def plot_information_flow(simulation_list):
for ind_key in range(len(simulation_list)):
print('ind_key = %d' % ind_key)
simulation_key = simulation_list[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)
retrieved_saved = \
file_handling.load_retrieved_several(n_seeds[ind_key], simulation_key)
m_saved, mi_saved, control, shuffled = get_mi(retrieved_saved,
retrieved_saved)
corrected = np.array(mi_saved)[:, None] - np.array(shuffled)
# print((np.array(mi_saved)).shape)
# print((np.array(shuffled)).shape)
# print(corrected.shape)
plt.title('Information flow, shuffled bias estimate')
plt.plot(m_saved,
corrected,
'-o',
color=color_s[ind_key],
label=r'$g_A$=%.1f, $w$=%.1f' % (g_A, w))
plt.plot(m_saved, shuffled, ':', color=color_s[ind_key], label='bias')
# plt.yscale('log', basey=10)
plt.ylim([ymin, ymax])
plt.xlabel(r'Shift $\Delta n$')
plt.legend(loc='upper right')
def plot_information_flow_apf(simulation_list):
g_A_s = np.array([0., 0.5, 1.])
apf_s = np.array([0., 0.05, 0.1, 0.2, 0.4])
n_gA = len(g_A_s)
n_apf = len(apf_s)
for ind_key in range(len(simulation_list)):
print('ind_key = %d' % ind_key)
simulation_key = simulation_list[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,
muted_prop) = file_handling.load_parameters(simulation_key)
retrieved_saved = file_handling.load_retrieved_several(
n_seeds[ind_key], simulation_key)
m_saved, mi_saved, control, shuffled, auto_corr, auto_corr_shuff = \
get_mi(retrieved_saved, retrieved_saved)
auto_corr[1] = 0
corrected = np.array(mi_saved)[:, None] - np.array(shuffled)
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]
print((np.array(mi_saved)).shape)
print((np.array(shuffled)).shape)
print(corrected.shape)
plt.figure('Mi')
plt.subplot(n_gA // 2 + n_gA % 2, 2, ind_gA + 1)
plt.title('g_A=%.1f, w=%.1f' % (g_A, w))
plt.plot(m_saved,
corrected,
'-o',
color=color_s[ind_apf],
label=r'$a_{pf}$=%.2f' % a_pf)
plt.plot(m_saved, shuffled, ':', color=color_s[ind_apf], label='bias')
plt.yscale('log', basey=10)
plt.ylim([ymin, ymax])
plt.xlabel(r'Shift $\Delta n$')
plt.legend(loc='upper right')
plt.figure('Autocor')
plt.subplot(n_apf // 2 + n_apf % 2, 2, ind_apf + 1)
plt.plot(m_saved,
auto_corr,
'-o',
color=color_s[ind_gA],
label=r'$g_A$=%.1f' % g_A)
plt.plot(m_saved, auto_corr_shuff, ':', color=color_s[ind_gA])
plt.yscale('log')
plt.title(r'$a_{pf}$=%.2f' % a_pf)
plt.ylabel('Correlation')
plt.xlabel(r'$\Delta n$')
plt.figure('Mi')
plt.tight_layout()
plt.figure('Autocor')
plt.legend()
plt.tight_layout()
plt.rc('ytick', labelsize=BIGGER_SIZE) # fontsize of the tick labels
plt.rc('legend', fontsize=BIGGER_SIZE) # legend fontsize
plt.rc('figure', titlesize=HUGE_SIZE) # fontsize of the figure title
# simulations = ['a2cc92e57feefe09afa4b7d522648850']
# simulations = ['f30d8a2438252005f6a9190c239c01c1']
simulations = [
'f35c969f14b35efe505be6e417c03656', '9e0fbd728bd38ee6eb130d85f35faa9a'
]
# simulations = ['b18e30bc89dbcb5bc2148fb9c6e0c51d']
# simulations = ['ff9fe40ed43a94577c1cc2fea6453bf0']
n_seeds = 1
key = simulations[0]
retrieved = file_handling.load_retrieved_several(n_seeds, key)
crossover = file_handling.load_crossover_several(n_seeds, key)
trans_time = file_handling.load_transition_time(0, key)[0]
(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, kick_prop) = \
file_handling.load_parameters(key)
ksi_i_mu, delta__ksi_i_mu__k, J_i_j_k_l, \
C_i_j = file_handling.load_network(key)
pair_crossovers = [[] for pair in range(p**2)]
previous = []
following = []
crossovers = []
yy = np.arange(0, max_count + 1, 1)
XX, YY = np.meshgrid(xx, yy)
for cycle in cycle_count:
data[cycle_count[cycle], len(cycle)] += 1
plt.pcolor(XX, YY, data, norm=colors.LogNorm(vmin=1, vmax=5e3))
plt.xlim(1, max_cycle)
plt.ylim(1, 1000)
cbar = plt.colorbar()
plt.yscale('log')
for ind_key in range(1):
print('ind_key = %d' % ind_key)
simulation_key = simulations[ind_key]
ryom_name = ryom_data[ind_key]
retrieved = file_handling.load_retrieved_several(1, simulation_key)
plt.subplot(311)
plot_cycles(retrieved, simulation_key)
plt.title("Latching sequence")
retrieved_random = get_eq_random(retrieved, simulation_key)
retrieved_markov = get_eq_markov(retrieved, simulation_key)
plt.subplot(312)
plt.title("Markov sequence")
plot_cycles(retrieved_markov, simulation_key)
plt.ylabel('Repetitions')
plt.subplot(313)
plt.title("Random sequence")
plot_cycles(retrieved_random, simulation_key)