Add the mode param for border management.

dipy/align/aniso2iso.py

@@ -3,14 +3,15 @@
 import numpy as np
 from scipy.ndimage import affine_transform
 
-def resample(data,affine,zooms,new_zooms,order=1):
-    ''' Resample data from anisotropic to isotropic voxel size
-
+
+def resample(data, affine, zooms, new_zooms, order=1, mode='constant', cval=0):
+    """Resample data from anisotropic to isotropic voxel size
+
     Parameters
     ----------
-    data : array, shape (I,J,K) or (I,J,K,N) 
+    data : array, shape (I,J,K) or (I,J,K,N)
         3d volume or 4d volume with datasets
-    affine : array, shape (4,4) 
+    affine : array, shape (4,4)
         mapping from voxel coordinates to world coordinates
     zooms : tuple, shape (3,)
         voxel size for (i,j,k) dimensions
@@ -20,57 +21,69 @@ def resample(data,affine,zooms,new_zooms,order=1):
         order of interpolation for resampling/reslicing,
         0 nearest interpolation, 1 trilinear etc..
         if you don't want any smoothing 0 is the option you need.
-
+    mode : string ('constant', 'nearest', 'reflect' or 'wrap')
+        Points outside the boundaries of the input are filled according
+        to the given mode.
+    cval : float
+        Value used for points outside the boundaries of the input if
+        mode='constant'.
+
     Returns
     -------
-    data2 : array, shape (I,J,K) or (I,J,K,N) 
+    data2 : array, shape (I,J,K) or (I,J,K,N)
         datasets resampled into isotropic voxel size
     affine2 : array, shape (4,4)
         new affine for the resampled image
-        
+
     Notes
     -----
-    It is also possible with this function to resample/reslice from isotropic voxel size to anisotropic 
-    or from isotropic to isotropic or even from anisotropic to anisotropic, as long as you provide
-    the correct zooms (voxel sizes) and new_zooms (new voxel sizes). It is fairly easy to get the correct
-    zooms using nibabel as show in the example below. 
-
+    It is also possible with this function to resample/reslice from isotropic
+    voxel size to anisotropic or from isotropic to isotropic or even from
+    anisotropic to anisotropic, as long as you provide the correct zooms
+    (voxel sizes) and new_zooms (new voxel sizes). It is fairly easy to get the
+    correct zooms using nibabel as show in the example below.
+
     Examples
     --------
     >>> import nibabel as nib
     >>> from dipy.align.aniso2iso import resample
-    >>> from dipy.data import get_data    
-    >>> fimg=get_data('aniso_vox')    
-    >>> img=nib.load(fimg)
-    >>> data=img.get_data()
+    >>> from dipy.data import get_data
+    >>> fimg = get_data('aniso_vox')
+    >>> img = nib.load(fimg)
+    >>> data = img.get_data()
     >>> data.shape
     (58, 58, 24)
-    >>> affine=img.get_affine()
-    >>> zooms=img.get_header().get_zooms()[:3]
-    >>> zooms      
+    >>> affine = img.get_affine()
+    >>> zooms = img.get_header().get_zooms()[:3]
+    >>> zooms
     (4.0, 4.0, 5.0)
-    >>> new_zooms=(3.,3.,3.)
+    >>> new_zooms = (3.,3.,3.)
     >>> new_zooms
     (3.0, 3.0, 3.0)
-    >>> data2,affine2=resample(data,affine,zooms,new_zooms)
+    >>> data2, affine2 = resample(data, affine, zooms, new_zooms)
     >>> data2.shape
     (77, 77, 40)
-
-    '''        
-    R=np.diag(np.array(new_zooms)/np.array(zooms))    
-    new_shape=np.array(zooms)/np.array(new_zooms) * np.array(data.shape[:3])
-    new_shape=np.round(new_shape).astype('i8')   
-    if data.ndim==3:
-        data2=affine_transform(input=data,matrix=R,offset=np.zeros(3,),output_shape=tuple(new_shape),order=order)
-    if data.ndim==4:
+    """
+    R = np.diag(np.array(new_zooms)/np.array(zooms))
+    new_shape = np.array(zooms)/np.array(new_zooms) * np.array(data.shape[:3])
+    new_shape = np.round(new_shape).astype('i8')
+    if data.ndim == 3:
+        data2 = affine_transform(input=data, matrix=R, offset=np.zeros(3,),
+                                 output_shape=tuple(new_shape),
+                                 order=order, mode=mode, cval=cval)
+    if data.ndim == 4:
         data2l=[] 
         for i in range(data.shape[-1]):
-            tmp=affine_transform(input=data[...,i],matrix=R,offset=np.zeros(3,),output_shape=tuple(new_shape),order=order)
+            tmp = affine_transform(input=data[..., i], matrix=R,
+                                   offset=np.zeros(3,),
+                                   output_shape=tuple(new_shape),
+                                   order=order, mode=mode, cval=cval)
             data2l.append(tmp)        
-        data2=np.zeros(tmp.shape+(data.shape[-1],),data.dtype)
+        data2 = np.zeros(tmp.shape+(data.shape[-1],), data.dtype)
         for i in range(data.shape[-1]):
-            data2[...,i]=data2l[i]        
-    Rx=np.eye(4)
-    Rx[:3,:3]=R
-    affine2=np.dot(affine,Rx)
-    return data2,affine2
+            data2[..., i] = data2l[i]
+
+    Rx = np.eye(4)
+    Rx[:3, :3] = R
+    affine2 = np.dot(affine, Rx)
+    return data2, affine2