def make_symmetric(mat): n_mat = np.ndarray.copy(mat) for r in range(n_mat.shape[0]): for c in range(n_mat.shape[0]): n_mat[c, r] = n_mat[r, c] return n_mat nodes = 10 num_dim = int((((nodes**2) - nodes) / 2)) seed_tract = 123 rng = np.random.RandomState(seed_tract) t_mat_v = rng.uniform(low=1, high=50, size=(num_dim)) tract_mat = hf.p2matrix(t_mat_v, nodes) # constant w_mat matches where tract_mat is 0 w_mat = np.ones((nodes, nodes)) zero_ind = tract_mat == 0 w_mat[zero_ind] = 0 np.fill_diagonal(w_mat, 0) #cv matrix seed2 = 200 rng = np.random.RandomState(seed2) c_mat_v = rng.uniform(low=500, high=10000, size=(num_dim)) c_mat = hf.p2matrix(c_mat_v, nodes) c_mat = np.reciprocal(c_mat) np.fill_diagonal(c_mat, 0)
w_mat = np.ones((nodes, nodes)) zero_ind = tract_mat == 0 w_mat[zero_ind] = 0 np.fill_diagonal(w_mat, 0) #cv matrix tract_p = hf.matrix2p(tract_mat) tract_p = np.array(tract_p) num_dim = tract_p[tract_p > 0].shape[0] seed2 = 200 num_dim_ = int((((nodes**2) - nodes) / 2)) rng = np.random.RandomState(seed2) c_mat_v = rng.uniform(low=500, high=10000, size=(num_dim_)) c_mat = hf.p2matrix(c_mat_v, nodes) c_mat = np.reciprocal(c_mat) np.fill_diagonal(c_mat, 0) #simulating network #WILSON-COWAN PARAMS """Set seed for the wc_model_sim in the residuals fxn (so all potential solutions get tested with same noise variable)""" wc_seed = 0 wc_params = { 'c1': 1.6, 'c2': -4.7, 'c3': 3, 'c4': -0.63, 'I_e': 1.8, 'I_i': -0.2,