def test_matrix_rank(model):
"""
Show rank of the S-Matrix.
This test will return the rank of the S-Matrix of the model.
This test is not scored, as the rank depends on the S-Matrix constructed,
which is system-specific.
"""
ann = test_matrix_rank.annotation
ann["data"] = matrix.matrix_rank(model)
ann["message"] = wrapper.fill("""The rank of the S-Matrix is {}.""".format(
ann["data"]))
def test_matrix_rank(model):
"""
Show the rank of the stoichiometric matrix.
The rank of the stoichiometric matrix is system specific. It is
calculated using singular value decomposition (SVD).
Implementation:
Compose the stoichiometric matrix, then estimate the rank, i.e. the
dimension of the column space, of a matrix. The algorithm used by this
function is based on the singular value decomposition of the matrix.
"""
ann = test_matrix_rank.annotation
ann["data"] = matrix.matrix_rank(model)
# Report the rank scaled by the number of reactions.
ann["metric"] = ann["data"] / len(model.reactions)
ann["message"] = wrapper.fill(
"""The rank of the S-Matrix is {}.""".format(ann["data"]))
def test_matrix_rank(model, num):
assert matrix.matrix_rank(model) == num