def test_deprecations():
g = nk.graph.Edgeless(3)
with pytest.warns(FutureWarning):
Spin(s=0.5, graph=g)
with pytest.warns(FutureWarning):
with pytest.raises(ValueError):
Spin(s=0.5, graph=g, N=3)
def test_state_iteration():
hilbert = Spin(s=0.5, N=10)
reference = [
np.array(el) for el in itertools.product([-1.0, 1.0], repeat=10)
]
for state, ref in zip(hilbert.states(), reference):
assert np.allclose(state, ref)
def test_composite_hilbert_spin():
hi1 = Spin(s=1 / 2, N=8)
hi2 = Spin(s=3 / 2, N=8)
hi = hi1 * hi2
assert hi.size == hi1.size + hi2.size
for i in range(hi.size):
assert hi.size_at_index(i) == 2 if i < 8 else 4
def test_hilbert_index_discrete(hi: DiscreteHilbert):
log_max_states = np.log(nk.hilbert._abstract_hilbert.max_states)
if hi.is_indexable:
local_sizes = [hi.size_at_index(i) for i in range(hi.size)]
assert np.sum(np.log(local_sizes)) < log_max_states
assert np.allclose(hi.states_to_numbers(hi.all_states()),
range(hi.n_states))
# batched version of number to state
n_few = min(hi.n_states, 100)
few_states = np.zeros(shape=(n_few, hi.size))
for k in range(n_few):
few_states[k] = hi.numbers_to_states(k)
assert np.allclose(hi.numbers_to_states(np.asarray(range(n_few))),
few_states)
else:
assert not hi.is_indexable
with pytest.raises(RuntimeError):
hi.n_states
# Check that a large hilbert space raises error when constructing matrices
g = nk.graph.Hypercube(length=100, n_dim=1)
op = nk.operator.Heisenberg(hilbert=Spin(s=0.5, N=g.n_nodes), graph=g)
with pytest.raises(RuntimeError):
op.to_dense()
with pytest.raises(RuntimeError):
op.to_sparse()
def merge_dicts(x, y):
z = x.copy() # start with x's keys and values
z.update(y) # modifies z with y's keys and values & returns None
return z
machines = {}
dm_machines = {}
# TESTS FOR SPIN HILBERT
# Constructing a 1d lattice
g = nk.graph.Hypercube(length=4, n_dim=1)
# Hilbert space of spins from given graph
hi = Spin(s=0.5, N=g.n_nodes)
if test_jax:
import jax
import jax.experimental
import jax.experimental.stax
def initializer(rng, shape):
return np.random.normal(scale=0.05, size=shape)
# machines["Jax Real"] = nk.machine.Jax(
# hi,
# jax.experimental.stax.serial(
# jax.experimental.stax.Dense(4, initializer, initializer),
# jax.experimental.stax.Relu,
# jax.experimental.stax.Dense(2, initializer, initializer),
Qubit,
Spin,
)
import netket.experimental as nkx
import jax
import jax.numpy as jnp
from .. import common
pytestmark = common.skipif_mpi
hilberts = {}
# Spin 1/2
hilberts["Spin 1/2"] = Spin(s=0.5, N=20)
# Spin 1/2 with total Sz
hilberts["Spin[0.5, N=20, total_sz=1"] = Spin(s=0.5, total_sz=1.0, N=20)
hilberts["Spin[0.5, N=5, total_sz=-1.5"] = Spin(s=0.5, total_sz=-1.5, N=5)
# Spin 1/2 with total Sz
hilberts["Spin 1 with total Sz, even sites"] = Spin(s=1.0, total_sz=5.0, N=6)
# Spin 1/2 with total Sz
hilberts["Spin 1 with total Sz, odd sites"] = Spin(s=1.0, total_sz=2.0, N=7)
# Spin 3
hilberts["Spin 3"] = Spin(s=3, N=25)
# Boson
def merge_dicts(x, y):
z = x.copy() # start with x's keys and values
z.update(y) # modifies z with y's keys and values & returns None
return z
machines = {}
dm_machines = {}
# TESTS FOR SPIN HILBERT
# Constructing a 1d lattice
g = nk.graph.Hypercube(length=4, n_dim=1)
# Hilbert space of spins from given graph
hi = Spin(s=0.5, graph=g)
if test_jax:
import jax
import jax.experimental
import jax.experimental.stax
def initializer(rng, shape):
return np.random.normal(scale=0.05, size=shape)
# machines["Jax Real"] = nk.machine.Jax(
# hi,
# jax.experimental.stax.serial(
# jax.experimental.stax.Dense(4, initializer, initializer),
# jax.experimental.stax.Relu,
# jax.experimental.stax.Dense(2, initializer, initializer),
