X-Git-Url: http://gitweb.michael.orlitzky.com/?a=blobdiff_plain;f=mjo%2Feja%2Feja_algebra.py;h=055bbbda83d7576fda8f5e1794d759c2eee467bc;hb=f807c8c9046723a059eb4eea03dff89293510160;hp=04929467d114dbb23cc4fcc15f11c80df58addf0;hpb=87004c70854aee37fcacd69b15c39d47088bd861;p=sage.d.git

diff --git a/mjo/eja/eja_algebra.py b/mjo/eja/eja_algebra.py
index 0492946..055bbbd 100644
--- a/mjo/eja/eja_algebra.py
+++ b/mjo/eja/eja_algebra.py
@@ -903,7 +903,7 @@ class HadamardEJA(FiniteDimensionalEuclideanJordanAlgebra):
         return x.to_vector().inner_product(y.to_vector())
 
 
-def random_eja(field=AA, nontrivial=False):
+def random_eja(field=AA):
     """
     Return a "random" finite-dimensional Euclidean Jordan Algebra.
 
@@ -917,21 +917,17 @@ def random_eja(field=AA, nontrivial=False):
         Euclidean Jordan algebra of dimension...
 
     """
-    eja_classes = [HadamardEJA,
-                   JordanSpinEJA,
-                   RealSymmetricEJA,
-                   ComplexHermitianEJA,
-                   QuaternionHermitianEJA]
-    if not nontrivial:
-        eja_classes.append(TrivialEJA)
-    classname = choice(eja_classes)
+    classname = choice([TrivialEJA,
+                        HadamardEJA,
+                        JordanSpinEJA,
+                        RealSymmetricEJA,
+                        ComplexHermitianEJA,
+                        QuaternionHermitianEJA])
     return classname.random_instance(field=field)
 
 
 
 
-
-
 class MatrixEuclideanJordanAlgebra(FiniteDimensionalEuclideanJordanAlgebra):
     @staticmethod
     def _max_test_case_size():
@@ -1006,8 +1002,10 @@ class MatrixEuclideanJordanAlgebra(FiniteDimensionalEuclideanJordanAlgebra):
             # we simply undo the basis_normalizer scaling that we
             # performed earlier.
             #
-            # TODO: make this access safe.
-            XS = a[0].variables()
+            # The a[0] access here is safe because trivial algebras
+            # won't have any basis normalizers and therefore won't
+            # make it to this "else" branch.
+            XS = a[0].parent().gens()
             subs_dict = { XS[i]: self._basis_normalizers[i]*XS[i]
                           for i in range(len(XS)) }
             return tuple( a_i.subs(subs_dict) for a_i in a )