def rs_plot(N, rs, incentive_func=None, eta=None, w=None, mutation_func=None, mu_ab=0.001, mu_ba=0.001, test_func=None):
"""Plot entropy rate over a range of fitness values."""
ms = [ [[r,r],[1,1]] for r in rs]
params = [constant_generator(N), ms]
for p in [incentive_func, eta, w, mutation_func, mu_ab, mu_ba, test_func]:
params.append(constant_generator(p))
es = run_batches(params)
print es[-1]
pyplot.plot(rs, [x[-1] for x in es])
return es
def mu_plot(N, m, incentive_func=None, eta=None, w=None, mutation_func=None, mus=None, test_func=None):
"""Plot entropy rate over a range of mutation probabilities."""
params = [constant_generator(N)]
for p in [m, incentive_func, eta, w, mutation_func]:
params.append(constant_generator(p))
params.append(mus)
params.append(mus)
params.append(constant_generator(test_func))
es = run_batches(params)
print es[-1]
pyplot.plot([-math.log(mu) for mu in mus], [x[-1] for x in es])
return es
def compute_N_r_heatmap_data(Ns, rs, incentive_func=None, eta=None, w=None, mutation_func=None, mu_ab=0.001, mu_ba=0.001, test_func=None):
data = []
for r in rs:
print r
m = [[r,r],[1,1]]
params = [Ns]
for p in [m, incentive_func, eta, w, mutation_func, mu_ab, mu_ba, test_func]:
params.append(constant_generator(p))
es = run_batches(params)
for (args, e) in es:
N, m, incentive_func, eta, w, mutation_func, mu_ab, mu_ba = args
data.append([N, r, mu_ab, mu_ba, e])
return data
def static_plot(Ns, m, incentive_func=None, eta=None, w=None, mutation_func=None, mu_ab=0.001, mu_ba=0.001, test_func=None):
"""Plot entropy rate over a range of population sizes."""
params = [Ns]
for p in [m, incentive_func, eta, w, mutation_func, mu_ab, mu_ba, test_func]:
params.append(constant_generator(p))
es = run_batches(params)
#k = 0.5 + 0.5* math.log(math.pi / 2)
#print mu_ab, (es[-1][-1] - k) / (0.5 * math.log(Ns[-1]))
#pyplot.plot(Ns, [(es[i][-1] -k)/(0.5 * math.log(Ns[i])) for i in range(len(es))])
#print mu_ab, (es[-1][-1] - k) / (0.5 * math.log(Ns[-1]))
print mu_ab, es[-1][-1] - binary_entropy(mu_ab)
pyplot.plot(Ns, [es[i][-1] - binary_entropy(mu_ab) for i in range(len(es))])
return es
def compute_N_mu_heatmap_data(Ns, mus, r, incentive_func=None, eta=None, w=None, mutation_func=None, test_func=None):
data = []
m = [[r,r],[1,1]]
for mu in mus:
print mu
params = [Ns]
for p in [m, incentive_func, eta, w, mutation_func, mu, mu, test_func]:
params.append(constant_generator(p))
es = run_batches(params)
for (args, e) in es:
N, m, incentive_func, eta, w, mutation_func, mu_ab, mu_ba = args
data.append([N, r, mu, -math.log(e)])
return data
def rs_plot(N,
rs,
incentive_func=None,
eta=None,
w=None,
mutation_func=None,
mu_ab=0.001,
mu_ba=0.001,
test_func=None):
"""Plot entropy rate over a range of fitness values."""
ms = [[[r, r], [1, 1]] for r in rs]
params = [constant_generator(N), ms]
for p in [incentive_func, eta, w, mutation_func, mu_ab, mu_ba, test_func]:
params.append(constant_generator(p))
es = run_batches(params)
print es[-1]
pyplot.plot(rs, [x[-1] for x in es])
return es
def mu_plot(N,
m,
incentive_func=None,
eta=None,
w=None,
mutation_func=None,
mus=None,
test_func=None):
"""Plot entropy rate over a range of mutation probabilities."""
params = [constant_generator(N)]
for p in [m, incentive_func, eta, w, mutation_func]:
params.append(constant_generator(p))
params.append(mus)
params.append(mus)
params.append(constant_generator(test_func))
es = run_batches(params)
print es[-1]
pyplot.plot([-math.log(mu) for mu in mus], [x[-1] for x in es])
return es
def compute_N_mu_heatmap_data(Ns,
mus,
r,
incentive_func=None,
eta=None,
w=None,
mutation_func=None,
test_func=None):
data = []
m = [[r, r], [1, 1]]
for mu in mus:
print mu
params = [Ns]
for p in [m, incentive_func, eta, w, mutation_func, mu, mu, test_func]:
params.append(constant_generator(p))
es = run_batches(params)
for (args, e) in es:
N, m, incentive_func, eta, w, mutation_func, mu_ab, mu_ba = args
data.append([N, r, mu, -math.log(e)])
return data
def static_plot(Ns,
m,
incentive_func=None,
eta=None,
w=None,
mutation_func=None,
mu_ab=0.001,
mu_ba=0.001,
test_func=None):
"""Plot entropy rate over a range of population sizes."""
params = [Ns]
for p in [
m, incentive_func, eta, w, mutation_func, mu_ab, mu_ba, test_func
]:
params.append(constant_generator(p))
es = run_batches(params)
#k = 0.5 + 0.5* math.log(math.pi / 2)
#print mu_ab, (es[-1][-1] - k) / (0.5 * math.log(Ns[-1]))
#pyplot.plot(Ns, [(es[i][-1] -k)/(0.5 * math.log(Ns[i])) for i in range(len(es))])
#print mu_ab, (es[-1][-1] - k) / (0.5 * math.log(Ns[-1]))
print mu_ab, es[-1][-1] - binary_entropy(mu_ab)
pyplot.plot(Ns,
[es[i][-1] - binary_entropy(mu_ab) for i in range(len(es))])
return es
def compute_N_r_heatmap_data(Ns,
rs,
incentive_func=None,
eta=None,
w=None,
mutation_func=None,
mu_ab=0.001,
mu_ba=0.001,
test_func=None):
data = []
for r in rs:
print r
m = [[r, r], [1, 1]]
params = [Ns]
for p in [
m, incentive_func, eta, w, mutation_func, mu_ab, mu_ba,
test_func
]:
params.append(constant_generator(p))
es = run_batches(params)
for (args, e) in es:
N, m, incentive_func, eta, w, mutation_func, mu_ab, mu_ba = args
data.append([N, r, mu_ab, mu_ba, e])
return data
