs big and small delta.
bvecs(N,3) ndarray: The direction, represented as a unit vector, of each gradient.
b0s_mask(N,) ndarray: Boolean array indicating which gradients have no diffusion weighting, ie b-value is close to 0.
b0_thresholdfloat: Gradients with b-value less than or equal to b0_threshold are considered to not have diffusion weighting.
btens(N,3,3) ndarray: The b-tensor of each gradient direction.

Methods

b0s_mask
bvals
bvecs
gradient_strength
qvals
tau

__init__(gradients, big_delta=None, small_delta=None, b0_threshold=50, btens=None): Constructor for GradientTable class

b0s_mask()

bvals()

bvecs()

gradient_strength()

property info

qvals()

tau()

`HemiSphere`

class dipy.data.HemiSphere(x=None, y=None, z=None, theta=None, phi=None, xyz=None, faces=None, edges=None, tol=1e-05)

Bases: Sphere

Points on the unit sphere.

A HemiSphere is similar to a Sphere but it takes antipodal symmetry into account. Antipodal symmetry means that point v on a HemiSphere is the same as the point -v. Duplicate points are discarded when constructing a HemiSphere (including antipodal duplicates). edges and faces are remapped to the remaining points as closely as possible.

The HemiSphere can be constructed using one of three conventions:

HemiSphere(x, y, z)
HemiSphere(xyz=xyz)
HemiSphere(theta=theta, phi=phi)

Parameters:

x, y, z1-D array_like: Vertices as x-y-z coordinates.
theta, phi1-D array_like: Vertices as spherical coordinates. Theta and phi are the inclination and azimuth angles respectively.
xyz(N, 3) ndarray: Vertices as x-y-z coordinates.
faces(N, 3) ndarray: Indices into vertices that form triangular faces. If unspecified, the faces are computed using a Delaunay triangulation.
edges(N, 2) ndarray: Edges between vertices. If unspecified, the edges are derived from the faces.
tolfloat: Angle in degrees. Vertices that are less than tol degrees apart are treated as duplicates.

See also

Sphere

Attributes:

x
y
z

Methods

`find_closest`(xyz)	Find the index of the vertex in the Sphere closest to the input vector, taking into account antipodal symmetry
`from_sphere`(sphere[, tol])	Create instance from a Sphere
`mirror`()	Create a full Sphere from a HemiSphere
`subdivide`([n])	Create a more subdivided HemiSphere

edges
faces
vertices

__init__(x=None, y=None, z=None, theta=None, phi=None, xyz=None, faces=None, edges=None, tol=1e-05): Create a HemiSphere from points

faces()

find_closest(xyz)

Find the index of the vertex in the Sphere closest to the input vector, taking into account antipodal symmetry

Parameters:

xyzarray-like, 3 elements: A unit vector

Returns:

idxint: The index into the Sphere.vertices array that gives the closest vertex (in angle).

classmethod from_sphere(sphere, tol=1e-05): Create instance from a Sphere

mirror(): Create a full Sphere from a HemiSphere

subdivide(n=1)

Create a more subdivided HemiSphere

See Sphere.subdivide for full documentation.

`Sphere`

class dipy.data.Sphere(x=None, y=None, z=None, theta=None, phi=None, xyz=None, faces=None, edges=None)

Bases: object

Points on the unit sphere.

The sphere can be constructed using one of three conventions:

Sphere(x, y, z)
Sphere(xyz=xyz)
Sphere(theta=theta, phi=phi)

Parameters:

x, y, z1-D array_like: Vertices as x-y-z coordinates.
theta, phi1-D array_like: Vertices as spherical coordinates. Theta and phi are the inclination and azimuth angles respectively.
xyz(N, 3) ndarray: Vertices as x-y-z coordinates.
faces(N, 3) ndarray: Indices into vertices that form triangular faces. If unspecified, the faces are computed using a Delaunay triangulation.
edges(N, 2) ndarray: Edges between vertices. If unspecified, the edges are derived from the faces.

Attributes:

x
y
z

Methods

`find_closest`(xyz)	Find the index of the vertex in the Sphere closest to the input vector
`subdivide`([n])	Subdivides each face of the sphere into four new faces.

edges
faces
vertices

__init__(x=None, y=None, z=None, theta=None, phi=None, xyz=None, faces=None, edges=None)

edges()

faces()

find_closest(xyz)

Find the index of the vertex in the Sphere closest to the input vector

Parameters:

xyzarray-like, 3 elements: A unit vector

Returns:

idxint: The index into the Sphere.vertices array that gives the closest vertex (in angle).

subdivide(n=1)

Subdivides each face of the sphere into four new faces.

New vertices are created at a, b, and c. Then each face [x, y, z] is divided into faces [x, a, c], [y, a, b], [z, b, c], and [a, b, c].

      y
      /\
     /  \
   a/____\b
   /\    /\
  /  \  /  \
 /____\/____\
x      c     z

Parameters:

nint, optional: The number of subdivisions to preform.

Returns:

new_sphereSphere: The subdivided sphere.

vertices()

property x

property y

property z

as_native_array

dipy.data.as_native_array(arr)

Return arr as native byteordered array

If arr is already native byte ordered, return unchanged. If it is opposite endian, then make a native byte ordered copy and return that

Parameters:

arrndarray

Returns:

native_arrndarray: If arr was native order, this is just arr. Otherwise it’s a new array such that np.all(native_arr == arr), with native byte ordering.

dirname

dipy.data.dirname(p): Returns the directory component of a pathname

dsi_deconv_voxels

dipy.data.dsi_deconv_voxels()

dsi_voxels

dipy.data.dsi_voxels()

exists

dipy.data.exists(path): Test whether a path exists. Returns False for broken symbolic links

fetch_bundle_atlas_hcp842

dipy.data.fetch_bundle_atlas_hcp842(): Download atlas tractogram from the hcp842 dataset with 80 bundles

fetch_bundle_fa_hcp

dipy.data.fetch_bundle_fa_hcp(): Download map of FA within two bundles in oneof the hcp dataset subjects

fetch_bundles_2_subjects

dipy.data.fetch_bundles_2_subjects(): Download 2 subjects from the SNAIL dataset with their bundles

fetch_cenir_multib

dipy.data.fetch_cenir_multib(with_raw=False)

Fetch ‘HCP-like’ data, collected at multiple b-values.

Parameters:

with_rawbool: Whether to fetch the raw data. Per default, this is False, which means that only eddy-current/motion corrected data is fetched

Notes

Details of the acquisition and processing, and additional meta-data are available through UW researchworks:

https://digital.lib.washington.edu/researchworks/handle/1773/33311

fetch_cfin_multib

dipy.data.fetch_cfin_multib(): Download CFIN multi b-value diffusion data

fetch_gold_standard_io

dipy.data.fetch_gold_standard_io(): Downloads the gold standard for streamlines io testing.

fetch_hbn

dipy.data.fetch_hbn(subjects, path=None)

Fetch preprocessed data from the Healthy Brain Network POD2 study [1, 2]_.

Parameters:

subjectslist: Identifiers of the subjects to download. For example: [“NDARAA948VFH”, “NDAREK918EC2”].
pathstring, optional: Path to save files into. Default: ‘~/.dipy’

Returns:

dict with remote and local names of these files,
path to BIDS derivative dataset

Notes

[1]

Alexander LM, Escalera J, Ai L, et al. An open resource for transdiagnostic research in pediatric mental health and learning disorders. Sci Data. 2017;4:170181.

