def test_create_basins_piechart():
    try:
        primes = get_primes("n5s3")
        attractors = compute_attractors(primes, update="asynchronous")
        compute_basins(attractors)
        fname_image = get_tests_path_out(fname="basins_piechart")
        create_basins_piechart(attractors, fname_image=fname_image)
    except NameError:
        pass
예제 #2
0
def test_compute_json():
    primes = get_primes("n5s3")
    fname = get_tests_path_out(fname="n5s3_attrs.json")
    attractors = compute_attractors(primes=primes,
                                    update="asynchronous",
                                    fname_json=fname,
                                    max_output=2)

    assert attractors
예제 #3
0

from pyboolnet.phenotypes import compute_phenotypes, compute_phenotype_diagram, create_phenotypes_piechart
from pyboolnet.attractors import compute_attractors
from pyboolnet.repository import get_primes


if __name__ == "__main__":
    # compute the commitment diagram

    primes = get_primes("arellano_rootstem")
    print(sorted(primes))

    attractors = compute_attractors(primes, "asynchronous")
    markers = ["WOX", "MGP"]
    phenotypes = compute_phenotypes(attractors, markers, fname_json="phenos.json")

    # inspect marker patterns

    for pheno in phenotypes["phenotypes"]:
        print(pheno["name"])
        print(pheno["pattern"])

    # draw diagram

    diagram = compute_phenotype_diagram(phenotypes, fname_image="phenos_diagram.pdf")

    # draw pie chart

    create_phenotypes_piechart(diagram, fname_image="phenos_piechart.pdf")
예제 #4
0
from pyboolnet.repository import get_primes
from pyboolnet.state_transition_graphs import primes2stg
from pyboolnet.attractors import compute_attractors_tarjan, compute_attractors, find_attractor_state_by_randomwalk_and_ctl

if __name__ == "__main__":
    # attractor computation with Tarjan

    primes = get_primes("tournier_apoptosis")

    stg = primes2stg(primes, "asynchronous")
    steady, cyclic = compute_attractors_tarjan(stg)
    print(steady)
    print(cyclic)

    # random walk attractor detection

    state = find_attractor_state_by_randomwalk_and_ctl(primes, "asynchronous")
    print(state)

    # model checking based attractor detection

    attrs = compute_attractors(primes, "asynchronous", fname_json="attrs.json")

    print(attrs["is_complete"])
    for x in attrs["attractors"]:
        print(x["is_steady"])
        print(x["state"]["str"])
def test_compute_basins():
    primes = get_primes("n5s3")
    attractors = compute_attractors(primes, update="asynchronous")
    compute_basins(attractors)
def attractors(primes):
    return compute_attractors(primes, update="asynchronous")
def attractors():
    return compute_attractors(primes=get_primes("raf"), update="asynchronous")