data#
Read test or example data.
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provide some simulated voxel data |
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Provide skeletons generated from Local Skeleton Clustering (LSC). |
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provide triangulated spheres |
Spherical functions represented by spherical harmonic coefficients and evaluated on a discrete sphere. |
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Make a callable, similar to maptlotlib.pyplot.get_cmap. |
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Import semidefinite programming constraint matrices for different models. |
Module: data.fetcher#
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Computes the md5 of filename and check if it matches with the supplied string md5 |
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Downloads files to folder and checks their md5 checksums |
Download a 2-shell software phantom dataset |
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Download reduced freesurfer aparc image from stanford web site |
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Download a 3shell HARDI dataset with 192 gradient direction |
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Download a HARDI dataset with 160 gradient directions |
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Download ResDNN Tensorflow model weights for Nath et. |
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Download ResDNN Pytorch model weights for Nath et. |
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Download Synb0 model weights for Schilling et. |
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Download Synb0 test data for Schilling et. |
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Download DeepN4 model weights for Kanakaraj et. |
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Download DeepN4 test data for Kanakaraj et. |
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Download EVAC+ model weights for Park et. |
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Download EVAC+ model weights for Park et. |
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Download EVAC+ test data for Park et. |
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Download stanford track for examples |
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Download a DSI dataset with 203 gradient directions |
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Download t1 and b0 volumes from the same session |
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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. |
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Download b=0 datasets from multiple MR systems (GE, Philips, Siemens) and different magnetic fields (1.5T and 3T) |
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Download 2 subjects from the SNAIL dataset with their bundles |
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Download IVIM dataset |
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Download CFIN multi b-value diffusion data |
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Download 5 bundles in various file formats and their reference |
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Download atlas tractogram from the hcp842 dataset with 80 bundles |
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Download atlas tractogram from the hcp842 dataset with 30 bundles |
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Download tractogram of one of the hcp dataset subjects |
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Download map of FA within two bundles in one of the hcp dataset subjects |
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Downloads test-retest qt-dMRI acquisitions of two C57Bl6 mice. |
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Downloads the gold standard for streamlines io testing. |
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Download QTE data with linear and planar tensor encoding. |
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Download Rat Brain DDE data for CTI reconstruction (Rat #1 data from Henriques et al. MRM 2021). |
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Surface for testing and examples |
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Download QTE data with linear, planar, and spherical tensor encoding. |
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Download QTE data with linear, planar, and spherical tensor encoding. |
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Download FOD and seeds for PTT testing and examples |
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Download Bundle Warp dataset |
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Download DISCO 1 dataset: The Diffusion-Simulated Connectivity Dataset. |
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Download DISCO 2 dataset: The Diffusion-Simulated Connectivity Dataset. |
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Download DISCO 3 dataset: The Diffusion-Simulated Connectivity Dataset. |
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Download All DISCO datasets. |
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Provide full paths to example or test datasets. |
Load test-retest qt-dMRI acquisitions of two C57Bl6 mice. |
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Load GE 3T b0 image form the scil b0 dataset. |
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Load Siemens 1.5T b0 image from the scil b0 dataset. |
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Load ISBI 2013 2-shell synthetic dataset. |
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Load Sherbrooke 3-shell HARDI dataset. |
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Read stanford hardi data and label map. |
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Load Stanford HARDI dataset. |
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Load Taiwan NTU dataset. |
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Load t1 and b0 volumes from the same session. |
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Download images to be used for tissue classification |
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Load images to be used for tissue classification |
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Read the MNI template from disk. |
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Fetch 'HCP-like' data, collected at multiple b-values. |
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Read CENIR multi b-value data. |
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Read images and streamlines from 2 subjects of the SNAIL dataset. |
Load IVIM dataset. |
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Load CFIN multi b-value DWI data. |
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Load CFIN T1-weighted data. |
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Read q-space trajectory encoding data with linear and planar tensor encoding. |
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Read q-space trajectory encoding data with 70 between linear, planar, and spherical tensor encoding measurements. |
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Read q-space trajectory encoding data with 217 between linear, planar, and spherical tensor encoding. |
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Extract 5 'AF_L','CST_R' and 'CC_ForcepsMajor' trk files in out_dir folder. |
Load 5 small left arcuate fasciculus bundles. |
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Dumps a dict into a bids description at the given location |
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Fetch HCP diffusion data and arrange it in a manner that resembles the BIDS specification. |
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Fetch preprocessed data from the Healthy Brain Network POD2 study. |
DataError#
loads_compat#
get_sim_voxels#
- dipy.data.get_sim_voxels(*, name='fib1')[source]#
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(name='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(name='fib2') >>> sv2['fibres'] '2' >>> sv2['snr'] '80'
get_skeleton#
- dipy.data.get_skeleton(*, name='C1')[source]#
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(name='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')[source]#
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(name="symmetric362") >>> verts, faces = sphere.vertices, sphere.faces >>> verts.shape == (362, 3) True >>> faces.shape == (720, 3) True >>> verts, faces = get_sphere(name="not a sphere name") Traceback (most recent call last): ... DataError: No sphere called "not a sphere name"
get_3shell_gtab#
- dipy.data.get_3shell_gtab()#
get_isbi2013_2shell_gtab#
- dipy.data.get_isbi2013_2shell_gtab()#
get_gtab_taiwan_dsi#
- dipy.data.get_gtab_taiwan_dsi()#
dsi_voxels#
dsi_deconv_voxels#
mrtrix_spherical_functions#
- dipy.data.mrtrix_spherical_functions()[source]#
Spherical functions represented by spherical harmonic coefficients and evaluated on a discrete sphere.
