def test_reshape_raises():
Ds = [8, 9, 10, 11]
indices = [Index(U1Charge.random(-5, 5, Ds[n]), False) for n in range(4)]
arr = ChargeArray.random(indices)
with pytest.raises(ValueError):
arr.reshape([64, 65])
arr2 = arr.reshape([72, 110])
with pytest.raises(ValueError):
arr2.reshape([9, 8, 10, 11])
def test_ChargeArray_generic(dtype):
Ds = [8, 9, 10, 11]
indices = [Index(U1Charge.random(-5, 5, Ds[n]), False) for n in range(4)]
arr = ChargeArray.random(indices, dtype=dtype)
assert arr.ndim == 4
assert arr.dtype == dtype
np.testing.assert_allclose(arr.shape, Ds)
np.testing.assert_allclose(arr.flat_flows, [False, False, False, False])
for n in range(4):
assert charge_equal(indices[n]._charges[0], arr.flat_charges[n])
assert arr.sparse_shape[n] == indices[n]
def test_transpose():
Ds = np.array([8, 9, 10, 11])
flows = [True, False, True, False]
indices = [
Index(U1Charge.random(-5, 5, Ds[n]), flows[n]) for n in range(4)
]
arr = ChargeArray.random(indices)
order = [2, 1, 0, 3]
arr2 = arr.transpose(order)
np.testing.assert_allclose(Ds[order], arr2.shape)
np.testing.assert_allclose(arr2._order, [[2], [1], [0], [3]])
np.testing.assert_allclose(arr2.flows, [[True], [False], [True], [False]])
def test_ChargeArray_init():
np.random.seed(10)
D = 10
rank = 4
charges = [U1Charge.random(-5, 5, D) for _ in range(rank)]
data = np.random.uniform(0, 1, size=D**rank)
flows = np.random.choice([True, False], size=rank, replace=True)
order = [[n] for n in range(rank)]
arr = ChargeArray(data, charges, flows, order=order)
np.testing.assert_allclose(data, arr.data)
for c1, c2 in zip(charges, arr.charges):
assert charge_equal(c1, c2[0])
for c1, c2 in zip(charges, arr._charges):
assert charge_equal(c1, c2)
def test_transpose_data():
Ds = np.array([8, 9, 10, 11])
order = [2, 0, 1, 3]
flows = [True, False, True, False]
indices = [
Index(U1Charge.random(-5, 5, Ds[n]), flows[n]) for n in range(4)
]
arr = ChargeArray.random(indices)
data = np.ascontiguousarray(np.transpose(np.reshape(arr.data, Ds), order))
arr2 = arr.transpose(order).transpose_data()
data3 = np.reshape(arr2.data, Ds[order])
np.testing.assert_allclose(data, data3)
np.testing.assert_allclose(arr2.shape, Ds[order])
np.testing.assert_allclose(arr2._order, [[0], [1], [2], [3]])
np.testing.assert_allclose(arr2.flows, [[True], [True], [False], [False]])
def test_ChargeArray_reshape(dtype):
Ds = [8, 9, 10, 11]
indices = [Index(U1Charge.random(-5, 5, Ds[n]), False) for n in range(4)]
arr = ChargeArray.random(indices, dtype=dtype)
arr2 = arr.reshape([72, 110])
for n in range(2):
for m in range(2):
assert charge_equal(arr2.charges[n][m], indices[n * 2 + m].charges)
np.testing.assert_allclose(arr2.shape, [72, 110])
np.testing.assert_allclose(arr2._order, [[0, 1], [2, 3]])
np.testing.assert_allclose(arr2.flows, [[False, False], [False, False]])
assert arr2.ndim == 2
arr3 = arr.reshape(Ds)
for n in range(4):
assert charge_equal(arr3.charges[n][0], indices[n].charges)
np.testing.assert_allclose(arr3.shape, Ds)
np.testing.assert_allclose(arr3._order, [[0], [1], [2], [3]])
np.testing.assert_allclose(arr3.flows,
[[False], [False], [False], [False]])
assert arr3.ndim == 4
def test_transpose_reshape():
Ds = np.array([8, 9, 10, 11])
flows = [True, False, True, False]
indices = [
Index(U1Charge.random(-5, 5, Ds[n]), flows[n]) for n in range(4)
]
arr = ChargeArray.random(indices)
arr2 = arr.transpose([2, 0, 1, 3])
arr3 = arr2.reshape([80, 99])
np.testing.assert_allclose(arr3.shape, [80, 99])
np.testing.assert_allclose(arr3._order, [[2, 0], [1, 3]])
np.testing.assert_allclose(arr3.flows, [[True, True], [False, False]])
arr4 = arr3.transpose([1, 0])
np.testing.assert_allclose(arr4.shape, [99, 80])
np.testing.assert_allclose(arr4._order, [[1, 3], [2, 0]])
np.testing.assert_allclose(arr4.flows, [[False, False], [True, True]])
arr5 = arr4.reshape([9, 11, 10, 8])
np.testing.assert_allclose(arr5.shape, [9, 11, 10, 8])
np.testing.assert_allclose(arr5._order, [[1], [3], [2], [0]])
np.testing.assert_allclose(arr5.flows, [[False], [False], [True], [True]])
def test_transpose_reshape_transpose_data():
Ds = np.array([8, 9, 10, 11])
flows = [True, False, True, False]
indices = [
Index(U1Charge.random(-5, 5, Ds[n]), flows[n]) for n in range(4)
]
arr = ChargeArray.random(indices)
nparr = np.reshape(arr.data, Ds)
arr2 = arr.transpose([2, 0, 1, 3])
nparr2 = nparr.transpose([2, 0, 1, 3])
arr3 = arr2.reshape([80, 99])
nparr3 = nparr2.reshape([80, 99])
arr4 = arr3.transpose([1, 0])
nparr4 = nparr3.transpose([1, 0])
arr5 = arr4.reshape([9, 11, 10, 8])
nparr5 = nparr4.reshape([9, 11, 10, 8])
np.testing.assert_allclose(arr3.transpose_data().data,
np.ascontiguousarray(nparr3).flat)
np.testing.assert_allclose(arr4.transpose_data().data,
np.ascontiguousarray(nparr4).flat)
np.testing.assert_allclose(arr5.transpose_data().data,
np.ascontiguousarray(nparr5).flat)
def test_ChargeArray_todense(dtype):
Ds = [8, 9, 10, 11]
indices = [Index(U1Charge.random(-5, 5, Ds[n]), False) for n in range(4)]
arr = ChargeArray.random(indices, dtype=dtype)
np.testing.assert_allclose(arr.todense(), np.reshape(arr.data, Ds))