eja/euclidean_jordan_algebra.py: add subalgebra_generated_by() method.

diff --git a/mjo/eja/euclidean_jordan_algebra.py b/mjo/eja/euclidean_jordan_algebra.py
index 8487e0ecea385d00f45c176bdc43b45d31d617ff..097233fdad86dea01e11483d7b7525f0ab816f2a 100644 (file)
--- a/mjo/eja/euclidean_jordan_algebra.py
+++ b/mjo/eja/euclidean_jordan_algebra.py
@@ -108,6 +108,38 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra):
             return self.span_of_powers().dimension()
 
 
+        def subalgebra_generated_by(self):
+            """
+            Return the subalgebra of the parent EJA generated by this element.
+            """
+            # First get the subspace spanned by the powers of myself...
+            V = self.span_of_powers()
+            F = self.base_ring()
+
+            # Now figure out the entries of the right-multiplication
+            # matrix for the successive basis elements b0, b1,... of
+            # that subspace.
+            mats = []
+            for b_right in V.basis():
+                eja_b_right = self.parent()(b_right)
+                b_right_rows = []
+                # The first row of the right-multiplication matrix by
+                # b1 is what we get if we apply that matrix to b1. The
+                # second row of the right multiplication matrix by b1
+                # is what we get when we apply that matrix to b2...
+                for b_left in V.basis():
+                    eja_b_left = self.parent()(b_left)
+                    # Multiply in the original EJA, but then get the
+                    # coordinates from the subalgebra in terms of its
+                    # basis.
+                    this_row = V.coordinates((eja_b_left*eja_b_right).vector())
+                    b_right_rows.append(this_row)
+                b_right_matrix = matrix(F, b_right_rows)
+                mats.append(b_right_matrix)
+
+            return FiniteDimensionalEuclideanJordanAlgebra(F, mats)
+
+
         def minimal_polynomial(self):
             return self.matrix().minimal_polynomial()