:py:mod:`AFQ.data.fetch`
========================

.. py:module:: AFQ.data.fetch


Module Contents
---------------


Functions
~~~~~~~~~

.. autoapisummary::

   AFQ.data.fetch.read_callosum_templates
   AFQ.data.fetch.read_templates
   AFQ.data.fetch.read_or_templates
   AFQ.data.fetch.read_stanford_hardi_tractography
   AFQ.data.fetch.organize_stanford_data



Attributes
~~~~~~~~~~

.. autoapisummary::

   AFQ.data.fetch.fetch_callosum_templates
   AFQ.data.fetch.fetch_templates
   AFQ.data.fetch.fetch_or_templates
   AFQ.data.fetch.fetch_stanford_hardi_tractography
   AFQ.data.fetch.fetch_stanford_hardi_lv1


.. py:data:: fetch_callosum_templates

   

.. py:function:: read_callosum_templates(as_img=True, resample_to=False)

   
   Load AFQ callosum templates from file


   :Parameters:

       **as_img** : bool, optional
           If True, values are `Nifti1Image`. Otherwise, values are
           paths to Nifti files. Default: True

       **resample_to** : str or nibabel image class instance, optional
           A template image to resample to. Typically, this should be the
           template to which individual-level data are registered. Defaults to
           the MNI template. Default: False

   :Returns:

       dict with: keys: names of template ROIs and values: nibabel Nifti1Image
           ..

       objects from each of the ROI nifti files.
           ..













   ..
       !! processed by numpydoc !!

.. py:data:: fetch_templates

   

.. py:function:: read_templates(as_img=True, resample_to=False)

   
   Load AFQ templates from file


   :Parameters:

       **as_img** : bool, optional
           If True, values are `Nifti1Image`. Otherwise, values are
           paths to Nifti files. Default: True

       **resample_to** : str or nibabel image class instance, optional
           A template image to resample to. Typically, this should be the
           template to which individual-level data are registered. Defaults to
           the MNI template. Default: False

   :Returns:

       dict with: keys: names of template ROIs and values: nibabel Nifti1Image
           ..

       objects from each of the ROI nifti files.
           ..













   ..
       !! processed by numpydoc !!

.. py:data:: fetch_or_templates

   

.. py:function:: read_or_templates(as_img=True, resample_to=False)

   
   Load AFQ OR templates from file


   :Parameters:

       **as_img** : bool, optional
           If True, values are `Nifti1Image`. Otherwise, values are
           paths to Nifti files. Default: True

       **resample_to** : str or nibabel image class instance, optional
           A template image to resample to. Typically, this should be the
           template to which individual-level data are registered. Defaults to
           the MNI template. Default: False

   :Returns:

       dict with: keys: names of template ROIs and values: nibabel Nifti1Image
           ..

       objects from each of the ROI nifti files.
           ..













   ..
       !! processed by numpydoc !!

.. py:data:: fetch_stanford_hardi_tractography

   

.. py:function:: read_stanford_hardi_tractography()

   
   Reads a minimal tractography from the Stanford dataset.
















   ..
       !! processed by numpydoc !!

.. py:function:: organize_stanford_data(path=None, clear_previous_afq=None)

   
   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

   :Parameters:

       **path** : str or None
           Path to download dataset to, by default it is ~/AFQ_data/.

       **clear_previous_afq** : str or None
           Whether to clear previous afq results in the stanford
           hardi dataset. If not None, can be "all", "track", "recog", "prof".
           Default: None














   ..
       !! processed by numpydoc !!

.. py:data:: fetch_stanford_hardi_lv1