def test_addition_adaptive_4_kernels(self, adata: AnnData):
a, b, c, d = np.random.uniform(0, 10, 4)
s = a + b + c + d
adata.uns["velocity_variances"] = vv = np.random.random(
size=(adata.n_obs, adata.n_obs))
adata.uns["connectivity_variances"] = cv = np.random.random(
size=(adata.n_obs, adata.n_obs))
vk, ck = create_kernels(
adata,
velocity_variances="velocity_variances",
connectivity_variances="connectivity_variances",
)
vk1, ck1 = create_kernels(
adata,
velocity_variances="velocity_variances",
connectivity_variances="connectivity_variances",
)
k = a * vk ^ b * ck ^ c * vk1 ^ d * ck1
expected = _normalize(a / s * vv * vk.transition_matrix +
b / s * cv * ck.transition_matrix +
c / s * vv * vk1.transition_matrix +
d / s * cv * ck1.transition_matrix)
np.testing.assert_allclose(k.transition_matrix.A, expected)
def test_simple_addition(self, adata: AnnData):
vk, ck = create_kernels(adata) # diagonal + upper diag
k = (vk + ck).compute_transition_matrix()
expected = np.eye(adata.n_obs) * 0.75 + np.eye(adata.n_obs, k=1) * 0.25
expected[-1, -1] = 1
np.testing.assert_allclose(k.transition_matrix.A, expected)
def test_addtion_with_constant(self, adata: AnnData):
vk, ck = create_kernels(adata) # diagonal + upper diag
k = (2 * vk + 3 * ck).compute_transition_matrix()
expected = (np.eye(adata.n_obs) * (2 / 5) + np.eye(adata.n_obs) *
(3 / 5) * 0.5 + np.eye(adata.n_obs, k=1) * (3 / 5) * 0.5)
expected[-1, -1] = 1
np.testing.assert_allclose(k.transition_matrix.A, expected)
def test_addition_adaptive(self, adata):
adata.uns["velocity_variances"] = vv = np.random.random(
size=(adata.n_obs, adata.n_obs))
adata.uns["connectivity_variances"] = cv = np.random.random(
size=(adata.n_obs, adata.n_obs))
vk, ck = create_kernels(adata)
k = vk ^ ck
expected = _normalize(0.5 * vv * vk.transition_matrix +
0.5 * cv * ck.transition_matrix)
np.testing.assert_allclose(k.transition_matrix.A, expected)
def test_addition_adaptive_wrong_variances(self, adata):
a, b = np.random.uniform(0, 10, 2)
s = a + b
adata.uns["velocity_variances"] = np.random.random(size=(adata.n_obs,
adata.n_obs))
adata.uns["connectivity_variances"] = np.random.random(
size=(adata.n_obs, adata.n_obs))
vk, ck = create_kernels(adata)
k = a * vk ^ b * ck
expected = _normalize(a / s * vk.transition_matrix +
b / s * ck.transition_matrix)
assert not np.allclose(k.transition_matrix.A, expected.A)
def test_addition_adaptive_constants(self, adata):
a, b = np.random.uniform(0, 10, 2)
s = a + b
adata.uns["velocity_variances"] = vv = np.random.random(
size=(adata.n_obs, adata.n_obs))
adata.uns["connectivity_variances"] = cv = np.random.random(
size=(adata.n_obs, adata.n_obs))
vk, ck = create_kernels(adata) # diagonal + upper diag
k = a * vk ^ b * ck
expected = _normalize(a / s * vv * vk.transition_matrix +
b / s * cv * ck.transition_matrix)
np.testing.assert_allclose(k.transition_matrix.A, expected)
def test_addition_3_kernels(self, adata: AnnData):
vk, ck = create_kernels(adata) # diagonal + upper diag
vk1 = VelocityKernel(adata)
vk1._transition_matrix = np.eye(adata.n_obs, k=-1) / 2 + np.eye(
adata.n_obs) / 2
vk1._transition_matrix[0, 0] = 1
np.testing.assert_allclose(np.sum(ck._transition_matrix, axis=1),
1) # sanity check
k = (vk + ck + vk1).compute_transition_matrix()
expected = (np.eye(adata.n_obs) * (1 / 3 + 1 / 6 + 1 / 6) +
np.eye(adata._n_obs, k=1) * 1 / 6 +
np.eye(adata.n_obs, k=-1) * 1 / 6)
expected[0, 0] = expected[-1, -1] = 2 / 3 + 1 / 3 * 0.5
expected[0, 1] = expected[-1, -2] = 1 - expected[0, 0]
np.testing.assert_allclose(k.transition_matrix.A, expected)
