from sage.rings.all import (ZZ, QQ, AA, QQbar, RR, RLF, CLF,
PolynomialRing,
QuadraticField)
-from mjo.eja.eja_element import (CartesianProductEJAElement,
- FiniteDimensionalEJAElement)
+from mjo.eja.eja_element import FiniteDimensionalEJAElement
from mjo.eja.eja_operator import FiniteDimensionalEJAOperator
-from mjo.eja.eja_utils import _mat2vec
+from mjo.eja.eja_utils import _all2list, _mat2vec
class FiniteDimensionalEJA(CombinatorialFreeModule):
r"""
check_axioms=True,
prefix='e'):
+ # Keep track of whether or not the matrix basis consists of
+ # tuples, since we need special cases for them damned near
+ # everywhere. This is INDEPENDENT of whether or not the
+ # algebra is a cartesian product, since a subalgebra of a
+ # cartesian product will have a basis of tuples, but will not
+ # in general itself be a cartesian product algebra.
+ self._matrix_basis_is_cartesian = False
+ n = len(basis)
+ if n > 0:
+ if hasattr(basis[0], 'cartesian_factors'):
+ self._matrix_basis_is_cartesian = True
+
if check_field:
if not field.is_subring(RR):
# Note: this does return true for the real algebraic
# The field for a cartesian product algebra comes from one
# of its factors and is the same for all factors, so
# there's no need to "reapply" it on product algebras.
- basis = tuple( b.change_ring(field) for b in basis )
+ if self._matrix_basis_is_cartesian:
+ # OK since if n == 0, the basis does not consist of tuples.
+ P = basis[0].parent()
+ basis = tuple( P(tuple(b_i.change_ring(field) for b_i in b))
+ for b in basis )
+ else:
+ basis = tuple( b.change_ring(field) for b in basis )
if check_axioms:
# Call the superclass constructor so that we can use its from_vector()
# method to build our multiplication table.
- n = len(basis)
CombinatorialFreeModule.__init__(self,
field,
range(n),
# we see in things like x = 1*e1 + 2*e2.
vector_basis = basis
- def flatten(b):
- # flatten a vector, matrix, or cartesian product of those
- # things into a long list.
- if cartesian_product:
- return sum(( b_i.list() for b_i in b ), [])
- else:
- return b.list()
-
degree = 0
if n > 0:
- degree = len(flatten(basis[0]))
+ degree = len(_all2list(basis[0]))
# Build an ambient space that fits our matrix basis when
# written out as "long vectors."
# Save a copy of the un-orthonormalized basis for later.
# Convert it to ambient V (vector) coordinates while we're
# at it, because we'd have to do it later anyway.
- deortho_vector_basis = tuple( V(flatten(b)) for b in basis )
+ deortho_vector_basis = tuple( V(_all2list(b)) for b in basis )
from mjo.eja.eja_utils import gram_schmidt
basis = tuple(gram_schmidt(basis, inner_product))
# Now create the vector space for the algebra, which will have
# its own set of non-ambient coordinates (in terms of the
# supplied basis).
- vector_basis = tuple( V(flatten(b)) for b in basis )
+ vector_basis = tuple( V(_all2list(b)) for b in basis )
W = V.span_of_basis( vector_basis, check=check_axioms)
if orthonormalize:
# The jordan product returns a matrixy answer, so we
# have to convert it to the algebra coordinates.
elt = jordan_product(q_i, q_j)
- elt = W.coordinate_vector(V(flatten(elt)))
+ elt = W.coordinate_vector(V(_all2list(elt)))
self._multiplication_table[i][j] = self.from_vector(elt)
if not orthonormalize:
...
ValueError: not an element of this algebra
+ Tuples work as well, provided that the matrix basis for the
+ algebra consists of them::
+
+ sage: J1 = HadamardEJA(3)
+ sage: J2 = RealSymmetricEJA(2)
+ sage: J = cartesian_product([J1,J2])
+ sage: J( (J1.matrix_basis()[1], J2.matrix_basis()[2]) )
+ e(0, 1) + e(1, 2)
+
TESTS:
Ensure that we can convert any element of the two non-matrix
# closure whereas the base ring of the 3-by-3 identity matrix
# 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"
+ # is that we're already converting everything to long vectors,
+ # and that strategy works for tuples as well.
+ #
# We pass check=False because the matrix basis is "guaranteed"
# to be linearly independent... right? Ha ha.
- V = VectorSpace(self.base_ring(), elt.nrows()*elt.ncols())
- W = V.span_of_basis( (_mat2vec(s) for s in self.matrix_basis()),
+ elt = _all2list(elt)
+ V = VectorSpace(self.base_ring(), len(elt))
+ W = V.span_of_basis( (V(_all2list(s)) for s in self.matrix_basis()),
check=False)
try:
- coords = W.coordinate_vector(_mat2vec(elt))
+ coords = W.coordinate_vector(V(elt))
except ArithmeticError: # vector is not in free module
raise ValueError(msg)
def subalgebra(self, basis, **kwargs):
r"""
Create a subalgebra of this algebra from the given basis.
