data

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.

Returns the directory component of a pathname

Download atlas tractogram from the hcp842 dataset with 80 bundles

Download map of FA within two bundles in oneof the hcp dataset subjects

Download 2 subjects from the SNAIL dataset with their bundles

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

Download CFIN multi b-value diffusion data

Downloads the gold standard for streamlines io testing.

Download a 2-shell software phantom dataset

Download IVIM dataset

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.

Download ResDNN model weights for Nath et. al 2018

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

Download a 3shell HARDI dataset with 192 gradient direction

Download a HARDI dataset with 160 gradient directions

Download reduced freesurfer aparc image from stanford web site

Download t1 and b0 volumes from the same session

Download a DSI dataset with 203 gradient directions

Download tractogram of one of the hcp dataset subjects

Download images to be used for tissue classification

Returns

file1string

file2string

Make a callable, similar to maptlotlib.pyplot.get_cmap.

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

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'

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']

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"

Returns

file1string

Returns

file1string

file2string

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

1. an array of shape (N,) or (1, N) or (N, 1) with the b-values.

2. a path for the file which contains an array like the above (1).

3. 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.

4. a path for the file which contains an array like the one at (3).

bvecscan be any of two options

1. an array of shape (N, 3) or (3, N) with the b-vectors.

2. 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

1. 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.

2. 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.

3. 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

1. 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__.

2. We assume that the minimum number of b-values is 7.

3. 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 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))

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.

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 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 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

Load CFIN multi b-value DWI data.

Returns

imgobj,

Nifti1Image

gtabobj,

GradientTable

Load CFIN T1-weighted data.

Returns

imgobj,

Nifti1Image

Load ISBI 2013 2-shell synthetic dataset.

Returns

imgobj,

Nifti1Image

gtabobj,

GradientTable

Load IVIM dataset.

Returns

imgobj,

Nifti1Image

gtabobj,

GradientTable

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

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.

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 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.

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

Returns

imgobj,

Nifti1Image

Load Sherbrooke 3-shell HARDI dataset.

Returns

imgobj,

Nifti1Image

gtabobj,

GradientTable

Load Stanford HARDI dataset.

Returns

imgobj,

Nifti1Image

gtabobj,

GradientTable

Read stanford hardi data and label map.

Load t1 and b0 volumes from the same session.

Returns

t1obj,

Nifti1Image

b0obj,

Nifti1Image

Load Taiwan NTU dataset.

Returns

imgobj,

Nifti1Image

gtabobj,

GradientTable

Load images to be used for tissue classification

Parameters

constraststr

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

Returns

imageobj,

Nifti1Image

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

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

copy data from file-like object fsrc to file-like object fdst

Download atlas tractogram from the hcp842 dataset with 80 bundles

Download map of FA within two bundles in oneof the hcp dataset subjects

Download 2 subjects from the SNAIL dataset with their bundles

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

Download CFIN multi b-value diffusion data

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.

Download 5 bundles in various file formats and their reference

Surface for testing and examples

Downloads the gold standard for streamlines io testing.

Download a 2-shell software phantom dataset

Download IVIM dataset

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.

Downloads test-retest qt-dMRI acquisitions of two C57Bl6 mice.

Download QTE data with linear and planar tensor encoding.

Download ResDNN model weights for Nath et. al 2018

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

Download a 3shell HARDI dataset with 192 gradient direction

Download a HARDI dataset with 160 gradient directions

Download reduced freesurfer aparc image from stanford web site

Download t1 and b0 volumes from the same session

Download a DSI dataset with 203 gradient directions

Download tractogram of one of the hcp dataset subjects

Download images to be used for tissue classification

Returns

file1string

file2string

Returns

bundles_listall bundles (list)

ref_anatreference

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

Returns

file1string

Returns

file1string

file2string

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

1. an array of shape (N,) or (1, N) or (N, 1) with the b-values.

2. a path for the file which contains an array like the above (1).

3. 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.

4. a path for the file which contains an array like the one at (3).

bvecscan be any of two options

1. an array of shape (N, 3) or (3, N) with the b-vectors.

2. 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

1. 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.

2. 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.

3. 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

1. 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__.

2. We assume that the minimum number of b-values is 7.

3. 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

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

1. an array of shape (N, 3) or (3, N) with the b-vectors.

2. 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

1. 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__.

2. We assume that the minimum number of b-values is 7.

3. 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 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))

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

Returns a md5 hash object; optionally initialized with a string

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 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 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 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

Load CFIN multi b-value DWI data.

Returns

imgobj,

Nifti1Image

gtabobj,

GradientTable

Load CFIN T1-weighted data.

Returns

imgobj,

Nifti1Image

Load ISBI 2013 2-shell synthetic dataset.

Returns

imgobj,

Nifti1Image

gtabobj,

GradientTable

Load IVIM dataset.

Returns

imgobj,

Nifti1Image

gtabobj,

GradientTable

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

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.

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"]) 

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 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.

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

Returns

imgobj,

Nifti1Image

Load Sherbrooke 3-shell HARDI dataset.

Returns

imgobj,

Nifti1Image

gtabobj,

GradientTable

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

Returns

imgobj,

Nifti1Image

Load Stanford HARDI dataset.

Returns

imgobj,

Nifti1Image

gtabobj,

GradientTable

Read stanford hardi data and label map.

Load t1 and b0 volumes from the same session.

Returns

t1obj,

Nifti1Image

b0obj,

Nifti1Image

Load Taiwan NTU dataset.

Returns

imgobj,

Nifti1Image

gtabobj,

GradientTable

Load images to be used for tissue classification

Parameters

constraststr

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

Returns

imageobj,

Nifti1Image

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.