- Returns:
- func_coefarray (2, 3, 4, 45)
Functions represented by the coefficients associated with the mrtrix spherical harmonic basis of maximal order (l) 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.
get_cmap#
two_cingulum_bundles#
matlab_life_results#
load_sdp_constraints#
- dipy.data.load_sdp_constraints(model_name, *, order=None)[source]#
Import semidefinite programming constraint matrices for different models.
Generated as described for example in [1].
- Parameters:
- model_namestring
A string identifying the model that is to be constrained.
- orderunsigned int, optional
A non-negative integer that represent the order or instance of the model. Default: None.
- Returns:
- sdp_constraintsarray
Constraint matrices
Notes
The constraints will be loaded from a file named ‘id_constraint_order.npz’.
References
copyfileobj_withprogress#
check_md5#
fetch_data#
- dipy.data.fetcher.fetch_data(files, folder, *, data_size=None, use_headers=False)[source]#
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.
- use_headersbool, optional
Whether to use headers when downloading files.
- 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_isbi2013_2shell#
- dipy.data.fetcher.fetch_isbi2013_2shell()#
Download a 2-shell software phantom dataset
fetch_stanford_labels#
- dipy.data.fetcher.fetch_stanford_labels()#
Download reduced freesurfer aparc image from stanford web site
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_resdnn_tf_weights#
- dipy.data.fetcher.fetch_resdnn_tf_weights()#
Download ResDNN Tensorflow model weights for Nath et. al 2018
fetch_resdnn_torch_weights#
- dipy.data.fetcher.fetch_resdnn_torch_weights()#
Download ResDNN Pytorch model weights for Nath et. al 2018
fetch_synb0_weights#
- dipy.data.fetcher.fetch_synb0_weights()#
Download Synb0 model weights for Schilling et. al 2019
fetch_synb0_test#
- dipy.data.fetcher.fetch_synb0_test()#
Download Synb0 test data for Schilling et. al 2019
fetch_deepn4_weights#
- dipy.data.fetcher.fetch_deepn4_weights()#
Download DeepN4 model weights for Kanakaraj et. al 2024
fetch_deepn4_test#
- dipy.data.fetcher.fetch_deepn4_test()#
Download DeepN4 test data for Kanakaraj et. al 2024
fetch_evac_tf_weights#
- dipy.data.fetcher.fetch_evac_tf_weights()#
Download EVAC+ model weights for Park et. al 2022
fetch_evac_torch_weights#
- dipy.data.fetcher.fetch_evac_torch_weights()#
Download EVAC+ model weights for Park et. al 2022
fetch_evac_test#
- dipy.data.fetcher.fetch_evac_test()#
Download EVAC+ test data for Park et. al 2022
fetch_stanford_t1#
- dipy.data.fetcher.fetch_stanford_t1()#
fetch_stanford_pve_maps#
- dipy.data.fetcher.fetch_stanford_pve_maps()#
fetch_stanford_tracks#
- dipy.data.fetcher.fetch_stanford_tracks()#
Download stanford track for examples
fetch_taiwan_ntu_dsi#
- dipy.data.fetcher.fetch_taiwan_ntu_dsi()#
Download a DSI dataset with 203 gradient directions
fetch_syn_data#
- dipy.data.fetcher.fetch_syn_data()#
Download t1 and b0 volumes from the same session
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:
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.
