AFQ¶
Subpackages¶
Package Contents¶
Functions¶
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Load AFQ callosum templates from file |
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Load AFQ OR templates from file |
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Load AFQ templates from file |
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Fetch HCP diffusion data and arrange it in a manner that resembles the |
Reads a minimal tractography from the Stanford dataset. |
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If necessary, downloads the Stanford HARDI dataset into DIPY directory and |
Attributes¶
- AFQ.read_callosum_templates(resample_to=False)[source]¶
Load AFQ callosum templates from file
- Returns
- dict with: keys: names of template ROIs and values: nibabel Nifti1Image
- objects from each of the ROI nifti files.
- AFQ.read_or_templates(resample_to=False)[source]¶
Load AFQ OR templates from file
- Returns
- dict with: keys: names of template ROIs and values: nibabel Nifti1Image
- objects from each of the ROI nifti files.
- AFQ.read_templates(resample_to=False)[source]¶
Load AFQ templates from file
- Returns
- dict with: keys: names of template ROIs and values: nibabel Nifti1Image
- objects from each of the ROI nifti files.
- AFQ.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 [1] specification.
- Parameters
- subjectslist
Each item is an integer, identifying one of the HCP subjects
- hcp_bucketstring, optional
The name of the HCP S3 bucket. Default: “hcp-openaccess”
- profile_namestring, optional
The name of the AWS profile used for access. Default: “hcp”
- pathstring, optional
Path to save files into. Default: ‘~/AFQ_data’
- studystring, optional
Which HCP study to grab. Default: ‘HCP_1200’
- aws_access_key_idstring, optional
AWS credentials to HCP AWS S3. Will only be used if profile_name is set to False.
- aws_secret_access_keystring, optional
AWS credentials to HCP AWS S3. Will only be used if profile_name is set to False.
- Returns
- dict with remote and local names of these files,
- path to BIDS derivative dataset
Notes
To use this function with its default setting, you need to have a file ‘~/.aws/credentials’, that includes a section:
[hcp] AWS_ACCESS_KEY_ID=XXXXXXXXXXXXXXXX AWS_SECRET_ACCESS_KEY=XXXXXXXXXXXXXXXX
The keys are credentials that you can get from HCP (see https://wiki.humanconnectome.org/display/PublicData/How+To+Connect+to+Connectome+Data+via+AWS) # noqa
Local filenames are changed to match our expected conventions.
- 1
Gorgolewski et al. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data, 3::160044. DOI: 10.1038/sdata.2016.44.
- AFQ.read_stanford_hardi_tractography()[source]¶
Reads a minimal tractography from the Stanford dataset.
- AFQ.organize_stanford_data(path=None, clear_previous_afq=False)[source]¶
If necessary, downloads the Stanford HARDI dataset into DIPY directory and creates a BIDS compliant file-system structure in AFQ data directory:
~/AFQ_data/ └── stanford_hardi ├── dataset_description.json └── derivatives
├── freesurfer │ ├── dataset_description.json │ └── sub-01 │ └── ses-01 │ └── anat │ ├── sub-01_ses-01_T1w.nii.gz │ └── sub-01_ses-01_seg.nii.gz └── vistasoft
├── dataset_description.json └── sub-01
- └── ses-01
- └── dwi
├── sub-01_ses-01_dwi.bval ├── sub-01_ses-01_dwi.bvec └── sub-01_ses-01_dwi.nii.gz
If clear_previous_afq is True and there is an afq folder in derivatives, it will be removed.