data

str(object=’’) -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.__str__() (if defined) or repr(object). encoding defaults to sys.getdefaultencoding(). errors defaults to ‘strict’.

provide some simulated voxel data

>>> 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).

>>> 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

>>> 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"

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)

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)

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

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

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

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

Downloads files to folder and checks their md5 checksums

Download a 2-shell software phantom dataset

Download reduced freesurfer aparc image from stanford web site

Download a 3shell HARDI dataset with 192 gradient direction

Download a HARDI dataset with 160 gradient directions

Download ResDNN model weights for Nath et. al 2018

Download Synb0 model weights for Schilling et. al 2019

Download Synb0 test data for Schilling et. al 2019

Download EVAC+ model weights for Park et. al 2022

Download EVAC+ test data for Park et. al 2022

Download stanford track for examples

Download a DSI dataset with 203 gradient directions

Download t1 and b0 volumes from the same session

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:

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

Download 2 subjects from the SNAIL dataset with their bundles

Download IVIM dataset

Download CFIN multi b-value diffusion data

Download 5 bundles in various file formats and their reference

Download atlas tractogram from the hcp842 dataset with 80 bundles

Download tractogram of one of the hcp dataset subjects

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

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

Downloads the gold standard for streamlines io testing.

Download QTE data with linear and planar tensor encoding.

Surface for testing and examples

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

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

Download FOD and seeds for PTT testing and examples

Download Bundle Warp dataset

Provide full paths to example or test datasets.

>>> 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

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 ——- data : list of length 4 contains the dwi datasets ordered as (subject1_test, subject1_retest, subject2_test, subject2_retest) cc_masks : list of length 4 contains the corpus callosum masks ordered in the same order as data. gtabs : list 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.

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

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

Load ISBI 2013 2-shell synthetic dataset.

Load Sherbrooke 3-shell HARDI dataset.

Read stanford hardi data and label map.

Load Stanford HARDI dataset.

Load Taiwan NTU dataset.

Load t1 and b0 volumes from the same session.

Download images to be used for tissue classification

Load images to be used for tissue classification

Read the MNI template from disk.

>>> # 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"]) 

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

Read CENIR multi b-value data.

Read images and streamlines from 2 subjects of the SNAIL dataset. Parameters ———- subj_id : string Either subj_1 or subj_2. metrics : array-like Either [‘fa’] or [‘t1’] or [‘fa’, ‘t1’] bundles : array-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 ——- dix : dict 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.

Load IVIM dataset.

Load CFIN multi b-value DWI data.

Load CFIN T1-weighted data.

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

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

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

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

Load 5 small left arcuate fasciculus bundles.

Dumps a dict into a bids description at the given location

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

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