def test_matrix_norm_3d(self):
""" Test normalization/cleanup of 3D (untraceable) flow matrix"""
Z = np.array([[[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ]],
[[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0.75]],
[[3.7 , 0. , 0. ],
[0. , 0.75, 0. ],
[0. , 2. , 0. ],
[0. , 0. , 0. ]]])
A0 = np.array([[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0.06818182],
[1.85 , 0. , 0.75 , 0.66666667, 0. ]])
nn_out0 = np.array([True, True, False, False, True, False, False,
True, False, False, False, True])
A, __, nn_in, nn_out = ac.matrix_norm(Z, self.V)
npt.assert_allclose(A, A0)
npt.assert_equal(nn_in, np.array([True, True, True]))
npt.assert_equal(nn_out, nn_out0)
def test_matrix_norm_3d(self):
""" Test normalization/cleanup of 3D (untraceable) flow matrix"""
Z = np.array([[[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ]],
[[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0.75]],
[[3.7 , 0. , 0. ],
[0. , 0.75, 0. ],
[0. , 2. , 0. ],
[0. , 0. , 0. ]]])
A0 = np.array([[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0.06818182],
[1.85 , 0. , 0.75 , 0.66666667, 0. ]])
nn_out0 = np.array([True, True, False, False, True, False, False,
True, False, False, False, True])
A, __, nn_in, nn_out = ac.matrix_norm(Z, self.V)
npt.assert_allclose(A, A0)
npt.assert_equal(nn_in, np.array([True, True, True]))
npt.assert_equal(nn_out, nn_out0)
def test_matrix_norm_2d(self):
""" Test normalization/cleanup of 2D (aggregated flow matrix
There is 1 null production (second product not produced)."""
Z = np.array([[0., 0., 1.],
[0., 0., 0],
[3., 0., 0.5]])
V = np.array([[4, 1, 0],
[0, 0, 0],
[0, 0, 2.]])
A0 = np.array([[0. , 0.5 ],
[0.6 , 0.25]])
A, __, nn_in, nn_out = ac.matrix_norm(Z, V)
npt.assert_allclose(A, A0)
npt.assert_equal(nn_in, nn_out, np.array([True, False, True]))
def test_matrix_norm_2d(self):
""" Test normalization/cleanup of 2D (aggregated flow matrix
There is 1 null production (second product not produced)."""
Z = np.array([[0., 0., 1.],
[0., 0., 0],
[3., 0., 0.5]])
V = np.array([[4, 1, 0],
[0, 0, 0],
[0, 0, 2.]])
A0 = np.array([[0. , 0.5 ],
[0.6 , 0.25]])
A, __, nn_in, nn_out = ac.matrix_norm(Z, V)
npt.assert_allclose(A, A0)
npt.assert_equal(nn_in, nn_out, np.array([True, False, True]))
def test_matrix_norm_4d(self):
""" Test normalization/cleanup of 4D (traceable) flow matrix"""
Z0 = np.array([[[[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ]],
[[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ]],
[[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ]]],
[[[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ]],
[[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ]],
[[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ]]],
[[[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ]],
[[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0.75]],
[[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ]]],
[[[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ]],
[[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ]],
[[3.7 , 0. , 0. ],
[0. , 0.75, 0. ],
[0. , 2. , 0. ],
[0. , 0. , 0. ]]]])
A0 = np.array([
[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0.06818182],
[1.85 , 0. , 0.75 , 0.66666667, 0. ]])
A, __, nn_in, nn_out = ac.matrix_norm(Z0, self.V)
npt.assert_allclose(A, A0)
def test_matrix_norm_4d(self):
""" Test normalization/cleanup of 4D (traceable) flow matrix"""
Z0 = np.array([[[[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ]],
[[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ]],
[[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ]]],
[[[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ]],
[[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ]],
[[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ]]],
[[[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ]],
[[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0.75]],
[[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ]]],
[[[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ]],
[[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ],
[0. , 0. , 0. ]],
[[3.7 , 0. , 0. ],
[0. , 0.75, 0. ],
[0. , 2. , 0. ],
[0. , 0. , 0. ]]]])
A0 = np.array([
[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0.06818182],
[1.85 , 0. , 0.75 , 0.66666667, 0. ]])
A, __, nn_in, nn_out = ac.matrix_norm(Z0, self.V)
npt.assert_allclose(A, A0)