def test_drop_io_dim():
aff = np.diag([1,2,3,1])
in_dims = list('ijk')
out_dims = list('xyz')
cm = Affine.from_params(in_dims, out_dims, aff)
yield assert_raises(ValueError, drop_io_dim, cm, 'q')
for i, d in enumerate(in_dims):
cm2 = drop_io_dim(cm, d)
yield assert_equal(cm2.ndim, (2,2))
ods = out_dims[:]
ids = in_dims[:]
ods.pop(i)
ids.pop(i)
yield assert_equal(ods, cm2.output_coords.coord_names)
yield assert_equal(ids, cm2.input_coords.coord_names)
a2 = cm2.affine
yield assert_equal(a2.shape, (3,3))
ind_a = np.ones((4,4), dtype=np.bool)
ind_a[:,i] = False
ind_a[i,:] = False
aff2 = cm.affine[ind_a].reshape((3,3))
yield assert_array_equal(aff2, a2)
# Check non-orth case
waff = np.diag([1,2,3,1])
wrong_i = (i+1) % 3
waff[wrong_i,i] = 2 # not OK if in same column as that being removed
wcm = Affine.from_params(in_dims, out_dims, waff)
yield assert_raises(ValueError, drop_io_dim, wcm, d)
raff = np.diag([1,2,3,1])
raff[i,wrong_i] = 2 # is OK if in same row
rcm = Affine.from_params(in_dims, out_dims, raff)
cm2 = drop_io_dim(rcm, d)
# dims out of range give error
yield assert_raises(ValueError, drop_io_dim, cm, 'p')
# only works for affine case
cm = CoordinateMap(lambda x:x, cm.input_coords, cm.output_coords)
yield assert_raises(AttributeError, drop_io_dim, cm, 'i')
# Check non-square case
aff = np.array([[1.,0,0,0],
[0,2,0,0],
[0,0,3,0],
[0,0,0,1],
[0,0,0,1]])
cm = Affine.from_params(in_dims, out_dims + ['t'], aff)
cm2 = drop_io_dim(cm, 'i')
yield assert_array_equal(cm2.affine,
[[2,0,0],
[0,3,0],
[0,0,1],
[0,0,1]])
yield assert_raises(ValueError, drop_io_dim, cm, 't')
def test_drop_io_dim():
# test ordinary case of 4d to 3d
cm4d = AffineTransform.from_params('ijkl', 'xyzt', np.diag([1,2,3,4,1]))
cm3d = drop_io_dim(cm4d, 't')
yield assert_array_equal(cm3d.affine, np.diag([1, 2, 3, 1]))
# 3d to 2d
cm3d = AffineTransform.from_params('ijk', 'xyz', np.diag([1,2,3,1]))
cm2d = drop_io_dim(cm3d, 'z')
yield assert_array_equal(cm2d.affine, np.diag([1, 2, 1]))
# test zero scaling for dropped dimension
cm3d = AffineTransform.from_params('ijk', 'xyz', np.diag([1, 2, 0, 1]))
cm2d = drop_io_dim(cm3d, 'z')
yield assert_array_equal(cm2d.affine, np.diag([1, 2, 1]))
# test not diagonal but orthogonal
aff = np.array([[1, 0, 0, 0],
[0, 0, 2, 0],
[0, 3, 0, 0],
[0, 0, 0, 1]])
cm3d = AffineTransform.from_params('ijk', 'xyz', aff)
cm2d = drop_io_dim(cm3d, 'z')
yield assert_array_equal(cm2d.affine, np.diag([1, 2, 1]))
cm2d = drop_io_dim(cm3d, 'k')
yield assert_array_equal(cm2d.affine, np.diag([1, 3, 1]))
# and with zeros scaling for orthogonal dropped dimension
aff[2] = 0
cm3d = AffineTransform.from_params('ijk', 'xyz', aff)
cm2d = drop_io_dim(cm3d, 'z')
yield assert_array_equal(cm2d.affine, np.diag([1, 2, 1]))
def fix_analyze_image(img, fliplr=False):
cmap = img.coordmap
if fliplr:
x_idx = cmap.function_range.index('x')
cmap.affine[x_idx] *= -1
if (len(img.shape) < 4) or img.shape[3] != 1:
return img
try:
from nipy.core.reference.coordinate_map import drop_io_dim
except:
print 'not fixing this image... NIPY API changed'
return img
# affine is a 5x5: need to get rid of row 3 and col 3
arr = np.asarray(img).reshape(img.shape[:3])
cmap = drop_io_dim(cmap, 't')
return ni_api.Image(arr, cmap)
def get_3D_coordmap(img):
'''
Gets a 3D CoordinateMap from img.
Parameters
----------
img: nib.Nifti1Image or nipy Image
Returns
-------
nipy.core.reference.coordinate_map.CoordinateMap
'''
if isinstance(img, nib.Nifti1Image):
img = nifti2nipy(img)
if img.ndim == 4:
from nipy.core.reference.coordinate_map import drop_io_dim
cm = drop_io_dim(img.coordmap, 3)
else:
cm = img.coordmap
return cm
def get_3D_coordmap(img):
'''
Gets a 3D CoordinateMap from img.
Parameters
----------
img: nib.Nifti1Image or nipy Image
Returns
-------
nipy.core.reference.coordinate_map.CoordinateMap
'''
if isinstance(img, nib.Nifti1Image):
img = nifti2nipy(img)
if img.ndim == 4:
from nipy.core.reference.coordinate_map import drop_io_dim
cm = drop_io_dim(img.coordmap, 3)
else:
cm = img.coordmap
return cm
