def test_semistandard_marked_setvalued():
mu = ()
assert Tableau.semistandard_marked_setvalued(0, mu) == {Tableau()}
assert Tableau.semistandard_marked_setvalued(1, mu) == {Tableau()}
assert Tableau.semistandard_marked_setvalued(2, mu) == {Tableau()}
mu = (1, )
assert Tableau.semistandard_marked_setvalued(1, mu) == {
Tableau({(1, 1): 1}),
Tableau({(1, 1): -1}),
Tableau({(1, 1): (-1, 1)})
}
assert Tableau.semistandard_marked_setvalued(2, mu) == {
Tableau({(1, 1): 1}),
Tableau({(1, 1): -1}),
Tableau({(1, 1): (-1, 1)}),
Tableau({(1, 1): 2}),
Tableau({(1, 1): -2}),
Tableau({(1, 1): (-2, 2)}),
Tableau({(1, 1): (1, 2)}),
Tableau({(1, 1): (1, -2)}),
Tableau({(1, 1): (1, -2, 2)}),
Tableau({(1, 1): (-1, 2)}),
Tableau({(1, 1): (-1, -2)}),
Tableau({(1, 1): (-1, -2, 2)}),
Tableau({(1, 1): (-1, 1, 2)}),
Tableau({(1, 1): (-1, 1, -2)}),
Tableau({(1, 1): (-1, 1, -2, 2)})
}
def test_skew_semistandard_marked_setvalued():
for n in [1, 2, 3]:
mu = (2, 1)
nu = (1, )
tabs = Tableau.semistandard_marked_setvalued(n, mu, nu)
assert len(tabs) == (2**(2 * n) - 1)**2
def _slow_stable_grothendieck_s(cls, num_variables, mu, nu=()):
return BETA**(sum(nu) - sum(mu)) * cls._slow_vectorize(
num_variables,
Tableau.semistandard_marked_setvalued(num_variables, mu, nu), BETA)