[2]

Richie-Halford A, Cieslak M, Ai L, et al. An analysis-ready and quality controlled resource for pediatric brain white-matter research. Scientific Data. 2022;9(1):1-27.

fetch_isbi2013_2shell

dipy.data.fetch_isbi2013_2shell(): Download a 2-shell software phantom dataset

fetch_ivim

dipy.data.fetch_ivim(): Download IVIM dataset

fetch_mni_template

dipy.data.fetch_mni_template()

fetch the MNI 2009a T1 and T2, and 2009c T1 and T1 mask files Notes —– The templates were downloaded from the MNI (McGill University) website in July 2015.

The following publications should be referenced when using these templates:

[1]

VS Fonov, AC Evans, K Botteron, CR Almli, RC McKinstry, DL Collins and BDCG, Unbiased average age-appropriate atlases for pediatric studies, NeuroImage, 54:1053-8119, DOI: 10.1016/j.neuroimage.2010.07.033

[2]

VS Fonov, AC Evans, RC McKinstry, CR Almli and DL Collins, Unbiased nonlinear average age-appropriate brain templates from birth to adulthood, NeuroImage, 47:S102 Organization for Human Brain Mapping 2009 Annual Meeting, DOI: https://doi.org/10.1016/S1053-8119(09)70884-5

License for the MNI templates:

Copyright (C) 1993-2004, Louis Collins McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University. Permission to use, copy, modify, and distribute this software and its documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appear in all copies. The authors and McGill University make no representations about the suitability of this software for any purpose. It is provided “as is” without express or implied warranty. The authors are not responsible for any data loss, equipment damage, property loss, or injury to subjects or patients resulting from the use or misuse of this software package.

fetch_resdnn_weights

dipy.data.fetch_resdnn_weights(): Download ResDNN model weights for Nath et. al 2018

fetch_scil_b0

dipy.data.fetch_scil_b0(): Download b=0 datasets from multiple MR systems (GE, Philips, Siemens) and different magnetic fields (1.5T and 3T)

fetch_sherbrooke_3shell

dipy.data.fetch_sherbrooke_3shell(): Download a 3shell HARDI dataset with 192 gradient direction

fetch_stanford_hardi

dipy.data.fetch_stanford_hardi(): Download a HARDI dataset with 160 gradient directions

fetch_stanford_labels

dipy.data.fetch_stanford_labels(): Download reduced freesurfer aparc image from stanford web site

fetch_stanford_pve_maps

dipy.data.fetch_stanford_pve_maps()

fetch_stanford_t1

dipy.data.fetch_stanford_t1()

fetch_syn_data

dipy.data.fetch_syn_data(): Download t1 and b0 volumes from the same session

fetch_taiwan_ntu_dsi

dipy.data.fetch_taiwan_ntu_dsi(): Download a DSI dataset with 203 gradient directions

fetch_target_tractogram_hcp

dipy.data.fetch_target_tractogram_hcp(): Download tractogram of one of the hcp dataset subjects

fetch_tissue_data

dipy.data.fetch_tissue_data(): Download images to be used for tissue classification

get_3shell_gtab

dipy.data.get_3shell_gtab()

get_bundle_atlas_hcp842

dipy.data.get_bundle_atlas_hcp842()

Returns:

file1string
file2string

get_cmap

dipy.data.get_cmap(name): Make a callable, similar to maptlotlib.pyplot.get_cmap.

get_fnames

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

Provide full paths to example or test datasets.

Parameters:

namestr

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)
‘test_piesno’ slice of N=8, K=14 diffusion data
‘reg_c’ small 2D image used for validating registration
‘reg_o’ small 2D image used for validation registration
‘cb_2’ two vectorized cingulum bundles

Returns:

fnamestuple: filenames for dataset

Examples

>>> import numpy as np
>>> from dipy.io.image import load_nifti
>>> from dipy.data import get_fnames
>>> fimg, fbvals, fbvecs = get_fnames('small_101D')
>>> bvals=np.loadtxt(fbvals)
>>> bvecs=np.loadtxt(fbvecs).T
>>> data, affine = load_nifti(fimg)
>>> data.shape == (6, 10, 10, 102)
True
>>> bvals.shape == (102,)
True
>>> bvecs.shape == (102, 3)
True

get_gtab_taiwan_dsi

dipy.data.get_gtab_taiwan_dsi()

get_isbi2013_2shell_gtab

dipy.data.get_isbi2013_2shell_gtab()

get_sim_voxels

dipy.data.get_sim_voxels(name='fib1')

provide some simulated voxel data

Parameters:

namestr, which file?: ‘fib0’, ‘fib1’ or ‘fib2’

Returns:

dixdictionary, 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)
True
>>> sv['fibres']
'1'
>>> sv['gradients'].shape == (102, 3)
True
>>> sv['bvals'].shape == (102,)
True
>>> sv['snr']
'60'
>>> sv2=get_sim_voxels('fib2')
>>> sv2['fibres']
'2'
>>> sv2['snr']
'80'

get_skeleton

dipy.data.get_skeleton(name='C1')

Provide skeletons generated from Local Skeleton Clustering (LSC).

Parameters:

namestr, ‘C1’ or ‘C3’

Returns:

dixdictionary

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']

get_sphere

dipy.data.get_sphere(name='symmetric362')

provide triangulated spheres

Parameters:

namestr: which sphere - one of: * ‘symmetric362’ * ‘symmetric642’ * ‘symmetric724’ * ‘repulsion724’ * ‘repulsion100’ * ‘repulsion200’

Returns:

spherea 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)
True
>>> faces.shape == (720, 3)
True
>>> verts, faces = get_sphere('not a sphere name') 
Traceback (most recent call last):
    ...
DataError: No sphere called "not a sphere name"

get_target_tractogram_hcp

dipy.data.get_target_tractogram_hcp()

Returns:

file1string

get_two_hcp842_bundles

dipy.data.get_two_hcp842_bundles()

Returns:

file1string
file2string

gradient_table

dipy.data.gradient_table(bvals, bvecs=None, big_delta=None, small_delta=None, b0_threshold=50, atol=0.01, btens=None)

A general function for creating diffusion MR gradients.

It reads, loads and prepares scanner parameters like the b-values and b-vectors so that they can be useful during the reconstruction process.

Parameters:

bvalscan be any of the four options

an array of shape (N,) or (1, N) or (N, 1) with the b-values.
a path for the file which contains an array like the above (1).
an array of shape (N, 4) or (4, N). Then this parameter is considered to be a b-table which contains both bvals and bvecs. In this case the next parameter is skipped.
a path for the file which contains an array like the one at (3).

bvecscan be any of two options

an array of shape (N, 3) or (3, N) with the b-vectors.
a path for the file which contains an array like the previous.

big_deltafloat

acquisition pulse separation time in seconds (default None)

small_deltafloat

acquisition pulse duration time in seconds (default None)

b0_thresholdfloat

All b-values with values less than or equal to bo_threshold are considered as b0s i.e. without diffusion weighting.

atolfloat

All b-vectors need to be unit vectors up to a tolerance.

btenscan be any of three options

a string specifying the shape of the encoding tensor for all volumes in data. Options: ‘LTE’, ‘PTE’, ‘STE’, ‘CTE’ corresponding to linear, planar, spherical, and “cigar-shaped” tensor encoding. Tensors are rotated so that linear and cigar tensors are aligned with the corresponding gradient direction and the planar tensor’s normal is aligned with the corresponding gradient direction. Magnitude is scaled to match the b-value.
an array of strings of shape (N,), (N, 1), or (1, N) specifying encoding tensor shape for each volume separately. N corresponds to the number volumes in data. Options for elements in array: ‘LTE’, ‘PTE’, ‘STE’, ‘CTE’ corresponding to linear, planar, spherical, and “cigar-shaped” tensor encoding. Tensors are rotated so that linear and cigar tensors are aligned with the corresponding gradient direction and the planar tensor’s normal is aligned with the corresponding gradient direction. Magnitude is scaled to match the b-value.
an array of shape (N,3,3) specifying the b-tensor of each volume exactly. N corresponds to the number volumes in data. No rotation or scaling is performed.

