crossovers = []
C_as = []
C_ad = []
C_ai = []
C_0 = []
for seed in range(n_seeds):
for ind_cue in range(p):
for ind_trans in range(3, len(retrieved[seed][ind_cue]) - 1):
pattA = retrieved[seed][ind_cue][ind_trans]
pattB = retrieved[seed][ind_cue][ind_trans + 1]
pair_crossovers[pattA * p + pattB].append(
crossover[seed][ind_cue][ind_trans + 1])
crossovers.append(crossover[seed][ind_cue][ind_trans + 1])
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)
plt.figure('Crossover histo')
plt.hist(crossovers, bins=100)
prop_high = np.zeros(p**2)
prop_low = np.zeros(p**2)
means = np.zeros(p**2)
pair_crossovers = [[] for pair in range(p**2)]
previous = []
following = []
marriage_time = []
C_as = []
C_ad = []
C_ai = []
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')
max_m_mu_saved.append(tmp[5])
max2_m_mu_saved.append(tmp[6])
indTrans += 1
ind_max_prev = ind_max
plt.close('all')
plt.figure(1)
plt.subplot(311)
plt.plot(tS[:, None], m_mu_plot)
plt.title('C1 = ' + str(C1) + ', C2 = ' + str(C2))
plt.ylabel('overlap')
plt.xlim(0, tSim)
plt.subplot(312)
plt.title(str(retrieved_saved))
plt.scatter(
tTrans,
N * a * correlations.active_same_state(
ksi_i_mu[:, retrieved_saved], ksi_i_mu[:, outsider_saved]),
label='C1')
plt.scatter(
tTrans,
N * a * correlations.active_diff_state(
ksi_i_mu[:, retrieved_saved], ksi_i_mu[:, outsider_saved]),
label='C2')
plt.xlim(0, tSim)
plt.ylim(-1, 21)
plt.legend()
plt.subplot(313)
plt.plot(tS[:, None], m_mu_plot[:, :2])
plt.xlim(0, tSim)
plt.tight_layout()
plt.savefig('scan_corr/C1_' + str(C1) + '_C2_' + str(C2))
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)
ax_order = 'xC1yC2'
if ax_order == 'xC1yC2':
xx = correlations.active_same_state(ksi_i_mu[:, retrieved_saved],
ksi_i_mu[:, previously_saved])
yy = correlations.active_diff_state(ksi_i_mu[:, retrieved_saved],
ksi_i_mu[:, previously_saved])
zz = correlations.active_inactive(ksi_i_mu[:, retrieved_saved],
ksi_i_mu[:, previously_saved])
XX = C1C2C0[:, 0]
YY = C1C2C0[:, 1]
else:
xx = correlations.active_diff_state(ksi_i_mu[:, retrieved_saved],
ksi_i_mu[:, previously_saved])
yy = correlations.active_same_state(ksi_i_mu[:, retrieved_saved],
ksi_i_mu[:, previously_saved])
zz = correlations.active_inactive(ksi_i_mu[:, retrieved_saved],
ksi_i_mu[:, previously_saved])
XX = C1C2C0[:, 1]
YY = C1C2C0[:, 0]
pair_covar_norm = pair_coactivation.copy()
C1C2C0 = np.zeros(((p * (p - 1)) // 2, 3))
shared_inact = pair_coactivation.copy()
cpt = 0
M = correlations.shared_inactive(ksi_i_mu)
for patt1 in range(p):
for patt2 in range(patt1 + 1, p):
shared_density[cpt] = shared_parents[patt1, patt2]
pair_coactivation[cpt] = coactivation[patt1, patt2]
pair_covariance[cpt] = covariance[patt1, patt2]
pair_coac_norm[cpt] = coactivation_norm[patt1, patt2]
pair_covar_norm[cpt] = covariance_norm[patt1, patt2]
C1C2C0[cpt, 0] = \
correlations.active_same_state(ksi_i_mu[:, patt1],
ksi_i_mu[:, patt2])
C1C2C0[cpt, 1] = \
correlations.active_diff_state(ksi_i_mu[:, patt1],
ksi_i_mu[:, patt2])
C1C2C0[cpt, 2] = \
correlations.active_inactive(ksi_i_mu[:, patt1],
ksi_i_mu[:, patt2])
shared_inact[cpt] = M[patt1, patt2]
cpt += 1
ind_gA = [i for i in range(len(g_A_s)) if g_A_s[i] == g_A][0]
ind_apf = [i for i in range(len(apf_s)) if apf_s[i] == a_pf][0]
jitterx = 0.15 * (rd.rand((p * (p - 1) // 2)) + ind_gA)
jittery = np.max(coactivation) * (0.05 * rd.rand((p * (p - 1) // 2)))
# plt.figure('Shared parents')
plt.figure('evolution')
trans_time = file_handling.load_transition_time(0, key)[ind_cue]
retrieved = file_handling.load_retrieved(0, key)[ind_cue]
plt.plot(tSnap, overlap)
for ind_trans in range(len(trans_time)):
plt.text(trans_time[ind_trans], 0.8, str(retrieved[ind_trans]))
plt.figure('Scatter')
C0, C1, C2 = [], [], []
for ind_trans in range(len(valid_transitions)):
pair = valid_transitions[ind_trans]
if pair[0] != -1:
pattA = ksi_i_mu[:, pair[0]]
pattB = ksi_i_mu[:, pair[1]]
C0.append(correlations.active_inactive(pattA, pattB))
C1.append(correlations.active_same_state(pattA, pattB))
C2.append(correlations.active_diff_state(pattA, pattB))
if correlations.active_same_state(pattA, pattB) > 0.8:
print(pair[0], pair[1])
plt.scatter(C1, C0, color='b')
C0, C1, C2 = [], [], []
for ind_trans in range(len(invalid_transitions)):
pair = invalid_transitions[ind_trans]
if pair[0] != -1:
pattA = ksi_i_mu[:, pair[0]]
pattB = ksi_i_mu[:, pair[1]]
C0.append(correlations.active_inactive(pattA, pattB))
C1.append(correlations.active_same_state(pattA, pattB))
C2.append(correlations.active_diff_state(pattA, pattB))