ENH: API change in TransformChain - new composition convention

nitransforms/linear.py

@@ -13,6 +13,7 @@
 from scipy import ndimage as ndi
 
 from nibabel.loadsave import load as _nbload
+from nibabel.affines import from_matvec
 
 from nitransforms.base import (
     ImageGrid,
@@ -218,6 +219,24 @@ def from_filename(cls, filename, fmt=None, reference=None, moving=None):
             f"Could not open <{filename}> (formats tried: {', '.join(fmtlist)})."
         )
 
+    @classmethod
+    def from_matvec(cls, mat=None, vec=None, reference=None):
+        """
+        Create an affine from a matrix and translation pair.
+
+        Example
+        -------
+        >>> Affine.from_matvec(vec=(4, 0, 0))  # doctest: +NORMALIZE_WHITESPACE
+        array([[1., 0., 0., 4.],
+               [0., 1., 0., 0.],
+               [0., 0., 1., 0.],
+               [0., 0., 0., 1.]])
+
+        """
+        mat = mat if mat is not None else np.eye(3)
+        vec = vec if vec is not None else np.zeros((3,))
+        return cls(from_matvec(mat, vector=vec), reference=reference)
+
     def __repr__(self):
         """
         Change representation to the internal matrix.

nitransforms/manip.py

@@ -8,6 +8,7 @@
 ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
 """Common interface for transforms."""
 from collections.abc import Iterable
+import numpy as np
 
 from .base import (
     TransformBase,
@@ -74,8 +75,8 @@ def transforms(self):
     @transforms.setter
     def transforms(self, value):
         self._transforms = _as_chain(value)
-        if self.transforms[-1].reference:
-            self.reference = self.transforms[-1].reference
+        if self.transforms[0].reference:
+            self.reference = self.transforms[0].reference
 
     def append(self, x):
         """
@@ -131,19 +132,56 @@ def map(self, x, inverse=False):
             raise TransformError("Cannot apply an empty transforms chain.")
 
         transforms = self.transforms
-        if not inverse:
-            transforms = self.transforms[::-1]
+        if inverse:
+            transforms = list(reversed(self.transforms))
 
         for xfm in transforms:
-            x = xfm(x, inverse=inverse)
+            x = xfm.map(x, inverse=inverse)
 
         return x
 
-    def asaffine(self):
-        """Combine a succession of linear transforms into one."""
-        retval = self.transforms[-1]
-        for xfm in self.transforms[:-1][::-1]:
-            retval @= xfm
+    def asaffine(self, indices=None):
+        """
+        Combine a succession of linear transforms into one.
+
+        Example
+        ------
+        >>> chain = TransformChain(transforms=[
+        ...     Affine.from_matvec(vec=(2, -10, 3)),
+        ...     Affine.from_matvec(vec=(-2, 10, -3)),
+        ... ])
+        >>> chain.asaffine()
+        array([[1., 0., 0., 0.],
+               [0., 1., 0., 0.],
+               [0., 0., 1., 0.],
+               [0., 0., 0., 1.]])
+
+        >>> chain = TransformChain(transforms=[
+        ...     Affine.from_matvec(vec=(1, 2, 3)),
+        ...     Affine.from_matvec(mat=[[0, 1, 0], [0, 0, 1], [1, 0, 0]]),
+        ... ])
+        >>> chain.asaffine()
+        array([[0., 1., 0., 2.],
+               [0., 0., 1., 3.],
+               [1., 0., 0., 1.],
+               [0., 0., 0., 1.]])
+
+        >>> np.allclose(
+        ...     chain.map((4, -2, 1)),
+        ...     chain.asaffine().map((4, -2, 1)),
+        ... )
+        True
+
+        Parameters
+        ----------
+        indices : :obj:`numpy.array_like`
+            The indices of the values to extract.
+
+        """
+        affines = self.transforms if indices is None else np.take(self.transforms, indices)
+        retval = affines[0]
+        for xfm in affines[1:]:
+            retval = xfm @ retval
         return retval
 
     @classmethod
@@ -157,9 +195,9 @@ def from_filename(cls, filename, fmt="X5", reference=None, moving=None):
             xforms = itk.ITKCompositeH5.from_filename(filename)
             for xfmobj in xforms:
                 if isinstance(xfmobj, itk.ITKLinearTransform):
-                    retval.append(Affine(xfmobj.to_ras(), reference=reference))
+                    retval.insert(0, Affine(xfmobj.to_ras(), reference=reference))
                 else:
-                    retval.append(DisplacementsFieldTransform(xfmobj))
+                    retval.insert(0, DisplacementsFieldTransform(xfmobj))
 
             return TransformChain(retval)