Update DDF v10

py4DSTEM/process/diffraction/digital_dark_field.py

@@ -423,30 +423,6 @@ def pointlist_to_array(
     return points_array
 
 
-def pointlist_differences(aperture_position, points_array):
-    """
-    calculates Euclidean distances between a specific aperture position
-    and a whole list of detected points for a dataset
-
-    Parameters
-    ----------
-    aperture_position: tuple
-        2-element vector of the diffraction space shape of a position of an aperture
-    points_array: numpy array
-        as produced by pointlist_to_array and defined in docstring for that function
-
-    Returns
-    ----------
-    diff: numpy array
-        the Euclidean distances as a 1D numpy array
-    """
-    subtractor = np.array(
-        [[aperture_position[0], aperture_position[1]] * points_array.shape[0]]
-    ).reshape((points_array.shape[0], 2))
-    diff = ((points_array[:, :2] - subtractor) ** 2).sum(axis=1) ** 0.5
-    return diff
-
-
 def DDFimage(points_array, aperture_positions, Rshape=None, tol=1):
     """
     Calculates a Digital Dark Field image from a list of detected diffraction peak positions in a points_array and a list of aperture_positions, within a defined matching tolerance
@@ -478,19 +454,27 @@ def DDFimage(points_array, aperture_positions, Rshape=None, tol=1):
         )
 
     image = np.zeros(Rshape)
+
     for aperture_index in tqdmnd(len(aperture_positions)):
+        # Pick one of the aperture positions
         aperture_position = aperture_positions[aperture_index]
-        intensities = np.vstack(
+        # Calculate vector differences
+        differences = points_array[:, :2] - aperture_position
+        # Calculate normalised distances
+        diffnorm = (differences**2).sum(axis=1) ** 0.5
+        # Append these norms to the ends of each row for each spot
+        intensities = np.hstack(
             (
-                points_array[:, 2:5].T,
-                pointlist_differences(aperture_position, points_array),
+                points_array[:, 2:5],
+                diffnorm[:, np.newaxis],
             )
-        ).T
+        )
+        # Delete all spots for which the norm is larger than tolerance
         intensities2 = np.delete(intensities, np.where(intensities[:, 3] > tol), axis=0)
-        for row in range(intensities2[:, 0].shape[0]):
-            image[
-                intensities2[row, 1].astype(int), intensities2[row, 2].astype(int)
-            ] += intensities2[row, 0]
+        # Update the image with the remaining intensities sliced by the Rx and Ry positions
+        image[
+            intensities2[:, 1].astype(int), intensities2[:, 2].astype(int)
+        ] += intensities2[:, 0]
     return image