def bomze_figures(N=60, beta=1, process="incentive", directory=None):
"""
Makes plots of the stationary distribution and expected divergence for each
of the plots in Bomze's classification.
"""
if not directory:
directory = "bomze_paper_figures_%s" % process
ensure_directory(directory)
for i, m in enumerate(bomze_matrices()):
mu = 3. / 2 * 1. / N
fitness_landscape = linear_fitness_landscape(m)
incentive = fermi(fitness_landscape, beta=beta)
edges = incentive_process.multivariate_transitions(N,
incentive,
num_types=3,
mu=mu)
d1 = stationary_distribution(edges)
filename = os.path.join(directory, "%s_%s_stationary.eps" % (i, N))
figure, tax = ternary.figure(scale=N)
tax.heatmap(d1)
tax.savefig(filename=filename)
pyplot.close(figure)
for q_d in [0., 1.]:
d2 = expected_divergence(edges, q_d=q_d)
filename = os.path.join(directory, "%s_%s_%s.eps" % (i, N, q_d))
figure, tax = ternary.figure(scale=N)
tax.heatmap(d2)
tax.savefig(filename=filename)
pyplot.close(figure)
def bomze_figures(N=60, beta=1, process="incentive", directory=None):
"""
Makes plots of the stationary distribution and expected divergence for each
of the plots in Bomze's classification.
"""
if not directory:
directory = "bomze_paper_figures_%s" % process
ensure_directory(directory)
for i, m in enumerate(bomze_matrices()):
mu = 3./2 * 1./N
fitness_landscape = linear_fitness_landscape(m)
incentive = fermi(fitness_landscape, beta=beta)
edges = incentive_process.multivariate_transitions(N, incentive,
num_types=3, mu=mu)
d1 = stationary_distribution(edges)
filename = os.path.join(directory, "%s_%s_stationary.eps" % (i, N))
figure, tax = ternary.figure(scale = N)
tax.heatmap(d1)
tax.savefig(filename=filename)
pyplot.close(figure)
for q_d in [0., 1.]:
d2 = expected_divergence(edges, q_d=q_d)
filename = os.path.join(directory, "%s_%s_%s.eps" % (i, N, q_d))
figure, tax = ternary.figure(scale = N)
tax.heatmap(d2)
tax.savefig(filename=filename)
pyplot.close(figure)
def test_bomze_matrices():
"""
Check that the data file with the Bomze classification matrices is present
and loads the correct number of matrices.
"""
matrices = list(bomze_matrices())
assert_equal(len(matrices), 49)
def test_bomze_matrices():
"""
Check that the data file with the Bomze classification matrices is present
and loads the correct number of matrices.
"""
matrices = list(bomze_matrices())
assert_equal(len(matrices), 49)
