def plot_lamb_hist_kwang_il(simulation_list):
labels = []
# def plot_lamb_hist(simulation_list):
for ind_key in range(len(simulations)):
print('ind_key = %d' % ind_key)
simulation_key = simulations[ind_key]
(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(simulation_key+'.pkl')
labels.append(r"$g_A$=%.2f" % g_A)
lamb = file_handling.load_overlap(simulation_key + '.txt')
lamb_1D = np.zeros(file_handling.event_counter(lamb, p))
cpt = 0
for ind_cue in range(len(lamb)):
for ind_trans in range(len(lamb[ind_cue])):
lamb_1D[cpt] = lamb[ind_cue][ind_trans]
cpt += 1
sns.distplot(lamb_1D)
plt.xlabel(r'$\lambda$')
plt.ylabel('Density')
plt.title(r'w=' + str(w) + ', Gsln')
plt.legend(labels)
def compare_mi_crossovere(simulation_list):
for ind_sim in range(len(simulation_list)):
simulation = simulation_list[ind_sim]
retrieved_saved = file_handling.load_retrieved(simulation)
lamb = file_handling.load_overlap(simulation)
(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(simulation)
thresholds = np.linspace(0.1, 0.9, 10)
mi_high_s = thresholds.copy()
mi_low_s = thresholds.copy()
shuffled_high_s = thresholds.copy()
shuffled_low_s = thresholds.copy()
random_high_s = thresholds.copy()
random_low_s = thresholds.copy()
for ii in range(len(thresholds)):
threshold = thresholds[ii]
mi_high, mi_low, shuffled_high, shuffled_low, random_high, random_low = \
get_mi_crossovers(retrieved_saved, lamb, threshold)
mi_high_s[ii] = mi_high
mi_low_s[ii] = mi_low
shuffled_high_s[ii] = shuffled_high
shuffled_low_s[ii] = shuffled_low
random_low_s[ii] = random_high
random_high_s[ii] = random_low
plt.subplot(3, 2, ind_sim + 1)
plt.plot(thresholds,
mi_high_s - shuffled_high_s,
'b',
label='Corrected high')
plt.plot(thresholds,
mi_low_s - shuffled_low_s,
'g',
label='Corrected low')
plt.plot(thresholds, mi_high_s, ':b', label='Original high')
plt.plot(thresholds, mi_low_s, ':g', label='Original how')
plt.plot(thresholds, shuffled_high_s, '--b', label='Bias high')
plt.plot(thresholds, shuffled_low_s, '--g', label='Bias low')
plt.ylabel('Mutual information in pairs (m=2)')
plt.xlim(0, 1)
plt.title('w=%.2f, g_A=%.2f' % (w, g_A))
if ind_sim == 0:
plt.legend()
if ind_sim in [4, 5]:
plt.xlabel('Overlap threshold')
def find_neighbors(key):
(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)
graph_all = nx.Graph()
graph_high = nx.Graph()
graph_low = nx.Graph()
num_trans_all = np.zeros((p, p), dtype=int)
num_trans_high = np.zeros((p, p), dtype=int)
num_trans_low = np.zeros((p, p), dtype=int)
retrieved_saved = file_handling.load_retrieved(key)
lamb = file_handling.load_overlap(key)
threshold = median([
lamb[ind_cue][trans] for ind_cue in range(p)
for trans in range(len(lamb[ind_cue]))
])
neighbors = [[] for pat in range(p)]
for cue_ind in range(p):
if len(retrieved_saved[cue_ind][:ind_max[cue_ind]]) >= 3:
# print(len(retrieved_saved[cue_ind]))
duration = len(retrieved_saved[cue_ind])
if cue_ind != retrieved_saved[cue_ind][0]:
duration += 1
# ind_max[cue_ind] = duration
sequence = []
if cue_ind != retrieved_saved[cue_ind][0]:
sequence.append(cue_ind)
sequence += retrieved_saved[cue_ind]
sequence = sequence[3:ind_max[cue_ind]]
for ind_trans in range(len(sequence) - 1):
patt1 = sequence[ind_trans]
patt2 = sequence[ind_trans + 1]
num_trans_all[patt1, patt2] += 1
if lamb[cue_ind][ind_trans + 1] >= threshold:
num_trans_high[patt1, patt2] += 1
else:
num_trans_low[patt1, patt2] += 1
# if True:
if patt2 not in neighbors[patt1]:
neighbors[patt1].append(patt2)
for patt1 in range(p):
for patt2 in range(p):
if patt1 != patt2:
