def generate_T2s():
scenarii = json.loads(open('scenarii.json').read())
for key, info in scenarii.items():
# Define the switch points
times = np.linspace(0, info['maxTime'], info['switches'] + 1,
endpoint=False)
# Build the theoretical model
model = StSICMR(info['true_n'], times, info['true_M'])
# Choose points in which to evaluate the model
steps = generate_times(info['maxTime'])
scenarii[key]['times'] = list(steps)
scenarii[key]['lambdas'] = [model.lambda_s(s) for s in steps]
scenarii[key]['true_T'] = list(times)
# Save it a s a .json file
with open('scenarii.json', 'w') as outfile:
json.dump(scenarii, outfile)
for i in range(1, n_tw+1)])
print (psmc_t_vector)
#t_vector = psmc_t_vector
t_vector = np.arange(0, 50, 0.1)
N_ref = 1000
path2ms = '../bin'
if __name__ == "__main__":
model = StSICMR(n, T_list, M_list)
T_list_ms = np.true_divide(np.array(T_list), 2)
cmd = create_ms_command_T2(n_obs, n, T_list_ms, M_list, sampling)
obs_T2 = simulate_T2_ms(cmd, path2ms)
(x, empirical_lambda) = compute_empirical_hist(obs_T2, t_vector, N_ref,
path2ms)
theoretical_lambda = [model.lambda_s(t) for t in t_vector]
times = 2 * N_ref * np.array(x)
empirical_sizes = N_ref * np.array(empirical_lambda)
theoretical_sizes = N_ref * np.array(theoretical_lambda)
# Now we plot
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.step(times, empirical_sizes, '-r', where='post', label='empirical')
ax.step(times, theoretical_sizes, '-b', where='post', label='theoretical')
ax.set_xlim(1e3, 2*N_ref*t_vector[-1])
ax.set_ylim(0, 1e5)
ax.set_xscale('log')
print (empirical_lambda[0])
print (theoretical_lambda[0])
