`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`()
`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_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_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])	provides filenames of some test datasets or other useful parametrisations
`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
`gradient_table`(bvals[, bvecs, big_delta, …])	A general function for creating diffusion MR gradients.
`load`(filename, **kwargs)	Load file given filename, guessing at file type
`loads_compat`(bytes)
`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_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_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_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.
`sticks_and_ball`(gtab[, d, S0, angles, …])	Simulate the signal for a Sticks & Ball model.
`two_cingulum_bundles`()

Module: `data.fetcher`¶

`FetcherError`
`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, …[, …])
`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_gold_standard_io`()	Downloads the gold standard for streamlines io testing.
`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_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_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.
`md5`	Returns a md5 hash object; optionally initialized with a string
`pjoin`(a, *p)	Join two or more pathname components, inserting ‘/’ as needed.
`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_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_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 form 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
`update_progressbar`(progress, total_length)	Show progressbar
`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__($self, /, *args, **kwargs)¶: Initialize self. See help(type(self)) for accurate signature.

`GradientTable`¶

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

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.

`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: dipy.core.sphere.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.

`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)¶: Initialize self. See help(type(self)) for accurate signature.

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/____
/\    /            /  \  /             /____\/____          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()¶

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_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

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_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')¶

provides filenames of some test datasets or other useful parametrisations

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.data import get_fnames
>>> fimg,fbvals,fbvecs=get_fnames('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)
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') #doctest: +IGNORE_EXCEPTION_DETAIL
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

gradient_table¶

dipy.data.gradient_table(bvals, bvecs=None, big_delta=None, small_delta=None, 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

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.

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¶

dipy.data.load(filename, **kwargs)¶

Load file given filename, guessing at file type

Parameters

filenamestring: specification of file to load
**kwargskeyword arguments: Keyword arguments to format-specific load

Returns

imgSpatialImage: Image of guessed type

loads_compat¶

dipy.data.loads_compat(bytes)¶

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 mxtrix 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_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.
metricslist: Either [‘fa’] or [‘t1’] or [‘fa’, ‘t1’]
bundleslist: 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_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: 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

Examples

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

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

sticks_and_ball¶

dipy.data.sticks_and_ball(gtab, d=0.0015, S0=1.0, angles=[(0, 0), (90, 0)], fractions=[35, 35], snr=20)¶

Simulate the signal for a Sticks & Ball model.

Parameters

gtabGradientTable: Signal measurement directions.
dfloat: Diffusivity value.
S0float: Unweighted signal value.
anglesarray (K,2) or (K, 3): List of K polar angles (in degrees) for the sticks or array of K sticks as unit vectors.
fractionsfloat: Percentage of each stick. Remainder to 100 specifies isotropic component.
snrfloat: Signal to noise ratio, assuming Rician noise. If set to None, no noise is added.

Returns

S(N,) ndarray: Simulated signal.
sticks(M,3): Sticks in cartesian coordinates.

References

1: Behrens et al., “Probabilistic diffusion tractography with multiple fiber orientations: what can we gain?”, Neuroimage, 2007.

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__($self, /, *args, **kwargs)¶: Initialize self. See help(type(self)) for accurate signature.

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=16384)¶: 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)¶

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_gold_standard_io¶

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

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: 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

fetch_qtdMRI_test_retest_2subjects¶

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

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_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)¶

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.

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)

md5¶

dipy.data.fetcher.md5()¶: Returns a md5 hash object; optionally initialized with a string

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_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.
metricslist: Either [‘fa’] or [‘t1’] or [‘fa’, ‘t1’]
bundleslist: 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_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: 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

Examples

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_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 form 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

update_progressbar¶

dipy.data.fetcher.update_progressbar(progress, total_length)¶

Show progressbar

Takes a number between 0 and 1 to indicate progress from 0 to 100%.

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 methods such as

geturl() - return the URL of the resource retrieved, commonly used to determine if a redirect was followed
info() - return the meta-information of the page, such as headers, in the form of an email.message_from_string() instance (see Quick Reference to HTTP Headers)
getcode() - return the HTTP status code of the response. Raises URLError on errors.

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.

`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

b0s_mask
bvals
bvecs
gradient_strength
qvals
tau

data¶

Module: data.fetcher¶

DataError¶

GradientTable¶

HemiSphere¶

Sphere¶

as_native_array¶

dirname¶

dsi_deconv_voxels¶

dsi_voxels¶

fetch_bundle_atlas_hcp842¶

fetch_bundle_fa_hcp¶

fetch_bundles_2_subjects¶

fetch_cenir_multib¶

fetch_cfin_multib¶

fetch_gold_standard_io¶

fetch_isbi2013_2shell¶

fetch_ivim¶

fetch_mni_template¶

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¶

gradient_table¶

load¶

loads_compat¶

matlab_life_results¶

mrtrix_spherical_functions¶

pjoin¶

read_bundles_2_subjects¶

read_cenir_multib¶

read_cfin_dwi¶

read_cfin_t1¶

read_isbi2013_2shell¶

read_ivim¶

read_mni_template¶

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¶

sticks_and_ball¶

two_cingulum_bundles¶

FetcherError¶

check_md5¶

copyfileobj¶

copyfileobj_withprogress¶

fetch_bundle_atlas_hcp842¶

fetch_bundle_fa_hcp¶

fetch_bundles_2_subjects¶

fetch_cenir_multib¶

fetch_cfin_multib¶

fetch_data¶

fetch_file_formats¶

fetch_gold_standard_io¶

fetch_isbi2013_2shell¶

fetch_ivim¶

fetch_mni_template¶

fetch_qtdMRI_test_retest_2subjects¶

`data`¶

Module: `data.fetcher`¶

`DataError`¶

`GradientTable`¶

`HemiSphere`¶

`Sphere`¶

`FetcherError`¶