Flatten a vector, matrix, or cartesian product of those things
into a long list.
- EXAMPLES::
+ If the entries of the matrix themselves belong to a real vector
+ space (such as the complex numbers which can be thought of as
+ pairs of real numbers), they will also be expanded in vector form
+ and flattened into the list.
+
+ SETUP::
sage: from mjo.eja.eja_utils import _all2list
+ sage: from mjo.octonions import Octonions, OctonionMatrixAlgebra
+
+ EXAMPLES::
+
+ sage: _all2list([[1]])
+ [1]
+
+ ::
+
sage: V1 = VectorSpace(QQ,2)
sage: V2 = MatrixSpace(QQ,2)
sage: x1 = V1([1,1])
sage: _all2list(M((x2,y2)))
[1, -1, 0, 1, 1, 0]
+ ::
+
+ sage: _all2list(Octonions().one())
+ [1, 0, 0, 0, 0, 0, 0, 0]
+ sage: _all2list(OctonionMatrixAlgebra(1).one())
+ [1, 0, 0, 0, 0, 0, 0, 0]
+
+ ::
+
+ sage: V1 = VectorSpace(QQ,2)
+ sage: V2 = OctonionMatrixAlgebra(1,field=QQ)
+ sage: C = cartesian_product([V1,V2])
+ sage: x1 = V1([3,4])
+ sage: y1 = V2.one()
+ sage: _all2list(C( (x1,y1) ))
+ [3, 4, 1, 0, 0, 0, 0, 0, 0, 0]
+
"""
- if hasattr(x, 'list'):
- # Easy case...
- return x.list()
- else:
- # But what if it's a tuple or something else? This has to
- # handle cartesian products of cartesian products, too; that's
- # why it's recursive.
- return sum( map(_all2list,x), [] )
+ if hasattr(x, 'to_vector'):
+ # This works on matrices of e.g. octonions directly, without
+ # first needing to convert them to a list of octonions and
+ # then recursing down into the list. It also avoids the wonky
+ # list(x) when x is an element of a CFM. I don't know what it
+ # returns but it aint the coordinates. This will fall through
+ # to the iterable case the next time around.
+ return _all2list(x.to_vector())
+
+ try:
+ xl = list(x)
+ except TypeError: # x is not iterable
+ return [x]
+
+ if xl == [x]:
+ # Avoid the retardation of list(QQ(1)) == [1].
+ return [x]
+
+ return sum(list( map(_all2list, xl) ), [])
+
+
def _mat2vec(m):
return vector(m.base_ring(), m.list())
"""
if inner_product is None:
inner_product = lambda x,y: x.inner_product(y)
- norm = lambda x: inner_product(x,x).sqrt()
+ def norm(x):
+ ip = inner_product(x,x)
+ # Don't expand the given field; the inner-product's codomain
+ # is already correct. For example QQ(2).sqrt() returns sqrt(2)
+ # in SR, and that will give you weird errors about symbolics
+ # when what's really going wrong is that you're trying to
+ # orthonormalize in QQ.
+ return ip.parent()(ip.sqrt())
v = list(v) # make a copy, don't clobber the input