Returns:

gradientsGradientTable: A GradientTable with all the gradient information.

Notes

Often b0s (b-values which correspond to images without diffusion weighting) have 0 values however in some cases the scanner cannot provide b0s of an exact 0 value and it gives a bit higher values e.g. 6 or 12. This is the purpose of the b0_threshold in the __init__.
We assume that the minimum number of b-values is 7.
B-vectors should be unit vectors.

Examples

>>> from dipy.core.gradients import gradient_table
>>> bvals = 1500 * np.ones(7)
>>> bvals[0] = 0
>>> sq2 = np.sqrt(2) / 2
>>> bvecs = np.array([[0, 0, 0],
...                   [1, 0, 0],
...                   [0, 1, 0],
...                   [0, 0, 1],
...                   [sq2, sq2, 0],
...                   [sq2, 0, sq2],
...                   [0, sq2, sq2]])
>>> gt = gradient_table(bvals, bvecs)
>>> gt.bvecs.shape == bvecs.shape
True
>>> gt = gradient_table(bvals, bvecs.T)
>>> gt.bvecs.shape == bvecs.T.shape
False

load_nifti

dipy.data.load_nifti(fname, return_img=False, return_voxsize=False, return_coords=False, as_ndarray=True)

Load data and other information from a nifti file.

Parameters:

fnamestr: Full path to a nifti file.
return_imgbool, optional: Whether to return the nibabel nifti img object. Default: False
return_voxsize: bool, optional: Whether to return the nifti header zooms. Default: False
return_coordsbool, optional: Whether to return the nifti header aff2axcodes. Default: False
as_ndarray: bool, optional: convert nibabel ArrayProxy to a numpy.ndarray. If you want to save memory and delay this casting, just turn this option to False (default: True)

Returns:

A tuple, with (at the most, if all keyword args are set to True):
(data, img.affine, img, vox_size, nib.aff2axcodes(img.affine))

See also

load_nifti_data

load_npz

dipy.data.load_npz(file)

Load a sparse matrix from a file using .npz format.

Parameters:

filestr or file-like object: Either the file name (string) or an open file (file-like object) where the data will be loaded.

Returns:

resultcsc_matrix, csr_matrix, bsr_matrix, dia_matrix or coo_matrix: A sparse matrix containing the loaded data.

Raises:

OSError: If the input file does not exist or cannot be read.

See also

scipy.sparse.save_npz: Save a sparse matrix to a file using .npz format.
numpy.load: Load several arrays from a .npz archive.

Examples

Store sparse matrix to disk, and load it again:

>>> import numpy as np
>>> import scipy.sparse
>>> sparse_matrix = scipy.sparse.csc_matrix(np.array([[0, 0, 3], [4, 0, 0]]))
>>> sparse_matrix
<2x3 sparse matrix of type '<class 'numpy.int64'>'
   with 2 stored elements in Compressed Sparse Column format>
>>> sparse_matrix.toarray()
array([[0, 0, 3],
       [4, 0, 0]], dtype=int64)

>>> scipy.sparse.save_npz('/tmp/sparse_matrix.npz', sparse_matrix)
>>> sparse_matrix = scipy.sparse.load_npz('/tmp/sparse_matrix.npz')

>>> sparse_matrix
<2x3 sparse matrix of type '<class 'numpy.int64'>'
    with 2 stored elements in Compressed Sparse Column format>
>>> sparse_matrix.toarray()
array([[0, 0, 3],
       [4, 0, 0]], dtype=int64)

load_sdp_constraints

dipy.data.load_sdp_constraints(model_name, order=None)

Import semidefinite programming constraint matrices for different models, generated as described for example in [1].

Parameters:

model_namestring: A string identifying the model that is to be constrained.
orderunsigned int, optional: A non-negative integer that represent the order or instance of the model. Default: None.

Returns:

sdp_constraintsarray: Constraint matrices

Notes

The constraints will be loaded from a file named ‘id_constraint_order.npz’.

References

[1] (1,2)

Dela Haije et al. “Enforcing necessary non-negativity constraints for common diffusion MRI models using sum of squares programming”. NeuroImage 209, 2020, 116405.

loads_compat

dipy.data.loads_compat(byte_data)

matlab_life_results

dipy.data.matlab_life_results()

mrtrix_spherical_functions

dipy.data.mrtrix_spherical_functions()

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

Returns:

func_coefarray (2, 3, 4, 45): Functions represented by the coefficients associated with the mrtrix spherical harmonic basis of order 8.
func_discretearray (2, 3, 4, 81): Functions evaluated on sphere.
sphereSphere: 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.

pjoin

dipy.data.pjoin(a, *p): Join two or more pathname components, inserting ‘/’ as needed. If any component is an absolute path, all previous path components will be discarded. An empty last part will result in a path that ends with a separator.

read_DiB_217_lte_pte_ste

dipy.data.read_DiB_217_lte_pte_ste()

Read q-space trajectory encoding data with 217 between linear, planar, and spherical tensor encoding.

Returns:

data_imgnibabel.nifti1.Nifti1Image: dMRI data image.
mask_imgnibabel.nifti1.Nifti1Image: Brain mask image.
gtabdipy.core.gradients.GradientTable: Gradient table.

read_DiB_70_lte_pte_ste

dipy.data.read_DiB_70_lte_pte_ste()

Read q-space trajectory encoding data with 70 between linear, planar, and spherical tensor encoding measurements.

Returns:

data_imgnibabel.nifti1.Nifti1Image: dMRI data image.
mask_imgnibabel.nifti1.Nifti1Image: Brain mask image.
gtabdipy.core.gradients.GradientTable: Gradient table.

read_bundles_2_subjects

dipy.data.read_bundles_2_subjects(subj_id='subj_1', metrics=('fa',), bundles=('af.left', 'cst.right', 'cc_1'))

Read images and streamlines from 2 subjects of the SNAIL dataset.

Parameters:

subj_idstring: Either subj_1 or subj_2.
metricsarray-like: Either [‘fa’] or [‘t1’] or [‘fa’, ‘t1’]
bundlesarray-like: E.g., [‘af.left’, ‘cst.right’, ‘cc_1’]. See all the available bundles in the exp_bundles_maps/bundles_2_subjects directory of your $HOME/.dipy folder.

Returns:

dixdict: Dictionary with data of the metrics and the bundles as keys.

Notes

If you are using these datasets please cite the following publications.

References

[1]

Renauld, E., M. Descoteaux, M. Bernier, E. Garyfallidis,

K. Whittingstall, “Morphology of thalamus, LGN and optic radiation do not influence EEG alpha waves”, Plos One (under submission), 2015.

[2]

Garyfallidis, E., O. Ocegueda, D. Wassermann,

M. Descoteaux. Robust and efficient linear registration of fascicles in the space of streamlines , Neuroimage, 117:124-140, 2015.

read_cenir_multib

dipy.data.read_cenir_multib(bvals=None)

Read CENIR multi b-value data.

Parameters:

bvalslist or int: The b-values to read from file (200, 400, 1000, 2000, 3000).

