Moved the apply transform code to align.py file.

1) Refactored the apply transform code.
2) Renamed the binary for apply transform workflow.
Future Work:
1) Need to do more manual testing for the apply transform workflow.
2) Need to write test cases for the apply transform workflow.

bin/dipy_align_transform → bin/dipy_apply_transform

@@ -6,7 +6,7 @@ method from the workflows.
 """
 
 from dipy.workflows.flow_runner import run_flow
-from dipy.workflows.apply_transform import ApplyTransformFlow
+from dipy.workflows.align import ApplyTransformFlow
 
 if __name__ == "__main__":
     run_flow(ApplyTransformFlow())

dipy/align/imaffine.py

@@ -954,7 +954,7 @@ def _init_optimizer(self, static, moving, transform, params0,
 
     def optimize(self, static, moving, transform, params0,
                  static_grid2world=None, moving_grid2world=None,
-                 starting_affine=None):
+                 starting_affine=None, ret_metric=False):
         r''' Starts the optimization process
 
         Parameters
@@ -993,11 +993,22 @@ def optimize(self, static, moving, transform, params0,
             If None:
                 Start from identity.
             The default is None.
+        ret_metric : boolean, optional
+            if True, it returns the parameters for measuring the
+            similarity between the images (default 'False').
+            The metric containing optimal parameters and
+            the distance between the images.
 
         Returns
         -------
         affine_map : instance of AffineMap
             the affine resulting affine transformation
+
+        xopt : similarity metric
+            the metric of optimal parameters
+
+        fopt : distance
+            the distance between the images
         '''
         self._init_optimizer(static, moving, transform, params0,
                              static_grid2world, moving_grid2world,
@@ -1074,6 +1085,8 @@ def optimize(self, static, moving, transform, params0,
             self.params0 = self.transform.get_identity_parameters()
 
         affine_map.set_affine(self.starting_affine)
+        if ret_metric:
+            return affine_map, opt.xopt, opt.fopt
         return affine_map
 
 
@@ -1222,4 +1235,4 @@ def transform_origins(static, static_grid2world,
     affine_map = AffineMap(transform,
                            static.shape, static_grid2world,
                            moving.shape, moving_grid2world)
-    return affine_map
+    return affine_map

dipy/io/image.py

@@ -45,4 +45,22 @@ def load_affine_matrix(fname):
         fname : str
             The file containing the saved affine matrix.
     """
-    return np.loadtxt(fname)
+    return np.loadtxt(fname)
+
+def save_quality_assur_metric(fname, xopt, fopt):
+    """
+    Parameters
+    __________
+    fname: string
+        File name to save the metric values.
+    xopt: numpy array
+        The metric containing the
+        optimal parameters for
+        image registration.
+    fopt: int
+        The distance between the registered images.
+    """
+    np.savetxt(fname, xopt, header="Optimal Parameter metric")
+    with open(fname, 'a') as f:
+        f.write('# Distance after registration\n')
+        f.write(str(fopt))