References
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_bundles_2_subjects#
- dipy.data.fetcher.fetch_bundles_2_subjects()#
Download 2 subjects from the SNAIL dataset with their bundles
fetch_ivim#
- dipy.data.fetcher.fetch_ivim()#
Download IVIM dataset
fetch_cfin_multib#
- dipy.data.fetcher.fetch_cfin_multib()#
Download CFIN multi b-value diffusion data
fetch_file_formats#
- dipy.data.fetcher.fetch_file_formats()#
Download 5 bundles in various file formats and their reference
fetch_bundle_atlas_hcp842#
- dipy.data.fetcher.fetch_bundle_atlas_hcp842()#
Download atlas tractogram from the hcp842 dataset with 80 bundles
fetch_30_bundle_atlas_hcp842#
- dipy.data.fetcher.fetch_30_bundle_atlas_hcp842()#
Download atlas tractogram from the hcp842 dataset with 30 bundles
fetch_target_tractogram_hcp#
- dipy.data.fetcher.fetch_target_tractogram_hcp()#
Download tractogram of one of the hcp dataset subjects
fetch_bundle_fa_hcp#
- dipy.data.fetcher.fetch_bundle_fa_hcp()#
Download map of FA within two bundles in one of the hcp dataset subjects
fetch_qtdMRI_test_retest_2subjects#
- dipy.data.fetcher.fetch_qtdMRI_test_retest_2subjects()#
Downloads test-retest qt-dMRI acquisitions of two C57Bl6 mice.
fetch_gold_standard_io#
- dipy.data.fetcher.fetch_gold_standard_io()#
Downloads the gold standard for streamlines io testing.
fetch_qte_lte_pte#
- dipy.data.fetcher.fetch_qte_lte_pte()#
Download QTE data with linear and planar tensor encoding.
fetch_cti_rat1#
- dipy.data.fetcher.fetch_cti_rat1()#
Download Rat Brain DDE data for CTI reconstruction (Rat #1 data from Henriques et al. MRM 2021).
fetch_fury_surface#
- dipy.data.fetcher.fetch_fury_surface()#
Surface for testing and examples
fetch_DiB_70_lte_pte_ste#
- dipy.data.fetcher.fetch_DiB_70_lte_pte_ste()#
Download QTE data with linear, planar, and spherical tensor encoding. If using this data please cite F Szczepankiewicz, S Hoge, C-F Westin. Linear, planar and spherical tensor-valued diffusion MRI data by free waveform encoding in healthy brain, water, oil and liquid crystals. Data in Brief (2019),DOI: https://doi.org/10.1016/j.dib.2019.104208
fetch_DiB_217_lte_pte_ste#
- dipy.data.fetcher.fetch_DiB_217_lte_pte_ste()#
Download QTE data with linear, planar, and spherical tensor encoding. If using this data please cite F Szczepankiewicz, S Hoge, C-F Westin. Linear, planar and spherical tensor-valued diffusion MRI data by free waveform encoding in healthy brain, water, oil and liquid crystals. Data in Brief (2019),DOI: https://doi.org/10.1016/j.dib.2019.104208
fetch_ptt_minimal_dataset#
- dipy.data.fetcher.fetch_ptt_minimal_dataset()#
Download FOD and seeds for PTT testing and examples
fetch_bundle_warp_dataset#
- dipy.data.fetcher.fetch_bundle_warp_dataset()#
Download Bundle Warp dataset
fetch_disco1_dataset#
- dipy.data.fetcher.fetch_disco1_dataset()#
Download DISCO 1 dataset: The Diffusion-Simulated Connectivity Dataset. DOI: 10.17632/fgf86jdfg6.3
fetch_disco2_dataset#
- dipy.data.fetcher.fetch_disco2_dataset()#
Download DISCO 2 dataset: The Diffusion-Simulated Connectivity Dataset. DOI: 10.17632/fgf86jdfg6.3
fetch_disco3_dataset#
- dipy.data.fetcher.fetch_disco3_dataset()#
Download DISCO 3 dataset: The Diffusion-Simulated Connectivity Dataset. DOI: 10.17632/fgf86jdfg6.3
fetch_disco_dataset#
get_fnames#
- dipy.data.fetcher.get_fnames(*, name='small_64D')[source]#
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(name='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
read_qtdMRI_test_retest_2subjects#
- dipy.data.fetcher.read_qtdMRI_test_retest_2subjects()[source]#
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:tau-indices) in the corpus callosum of two mice [4]. The data itself and its details are publicly available and can be cited at [5].
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
read_scil_b0#
read_siemens_scil_b0#
read_isbi2013_2shell#
read_sherbrooke_3shell#
read_stanford_labels#
read_stanford_hardi#
read_stanford_t1#
read_stanford_pve_maps#
read_taiwan_ntu_dsi#
read_syn_data#
fetch_tissue_data#
read_tissue_data#
read_mni_template#
- dipy.data.fetcher.read_mni_template(*, version='a', contrast='T2')[source]#
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:
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.