Returns:

gtaba GradientTable class instance
imgnibabel.Nifti1Image

Notes

Details of the acquisition and processing, and additional meta-data are available through UW researchworks:

https://digital.lib.washington.edu/researchworks/handle/1773/33311

read_cfin_dwi

dipy.data.read_cfin_dwi()

Load CFIN multi b-value DWI data.

Returns:

imgobj,: Nifti1Image
gtabobj,: GradientTable

read_cfin_t1

dipy.data.read_cfin_t1()

Load CFIN T1-weighted data.

Returns:

imgobj,: Nifti1Image

read_five_af_bundles

dipy.data.read_five_af_bundles()

Load 5 small left arcuate fasciculus bundles.

Returns:

bundles: list of ArraySequence: List with loaded bundles.

read_isbi2013_2shell

dipy.data.read_isbi2013_2shell()

Load ISBI 2013 2-shell synthetic dataset.

Returns:

imgobj,: Nifti1Image
gtabobj,: GradientTable

read_ivim

dipy.data.read_ivim()

Load IVIM dataset.

Returns:

imgobj,: Nifti1Image
gtabobj,: GradientTable

read_mni_template

dipy.data.read_mni_template(version='a', contrast='T2')

Read the MNI template from disk.

Parameters:

version: string: There are two MNI templates 2009a and 2009c, so options available are: “a” and “c”.
contrastlist or string, optional: Which of the contrast templates to read. For version “a” two contrasts are available: “T1” and “T2”. Similarly for version “c” there are two options, “T1” and “mask”. You can input contrast as a string or a list

Returns:

listcontains the nibabel.Nifti1Image objects requested, according to the: order they were requested in the input.

Notes

The templates were downloaded from the MNI (McGill University) website in July 2015.

The following publications should be referenced when using these templates:

[1]

[2]

License for the MNI templates:

Examples

>>> # Get only the T1 file for version c:
>>> T1 = read_mni_template("c", contrast = "T1") 
>>> # Get both files in this order for version a:
>>> T1, T2 = read_mni_template(contrast = ["T1", "T2"]) 

read_qte_lte_pte

dipy.data.read_qte_lte_pte()

Read q-space trajectory encoding data with linear and planar tensor encoding.

Returns:

data_imgnibabel.nifti1.Nifti1Image: dMRI data image.
mask_imgnibabel.nifti1.Nifti1Image: Brain mask image.
gtabdipy.core.gradients.GradientTable: Gradient table.

read_scil_b0

dipy.data.read_scil_b0()

Load GE 3T b0 image form the scil b0 dataset.

Returns:

imgobj,: Nifti1Image

read_sherbrooke_3shell

dipy.data.read_sherbrooke_3shell()

Load Sherbrooke 3-shell HARDI dataset.

Returns:

imgobj,: Nifti1Image
gtabobj,: GradientTable

read_stanford_hardi

dipy.data.read_stanford_hardi()

Load Stanford HARDI dataset.

Returns:

imgobj,: Nifti1Image
gtabobj,: GradientTable

read_stanford_labels

dipy.data.read_stanford_labels(): Read stanford hardi data and label map.

read_stanford_pve_maps

dipy.data.read_stanford_pve_maps()

read_stanford_t1

dipy.data.read_stanford_t1()

read_syn_data

dipy.data.read_syn_data()

Load t1 and b0 volumes from the same session.

Returns:

t1obj,: Nifti1Image
b0obj,: Nifti1Image

read_taiwan_ntu_dsi

dipy.data.read_taiwan_ntu_dsi()

Load Taiwan NTU dataset.

Returns:

imgobj,: Nifti1Image
gtabobj,: GradientTable

read_tissue_data

dipy.data.read_tissue_data(contrast='T1')

Load images to be used for tissue classification

Parameters:

constraststr: ‘T1’, ‘T1 denoised’ or ‘Anisotropic Power’

Returns:

imageobj,: Nifti1Image

relist_streamlines

dipy.data.relist_streamlines(points, offsets)

Given a representation of a set of streamlines as a large array and an offsets array return the streamlines as a list of shorter arrays.

Parameters:

pointsarray
offsetsarray

Returns:

streamlines: sequence

two_cingulum_bundles

dipy.data.two_cingulum_bundles()

`FetcherError`

class dipy.data.fetcher.FetcherError

Bases: Exception

Attributes:

args

Methods

with_traceback

Exception.with_traceback(tb) -- set self.__traceback__ to tb and return self.

__init__(*args, **kwargs)

`TripWire`

class dipy.data.fetcher.TripWire(msg)

Bases: object

Class raising error if used

Standard use is to proxy modules that we could not import

Examples

>>> try:
...     import silly_module_name
... except ImportError:
...    silly_module_name = TripWire('We do not have silly_module_name')
>>> silly_module_name.do_silly_thing('with silly string') 
Traceback (most recent call last):
    ...
TripWireError: We do not have silly_module_name

Methods

__call__(*args, **kwargs)

Raise informative error while calling

__init__(msg)

`tqdm`

dipy.data.fetcher.tqdm: alias of tqdm_asyncio

check_md5

dipy.data.fetcher.check_md5(filename, stored_md5=None)

Computes the md5 of filename and check if it matches with the supplied string md5

Parameters:

filenamestring: Path to a file.
md5string: Known md5 of filename to check against. If None (default), checking is skipped

copyfileobj

dipy.data.fetcher.copyfileobj(fsrc, fdst, length=0): copy data from file-like object fsrc to file-like object fdst

copyfileobj_withprogress

dipy.data.fetcher.copyfileobj_withprogress(fsrc, fdst, total_length, length=16384)

extract_example_tracts

dipy.data.fetcher.extract_example_tracts(out_dir)

Extract 5 ‘AF_L’,’CST_R’ and ‘CC_ForcepsMajor’ trk files in out_dir folder.

Parameters:

out_dirstr: Folder in which to extract the files.

fetch_DiB_217_lte_pte_ste

dipy.data.fetcher.fetch_DiB_217_lte_pte_ste(): Download QTE data with linear, planar, and spherical tensor encoding. If using this data please cite F Szczepankiewicz, S Hoge, C-F Westin. Linear, planar and spherical tensor-valued diffusion MRI data by free waveform encoding in healthy brain, water, oil and liquid crystals. Data in Brief (2019),DOI: https://doi.org/10.1016/j.dib.2019.104208

fetch_DiB_70_lte_pte_ste

dipy.data.fetcher.fetch_DiB_70_lte_pte_ste(): Download QTE data with linear, planar, and spherical tensor encoding. If using this data please cite F Szczepankiewicz, S Hoge, C-F Westin. Linear, planar and spherical tensor-valued diffusion MRI data by free waveform encoding in healthy brain, water, oil and liquid crystals. Data in Brief (2019),DOI: https://doi.org/10.1016/j.dib.2019.104208

fetch_bundle_atlas_hcp842

dipy.data.fetcher.fetch_bundle_atlas_hcp842(): Download atlas tractogram from the hcp842 dataset with 80 bundles

fetch_bundle_fa_hcp

dipy.data.fetcher.fetch_bundle_fa_hcp(): Download map of FA within two bundles in oneof the hcp dataset subjects

fetch_bundles_2_subjects

dipy.data.fetcher.fetch_bundles_2_subjects(): Download 2 subjects from the SNAIL dataset with their bundles

fetch_cenir_multib

dipy.data.fetcher.fetch_cenir_multib(with_raw=False)

Fetch ‘HCP-like’ data, collected at multiple b-values.

