def get_vectors(R0_scale=1):
beta_vec_new = R0_scale * beta_vec
beta_vec_list_new = beta_vec_new.tolist()
beta_0 = R0_scale * np.random.gamma(R0_scale * R0_isol_mean, 1 / R0_scale,
N_nodes) / Trec_vec
beta_0_list = beta_0.tolist()
beta_1 = R0_scale * hp.power_law_pdf(np.random.uniform(0, 1, N_nodes), a0,
a1, scale_nonisol) / Trec_vec
beta_1_list = beta_vec_list_new
return beta_vec_list_new, Trec_vec_list, beta_0_list, beta_1_list
## 2.add distribution for R0
## 2.A self-isolated
isol_share = 0.7
N_nodes_isol = round(isol_share * N_nodes)
R0_scale = 100 #10**2
R0_isol_mean = 0.25 * 3.96 # 1.43
R0_isol_vec = np.random.gamma(R0_scale * R0_isol_mean, 1 / R0_scale,
N_nodes_isol)
## 2.B non-isolated
a0 = R0_isol_mean + np.std(R0_isol_vec) #2
a1 = 14 #16
scale_nonisol = 2.3 #1.65
N_nodes_nonisol = N_nodes - N_nodes_isol
R0_nonisol_vec = hp.power_law_pdf(np.random.uniform(0, 1, N_nodes_nonisol), a0,
a1, scale_nonisol)
## 2.C joint distribution
R0_vec = shuffle(np.transpose(
[*np.transpose(R0_isol_vec), *np.transpose(R0_nonisol_vec)]),
random_state=0)
R0_vec_list = R0_vec.tolist()
R0_mean = np.mean(R0_vec)
R0_std = np.std(R0_vec)
## 3. exctract Beta
beta_vec = R0_vec / Trec_vec
beta_vec_mean = np.mean(beta_vec)
## use this -->>
beta_vec_list = beta_vec.tolist()