fixed a mistake in the output format. The first entry in the output of Superpose3D() is now a number instead of a rank-0 tensor. The previous behavior (rank-0 tensor) was a mistake which my automated tests failed to catch. Changing this might break backward compatibility for some users.

Author: jewettaij

setup.py

@@ -81,9 +81,9 @@ def Superpose3D(X,    # <-- Nx3 array of coords for the "frozen" point cloud
 
   url='https://github.com/jewettaij/superpose3d',
 
-  download_url='https://github.com/jewettaij/superpose3d/archive/v1.1.1.zip',
+  download_url='https://github.com/jewettaij/superpose3d/archive/v1.2.0.zip',
 
-  version='1.1.1',
+  version='1.2.0',
 
   install_requires=[
       'numpy',

superpose3d/__init__.py

@@ -68,7 +68,7 @@ def Superpose3D(aaXf_orig,   # <-- coordinates for the "frozen" object
         aWeights = np.array(aWeights).reshape(N,1)
     aCenter_f = np.sum(aaXf_orig * aWeights, axis=0)
     aCenter_m = np.sum(aaXm_orig * aWeights, axis=0)
-    sum_weights = np.sum(aWeights, axis=0)
+    sum_weights = np.sum(aWeights)
 
     # Subtract the centers-of-mass from the original coordinates for each object
     """ # old code (using for-loops)

test_superpose3d.py

@@ -22,12 +22,13 @@ def test_superpose3d():
     w = [1.0, 1.0, 1.0, 1.0]
     result = Superpose3D(X, xscshift, w, True)
     # Does the RMSD returned in result[0] match the RMSD calculated manually?
-    R = np.matrix(result[1])              # rotation matrix
-    T = np.matrix(result[2]).transpose()  # translation vector (3x1 matrix)
-    c = result[3]                         # scalar
+    R = np.array(result[1])              # rotation matrix
+    T = np.array(result[2]).transpose()  # translation vector (3x1 matrix)
+    c = result[3]                        # scalar
     if len(X) > 0:
-        _x = np.matrix(xscshift).transpose()
-        _xprime = c*R*_x + T
+        _x = np.array(xscshift).transpose()
+        # _xprime = c*R*_x + T   <-- syntax is depreciated
+        _xprime = c*np.matmul(R,_x) + np.outer(T, np.array([1]*len(X)))
         xprime = np.array(_xprime.transpose()) # convert to length 3 numpy array
     else:
         xprime = np.array([])