Parameters:

with_rawbool: Whether to fetch the raw data. Per default, this is False, which means that only eddy-current/motion corrected data is fetched

Notes

Details of the acquisition and processing, and additional meta-data are available through UW researchworks:

https://digital.lib.washington.edu/researchworks/handle/1773/33311

fetch_cfin_multib

dipy.data.fetcher.fetch_cfin_multib(): Download CFIN multi b-value diffusion data

fetch_data

dipy.data.fetcher.fetch_data(files, folder, data_size=None)

Downloads files to folder and checks their md5 checksums

Parameters:

filesdictionary: For each file in files the value should be (url, md5). The file will be downloaded from url if the file does not already exist or if the file exists but the md5 checksum does not match.
folderstr: The directory where to save the file, the directory will be created if it does not already exist.
data_sizestr, optional: A string describing the size of the data (e.g. “91 MB”) to be logged to the screen. Default does not produce any information about data size.
Raises
——
FetcherError: Raises if the md5 checksum of the file does not match the expected value. The downloaded file is not deleted when this error is raised.

fetch_file_formats

dipy.data.fetcher.fetch_file_formats(): Download 5 bundles in various file formats and their reference

fetch_fury_surface

dipy.data.fetcher.fetch_fury_surface(): Surface for testing and examples

fetch_gold_standard_io

dipy.data.fetcher.fetch_gold_standard_io(): Downloads the gold standard for streamlines io testing.

fetch_hbn

dipy.data.fetcher.fetch_hbn(subjects, path=None)

Fetch preprocessed data from the Healthy Brain Network POD2 study [1, 2]_.

Parameters:

subjectslist: Identifiers of the subjects to download. For example: [“NDARAA948VFH”, “NDAREK918EC2”].
pathstring, optional: Path to save files into. Default: ‘~/.dipy’

Returns:

dict with remote and local names of these files,
path to BIDS derivative dataset

Notes

[1]

Alexander LM, Escalera J, Ai L, et al. An open resource for transdiagnostic research in pediatric mental health and learning disorders. Sci Data. 2017;4:170181.

[2]

Richie-Halford A, Cieslak M, Ai L, et al. An analysis-ready and quality controlled resource for pediatric brain white-matter research. Scientific Data. 2022;9(1):1-27.

fetch_hcp

dipy.data.fetcher.fetch_hcp(subjects, hcp_bucket='hcp-openaccess', profile_name='hcp', path=None, study='HCP_1200', aws_access_key_id=None, aws_secret_access_key=None)

Fetch HCP diffusion data and arrange it in a manner that resembles the BIDS [1] specification.

Parameters:

subjectslist: Each item is an integer, identifying one of the HCP subjects
hcp_bucketstring, optional: The name of the HCP S3 bucket. Default: “hcp-openaccess”
profile_namestring, optional: The name of the AWS profile used for access. Default: “hcp”
pathstring, optional: Path to save files into. Default: ‘~/.dipy’
studystring, optional: Which HCP study to grab. Default: ‘HCP_1200’
aws_access_key_idstring, optional: AWS credentials to HCP AWS S3. Will only be used if profile_name is set to False.
aws_secret_access_keystring, optional: AWS credentials to HCP AWS S3. Will only be used if profile_name is set to False.

Returns:

dict with remote and local names of these files,
path to BIDS derivative dataset

Notes

To use this function with its default setting, you need to have a file ‘~/.aws/credentials’, that includes a section:

[hcp] AWS_ACCESS_KEY_ID=XXXXXXXXXXXXXXXX AWS_SECRET_ACCESS_KEY=XXXXXXXXXXXXXXXX

The keys are credentials that you can get from HCP (see https://wiki.humanconnectome.org/display/PublicData/How+To+Connect+to+Connectome+Data+via+AWS) # noqa

Local filenames are changed to match our expected conventions.

[1] (1,2)

Gorgolewski et al. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data, 3::160044. DOI: 10.1038/sdata.2016.44.

fetch_isbi2013_2shell

dipy.data.fetcher.fetch_isbi2013_2shell(): Download a 2-shell software phantom dataset

fetch_ivim

dipy.data.fetcher.fetch_ivim(): Download IVIM dataset

fetch_mni_template

dipy.data.fetcher.fetch_mni_template()

fetch the MNI 2009a T1 and T2, and 2009c T1 and T1 mask files Notes —– The templates were downloaded from the MNI (McGill University) website in July 2015.

The following publications should be referenced when using these templates:

[1]

[2]

License for the MNI templates:

fetch_qtdMRI_test_retest_2subjects

dipy.data.fetcher.fetch_qtdMRI_test_retest_2subjects(): Downloads test-retest qt-dMRI acquisitions of two C57Bl6 mice.

fetch_qte_lte_pte

dipy.data.fetcher.fetch_qte_lte_pte(): Download QTE data with linear and planar tensor encoding.

fetch_resdnn_weights

dipy.data.fetcher.fetch_resdnn_weights(): Download ResDNN model weights for Nath et. al 2018

fetch_scil_b0

dipy.data.fetcher.fetch_scil_b0(): Download b=0 datasets from multiple MR systems (GE, Philips, Siemens) and different magnetic fields (1.5T and 3T)

fetch_sherbrooke_3shell

dipy.data.fetcher.fetch_sherbrooke_3shell(): Download a 3shell HARDI dataset with 192 gradient direction

fetch_stanford_hardi

dipy.data.fetcher.fetch_stanford_hardi(): Download a HARDI dataset with 160 gradient directions

fetch_stanford_labels

dipy.data.fetcher.fetch_stanford_labels(): Download reduced freesurfer aparc image from stanford web site

fetch_stanford_pve_maps

dipy.data.fetcher.fetch_stanford_pve_maps()

fetch_stanford_t1

dipy.data.fetcher.fetch_stanford_t1()

fetch_syn_data

dipy.data.fetcher.fetch_syn_data(): Download t1 and b0 volumes from the same session

fetch_taiwan_ntu_dsi

dipy.data.fetcher.fetch_taiwan_ntu_dsi(): Download a DSI dataset with 203 gradient directions

fetch_target_tractogram_hcp

dipy.data.fetcher.fetch_target_tractogram_hcp(): Download tractogram of one of the hcp dataset subjects

fetch_tissue_data

dipy.data.fetcher.fetch_tissue_data(): Download images to be used for tissue classification

get_bundle_atlas_hcp842

dipy.data.fetcher.get_bundle_atlas_hcp842()

Returns:

file1string
file2string

get_file_formats

dipy.data.fetcher.get_file_formats()

Returns:

bundles_listall bundles (list)
ref_anatreference

get_fnames

dipy.data.fetcher.get_fnames(name='small_64D')

Provide full paths to example or test datasets.

Parameters:

namestr

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)
‘test_piesno’ slice of N=8, K=14 diffusion data
‘reg_c’ small 2D image used for validating registration
‘reg_o’ small 2D image used for validation registration
‘cb_2’ two vectorized cingulum bundles

Returns:

fnamestuple: filenames for dataset

Examples

>>> import numpy as np
>>> from dipy.io.image import load_nifti
>>> from dipy.data import get_fnames
>>> fimg, fbvals, fbvecs = get_fnames('small_101D')
>>> bvals=np.loadtxt(fbvals)
>>> bvecs=np.loadtxt(fbvecs).T
>>> data, affine = load_nifti(fimg)
>>> data.shape == (6, 10, 10, 102)
True
>>> bvals.shape == (102,)
True
>>> bvecs.shape == (102, 3)
True

get_target_tractogram_hcp

dipy.data.fetcher.get_target_tractogram_hcp()

Returns:

file1string

get_two_hcp842_bundles

dipy.data.fetcher.get_two_hcp842_bundles()

Returns:

file1string
file2string

gradient_table

dipy.data.fetcher.gradient_table(bvals, bvecs=None, big_delta=None, small_delta=None, b0_threshold=50, atol=0.01, btens=None)

A general function for creating diffusion MR gradients.

