invert QTransform using adjoint() and determinant()

pyqtgraph/functions.py

@@ -2324,26 +2324,32 @@ def isosurface(data, level):
         
     return vertexes, faces
 
-
     
+def _pinv_fallback(tr):
+    arr = np.array([tr.m11(), tr.m12(), tr.m13(),
+                    tr.m21(), tr.m22(), tr.m23(),
+                    tr.m31(), tr.m32(), tr.m33()])
+    arr.shape = (3, 3)
+    pinv = np.linalg.pinv(arr)
+    return QtGui.QTransform(*pinv.ravel().tolist())
+
+
 def invertQTransform(tr):
     """Return a QTransform that is the inverse of *tr*.
-    Rasises an exception if tr is not invertible.
+    A pseudo-inverse is returned if tr is not invertible.
     
     Note that this function is preferred over QTransform.inverted() due to
     bugs in that method. (specifically, Qt has floating-point precision issues
     when determining whether a matrix is invertible)
     """
     try:
-        import numpy.linalg
-        arr = np.array([[tr.m11(), tr.m12(), tr.m13()], [tr.m21(), tr.m22(), tr.m23()], [tr.m31(), tr.m32(), tr.m33()]])
-        inv = numpy.linalg.inv(arr)
-        return QtGui.QTransform(inv[0,0], inv[0,1], inv[0,2], inv[1,0], inv[1,1], inv[1,2], inv[2,0], inv[2,1])
-    except ImportError:
-        inv = tr.inverted()
-        if inv[1] is False:
-            raise Exception("Transform is not invertible.")
-        return inv[0]
+        det = tr.determinant()
+        detr = 1.0 / det    # let singular matrices raise ZeroDivisionError
+        inv = tr.adjoint()
+        inv *= detr
+        return inv
+    except ZeroDivisionError:
+        return _pinv_fallback(tr)
     
 
 def pseudoScatter(data, spacing=None, shuffle=True, bidir=False, method='exact'):