# os.setenv('OMP_NUM_THREADS', '1')
# os.environ["OMP_NUM_THREADS"] = "1"
initialVec = lib.splayState(i_ext_e,
rand(num_e),
lib.derivative)
v_e = initialVec[:, 0]
m_e = lib.m_e_inf(v_e)
h_e = initialVec[:, 1]
n_e = initialVec[:, 2]
q_e = np.zeros(num_e)
s_e = np.zeros(num_e)
v_i = -75.0 * np.ones(num_i)
m_i = lib.m_i_inf(v_i)
h_i = lib.h_i_inf(v_i)
n_i = lib.n_i_inf(v_i)
q_i = np.zeros(num_i)
s_i = np.zeros(num_i)
initialConditions = np.hstack((v_e, h_e, n_e, q_e, s_e,
v_i, h_i, n_i, q_i, s_i))
t = np.arange(0, t_final, dt)
sol = odeint(lib.derivativePopulation,
initialConditions,
t)
lfp = np.mean(sol[:, :num_e], axis=1)
start = time()
adjacency_matrix = nx.to_numpy_array(
nx.gnp_random_graph(num_i, p_gap, seed=seed))
# G_gap = adjacency_matrix * g_hat_gap / (p_gap * (num_i - 1.0))
# c = np.sum(G_gap, axis=1)
initialConditions = lib.splayState(i_ext_i, num_i, lib.derivativeSingle)
# v = initialConditions[:, 0]
v = np.random.uniform(-100, 50, num_i)
n = np.random.rand(num_i)
h = np.random.rand(num_i)
m = lib.m_i_inf(v)
# h = initialConditions[:, 1]
# n = initialConditions[:, 2]
q = np.zeros(num_i)
s = np.zeros(num_i)
x0 = np.hstack((v, h, n, q, s))
t = np.arange(0, t_final, dt)
sol = odeint(lib.derivative, x0, t)
lfp = np.mean(sol[:, :num_i], axis=1)
t_i_spikes = []
for i in range(num_i):
ts_i = lib.spikeDetection(t, sol[:, i], spikeThreshold)
t_i_spikes.append(ts_i)