It reads, loads and prepares scanner parameters like the b-values and b-vectors so that they can be useful during the reconstruction process.

Parameters:

bvalscan be any of the four options

an array of shape (N,) or (1, N) or (N, 1) with the b-values.
a path for the file which contains an array like the above (1).
an array of shape (N, 4) or (4, N). Then this parameter is considered to be a b-table which contains both bvals and bvecs. In this case the next parameter is skipped.
a path for the file which contains an array like the one at (3).

bvecscan be any of two options

an array of shape (N, 3) or (3, N) with the b-vectors.
a path for the file which contains an array like the previous.

big_deltafloat

acquisition pulse separation time in seconds (default None)

small_deltafloat

acquisition pulse duration time in seconds (default None)

b0_thresholdfloat

All b-values with values less than or equal to bo_threshold are considered as b0s i.e. without diffusion weighting.

atolfloat

All b-vectors need to be unit vectors up to a tolerance.

btenscan be any of three options

a string specifying the shape of the encoding tensor for all volumes in data. Options: ‘LTE’, ‘PTE’, ‘STE’, ‘CTE’ corresponding to linear, planar, spherical, and “cigar-shaped” tensor encoding. Tensors are rotated so that linear and cigar tensors are aligned with the corresponding gradient direction and the planar tensor’s normal is aligned with the corresponding gradient direction. Magnitude is scaled to match the b-value.
an array of strings of shape (N,), (N, 1), or (1, N) specifying encoding tensor shape for each volume separately. N corresponds to the number volumes in data. Options for elements in array: ‘LTE’, ‘PTE’, ‘STE’, ‘CTE’ corresponding to linear, planar, spherical, and “cigar-shaped” tensor encoding. Tensors are rotated so that linear and cigar tensors are aligned with the corresponding gradient direction and the planar tensor’s normal is aligned with the corresponding gradient direction. Magnitude is scaled to match the b-value.
an array of shape (N,3,3) specifying the b-tensor of each volume exactly. N corresponds to the number volumes in data. No rotation or scaling is performed.

Returns:

gradientsGradientTable: A GradientTable with all the gradient information.

Notes

Often b0s (b-values which correspond to images without diffusion weighting) have 0 values however in some cases the scanner cannot provide b0s of an exact 0 value and it gives a bit higher values e.g. 6 or 12. This is the purpose of the b0_threshold in the __init__.
We assume that the minimum number of b-values is 7.
B-vectors should be unit vectors.

Examples

>>> from dipy.core.gradients import gradient_table
>>> bvals = 1500 * np.ones(7)
>>> bvals[0] = 0
>>> sq2 = np.sqrt(2) / 2
>>> bvecs = np.array([[0, 0, 0],
...                   [1, 0, 0],
...                   [0, 1, 0],
...                   [0, 0, 1],
...                   [sq2, sq2, 0],
...                   [sq2, 0, sq2],
...                   [0, sq2, sq2]])
>>> gt = gradient_table(bvals, bvecs)
>>> gt.bvecs.shape == bvecs.shape
True
>>> gt = gradient_table(bvals, bvecs.T)
>>> gt.bvecs.shape == bvecs.T.shape
False

gradient_table_from_gradient_strength_bvecs

dipy.data.fetcher.gradient_table_from_gradient_strength_bvecs(gradient_strength, bvecs, big_delta, small_delta, b0_threshold=50, atol=0.01)

A general function for creating diffusion MR gradients.

It reads, loads and prepares scanner parameters like the b-values and b-vectors so that they can be useful during the reconstruction process.

Parameters:

gradient_strengthan array of shape (N,),

gradient strength given in T/mm

bvecscan be any of two options

an array of shape (N, 3) or (3, N) with the b-vectors.
a path for the file which contains an array like the previous.

big_deltafloat or array of shape (N,)

acquisition pulse separation time in seconds

small_deltafloat

acquisition pulse duration time in seconds

b0_thresholdfloat

All b-values with values less than or equal to bo_threshold are considered as b0s i.e. without diffusion weighting.

atolfloat

All b-vectors need to be unit vectors up to a tolerance.

Returns:

gradientsGradientTable: A GradientTable with all the gradient information.

Notes

Often b0s (b-values which correspond to images without diffusion weighting) have 0 values however in some cases the scanner cannot provide b0s of an exact 0 value and it gives a bit higher values e.g. 6 or 12. This is the purpose of the b0_threshold in the __init__.
We assume that the minimum number of b-values is 7.
B-vectors should be unit vectors.

Examples

>>> from dipy.core.gradients import (
...    gradient_table_from_gradient_strength_bvecs)
>>> gradient_strength = .03e-3 * np.ones(7)  # clinical strength at 30 mT/m
>>> big_delta = .03  # pulse separation of 30ms
>>> small_delta = 0.01  # pulse duration of 10ms
>>> gradient_strength[0] = 0
>>> sq2 = np.sqrt(2) / 2
>>> bvecs = np.array([[0, 0, 0],
...                   [1, 0, 0],
...                   [0, 1, 0],
...                   [0, 0, 1],
...                   [sq2, sq2, 0],
...                   [sq2, 0, sq2],
...                   [0, sq2, sq2]])
>>> gt = gradient_table_from_gradient_strength_bvecs(
...     gradient_strength, bvecs, big_delta, small_delta)

load_nifti

dipy.data.fetcher.load_nifti(fname, return_img=False, return_voxsize=False, return_coords=False, as_ndarray=True)

Load data and other information from a nifti file.

Parameters:

fnamestr: Full path to a nifti file.
return_imgbool, optional: Whether to return the nibabel nifti img object. Default: False
return_voxsize: bool, optional: Whether to return the nifti header zooms. Default: False
return_coordsbool, optional: Whether to return the nifti header aff2axcodes. Default: False
as_ndarray: bool, optional: convert nibabel ArrayProxy to a numpy.ndarray. If you want to save memory and delay this casting, just turn this option to False (default: True)

Returns:

A tuple, with (at the most, if all keyword args are set to True):
(data, img.affine, img, vox_size, nib.aff2axcodes(img.affine))

See also

load_nifti_data

load_nifti_data

dipy.data.fetcher.load_nifti_data(fname, as_ndarray=True)

Load only the data array from a nifti file.

Parameters:

fnamestr: Full path to the file.
as_ndarray: bool, optional: convert nibabel ArrayProxy to a numpy.ndarray. If you want to save memory and delay this casting, just turn this option to False (default: True)

Returns:

data: np.ndarray or nib.ArrayProxy

See also

load_nifti

load_trk

dipy.data.fetcher.load_trk(filename, reference, to_space=Space.RASMM, to_origin=Origin.NIFTI, bbox_valid_check=True, trk_header_check=True)

Load the stateful tractogram of the .trk format

Parameters:

filenamestring

Filename with valid extension

referenceNifti or Trk filename, Nifti1Image or TrkFile, Nifti1Header or

trk.header (dict), or ‘same’ if the input is a trk file. Reference that provides the spatial attribute. Typically a nifti-related object from the native diffusion used for streamlines generation

to_spaceEnum (dipy.io.stateful_tractogram.Space)

Space to which the streamlines will be transformed after loading

to_originEnum (dipy.io.stateful_tractogram.Origin)

Origin to which the streamlines will be transformed after loading: NIFTI standard, default (center of the voxel) TRACKVIS standard (corner of the voxel)

bbox_valid_checkbool

Verification for negative voxel coordinates or values above the volume dimensions. Default is True, to enforce valid file.

trk_header_checkbool

Verification that the reference has the same header as the spatial attributes as the input tractogram when a Trk is loaded

Returns:

outputStatefulTractogram: The tractogram to load (must have been saved properly)

md5

dipy.data.fetcher.md5(/, string=b'', *, usedforsecurity=True): Returns a md5 hash object; optionally initialized with a string

optional_package

dipy.data.fetcher.optional_package(name, trip_msg=None)

Return package-like thing and module setup for package name

Parameters:

namestr: package name
trip_msgNone or str: message to give when someone tries to use the return package, but we could not import it, and have returned a TripWire object instead. Default message if None.

