def test_grid_creation(self):
mask = self.dataset.masker.volume
# Test with mask
grid = imageutils.create_grid(image=mask, scale=4)
self.assertEquals(grid.shape, (91, 109, 91))
self.assertEquals(len(np.unique(grid.get_data())), 4359)
# Test without mask
grid = imageutils.create_grid(image=mask, scale=4, apply_mask=False)
self.assertGreater(len(np.unique(grid.get_data())), 4359)
def apply_grid(dataset, masker=None, scale=5, threshold=None):
""" Imposes a 3D grid on the brain volume and averages across all voxels that
fall within each cell.
Args:
dataset: Data to apply grid to. Either a Dataset instance, or a numpy array
with voxels in rows and features in columns.
masker: Optional Masker instance used to map between the created grid and
the dataset. This is only needed if dataset is a numpy array; if
dataset is a Dataset instance, the Masker in the dataset will be used.
scale: int; scaling factor (in mm) to pass onto create_grid().
threshold: Optional float to pass to reduce.average_within_regions().
Returns:
A tuple of length 2, where the first element is a numpy array of dimensions
n_cubes x n_studies, and the second element is a numpy array, with the same
dimensions as the Masker instance in the current Dataset, that maps voxel
identities onto cell IDs in the grid.
"""
if masker is None:
if isinstance(dataset, Dataset):
masker = dataset.masker
else:
raise ValueError(
"If dataset is a numpy array, a masker must be provided.")
grid = imageutils.create_grid(masker.volume, scale)
cm = masker.mask(grid, in_global_mask=True)
data = average_within_regions(dataset, cm, threshold)
return (data, grid)
def apply_grid(dataset, masker=None, scale=5, threshold=None):
""" Imposes a 3D grid on the brain volume and averages across all voxels
that fall within each cell.
Args:
dataset: Data to apply grid to. Either a Dataset instance, or a numpy
array with voxels in rows and features in columns.
masker: Optional Masker instance used to map between the created grid
and the dataset. This is only needed if dataset is a numpy array;
if dataset is a Dataset instance, the Masker in the dataset will
be used.
scale: int; scaling factor (in mm) to pass onto create_grid().
threshold: Optional float to pass to reduce.average_within_regions().
Returns:
A tuple of length 2, where the first element is a numpy array of
dimensions n_cubes x n_studies, and the second element is a numpy
array, with the same dimensions as the Masker instance in the current
Dataset, that maps voxel identities onto cell IDs in the grid.
"""
if masker is None:
if isinstance(dataset, Dataset):
masker = dataset.masker
else:
raise ValueError(
"If dataset is a numpy array, a masker must be provided.")
grid = imageutils.create_grid(masker.volume, scale)
cm = masker.mask(grid, in_global_mask=True)
data = average_within_regions(dataset, cm, threshold)
return (data, grid)
