metricList = [Metric.Energy, Metric.Consensus]
state = '$r_{stable}$'
c_len = 'length'
data = {c_len: [], state: [], 'fct': []}
for metric in metricList:
data[metric.name] = []
conf = create_config(c_type, n=args.n)
states = states_per_magnet(size, 0.0)
conf.set_states(states)
for i, p in enumerate(r_factor):
rules = rules_per_ratio(size, p)
conf.set_rules(rules)
evol, metrics = evolve(config=conf,
perm=pi,
steps=args.steps,
metricList=metricList,
cycleBreak=True)
data[c_len].append(cycle_length(evol)[0])
rstab = p / (size - p) if size != p else np.inf
data[state].append(rstab)
data['fct'].append(p)
for e in metrics:
data[e.name].append(metrics[e][-1])
datum = pd.DataFrame(data)
csv = datum.to_csv(encoding='utf-8', index=False)
print(csv)
for i, mag in enumerate(magnetList):
init_states = [State.ON] * round((size * (0.5 + mag / 2)) - 10**
(-9)) + [State.OFF] * round(
(size * (0.5 - mag / 2) + 10**(-9)))
rng.shuffle(init_states)
init_rule = np.array([Rule.STABLE] * size)
curr_rule = init_rule
conf.set_rules(init_rule)
conf.set_states(init_states)
curr_magnet = mag
stables = [0]
while curr_magnet < 1 and stables != []:
array, out = evolve(conf,
perm=pi,
steps=args.steps,
metricList=metricList,
cycleBreak=True)
curr_energy = out[Metric.Energy][-1]
curr_magnet = out[Metric.Consensus][-1]
length, start = cycle_length(array)
stables = [k for k in range(size) if curr_rule[k] == Rule.STABLE]
agree['init_magnet'].append(mag)
agree['length'].append(length)
agree['Magnetization_mean'].append(curr_magnet)
agree['Magnetization_std'].append(
np.std(consensus(array[start:start + length])))
agree['Magnetization_min'].append(
np.min(consensus(array[start:start + length])))
agree['Magnetization_max'].append(