def __xor__(A, B):
C = A * B
C = C.Alt_or_Sym(1)
k = A.order()
l = B.order()
p = C.product_of_dims()
r = k + l
n = len(C.elements[0])
scale = fact_m.fact(k + l) / (1.0 * fact_m.fact(k) * fact_m.fact(l))
for i in range(0, p):
I = multidx(i, r, n)
C[I] *= scale
return C
def __xor__(A, B): C = A * B C = C.Alt_or_Sym(1) k = A.order() l = B.order() p = C.product_of_dims() r = k + l n = len(C.elements[0]) scale = fact_m.fact(k+l) / (1.0 * fact_m.fact(k) * fact_m.fact(l)) for i in range(0, p): I = multidx(i, r, n) C[I] *= scale return C
def Alt_or_Sym(self, want_Alt):
# xxx what if scalar?
r = self.order()
n = len(self.elements[0])
p = self.product_of_dims()
C = make_zero_simple_tensor(r, n)
Sr = sni_gm.get_elements_str(r)
for sigma in Sr:
if (want_Alt and sigma.sgn() == -1):
sign = -1
else:
sign = 1
for i in range(0, p):
I = multidx(i, r, n)
J = sigma.of(I)
C[I] += sign * self[J]
scale = 1.0 / fact_m.fact(r)
for i in range(0, p):
I = multidx(i, r, n)
C[I] *= scale
return C
def Alt_or_Sym(self, want_Alt): # xxx what if scalar? r = self.order() n = len(self.elements[0]) p = self.product_of_dims() C = make_zero_simple_tensor(r,n) Sr = sni_gm.get_elements_str(r) for sigma in Sr: if (want_Alt and sigma.sgn() == -1): sign = -1 else: sign = 1 for i in range(0, p): I = multidx(i, r, n) J = sigma.of(I) C[I] += sign * self[J] scale = 1.0 / fact_m.fact(r) for i in range(0, p): I = multidx(i, r, n) C[I] *= scale return C
