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 = []
C_as = []
C_ad = []
def get_transition_times_seed(key, n_seeds):
trans_times = [[] for ii in range(n_seeds)]
for kick_seed in range(n_seeds):
trans_times[kick_seed] = file_handling.load_transition_time(
kick_seed, key)
return trans_times