ENH: Make double precision the default

ants/registration/apply_transforms.py

@@ -11,7 +11,7 @@
 def apply_transforms(fixed, moving, transformlist,
                      interpolator='linear', imagetype=0,
                      whichtoinvert=None, compose=None,
-                     defaultvalue=0, singleprecision=True, verbose=False, **kwargs):
+                     defaultvalue=0, singleprecision=False, verbose=False, **kwargs):
     """
     Apply a transform list to map an image from one domain to another.
     In image registration, one computes mappings between (usually) pairs
@@ -68,9 +68,8 @@ def apply_transforms(fixed, moving, transformlist,
         Default voxel value for mappings outside the image domain.
 
     singleprecision : boolean
-        if True, use float32 for computations and output data storage type. This reduces
-        memory storage and computational time for large images, but may result in a loss
-        of precision. Set to False to use double precision.
+        if True, use float32 for computations. This is useful for reducing memory
+        usage for large datasets, at the cost of precision.
 
     verbose : boolean
         print command and run verbose application of transform.

tests/test_registation.py

@@ -53,9 +53,9 @@ def test_example(self):
         self.assertTrue(ants.ants_image.image_physical_space_consistency(fixed, mywarpedimage,
                                                                          0.0001, datatype = False))
 
-        # Call with double precision for transforms, but should still return input type
+        # Call with float precision for transforms, but should still return input type
         mywarpedimage2 = ants.apply_transforms(
-            fixed=fixed, moving=moving, transformlist=mytx["fwdtransforms"], singleprecision=False
+            fixed=fixed, moving=moving, transformlist=mytx["fwdtransforms"], singleprecision=True
         )
         self.assertEqual(mywarpedimage2.pixeltype, moving.pixeltype)
         self.assertAlmostEqual(mywarpedimage.sum(), mywarpedimage2.sum(), places=3)