def normalize_real_part(vecs, transposed=False):
"""Take the real part of a set of vectors and normalize it. This is used
for returning real eigenvectors even when the PETSc scalar type is complex.
"""
k = vecs.shape[0] if transposed else vecs.shape[1]
vecs = np.ascontiguousarray(vecs.real)
for i in range(k):
where = (i, Ellipsis) if transposed else (Ellipsis, i)
qu.nmlz(vecs[where], inplace=True)
return vecs
def test_normalize_inplace_dop(self):
a = qu.qu([1, -1j], 'dop')
b = qu.nmlz(a, inplace=True)
assert_almost_equal(qu.trace(a), 1.0)
assert_almost_equal(qu.trace(b), 1.0)
def test_normalize_ket(self):
a = qu.qu([1, -1j], 'ket')
b = qu.nmlz(a, inplace=False)
assert_almost_equal(qu.trace(b.H @ b), 1.0)
assert_almost_equal(qu.trace(a.H @ a), 2.0)
def test_normalize_bra(self):
a = qu.qu([1, -1j], 'bra')
b = qu.nmlz(a, inplace=False)
assert_almost_equal(qu.trace(b @ b.H), 1.0)
def test_normalize_dop(self):
a = qu([1, -1j], 'dop')
b = nmlz(a, inplace=False)
assert_almost_equal(trace(b), 1.0)