-
- This is a simple wrapper around a subalgebra class constructor
- that can be overridden in subclasses.
"""
from mjo.eja.eja_subalgebra import FiniteDimensionalEJASubalgebra
return FiniteDimensionalEJASubalgebra(self, basis, **kwargs)
return self.zero().to_vector().parent().ambient_vector_space()
- Element = FiniteDimensionalEJAElement
class RationalBasisEJA(FiniteDimensionalEJA):
r"""
True
"""
+ Element = FiniteDimensionalEJAElement
+
+
def __init__(self, algebras, **kwargs):
CombinatorialFreeModule_CartesianProduct.__init__(self,
algebras,
return FiniteDimensionalEJAOperator(Ji,self,Ei.matrix())
- def _element_constructor_(self, elt):
- r"""
- Construct an element of this algebra from an ordered tuple.
-
- We just apply the element constructor from each of our factors
- to the corresponding component of the tuple, and package up
- the result.
-
- SETUP::
-
- sage: from mjo.eja.eja_algebra import (HadamardEJA,
- ....: RealSymmetricEJA)
-
- EXAMPLES::
-
- sage: J1 = HadamardEJA(3)
- sage: J2 = RealSymmetricEJA(2)
- sage: J = cartesian_product([J1,J2])
- sage: J( (J1.matrix_basis()[1], J2.matrix_basis()[2]) )
- e(0, 1) + e(1, 2)
- """
- m = len(self.cartesian_factors())
- try:
- z = tuple( self.cartesian_factors()[i](elt[i]) for i in range(m) )
- return self._cartesian_product_of_elements(z)
- except:
- raise ValueError("not an element of this algebra")
-
- def subalgebra(self, basis, **kwargs):
- r"""
- Create a subalgebra of this algebra from the given basis.
-
- This overrides the superclass method to use a special class
- for Cartesian products.
- """
- from mjo.eja.eja_subalgebra import CartesianProductEJASubalgebra
- return CartesianProductEJASubalgebra(self, basis, **kwargs)
-
- Element = CartesianProductEJAElement
-
FiniteDimensionalEJA.CartesianProduct = CartesianProductEJA
[0 0 0 0 0 0 1 0]
[0 0 0 0 0 0 0 1]
+ This also works in Cartesian product algebras::
+
+ sage: J1 = HadamardEJA(1)
+ sage: J2 = RealSymmetricEJA(2)
+ sage: J = cartesian_product([J1,J2])
+ sage: x = sum(J.gens())
+ sage: x.to_matrix()[0]
+ [1]
+ sage: x.to_matrix()[1]
+ [ 1 0.7071067811865475?]
+ [0.7071067811865475? 1]
+
"""
B = self.parent().matrix_basis()
W = self.parent().matrix_space()
- # This is just a manual "from_vector()", but of course
- # matrix spaces aren't vector spaces in sage, so they
- # don't have a from_vector() method.
- return W.linear_combination( zip(B, self.to_vector()) )
+ if self.parent()._matrix_basis_is_cartesian:
+ # Aaaaand linear combinations don't work in Cartesian
+ # product spaces, even though they provide a method
+ # with that name.
+ pairs = zip(B, self.to_vector())
+ return sum( ( W(tuple(alpha*b_i for b_i in b))
+ for (b,alpha) in pairs ),
+ W.zero())
+ else:
+ # This is just a manual "from_vector()", but of course
+ # matrix spaces aren't vector spaces in sage, so they
+ # don't have a from_vector() method.
+ return W.linear_combination( zip(B, self.to_vector()) )
"""
return self.trace_inner_product(self).sqrt()
-
-
-
-class CartesianProductEJAElement(FiniteDimensionalEJAElement):
-
- def to_matrix(self):
- r"""
- SETUP::
-
- sage: from mjo.eja.eja_algebra import (HadamardEJA,
- ....: RealSymmetricEJA)
-
- EXAMPLES::
-
- sage: J1 = HadamardEJA(1)
- sage: J2 = RealSymmetricEJA(2)
- sage: J = cartesian_product([J1,J2])
- sage: x = sum(J.gens())
- sage: x.to_matrix()[0]
- [1]
- sage: x.to_matrix()[1]
- [ 1 0.7071067811865475?]
- [0.7071067811865475? 1]
-
- """
- B = self.parent().matrix_basis()
- W = self.parent().matrix_space()
-
- # Aaaaand linear combinations don't work in Cartesian
- # product spaces, even though they provide a method
- # with that name.
- pairs = zip(B, self.to_vector())
- return sum( ( W(tuple(alpha*b_i for b_i in b))
- for (b,alpha) in pairs ),
- W.zero())
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