affines
¶
Utility routines for working with points and affine transforms
Errors in calculating or using affines 


Add diagonal elements steps and translations starts to affine 

Apply affine matrix aff to points pts 

Apply numpy dot product function from right to left on arrays 

Combine a matrix and vector into an homogeneous affine 

Estimate the obliquity an affine's axes represent 

Return a new affine matrix with updated voxel sizes (zooms) 

Split a transform into its matrix and vector components 

Return voxel size for each input axis given affine 
AffineError
¶
 class nibabel.affines.AffineError¶
Bases:
ValueError
Errors in calculating or using affines
 __init__(*args, **kwargs)¶
append_diag¶
 nibabel.affines.append_diag(aff, steps, starts=())¶
Add diagonal elements steps and translations starts to affine
Typical use is in expanding 4x4 affines to larger dimensions. Nipy is the main consumer because it uses NxM affines, whereas we generally only use 4x4 affines; the routine is here for convenience.
 Parameters:
 aff2D array
N by M affine matrix
 stepsscalar or sequence
diagonal elements to append.
 startsscalar or sequence
elements to append to last column of aff, representing translations corresponding to the steps. If empty, expands to a vector of zeros of the same length as steps
 Returns:
 aff_plus2D array
Now P by Q where L =
len(steps)
and P == N+L, Q=N+L
Examples
>>> aff = np.eye(4) >>> aff[:3,:3] = np.arange(9).reshape((3,3)) >>> append_diag(aff, [9, 10], [99,100]) array([[ 0., 1., 2., 0., 0., 0.], [ 3., 4., 5., 0., 0., 0.], [ 6., 7., 8., 0., 0., 0.], [ 0., 0., 0., 9., 0., 99.], [ 0., 0., 0., 0., 10., 100.], [ 0., 0., 0., 0., 0., 1.]])
apply_affine¶
 nibabel.affines.apply_affine(aff, pts, inplace=False)¶
Apply affine matrix aff to points pts
Returns result of application of aff to the right of pts. The coordinate dimension of pts should be the last.
For the 3D case, aff will be shape (4,4) and pts will have final axis length 3  maybe it will just be N by 3. The return value is the transformed points, in this case:
res = np.dot(aff[:3,:3], pts.T) + aff[:3,3:4] transformed_pts = res.T
This routine is more general than 3D, in that aff can have any shape (N,N), and pts can have any shape, as long as the last dimension is for the coordinates, and is therefore length N1.
 Parameters:
 aff(N, N) arraylike
Homogeneous affine, for 3D points, will be 4 by 4. Contrary to first appearance, the affine will be applied on the left of pts.
 pts(…, N1) arraylike
Points, where the last dimension contains the coordinates of each point. For 3D, the last dimension will be length 3.
 inplacebool, optional
If True, attempt to apply the affine directly to
pts
. If False, or inplace application fails, a freshly allocated array will be returned.
 Returns:
 transformed_pts(…, N1) array
transformed points
Examples
>>> aff = np.array([[0,2,0,10],[3,0,0,11],[0,0,4,12],[0,0,0,1]]) >>> pts = np.array([[1,2,3],[2,3,4],[4,5,6],[6,7,8]]) >>> apply_affine(aff, pts) array([[14, 14, 24], [16, 17, 28], [20, 23, 36], [24, 29, 44]]...)
Just to show that in the simple 3D case, it is equivalent to:
>>> (np.dot(aff[:3,:3], pts.T) + aff[:3,3:4]).T array([[14, 14, 24], [16, 17, 28], [20, 23, 36], [24, 29, 44]]...)
But pts can be a more complicated shape:
>>> pts = pts.reshape((2,2,3)) >>> apply_affine(aff, pts) array([[[14, 14, 24], [16, 17, 28]], [[20, 23, 36], [24, 29, 44]]]...)
dot_reduce¶
 nibabel.affines.dot_reduce(*args)¶
Apply numpy dot product function from right to left on arrays
For passed arrays \(A, B, C, ... Z\) returns \(A \dot B \dot C ... \dot Z\) where “.” is the numpy array dot product.
 Parameters:
 **argsarrays
Arrays that can be passed to numpy
dot
function
 Returns:
 dot_productarray
If there are N arguments, result of
arg[0].dot(arg[1].dot(arg[2].dot ... arg[N2].dot(arg[N1])))...
from_matvec¶
 nibabel.affines.from_matvec(matrix, vector=None)¶
Combine a matrix and vector into an homogeneous affine
Combine a rotation / scaling / shearing matrix and translation vector into a transform in homogeneous coordinates.
 Parameters:
 matrixarraylike
An NxM array representing the the linear part of the transform. A transform from an Mdimensional space to an Ndimensional space.
 vectorNone or arraylike, optional
None or an (N,) array representing the translation. None corresponds to an (N,) array of zeros.
 Returns:
