from UQuant.SemilinearSystem import SemiLinSystem
# build a pipe
pipe = SemiLinSystem(1.0, 5.0, 0.0, 1.0, 0.005, 7, 0.05)
# get the info of the pipe
pipe.info()
# check how pressure drop behaves when the friction coefficient is
# updated. Theoretically we have continuous dependence which can
# be seen also numerically.
coef = 0.0
for i in range(0, 100):
vec_c = [0.05 + coef, 0.01, 0.0, 0.04, 0.0, 0.005, 0.005]
pipe.run(vec_c)
pipe.get_lambda_average(vec_c)
pipe.get_presure_drop(time_instance=13)
if i % 10 == 1:
plt.plot(pipe.timeslices,
pipe.pressure_drop,
"-o",
label="cf = " + str(0.05 + coef))
coef += 0.001
print ">> Computation Done"
plt.legend(loc=2, borderaxespad=0.0)
plt.xticks([round(tn, 2) for tn in pipe.timeslices])
plt.xlabel("$t_n$", fontsize=24)
plt.ylabel("$\delta p_h(t_n)$", fontsize=24)
plt.tight_layout()
plt.show(block=True)
c_sound = 1.0
t_final = 5.0
x_l, x_r = [0.0, 1.0]
dx = 0.005
boundary_eps = 0.05
# true friction coefficient
true_friction = [0.075, 0.015, 0.035, 0.025, 0.035, 0.001, 0.03]
time_ins = 20
# construct and run the true pipe
pipe_true = SemiLinSystem(c_sound, t_final, x_l, x_r, dx, expan_coef,
boundary_eps)
pipe_true.run(true_friction)
y_obs = normal(0.0, 0.1, time_ins) + \
pipe_true.get_presure_drop(time_instance=time_ins, inplace=False)
# construct a pipe for computation
pipe = SemiLinSystem(c_sound, t_final, x_l, x_r, dx, expan_coef, boundary_eps)
def uni_prior(x):
'''
uniform prior
Parameters
----------
x: float * len(x)
'''
ax.set_aspect(aspect=6)
ax.legend(loc='upper left', bbox_to_anchor=(0.05, 1.275), fontsize=12)
if cluster != 0:
ax.tick_params(axis='y', labelleft=False)
plt.tight_layout()
plt.savefig("results/cluster_13.pgf")
plt.show(block=False)
# plot pressure drop
fig = plt.figure(figsize=(12, 6))
pipe_true.run(true_friction)
pipe_true.get_presure_drop(time_instance=time_ins)
markers = ['x', '^', 'P', 'o', '*', 'p']
for cluster in range(n_clusters):
ax = fig.add_subplot(1, n_clusters, cluster + 1)
plt.yticks(fontsize=18)
plt.xticks(fontsize=18)
plt.xlabel("$t$", fontsize=24)
mean_cluster.append(df.iloc[pred == cluster, :].mean().values)
pipe.run(mean_cluster[cluster])
pipe.get_presure_drop(time_instance=time_ins)
ax.plot(pipe.timeslices,
pipe.pressure_drop,
markers[cluster % len(markers)],