from sympy import I, Matrix, symbols, conjugate, Expr, Integer

from sympy.physics.quantum.dagger import adjoint, Dagger
from sympy.external import import_module
from sympy.utilities.pytest import skip


def test_scalars():
    x = symbols('x', complex=True)
    assert Dagger(x) == conjugate(x)
    assert Dagger(I*x) == -I*conjugate(x)

    i = symbols('i', real=True)
    assert Dagger(i) == i

    p = symbols('p')
    assert isinstance(Dagger(p), adjoint)

    i = Integer(3)
    assert Dagger(i) == i

    A = symbols('A', commutative=False)
    assert Dagger(A).is_commutative is False


def test_matrix():
    x = symbols('x')
    m = Matrix([[I, x*I], [2, 4]])
    assert Dagger(m) == m.H


class Foo(Expr):

    def _eval_adjoint(self):
        return I


def test_eval_adjoint():
    f = Foo()
    d = Dagger(f)
    assert d == I

np = import_module('numpy')


def test_numpy_dagger():
    if not np:
        skip("numpy not installed.")

    a = np.matrix([[1.0, 2.0j], [-1.0j, 2.0]])
    adag = a.copy().transpose().conjugate()
    assert (Dagger(a) == adag).all()


scipy = import_module('scipy', __import__kwargs={'fromlist': ['sparse']})


def test_scipy_sparse_dagger():
    if not np:
        skip("numpy not installed.")
    if not scipy:
        skip("scipy not installed.")
    else:
        sparse = scipy.sparse

    a = sparse.csr_matrix([[1.0 + 0.0j, 2.0j], [-1.0j, 2.0 + 0.0j]])
    adag = a.copy().transpose().conjugate()
    assert np.linalg.norm((Dagger(a) - adag).todense()) == 0.0