Plot transformed LDA data.

def plot_LDA(X_lda, y_lda, class_colors, exp_var, style, fig_size, label_pad,
             font_size, sids, dim=2, zangles=None, pt_size=250, out_fp=""):
    """
    Plot transformed LDA data.
    """
    cats = class_colors.keys()
    fig = plt.figure(figsize=fig_size)
    if dim == 3:
        try:
            assert X_lda.shape[1] >= 3
        except AssertionError:
            sys.exit("\nLinear Discriminant Analysis requires at least 4 groups of "
                     "samples to create a 3D figure. Please update group information or "
                     "use the default 2D view of the results.\n")
        if sids is not None:
            print("\nPoint annotations are available only for 2D figures.\n")
        ax = fig.add_subplot(111, projection="3d")
        ax.view_init(elev=zangles[1], azim=zangles[0])
        try:
            ax.set_zlabel("LD3 (Percent Explained Variance: {:.3f}%)".
                          format(exp_var[2]*100), fontsize=font_size, labelpad=label_pad)
        except:
            ax.set_zlabel("LD3", fontsize=font_size, labelpad=label_pad)
        for i, target_name in zip(range(len(cats)), cats):
            cat_x = X_lda[:, 0][y_lda == target_name]
            cat_y = X_lda[:, 1][y_lda == target_name]
            cat_z = X_lda[:, 2][y_lda == target_name]
            ax.scatter(xs=cat_x, ys=cat_y, zs=cat_z, label=target_name,
                       c=class_colors[target_name], alpha=0.85, s=pt_size, edgecolors="k",
                       zdir="z")
    else:
        ax = fig.add_subplot(111)
        for i, target_name in zip(range(len(cats)), cats):
            cat_x = X_lda[:, 0][y_lda == target_name]
            if X_lda.shape[1] == 1:
                cat_y = np.ones((cat_x.shape[0], 1)) + i
            else:
                cat_y = X_lda[:, 1][y_lda == target_name]
            ax.scatter(x=cat_x, y=cat_y, label=target_name, alpha=0.85, s=pt_size,
                       color=class_colors[target_name], edgecolors="k")
        # Annotate data points with sample IDs
        if sids is not None:
            for sample, point, group in zip(sids, X_lda, y_lda):
                try:
                    assert len(point) >= 2
                except AssertionError:
                    point = (point[0], cats.index(group)+1)
                finally:
                    ax.annotate(s=sample, xy=point[:2], xytext=(0, -15), ha="center",
                                va="center", textcoords="offset points")
    if X_lda.shape[1] == 1:
        plt.ylim((0.5, 2.5))
    try:
        ax.set_xlabel("LD1 (Percent Explained Variance: {:.3f}%)".
                      format(exp_var[0]*100), fontsize=font_size, labelpad=label_pad)
    except:
        ax.set_xlabel("LD1", fontsize=font_size, labelpad=label_pad)
    try:
        ax.set_ylabel("LD2 (Percent Explained Variance: {:.3f}%)".
                      format(exp_var[1]*100), fontsize=font_size, labelpad=label_pad)
    except:
        ax.set_ylabel("LD2", fontsize=font_size, labelpad=label_pad)

    leg = plt.legend(loc="best", scatterpoints=3, frameon=True, framealpha=1, fontsize=15)
    leg.get_frame().set_edgecolor('k')
    if dim == 2 and style:
        gu.ggplot2_style(ax)
        fc = "0.8"
    else:
        fc = "none"

    # save or display result
    if out_fp:
        plt.savefig(out_fp, facecolor=fc, edgecolor="none", dpi=300, bbox_inches="tight",
                    pad_inches=0.1)
    else:
        plt.show()