def plot_overlap(cue, key):
overlaps = file_handling.load_evolution(cue, 0, key)
print(overlaps)
(dt, tSim, N, S, p, num_fact, p_fact, dzeta, a_pf, eps, f_russo,
cm, a, U, w, tau_1, tau_2, tau_3_A, tau_3_B, g_A, beta, tau, t_0,
g, random_seed, p_0, n_p, nSnap, russo2008_mode, _) = \
file_handling.load_parameters(key)
tS = np.linspace(0, tSim, nSnap)
# plt.title(r"$a_{pf}$=%.2f, $w$=%.1f, $g_A$=%.1f" % (a_pf, w, g_A))
ax.set_title(r"$w$=%.1f, $a_{pf}$=%.2f" % (w, a_pf))
# retrieved = file_handling.load_retrieved(0, key)[0]
# trans_time = file_handling.load_transition_time(0, key)[0]
# for ind_trans in range(len(trans_time)-1):
# ax.text((trans_time[ind_trans+1]+trans_time[ind_trans])/2, 0.9, str(retrieved[ind_trans]), fontsize=15)
ax.plot(tS, overlaps)
cue = 0
sig_i_k = file_handling.load_activation_cue(0, 0, key)
r_i_k = file_handling.load_thresholds_cue(0, 0, key)
r_i_S_A = file_handling.load_thresholds_S_A_cue(0, 0, key)
r_i_S_B = file_handling.load_thresholds_S_B_cue(0, 0, key)
for ii in range(N):
for kk in range(S):
r_i_k[:, ii * S + kk] += r_i_S_B[:, ii]
h_i_k = np.zeros((len(tS), N * S))
for tt in range(len(tS)):
h_i_k[tt, :] = field(sig_i_k[tt, :])
m_mu = np.array(file_handling.load_evolution(cue, 0, key))
recorded = tS > 0.
tS = tS[recorded]
m_mu = m_mu[recorded, :]
sig_i_k = sig_i_k[recorded, :]
r_i_k = r_i_k[recorded, :]
h_i_k = h_i_k[recorded, :]
ind_trans = 5
# mu = retrieved[0][0][ind_trans]
# nu = retrieved[0][0][ind_trans+1]
mu = 11
nu = 39
color_dict = {
181: 'tab:blue',
C_0 = []
for ind_marriage in range(len(simult_ret)):
pattA = int(simult_ret[ind_marriage][0])
pattB = int(simult_ret[ind_marriage][1])
marriage_time.append(simult_ret[ind_marriage][3])
C_as.append(
correlations.active_same_state(ksi_i_mu[:, pattA], ksi_i_mu[:, pattB]))
C_ad.append(
correlations.active_diff_state(ksi_i_mu[:, pattA], ksi_i_mu[:, pattB]))
C_ai.append(
correlations.active_inactive(ksi_i_mu[:, pattA], ksi_i_mu[:, pattB]))
previous.append(pattA)
following.append(pattB)
overlaps = file_handling.load_evolution(0, 0, key)
tS = np.array(file_handling.load_time(0, key)[0])
recorded = tS > 0.
trans_time = file_handling.load_transition_time(0, key)[0]
C1C2C0 = correlations.cross_correlations(ksi_i_mu)
plt.close('scatter_cas')
fig = plt.figure('scatter_cas')
ax1 = plt.subplot2grid((5, 1), (0, 0))
ax2 = plt.subplot2grid((5, 1), (1, 0))
ax3 = plt.subplot2grid((5, 1), (2, 0), rowspan=3)
sns.distplot(C1C2C0[:, 0], norm_hist=True, kde=False, color='tab:red', ax=ax1)
ax1.set_title(r'Histogram of $C_{as}$ in all pairs')
ax1.set_xlim(0, 0.1)
crossovers = file_handling.load_crossover(0, key)
lamb = []
for ind_cue in range(p):
lamb += crossovers[ind_cue][1:]
lamb = np.array(lamb)
retrieved = file_handling.load_retrieved(0, key)
retrieved_saved = []
previously_saved = []
for ind_cue in range(p):
retrieved_saved += retrieved[ind_cue][1:]
previously_saved += retrieved[ind_cue][:-1]
tSnap = np.array(file_handling.load_time(0, key)[0])
m_mu_plot = file_handling.load_evolution(0, 0, key)
s = 2
shift = 1 / N / a / 5 # In order categories to be visible in scatter
lamb = np.array(lamb)
low_cor = lamb < 0.2
l_low_cor = r'$\lambda < 0.2$'
mid_low_cor = np.logical_and(0.2 <= lamb, lamb < 0.6)
l_mid_low_cor = r'$0.2 \leq \lambda < 0.6$'
mid_high_cor = np.logical_and(0.6 <= lamb, lamb < 0.8)
l_mid_high_cor = r'$0.6 \leq \lambda < 0.8$'
high_cor = 0.8 <= lamb
l_high_cor = r'$0.8 \leq \lambda $'
C1C2C0 = correlations.cross_correlations(ksi_i_mu)
# plt.axvline(tau_2, 0, 1, label=r'$\tau_2$', color='k')
# plt.figure('QZFZEFQZE')
# plt.hist(pair_std_time/pair_mean_time, bins=100)
ksi_i_mu, delta__ksi_i_mu__k, J_i_j_k_l, _ = file_handling.load_network(key)
trans = []
trans_time = []
valid_transitions = []
invalid_transitions = []
valid_time = []
invalid_time = []
is_valid = []
tSnap = np.linspace(0, tSim, nSnap)
for kick_seed in range(1):
for ind_cue in tqdm(range(1)):
overlap = file_handling.load_evolution(ind_cue, kick_seed, key)
retrieved = []
prev_ret = -1
waiting_validation = False
valid = True
patt_buffer = None
for ind_t in range(nSnap - 1):
pattA = np.argmax(overlap[ind_t, :])
max_m_mu = overlap[ind_t, pattA]
overlap[ind_t, pattA] = -np.inf
pattB = np.argmax(overlap[ind_t, :])
max2_m_mu = overlap[ind_t, pattB]
overlap[ind_t, pattA] = max_m_mu
if pattA != prev_ret and max_m_mu > 0.5:
trans.append((prev_ret, pattA))