htfa.io.Subject¶

class htfa.io.Subject(data, coords, affine=None, mask=None, image_shape=None, source=None, group_path=())[source]¶

One subject’s data in voxel/pixel-by-sample form.

Parameters:

datanumpy.ndarray: Values, shape (V, T), float64.
coordsnumpy.ndarray: Coordinates of the V locations, shape (V, D), float64.
affinenumpy.ndarray or None: (4, 4) voxel-to-world affine, for NIfTI-derived subjects.
masknumpy.ndarray or None: Boolean mask in image space (image_shape) selecting the V locations.
image_shapetuple or None: Shape of the image grid the data came from.
sourcestr or None: Path the data were read from, if any.
group_pathtuple of str: Folder-derived hierarchy, from outermost to innermost group. Empty for individual files, arrays, flat folders, and stacked inputs.

Examples

>>> import numpy as np
>>> from htfa.io import load
>>> (subject,) = load(np.random.rand(4, 5) + 1)
>>> subject.data.shape, subject.coords.shape
((20, 1), (20, 2))

__init__(data, coords, affine=None, mask=None, image_shape=None, source=None, group_path=())¶

Methods

`__init__`(data, coords[, affine, mask, ...])
`to_image`(values)	Place `values` back into image space, with NaN outside the mask.

Attributes

`affine`
`group_path`
`image_shape`
`mask`
`source`
`data`
`coords`