'''
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)