def test_graph_filtration(get_input_and_gate):
input_file = get_input_and_gate
gc = parse_sbol_xml_tree(input_file)
gn = NetworkGeneticCircuit(sbol_input=gc)
ogn = gn.filter_graph("bound")
assert len(ogn) == 6 # 3 sets of 2 elements = 6
# All nodes in the primary graph have a regular edge
assert len(gn.get_nodes()) == 18
def test_graph_construction(get_input_and_gate):
input_file = get_input_and_gate
gc = parse_sbol_xml_tree(input_file)
gn = NetworkGeneticCircuit(sbol_input=gc)
promoter_parts = gn.get_nodes_by_part("Promoter")
assert len(promoter_parts) == 5
bound_nodes = gn.get_bound_nodes()
assert len(bound_nodes) == 3
def test_scoring_and_requirements_interface(get_input_and_gate):
"""
If a scoring module does not implement the baseline features, it raises a
TypeError.
"""
input_file = get_input_and_gate
gc = parse_sbol_xml_tree(input_file)
gn = NetworkGeneticCircuit(sbol_input=gc)
req = GoodRequirements()
GoodScoring(gn, req)
with pytest.raises(TypeError):
BadScoring(gn, req)
with pytest.raises(TypeError):
BaseRequirement()
def test_gc_calculation(get_input_and_gate):
input_file = get_input_and_gate
gc = parse_sbol_xml_tree(input_file)
gn = NetworkGeneticCircuit(sbol_input=gc)
# Should be approximately 48% GC Content
assert round(gn.calculate_gc_content_percentage(), 2) == 0.48
def score(
requested_solvers: List[str] = typer.Argument(
...,
help="The Input Solvers",
),
sbol_filepath: str = typer.Option(
os.path.join(os.getcwd(), "tests", "test_cello",
"example_and_gate.xml"),
help="Filepath: location of the SBOL file that constitutes the genetic "
"circuit",
),
parameter_filepath: str = typer.Option(
os.path.join(os.getcwd(), "input.yml"),
help="Filepath: location of the SBOL file that constitutes the genetic "
"circuit",
),
out_filepath: str = typer.Option(
"output",
help="Filepath: location to write the output of the solved function"),
):
"""
Takes an SBOL file, evaluates the quality of a genetic circuit, and then outputs performance metrics.
"""
# We take in our input values and normalize them.
requested_solvers = [solver.lower() for solver in requested_solvers]
available_solvers = get_available_scorers()
for solver in requested_solvers:
if solver not in available_solvers:
raise RuntimeError(
f"Unable to find a scorer with the name {solver}, please "
f"investigate.")
# We then ensure that our filepaths are correct so we're not breaking down
# the line.
if not os.path.isfile(parameter_filepath):
raise RuntimeError(
f"Unable to locate input file {parameter_filepath}. Please Investigate."
)
# We assume that we'll have multiple forms of output, so what we're doing is
# validating the output is just an extant directory. If not, we try to
# generate one.
if not os.path.isdir(out_filepath):
os.mkdir(out_filepath)
# We now need to ensure that our files, while extant, contain the information
# required to compute the score. Individual errors are propagated via the
# function.
if not validate_input_file(
input_fp=parameter_filepath,
requested_scorers=requested_solvers,
):
raise RuntimeError(f"Input File failed to pass validation. Exiting.")
# We should now be good to go so we just move forward with parsing the input
# data and sending it off to the requested solvers.
gc = parse_sbol_xml_tree(sbol_filepath)
network = NetworkGeneticCircuit(gc)
requested_requirements = generate_requirement_classes(
parameter_filepath, requested_solvers)
scoring_map = get_scorer_map()
for solver, requirement in zip(requested_solvers, requested_requirements):
solver_class = scoring_map[solver]
try:
solver_obj = solver_class(network, requirement)
except TypeError:
solver_obj = solver_class(requirement)
solver_obj.score()
solver_obj.report()
def test_ingress_module(get_input_and_gate):
input_file = get_input_and_gate
gc = parse_sbol_xml_tree(input_file)
gc_dict = gc.as_dict()