Note

This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the user guide for the big picture.

nilearn.glm.first_level.mean_scaling

nilearn.glm.first_level.mean_scaling(Y, axis=0)

Scaling of the data to have percent of baseline change along the specified axis.

Parameters:

Yarray of shape (n_time_points, n_voxels)

The input data.

axisint, default=0

Axis along which the scaling mean should be calculated.

Returns:

Yarray of shape (n_time_points, n_voxels),

The data after mean-scaling, de-meaning and multiplication by 100.

meanarray of shape (n_voxels,)

The data mean.

Examples

>>> import numpy as np
>>> from nilearn.glm.first_level import mean_scaling
>>> Y = np.array([[1.0, 2.0, 3.0], [2.0, 4.0, 6.0]])
>>> Y_scaled, mean = mean_scaling(Y)
>>> Y_scaled.shape
(2, 3)
>>> mean
array([1.5, 3. , 4.5])
>>> bool(np.allclose(Y_scaled.mean(axis=0), 0))
True