Returns:

pkg_likemodule or TripWire instance: If we can import the package, return it. Otherwise return an object raising an error when accessed
have_pkgbool: True if import for package was successful, false otherwise
module_setupfunction: callable usually set as setup_module in calling namespace, to allow skipping tests.

Examples

Typical use would be something like this at the top of a module using an optional package:

>>> from dipy.utils.optpkg import optional_package
>>> pkg, have_pkg, setup_module = optional_package('not_a_package')

Of course in this case the package doesn’t exist, and so, in the module:

>>> have_pkg
False

and

>>> pkg.some_function() 
Traceback (most recent call last):
    ...
TripWireError: We need package not_a_package for these functions, but
``import not_a_package`` raised an ImportError

If the module does exist - we get the module

>>> pkg, _, _ = optional_package('os')
>>> hasattr(pkg, 'path')
True

Or a submodule if that’s what we asked for

>>> subpkg, _, _ = optional_package('os.path')
>>> hasattr(subpkg, 'dirname')
True

pjoin

dipy.data.fetcher.pjoin(a, *p): Join two or more pathname components, inserting ‘/’ as needed. If any component is an absolute path, all previous path components will be discarded. An empty last part will result in a path that ends with a separator.

read_DiB_217_lte_pte_ste

dipy.data.fetcher.read_DiB_217_lte_pte_ste()

Read q-space trajectory encoding data with 217 between linear, planar, and spherical tensor encoding.

Returns:

data_imgnibabel.nifti1.Nifti1Image: dMRI data image.
mask_imgnibabel.nifti1.Nifti1Image: Brain mask image.
gtabdipy.core.gradients.GradientTable: Gradient table.

read_DiB_70_lte_pte_ste

dipy.data.fetcher.read_DiB_70_lte_pte_ste()

Read q-space trajectory encoding data with 70 between linear, planar, and spherical tensor encoding measurements.

Returns:

data_imgnibabel.nifti1.Nifti1Image: dMRI data image.
mask_imgnibabel.nifti1.Nifti1Image: Brain mask image.
gtabdipy.core.gradients.GradientTable: Gradient table.

read_bundles_2_subjects

dipy.data.fetcher.read_bundles_2_subjects(subj_id='subj_1', metrics=('fa',), bundles=('af.left', 'cst.right', 'cc_1'))

Read images and streamlines from 2 subjects of the SNAIL dataset.

Parameters:

subj_idstring: Either subj_1 or subj_2.
metricsarray-like: Either [‘fa’] or [‘t1’] or [‘fa’, ‘t1’]
bundlesarray-like: E.g., [‘af.left’, ‘cst.right’, ‘cc_1’]. See all the available bundles in the exp_bundles_maps/bundles_2_subjects directory of your $HOME/.dipy folder.

Returns:

dixdict: Dictionary with data of the metrics and the bundles as keys.

Notes

If you are using these datasets please cite the following publications.

References

[1]

Renauld, E., M. Descoteaux, M. Bernier, E. Garyfallidis,

K. Whittingstall, “Morphology of thalamus, LGN and optic radiation do not influence EEG alpha waves”, Plos One (under submission), 2015.

[2]

Garyfallidis, E., O. Ocegueda, D. Wassermann,

M. Descoteaux. Robust and efficient linear registration of fascicles in the space of streamlines , Neuroimage, 117:124-140, 2015.

read_bvals_bvecs

dipy.data.fetcher.read_bvals_bvecs(fbvals, fbvecs)

Read b-values and b-vectors from disk.

Parameters:

fbvalsstr: Full path to file with b-values. None to not read bvals.
fbvecsstr: Full path of file with b-vectors. None to not read bvecs.

Returns:

bvalsarray, (N,) or None
bvecsarray, (N, 3) or None

Notes

Files can be either ‘.bvals’/’.bvecs’ or ‘.txt’ or ‘.npy’ (containing arrays stored with the appropriate values).

read_cenir_multib

dipy.data.fetcher.read_cenir_multib(bvals=None)

Read CENIR multi b-value data.

Parameters:

bvalslist or int: The b-values to read from file (200, 400, 1000, 2000, 3000).

Returns:

gtaba GradientTable class instance
imgnibabel.Nifti1Image

Notes

Details of the acquisition and processing, and additional meta-data are available through UW researchworks:

https://digital.lib.washington.edu/researchworks/handle/1773/33311

read_cfin_dwi

dipy.data.fetcher.read_cfin_dwi()

Load CFIN multi b-value DWI data.

Returns:

imgobj,: Nifti1Image
gtabobj,: GradientTable

read_cfin_t1

dipy.data.fetcher.read_cfin_t1()

Load CFIN T1-weighted data.

Returns:

imgobj,: Nifti1Image

read_five_af_bundles

dipy.data.fetcher.read_five_af_bundles()

Load 5 small left arcuate fasciculus bundles.

Returns:

bundles: list of ArraySequence: List with loaded bundles.

read_isbi2013_2shell

dipy.data.fetcher.read_isbi2013_2shell()

Load ISBI 2013 2-shell synthetic dataset.

Returns:

imgobj,: Nifti1Image
gtabobj,: GradientTable

read_ivim

dipy.data.fetcher.read_ivim()

Load IVIM dataset.

Returns:

imgobj,: Nifti1Image
gtabobj,: GradientTable

read_mni_template

dipy.data.fetcher.read_mni_template(version='a', contrast='T2')

Read the MNI template from disk.

Parameters:

version: string: There are two MNI templates 2009a and 2009c, so options available are: “a” and “c”.
contrastlist or string, optional: Which of the contrast templates to read. For version “a” two contrasts are available: “T1” and “T2”. Similarly for version “c” there are two options, “T1” and “mask”. You can input contrast as a string or a list

Returns:

listcontains the nibabel.Nifti1Image objects requested, according to the: order they were requested in the input.

Notes

The templates were downloaded from the MNI (McGill University) website in July 2015.

The following publications should be referenced when using these templates:

[1]

[2]

License for the MNI templates:

Examples

>>> # Get only the T1 file for version c:
>>> T1 = read_mni_template("c", contrast = "T1") 
>>> # Get both files in this order for version a:
>>> T1, T2 = read_mni_template(contrast = ["T1", "T2"]) 

read_qtdMRI_test_retest_2subjects

dipy.data.fetcher.read_qtdMRI_test_retest_2subjects()

Load test-retest qt-dMRI acquisitions of two C57Bl6 mice. These datasets were used to study test-retest reproducibility of time-dependent q-space indices (q:math:` au`-indices) in the corpus callosum of two mice [1]. The data itself and its details are publicly available and can be cited at [2].

The test-retest diffusion MRI spin echo sequences were acquired from two C57Bl6 wild-type mice on an 11.7 Tesla Bruker scanner. The test and retest acquisition were taken 48 hours from each other. The (processed) data consists of 80x160x5 voxels of size 110x110x500μm. Each data set consists of 515 Diffusion-Weighted Images (DWIs) spread over 35 acquisition shells. The shells are spread over 7 gradient strength shells with a maximum gradient strength of 491 mT/m, 5 pulse separation shells between [10.8 - 20.0]ms, and a pulse length of 5ms. We manually created a brain mask and corrected the data from eddy currents and motion artifacts using FSL’s eddy. A region of interest was then drawn in the middle slice in the corpus callosum, where the tissue is reasonably coherent.

