dimension=DIMENSION,
observe_every=observe_every,
)
s_ukf = s
data_ukf = data
H = np.array([[params["c"], (1 - params["a"] - params["b"])], [0, 1]])
# --- PLOTS --- #
# fig, ax = plot_discharge_predictions(
# data, filtered_prior=False, plusR=False)
# ax.set_ylim(-0.1, 4.5)
# plt.show()
fig, ax = plot_discharge_predictions(data, filtered_prior=False, plusR=False)
ax.set_title(
f"UNSCENTED KF ({DIMENSION}D) Predicted Discharge R: {R} Q: {Q00_s_noise} "
"\n $\sigma_{q_{obs}}$:"
f"{std_q_obs} "
"$\sigma_{r_{obs}}$:"
f"{std_r_obs} "
"$\sigma_{abc}$:"
f"{std_abc} "
"$\sigma_{S0}$:"
f"{std_S0}"
)
ax.set_ylim(-0.1, None)
plt.show()
# fig.savefig(plot_dir / f"001_discharge_preds_{int( random.random() * 100 )}")
)
# ------ INTERPRET OUTPUT ------
# calculate error metrics
r2 = calculate_r2_metrics(data)
print("R2 Metrics:")
print(f"Prior R2: {float(r2.loc[r2['run'] == 'prior', 'r2']):.2f}")
print(f"Posterior R2: {float(r2.loc[r2['run'] == 'posterior', 'r2']):.2f}")
# 1. Filtered/Prior vs. True Scatter
fig, axs = plot_prior_posterior_scatter_r2(data, s, r2)
plt.show()
fig.savefig(plot_dir / "004_discharge_scatter.png")
# 2. prior, filtered, true discharge Lineplot
fig, ax = plot_discharge_predictions(data)
plt.show()
fig.savefig(plot_dir / "005_prior_true_sim_discharge.png")
# 3. Plot the Storage Parameter (unobserved)
plot_state_storage(s, data)
plt.show()
fig.savefig(plot_dir / "006_prior_true_sim_storage.png")
fig, ax = plot_discharge_uncertainty(data)
plt.show()
fig.savefig(plot_dir / "007_discharge_uncertainty.png")
#
fig, ax = plt.subplots(figsize=(12, 4))
ax = plot_filtered_true_obs(data)
print("\\\\"
"\\alpha = "
f"{alpha}"
"\\\\"
"\\beta = "
f"{beta}"
"\\\\"
"\\kappa = "
f"{kappa}"
"\\\\")
# --- PLOTS ---
ylim = (-0.1, 2.5)
# ukf
fig, ax = plot_discharge_predictions(data_ukf,
filtered_prior=False,
plusR=True)
ax.set_title(
f"UNSCENTED KF ({UKF_DIMENSION}D) Predicted Discharge R: {R} Q: {Q00_s_noise} "
"\n $\sigma_{q_{obs}}$:"
f"{std_q_obs} "
"$\sigma_{r_{obs}}$:"
f"{std_r_obs} "
"$\sigma_{abc}$:"
f"{std_abc} "
"$\sigma_{S0}$:"
f"{std_S0}")
ax.set_ylim(ylim)
ax.set_xlim(-1, 50)
plt.show()
fig.savefig(f"/Users/tommylees/Downloads/00_unscented_kf.png")