if num_trans_all[patt1, patt2]:
graph_all.add_edge(patt1,
patt2,
weight=num_trans_all[patt1, patt2])
if num_trans_high[patt1, patt2]:
graph_high.add_edge(patt1,
patt2,
weight=num_trans_high[patt1, patt2])
if num_trans_low[patt1, patt2]:
graph_low.add_edge(patt1,
patt2,
weight=num_trans_low[patt1, patt2])
return neighbors, graph_all, num_trans_all, graph_low, num_trans_low, \
graph_high, num_trans_high, threshold
def compare_mi_crossover(simulation_list):
mi_high_s = np.zeros(len(simulation_list))
mi_low_s = mi_high_s.copy()
shuffled_high_s = mi_high_s.copy()
shuffled_low_s = mi_high_s.copy()
random_low_s = mi_high_s.copy()
random_high_s = mi_high_s.copy()
similarity_s = mi_high.copy()
similarity_shuf_s = mi_high.copy()
w_s = mi_high_s.copy()
g_A_s = mi_high_s.copy()
threshold_s = mi_high_s.copy()
for ind_sim in range(len(simulation_list)):
simulation = simulation_list[ind_sim]
retrieved_saved = file_handling.load_retrieved(simulation)
lamb = file_handling.load_overlap(simulation)
print('Events %d' % event_counter(lamb))
(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(simulation)
lamb_list = [
lamb[ind_cue][ind_trans] for ind_cue in range(len(lamb))
for ind_trans in range(2,
len(lamb[ind_cue]) - 1)
]
# print(lamb_list[:10])
threshold = median(lamb_list)
print(threshold)
mi_high, mi_low, shuffled_high, shuffled_low, random_high, random_low = \
get_mi_crossovers(retrieved_saved, lamb, threshold)
print(mi_high)
mi_high_s[ind_sim] = mi_high
mi_low_s[ind_sim] = mi_low
shuffled_high_s[ind_sim] = shuffled_high
shuffled_low_s[ind_sim] = shuffled_low
random_low_s[ind_sim] = random_high
random_high_s[ind_sim] = random_low
w_s[ind_sim] = w
g_A_s[ind_sim] = g_A
threshold_s[ind_sim] = threshold
ax1 = plt.subplot2grid((3, 2), (0, 0), colspan=2, rowspan=2)
ax1.plot(g_A_s, mi_high_s - shuffled_high_s, 'b-o', label='Corrected high')
ax1.plot(g_A_s, mi_low_s - shuffled_low_s, 'g-o', label='Corrected low')
ax1.plot(g_A_s, mi_high_s, ':b', label='Original high')
ax1.plot(g_A_s, mi_low_s, ':g', label='Original how')
ax1.plot(g_A_s, shuffled_high_s, '--b', label='Bias high')
ax1.plot(g_A_s, shuffled_low_s, '--g', label='Bias low')
ax1.set_ylabel('Mutual information in pairs (m=2)')
ax1.set_xlim(-0.1, 1.1)
ax1.set_title('High-and-low-crossover mutual information')
ax1.legend()
ax1.set_xlabel(r'$g_A$')
ax2 = plt.subplot2grid((3, 2), (2, 0))
ax2.plot(g_A_s, w_s, '-o')
ax2.set_xlim(-0.1, 1.1)
ax2.set_xlabel(r'$g_A$')
ax2.set_ylabel(r'$w$')
ax2.set_ylim(0.9, 1.5)
ax2.set_title('Latching border')
ax3 = plt.subplot2grid((3, 2), (2, 1))
ax3.plot(g_A_s, threshold_s, '-o')
ax3.set_xlim(-0.1, 1.1)
ax3.set_xlabel(r'$g_A$')
ax3.set_ylabel(r'$\lambda$')
ax3.set_ylim(0, 1)
ax3.set_title('Crossover threshold')
plt.tight_layout()
prefactor = 0.1
if len(seq_cut_high) < prefactor * p**2:
mi_high = np.NAN
shuffled_high = np.NAN
if len(seq_cut_low) < prefactor * p**2:
mi_low = np.NAN
shuffled_low = np.NAN
return mi_high, mi_low, shuffled_high, shuffled_low, random_high, random_low
for ind_sim in range(len(simulations)):
simulation = simulations[ind_sim]
retrieved_saved = file_handling.load_retrieved(simulation)
lamb = file_handling.load_overlap(simulation)
(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(simulation)
thresholds = np.linspace(0.1, 0.9, 10)
mi_high_s = thresholds.copy()
mi_low_s = thresholds.copy()
shuffled_high_s = thresholds.copy()
shuffled_low_s = thresholds.copy()
random_high_s = thresholds.copy()
random_low_s = thresholds.copy()
for ii in range(len(thresholds)):
threshold = thresholds[ii]