References
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"])
fetch_cenir_multib#
- dipy.data.fetcher.fetch_cenir_multib(*, with_raw=False)[source]#
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
read_cenir_multib#
- dipy.data.fetcher.read_cenir_multib(*, bvals=None)[source]#
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_bundles_2_subjects#
- dipy.data.fetcher.read_bundles_2_subjects(*, subj_id='subj_1', metrics=('fa',), bundles=('af.left', 'cst.right', 'cc_1'))[source]#
Read images and streamlines from 2 subjects of the SNAIL dataset.
See [6] and [7] for further details about the dataset and processing pipeline.
- Parameters:
- subj_idstring
Either
subj_1orsubj_2.- metricsarray-like
Either [‘fa’] or [‘t1’] or [‘fa’, ‘t1’]
- bundlesarray-like
E.g., [‘af.left’, ‘cst.right’, ‘cc_1’]. See all the available bundles in the
exp_bundles_maps/bundles_2_subjectsdirectory of yourDIPY_HOMEof$HOME/.dipyfolder.
- Returns:
- dixdict
Dictionary with data of the metrics and the bundles as keys.
Notes
If you are using these datasets please cite the following publications.
References
read_ivim#
read_cfin_dwi#
read_cfin_t1#
get_file_formats#
get_bundle_atlas_hcp842#
get_two_hcp842_bundles#
get_target_tractogram_hcp#
read_qte_lte_pte#
read_DiB_70_lte_pte_ste#
- dipy.data.fetcher.read_DiB_70_lte_pte_ste()[source]#
Read q-space trajectory encoding data with 70 between linear, planar, and spherical tensor encoding measurements.
- Returns:
- data_imgnibabel.nifti1.Nifti1Image
dMRI data image.
- mask_imgnibabel.nifti1.Nifti1Image
Brain mask image.
- gtabdipy.core.gradients.GradientTable
Gradient table.
read_DiB_217_lte_pte_ste#
- dipy.data.fetcher.read_DiB_217_lte_pte_ste()[source]#
Read q-space trajectory encoding data with 217 between linear, planar, and spherical tensor encoding.
- Returns:
- data_imgnibabel.nifti1.Nifti1Image
dMRI data image.
- mask_imgnibabel.nifti1.Nifti1Image
Brain mask image.
- gtabdipy.core.gradients.GradientTable
Gradient table.
extract_example_tracts#
read_five_af_bundles#
to_bids_description#
fetch_hcp#
- dipy.data.fetcher.fetch_hcp(subjects, hcp_bucket='hcp-openaccess', profile_name='hcp', path=None, study='HCP_1200', aws_access_key_id=None, aws_secret_access_key=None)[source]#
Fetch HCP diffusion data and arrange it in a manner that resembles the BIDS specification.
See [8] for details about the BIDS specification.
- Parameters:
- subjectslist
Each item is an integer, identifying one of the HCP subjects
- hcp_bucketstring, optional
The name of the HCP S3 bucket.
- profile_namestring, optional
The name of the AWS profile used for access.
- pathstring, optional
Path to save files into. Defaults to the value of the
DIPY_HOMEenvironment variable is set; otherwise, defaults to$HOME/.dipy.- studystring, optional
Which HCP study to grab.
- aws_access_key_idstring, optional
AWS credentials to HCP AWS S3. Will only be used if profile_name is set to False.
- aws_secret_access_keystring, optional
AWS credentials to HCP AWS S3. Will only be used if profile_name is set to False.
- Returns:
- dict with remote and local names of these files,
- path to BIDS derivative dataset
Notes
To use this function with its default setting, you need to have a file ‘~/.aws/credentials’, that includes a section:
[hcp] AWS_ACCESS_KEY_ID=XXXXXXXXXXXXXXXX AWS_SECRET_ACCESS_KEY=XXXXXXXXXXXXXXXX
The keys are credentials that you can get from HCP (see https://wiki.humanconnectome.org/display/PublicData/How+To+Connect+to+Connectome+Data+via+AWS)
Local filenames are changed to match our expected conventions.
References
fetch_hbn#
- dipy.data.fetcher.fetch_hbn(subjects, *, path=None, include_afq=False)[source]#
Fetch preprocessed data from the Healthy Brain Network POD2 study.
See [9] and [10] for further details about the study and processing pipeline.
- Parameters:
- subjectslist
Identifiers of the subjects to download. For example: [“NDARAA948VFH”, “NDAREK918EC2”].
- pathstring, optional
Path to save files into. Defaults to the value of the
DIPY_HOMEenvironment variable is set; otherwise, defaults to$HOME/.dipy.- include_afqbool, optional
Whether to include pyAFQ derivatives
- Returns:
- dict with remote and local names of these files,
- path to BIDS derivative dataset
References