def plot_posterior_mean(add_mean=False): # plot posterior mean ax, x, y = visual_util.scaled_1d_kde_plot(ensemble_mean_arr, shade=True, color='red', linewidth=0, bandwidth=0.25, density_scale=0.19) return ax
def plot_posterior_mean(**kwargs): # plot posterior mean ensemble_mean_arr = np.concatenate(ensemble_mean_val) ax, x, y = visual_util.scaled_1d_kde_plot(ensemble_mean_arr, shade=True, color='red', linewidth=0, bandwidth=0.25, density_scale=0.19, **kwargs) return ax
def plot_posterior_sample(add_mean=False): # plot uncalibrated posterior prediction ax, x, y = visual_util.scaled_1d_kde_plot(ensemble_sample_arr, shade=True, color='grey', linewidth=0, density_scale=None) if add_mean: mean_height = y[np.argmin(abs(x - np.mean(ensemble_mean_arr)))] visual_util.add_vertical_segment(np.mean(ensemble_mean_arr), mean_height, c='grey', alpha=0.8) return ax
def plot_posterior_sample(add_mode=False): # plot uncalibrated posterior prediction ensemble_sample_arr = np.concatenate(ensemble_sample_val) ax, x, y = visual_util.scaled_1d_kde_plot(ensemble_sample_arr, shade=True, color='grey', linewidth=0, density_scale=None) if add_mode: median_val = x[np.argmax(y)] median_height = y[np.argmin(abs(x - median_val))] visual_util.add_vertical_segment(median_val, median_height, c='grey', alpha=0.8) return ax
def plot_true_density(add_mean=False): # plot target ax, x, y = visual_util.scaled_1d_kde_plot(y_valid, shade=False, linewidth=2, linestyle='--', color='grey', density_scale=None) if add_mean: mean_height = y[np.argmin(abs(x - np.mean(y_valid)))] visual_util.add_vertical_segment(np.mean(y_valid), mean_height, c='grey', alpha=0.8, linestyle='--') # plot observed model predictions for pred_val in np.concatenate(list(base_pred_dict.values())[1:]): plt.plot([pred_val, pred_val], [0, 0.01], lw=3, c='black')