def product_on_basis(self, i, j):
+ r"""
+ Returns the Jordan product of the `i` and `j`th basis elements.
+
+ This completely defines the Jordan product on the algebra, and
+ is used direclty by our superclass machinery to implement
+ :meth:`product`.
+
+ SETUP::
+
+ sage: from mjo.eja.eja_algebra import random_eja
+
+ TESTS::
+
+ sage: set_random_seed()
+ sage: J = random_eja()
+ sage: n = J.dimension()
+ sage: ei = J.zero()
+ sage: ej = J.zero()
+ sage: ei_ej = J.zero()*J.zero()
+ sage: if n > 0:
+ ....: i = ZZ.random_element(n)
+ ....: j = ZZ.random_element(n)
+ ....: ei = J.gens()[i]
+ ....: ej = J.gens()[j]
+ ....: ei_ej = J.product_on_basis(i,j)
+ sage: ei*ej == ei_ej
+ True
+
+ """
# We only stored the lower-triangular portion of the
# multiplication table.
if j <= i:
sage: J(x.to_vector().column()) == x
True
+ We cannot coerce elements between algebras just because their
+ matrix representations are compatible::
+
+ sage: J1 = HadamardEJA(3)
+ sage: J2 = JordanSpinEJA(3)
+ sage: J2(J1.one())
+ Traceback (most recent call last):
+ ...
+ ValueError: not an element of this algebra
+ sage: J1(J2.zero())
+ Traceback (most recent call last):
+ ...
+ ValueError: not an element of this algebra
+
"""
msg = "not an element of this algebra"
- if elt == 0:
- # The superclass implementation of random_element()
- # needs to be able to coerce "0" into the algebra.
- return self.zero()
- elif elt in self.base_ring():
+ if elt in self.base_ring():
# Ensure that no base ring -> algebra coercion is performed
# by this method. There's some stupidity in sage that would
# otherwise propagate to this method; for example, sage thinks
raise ValueError(msg)
try:
+ # Try to convert a vector into a column-matrix...
elt = elt.column()
except (AttributeError, TypeError):
- # Try to convert a vector into a column-matrix
+ # and ignore failure, because we weren't really expecting
+ # a vector as an argument anyway.
pass
if elt not in self.matrix_space():
# could be QQ instead of QQbar.
#
# And, we also have to handle Cartesian product bases (when
- # the matric basis consists of tuples) here. The "good news"
+ # the matrix basis consists of tuples) here. The "good news"
# is that we're already converting everything to long vectors,
# and that strategy works for tuples as well.
#