neurosynth/analysis/reduce.py
def get_random_voxels(dataset, n_voxels):
    """ Returns mappable data for a random subset of voxels.

    May be useful as a baseline in predictive analyses--e.g., to compare
    performance of a more principled feature selection method with simple
    random selection.

    Args:
        dataset: A Dataset instance
        n_voxels: An integer specifying the number of random voxels to select.

    Returns:
        A 2D numpy array with (randomly-selected) voxels in rows and mappables
        in columns.
    """
    voxels = np.arange(dataset.masker.n_vox_in_vol)
    np.random.shuffle(voxels)
    selected = voxels[0:n_voxels]
    return dataset.get_image_data(voxels=selected)
def get_random_voxels(dataset, n_voxels):
    voxels = np.arange(dataset.masker.n_vox_in_vol)
    np.random.shuffle(voxels)
    selected = voxels[0:n_voxels]
    return dataset.get_image_data(voxels=selected)
def get_random_voxels ( dataset , n_voxels ) : voxels = np . arange ( dataset . masker . n_vox_in_vol ) np . random . shuffle ( voxels ) selected = voxels [ 0 : n_voxels ] return dataset . get_image_data ( voxels = selected )
Returns mappable data for a random subset of voxels.

    May be useful as a baseline in predictive analyses--e.g., to compare
    performance of a more principled feature selection method with simple
    random selection.

    Args:
        dataset: A Dataset instance
        n_voxels: An integer specifying the number of random voxels to select.

    Returns:
        A 2D numpy array with (randomly-selected) voxels in rows and mappables
        in columns.
Returns mappable data for a random subset of voxels .