 xformarray
An (N+1, M+1) homogeneous transform matrix.
See also
Examples
>>> from_matvec(np.diag([2, 3, 4]), [9, 10, 11]) array([[ 2, 0, 0, 9], [ 0, 3, 0, 10], [ 0, 0, 4, 11], [ 0, 0, 0, 1]])
The vector argument is optional:
>>> from_matvec(np.diag([2, 3, 4])) array([[2, 0, 0, 0], [0, 3, 0, 0], [0, 0, 4, 0], [0, 0, 0, 1]])
obliquity¶
 nibabel.affines.obliquity(affine)¶
Estimate the obliquity an affine’s axes represent
The term obliquity is defined here as the rotation of those axes with respect to the cardinal axes. This implementation is inspired by AFNI’s implementation. For further details about obliquity, check AFNI’s documentation.
 Parameters:
 affine2D arraylike
Affine transformation array. Usually shape (4, 4), but can be any 2D array.
 Returns:
 angles1D arraylike
The obliquity of each axis with respect to the cardinal axes, in radians.
rescale_affine¶
 nibabel.affines.rescale_affine(affine, shape, zooms, new_shape=None)¶
Return a new affine matrix with updated voxel sizes (zooms)
This function preserves the rotations and shears of the original affine, as well as the RAS location of the central voxel of the image.
 Parameters:
 affine(N, N) arraylike
NxN transform matrix in homogeneous coordinates representing an affine transformation from an (N1)dimensional space to an (N1)dimensional space. An example is a 4x4 transform representing rotations and translations in 3 dimensions.
 shape(N1,) arraylike
The extent of the (N1) dimensions of the original space
 zooms(N1,) arraylike
The size of voxels of the output affine
 new_shape(N1,) arraylike, optional
The extent of the (N1) dimensions of the space described by the new affine. If
None
, useshape
.
 Returns:
 affine(N, N) array
A new affine transform with the specified voxel sizes
to_matvec¶
 nibabel.affines.to_matvec(transform)¶
Split a transform into its matrix and vector components
The transformation must be represented in homogeneous coordinates and is split into its rotation matrix and translation vector components.
 Parameters:
 transformarraylike
NxM transform matrix in homogeneous coordinates representing an affine transformation from an (N1)dimensional space to an (M1)dimensional space. An example is a 4x4 transform representing rotations and translations in 3 dimensions. A 4x3 matrix can represent a 2dimensional plane embedded in 3 dimensional space.
 Returns:
 matrix(N1, M1) array
Matrix component of transform
 vector(M1,) array
Vector component of transform
See also
Examples
>>> aff = np.diag([2, 3, 4, 1]) >>> aff[:3,3] = [9, 10, 11] >>> to_matvec(aff) (array([[2, 0, 0], [0, 3, 0], [0, 0, 4]]), array([ 9, 10, 11]))
voxel_sizes¶
 nibabel.affines.voxel_sizes(affine)¶
Return voxel size for each input axis given affine
The affine is the mapping between array (voxel) coordinates and mm (world) coordinates.
The voxel size for the first voxel (array) axis is the distance moved in world coordinates when moving one unit along the first voxel (array) axis. This is the distance between the world coordinate of voxel (0, 0, 0) and the world coordinate of voxel (1, 0, 0). The world coordinate vector of voxel coordinate vector (0, 0, 0) is given by
v0 = affine.dot((0, 0, 0, 1)[:3]
. The world coordinate vector of voxel vector (1, 0, 0) isv1_ax1 = affine.dot((1, 0, 0, 1))[:3]
. The final 1 in the voxel vectors and the[:3]
at the end are because the affine works on homogeneous coordinates. The translations part of the affine istrans = affine[:3, 3]
, and the rotations, zooms and shearing part of the affine isrzs = affine[:3, :3]
. Because of the final 1 in the input voxel vector,v0 == rzs.dot((0, 0, 0)) + trans
, andv1_ax1 == rzs.dot((1, 0, 0)) + trans
, and the difference vector isrzs.dot((0, 0, 0))  rzs.dot((1, 0, 0)) == rzs.dot((1, 0, 0)) == rzs[:, 0]
. The distance vectors in world coordinates between (0, 0, 0) and (1, 0, 0), (0, 1, 0), (0, 0, 1) are given byrzs.dot(np.eye(3)) = rzs
. The voxel sizes are the Euclidean lengths of the distance vectors. So, the voxel sizes are the Euclidean lengths of the columns of the affine (excluding the last row and column of the affine). Parameters:
 affine2D arraylike
Affine transformation array. Usually shape (4, 4), but can be any 2D array.
 Returns:
 vox_sizes1D array
Voxel sizes for each input axis of affine. Usually 1D array length 3, but in general has length (N1) where input affine is shape (M, N).