def METHOD_NAME(ds, tendency, field):
    figures = []
    def insert_fig(key):
        plt.title(key)
        figures.append(report.MatplotlibFigure(plt.gcf()))
        plt.close(plt.gcf())
    truth = tendency(ds, field, "physics")
    pred = tendency(ds, field, "emulator")
    combined = vcm.combine_array_sequence(
        plot_vertical_accuracy_levels(truth, pred, global_mean), labels=["threshold"]
    ).load()
    def plot_precision(combined):
        i = interpolate_onto_var(
            combined, "true_positive_rate", np.linspace(0, 1, 40), "threshold"
        )
        plot_2d_z(i.precision, cmap=plt.get_cmap("viridis", 10))
        insert_fig("precision-recall")
    def plot_roc(combined):
        i = interpolate_onto_var(
            combined, "false_positive_rate", np.linspace(0, 1, 40), "threshold"
        )
        plot_2d_z(i.true_positive_rate, vmin=0.4, vmax=1, cmap=plt.get_cmap("Blues", 6))
        insert_fig("ROC")
    def plot_auc(combined):
        i = interpolate_onto_var(
            combined, "false_positive_rate", np.linspace(0, 1, 40), "threshold"
        )
        i.true_positive_rate.fillna(0).mean("false_positive_rate").rename("AUC").plot()
        plt.axhline(0.5, linestyle="--")
        plt.yticks(np.r_[0:1:0.1])
        plt.grid()
        insert_fig("AUC")
    plot_precision(combined)
    plot_roc(combined)
    plot_auc(combined)
    return figures