`AFQ.tasks.segmentation`#

Module Contents#

`segment`(data_imap, mapping_imap, tractography_imap, ...)	full path to a trk/trx file containing containing
`export_bundles`(base_fname, output_dir, bundles, ...)	dictionary of paths, where each path is
`export_sl_counts`(bundles)	full path to a JSON file containing streamline counts
`export_bundle_lengths`(bundles)	full path to a JSON file containing median bundle lengths
`export_density_maps`(bundles, data_imap)	full path to 4d nifti file containing streamline counts per voxel
`tract_profiles`(bundles, scalar_dict, data_imap[, ...])	full path to a CSV file containing tract profiles
`get_scalar_dict`(data_imap, mapping_imap[, scalars])	dicionary mapping scalar names
`get_segmentation_plan`(kwargs)

`has_trx`
`logger`

AFQ.tasks.segmentation.segment(data_imap, mapping_imap, tractography_imap, segmentation_params)[source]#

full path to a trk/trx file containing containing segmented streamlines, labeled by bundle

Parameters

segmentation_paramsdict, optional: The parameters for segmentation. Default: use the default behavior of the seg.Segmentation object.

AFQ.tasks.segmentation.export_bundles(base_fname, output_dir, bundles, tracking_params, segmentation_params)[source]#: dictionary of paths, where each path is a full path to a trk file containing the streamlines of a given bundle.

AFQ.tasks.segmentation.export_sl_counts(bundles)[source]#: full path to a JSON file containing streamline counts

AFQ.tasks.segmentation.export_bundle_lengths(bundles)[source]#: full path to a JSON file containing median bundle lengths

AFQ.tasks.segmentation.export_density_maps(bundles, data_imap)[source]#: full path to 4d nifti file containing streamline counts per voxel per bundle, where the 4th dimension encodes the bundle

AFQ.tasks.segmentation.tract_profiles(bundles, scalar_dict, data_imap, profile_weights='gauss', n_points_profile=100)[source]#

full path to a CSV file containing tract profiles

Parameters

profile_weightsstr, 1D array, 2D array callable, optional: How to weight each streamline (1D) or each node (2D) when calculating the tract-profiles. If callable, this is a function that calculates weights. If None, no weighting will be applied. If “gauss”, gaussian weights will be used. If “median”, the median of values at each node will be used instead of a mean or weighted mean. Default: “gauss”
n_points_profileint, optional: Number of points to resample each streamline to before calculating the tract-profiles. Default: 100

AFQ.tasks.segmentation.get_scalar_dict(data_imap, mapping_imap, scalars=['dti_fa', 'dti_md'])[source]#

dicionary mapping scalar names to their respective file paths

Parameters

scalarslist of strings and/or scalar definitions, optional: List of scalars to use. Can be any of: “dti_fa”, “dti_md”, “dki_fa”, “dki_md”, “dki_awf”, “dki_mk”. Can also be a scalar from AFQ.definitions.image. Default: [“dti_fa”, “dti_md”]