Returns:

datalist of length 4: contains the dwi datasets ordered as (subject1_test, subject1_retest, subject2_test, subject2_retest)
cc_maskslist of length 4: contains the corpus callosum masks ordered in the same order as data.
gtabslist of length 4: contains the qt-dMRI gradient tables of the data sets.

References

[1]

Fick, Rutger HJ, et al. “Non-Parametric GraphNet-Regularized Representation of dMRI in Space and Time”, Medical Image Analysis, 2017.

[2]

Wassermann, Demian, et al., “Test-Retest qt-dMRI datasets for `Non-Parametric GraphNet-Regularized Representation of dMRI in Space and Time’”. doi:10.5281/zenodo.996889, 2017.

read_qte_lte_pte

dipy.data.fetcher.read_qte_lte_pte()

Read q-space trajectory encoding data with linear and planar tensor encoding.

Returns:

data_imgnibabel.nifti1.Nifti1Image: dMRI data image.
mask_imgnibabel.nifti1.Nifti1Image: Brain mask image.
gtabdipy.core.gradients.GradientTable: Gradient table.

read_scil_b0

dipy.data.fetcher.read_scil_b0()

Load GE 3T b0 image form the scil b0 dataset.

Returns:

imgobj,: Nifti1Image

read_sherbrooke_3shell

dipy.data.fetcher.read_sherbrooke_3shell()

Load Sherbrooke 3-shell HARDI dataset.

Returns:

imgobj,: Nifti1Image
gtabobj,: GradientTable

read_siemens_scil_b0

dipy.data.fetcher.read_siemens_scil_b0()

Load Siemens 1.5T b0 image from the scil b0 dataset.

Returns:

imgobj,: Nifti1Image

read_stanford_hardi

dipy.data.fetcher.read_stanford_hardi()

Load Stanford HARDI dataset.

Returns:

imgobj,: Nifti1Image
gtabobj,: GradientTable

read_stanford_labels

dipy.data.fetcher.read_stanford_labels(): Read stanford hardi data and label map.

read_stanford_pve_maps

dipy.data.fetcher.read_stanford_pve_maps()

read_stanford_t1

dipy.data.fetcher.read_stanford_t1()

read_syn_data

dipy.data.fetcher.read_syn_data()

Load t1 and b0 volumes from the same session.

Returns:

t1obj,: Nifti1Image
b0obj,: Nifti1Image

read_taiwan_ntu_dsi

dipy.data.fetcher.read_taiwan_ntu_dsi()

Load Taiwan NTU dataset.

Returns:

imgobj,: Nifti1Image
gtabobj,: GradientTable

read_tissue_data

dipy.data.fetcher.read_tissue_data(contrast='T1')

Load images to be used for tissue classification

Parameters:

constraststr: ‘T1’, ‘T1 denoised’ or ‘Anisotropic Power’

Returns:

imageobj,: Nifti1Image

save_nifti

dipy.data.fetcher.save_nifti(fname, data, affine, hdr=None, dtype=None)

Save a data array into a nifti file.

Parameters:

fnamestr: The full path to the file to be saved.
datandarray: The array with the data to save.
affine4x4 array: The affine transform associated with the file.
hdrnifti header, optional: May contain additional information to store in the file header.

Returns:

None

to_bids_description

dipy.data.fetcher.to_bids_description(path, fname='dataset_description.json', BIDSVersion='1.4.0', **kwargs): Dumps a dict into a bids description at the given location

urlopen

dipy.data.fetcher.urlopen(url, data=None, timeout=<object object>, *, cafile=None, capath=None, cadefault=False, context=None)

Open the URL url, which can be either a string or a Request object.

data must be an object specifying additional data to be sent to the server, or None if no such data is needed. See Request for details.

urllib.request module uses HTTP/1.1 and includes a “Connection:close” header in its HTTP requests.

The optional timeout parameter specifies a timeout in seconds for blocking operations like the connection attempt (if not specified, the global default timeout setting will be used). This only works for HTTP, HTTPS and FTP connections.

If context is specified, it must be a ssl.SSLContext instance describing the various SSL options. See HTTPSConnection for more details.

The optional cafile and capath parameters specify a set of trusted CA certificates for HTTPS requests. cafile should point to a single file containing a bundle of CA certificates, whereas capath should point to a directory of hashed certificate files. More information can be found in ssl.SSLContext.load_verify_locations().

The cadefault parameter is ignored.

This function always returns an object which can work as a context manager and has the properties url, headers, and status. See urllib.response.addinfourl for more detail on these properties.

For HTTP and HTTPS URLs, this function returns a http.client.HTTPResponse object slightly modified. In addition to the three new methods above, the msg attribute contains the same information as the reason attribute — the reason phrase returned by the server — instead of the response headers as it is specified in the documentation for HTTPResponse.

For FTP, file, and data URLs and requests explicitly handled by legacy URLopener and FancyURLopener classes, this function returns a urllib.response.addinfourl object.

Note that None may be returned if no handler handles the request (though the default installed global OpenerDirector uses UnknownHandler to ensure this never happens).

In addition, if proxy settings are detected (for example, when a *_proxy environment variable like http_proxy is set), ProxyHandler is default installed and makes sure the requests are handled through the proxy.

data

Module: data.fetcher

DataError

GradientTable

HemiSphere

Sphere

as_native_array

dirname

dsi_deconv_voxels

dsi_voxels

exists

fetch_bundle_atlas_hcp842

fetch_bundle_fa_hcp

fetch_bundles_2_subjects

fetch_cenir_multib

fetch_cfin_multib

fetch_gold_standard_io

fetch_hbn

fetch_isbi2013_2shell

fetch_ivim

fetch_mni_template

fetch_resdnn_weights

fetch_scil_b0

fetch_sherbrooke_3shell

fetch_stanford_hardi

fetch_stanford_labels

fetch_stanford_pve_maps

fetch_stanford_t1

fetch_syn_data

fetch_taiwan_ntu_dsi

fetch_target_tractogram_hcp

fetch_tissue_data

get_3shell_gtab

get_bundle_atlas_hcp842

get_cmap

get_fnames

get_gtab_taiwan_dsi

get_isbi2013_2shell_gtab

get_sim_voxels

get_skeleton

get_sphere

get_target_tractogram_hcp

get_two_hcp842_bundles

gradient_table

load_nifti

load_npz

load_sdp_constraints

loads_compat

matlab_life_results

mrtrix_spherical_functions

pjoin

read_DiB_217_lte_pte_ste

read_DiB_70_lte_pte_ste

read_bundles_2_subjects

read_cenir_multib

read_cfin_dwi

read_cfin_t1

read_five_af_bundles

read_isbi2013_2shell

read_ivim

read_mni_template

read_qte_lte_pte

read_scil_b0

read_sherbrooke_3shell

read_stanford_hardi

read_stanford_labels

read_stanford_pve_maps

read_stanford_t1

read_syn_data

read_taiwan_ntu_dsi

read_tissue_data

relist_streamlines

two_cingulum_bundles

FetcherError

TripWire

tqdm

check_md5

copyfileobj

copyfileobj_withprogress

extract_example_tracts

`data`

Module: `data.fetcher`

`DataError`

`GradientTable`

`HemiSphere`

`Sphere`

`FetcherError`

`TripWire`

`tqdm`