''' PRIOR = uni_prior(x) if PRIOR > 10.0**-8: return likelihood(x) * PRIOR else: return 0.0 if __name__ == "__main__": import matplotlib.pyplot as plt ### Plotting # get the info of the true pipe pipe_true.info() # plot the pressure drop of the true pressure drop plt.plot(pipe_true.timeslices, pipe_true.pressure_drop, marker="o", label="true pressure drop") # plot the noisy pressure drop plt.plot(pipe_true.timeslices, y_obs, linestyle="--", marker="x", label="noisy pressure drop") plt.xlabel("$t_n$", fontsize=24) plt.ylabel("$\delta p_h^n$", fontsize=24) plt.yticks(fontsize=20) plt.xticks(fontsize=20)
and standard deviation of size 1. ''' LARGE_NUM = 1000000.0 pipe.run(x, progress_bool=False) # check if we have negative friction pipe.get_current_lambda_average() for i in pipe.lambda_avg: if i < 0: return LARGE_NUM S = pipe.get_presure_drop(time_instance=time_ins, inplace=False) out = 0.5 * dot(S - y_obs, S - y_obs) / std_noise return out if __name__ == "__main__": filename = "results/uq_pcn.dat" pipe.info() pcn = PCN(Nlikelihood, uniform_to_normal, normal_to_uniform, initial_point_mcmc) pcn.run(max_iter=4000, burning=400) pcn.write(filename, write_prob=True)