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(
np.max(consensus(array[start:start + length])))
agree['SpinGlass'].append(curr_energy)
agree['unstables'].append(size - len(stables))
# Choose a random stable node to change rule
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)
both = np.concatenate((inactive, active))
array[t] = order_update(array[t - 1].copy(), config, both, rules)
magn = metrics[Metric.Consensus](array[t], config, single=1)
t += 1
last = -1 if t > args.steps else t - 1
for metric in metricList:
out[metric.name].append(metrics[metric](array[last], config, single=1))
out[le].append(0)
out['order'].append('off_first')
out['init_magnet'].append(mag)
out['init_stab'].append(stab)
# Now random order
evol, vals = evolve(config=config,
steps=args.steps,
perm=pi,
metricList=metricList,
cycleBreak=True)
length, start = cycle_length(evol)
out[le].append(length)
for metric in metricList:
out[metric.name].append(vals[metric][-1])
out['order'].append('random')
out['init_magnet'].append(mag)
out['init_stab'].append(stab)
df = pd.DataFrame(out)
csv = df.to_csv(encoding='utf-8', index=False)
print(csv)