def test_get_target_dtype():
img = Nifti1Image(np.ones((2, 2, 2), dtype=np.float64), affine=np.eye(4))
assert_equal(img.get_data().dtype.kind, 'f')
dtype_kind_float = niimg._get_target_dtype(img.get_data().dtype,
target_dtype='auto')
assert_equal(dtype_kind_float, np.float32)
img2 = Nifti1Image(np.ones((2, 2, 2), dtype=np.int64), affine=np.eye(4))
assert_equal(img2.get_data().dtype.kind, 'i')
dtype_kind_int = niimg._get_target_dtype(img2.get_data().dtype,
target_dtype='auto')
assert_equal(dtype_kind_int, np.int32)
def test_get_target_dtype():
img = Nifti1Image(np.ones((2, 2, 2), dtype=np.float64), affine=np.eye(4))
assert get_data(img).dtype.kind == 'f'
dtype_kind_float = niimg._get_target_dtype(get_data(img).dtype,
target_dtype='auto')
assert dtype_kind_float == np.float32
img2 = Nifti1Image(np.ones((2, 2, 2), dtype=np.int64), affine=np.eye(4))
assert get_data(img2).dtype.kind == 'i'
dtype_kind_int = niimg._get_target_dtype(get_data(img2).dtype,
target_dtype='auto')
assert dtype_kind_int == np.int32
def test_get_target_dtype():
img = Nifti1Image(np.ones((2, 2, 2), dtype=np.float64), affine=np.eye(4))
assert_equal(img.get_data().dtype.kind, 'f')
dtype_kind_float = niimg._get_target_dtype(img.get_data().dtype,
target_dtype='auto')
assert_equal(dtype_kind_float, np.float32)
img2 = Nifti1Image(np.ones((2, 2, 2), dtype=np.int64), affine=np.eye(4))
assert_equal(img2.get_data().dtype.kind, 'i')
dtype_kind_int = niimg._get_target_dtype(img2.get_data().dtype,
target_dtype='auto')
assert_equal(dtype_kind_int, np.int32)
def our_load_niimg(niimg, dtype=None):
"""Load a niimg, check if it is a nibabel SpatialImage and cast if needed
Parameters:
-----------
niimg: Niimg-like object
See http://nilearn.github.io/manipulating_images/input_output.html.
Image to load.
dtype: {dtype, "auto"}
Data type toward which the data should be converted. If "auto", the
data will be converted to int32 if dtype is discrete and float32 if it
is continuous.
Returns:
--------
img: image
A loaded image object.
"""
from nilearn.image import new_img_like # avoid circular imports
if isinstance(niimg, _basestring):
# data is a filename, we load it
niimg = nibabel.load(niimg)
elif not isinstance(niimg, nibabel.spatialimages.SpatialImage):
raise TypeError("Data given cannot be loaded because it is"
" not compatible with nibabel format:\n"
+ short_repr(niimg))
try:
this_dtype = niimg.get_data_dtype()
except AttributeError:
# Nibabel bug
this_dtype = niimg.get_data().dtype
dtype = _get_target_dtype(this_dtype, dtype)
if dtype is not None:
niimg = new_img_like(niimg, niimg.get_data().astype(dtype),
get_affine(niimg))
return niimg