def generate_shock(prob, num_agent, num_time, buffer_time, save_dest, seed, init_state):
np.random.seed(seed)
data_rand = np.random.rand(num_agent, num_time+buffer_time)
data_i = np.ones((num_agent, num_time+buffer_time), dtype = int)
data_i[:, 0] = init_state
calc_trans(data_i, data_rand, prob)
data_i = data_i[:, buffer_time:]
np.save(save_dest + '.npy' , data_i)
split_shock(save_dest, num_agent, num_core)
if __name__ == '__main__':
f = open(nd_log_file, 'w')
# f.writelines('w, p, rc, dist, mom0, mom1, mom2, mom3\n')
f.writelines(
'p, rc, ome, varpi, dist, mom0, mom1, mom2, mom4, mom5, mom7, mom8\n')
f.close()
### generate shocks ###
np.random.seed(0)
data_rand = np.random.rand(num_pop, sim_time)
data_i_s = np.ones((num_pop, sim_time), dtype=int)
data_i_s[:,
0] = 7 #initial state. it does not matter if simulation is long enough.
calc_trans(data_i_s, data_rand, prob)
data_i_s = data_i_s[:, 2000:]
np.save(path_to_data_i_s + '.npy', data_i_s)
split_shock(path_to_data_i_s, 25_000, num_core)
del data_rand
np.random.seed(2)
data_rand = np.random.rand(
num_pop, sim_time + 1) #+1 is added since this matters in calculation
data_is_o = np.ones((num_pop, sim_time + 1), dtype=int)
data_is_o[:,
0] = 0 #initial state. it does not matter if simulation is long enough.
calc_trans(data_is_o, data_rand, prob_yo)
data_is_o = data_is_o[:, 2000:]
np.save(path_to_data_is_o + '.npy', data_is_o)
split_shock(path_to_data_is_o, 25_000, num_core)
f.writelines(
'p, rc, ome, varpi, dist, mom0, mom1, mom2, mom4, mom5, mom7\n')
f.close()
from markov import calc_trans, Stationary
num_pop = 100_000
sim_time = 3_000
data_i_s = np.ones((num_pop, sim_time), dtype=int)
#need to set initial state for zp
data_i_s[:, 0] = 7
# prob = np.load('./input_data/transition_matrix.npy')
np.random.seed(0)
data_rand = np.random.rand(num_pop, sim_time)
calc_trans(data_i_s, data_rand, prob)
data_i_s = data_i_s[:, 2000:]
np.save(path_to_data_i_s + '.npy', data_i_s)
### check
f = open(nd_log_file, 'w')
f.writelines(
np.array_str(np.bincount(data_i_s[:, 0]) /
np.sum(np.bincount(data_i_s[:, 0])),
precision=4,
suppress_small=True) + '\n')
f.writelines(
np.array_str(Stationary(prob), precision=4, suppress_small=True) +
'\n')
f.close()
