How to use the ergo.views.pca_projection function in ergo

if args.correlations:
        log.info("computing correlations of each feature with target")
        corr = compute_correlations_with_target(X,y)
        print_target_correlation_table(corr)
        log.info("computing features crosscorrelation")
        corr = calculate_corr(X)
        print_correlation_table(corr, min_corr=0.7)
        views.correlation_matrix(prj, corr, args.img_only)

    if args.pca:
        log.info("computing pca")
        pca = calculate_pca(X)
        log.info("computing pca projection")
        views.pca_projection(prj, pca, X, y, False)
        if args.D3:
            views.pca_projection(prj, pca, X, y, args.D3)
        views.pca_explained_variance(prj, pca, args.img_only)

    if args.stats:
        log.info("computing features stats")
        print_stats_table(X)

    inertia = False
    if args.cluster:
        if args.cluster_alg == 'kmeans':
            cluster_alg = kmeans_clustering
            if not args.nclusters:
                args.nclusters = len(set(np.argmax(y, axis=1)))
            args.nclusters = int(args.nclusters)
            if args.nmaxclusters:
                log.info("performing inertia analysis with clusters in the range (%d, %d)" % (args.nclusters, args.nmaxclusters))
                inertia = True

attributes = get_attributes(args.attributes, ncols)

    if args.correlations:
        log.info("computing correlations of each feature with target")
        corr = compute_correlations_with_target(X,y)
        print_target_correlation_table(corr)
        log.info("computing features crosscorrelation")
        corr = calculate_corr(X)
        print_correlation_table(corr, min_corr=0.7)
        views.correlation_matrix(prj, corr, args.img_only)

    if args.pca:
        log.info("computing pca")
        pca = calculate_pca(X)
        log.info("computing pca projection")
        views.pca_projection(prj, pca, X, y, False)
        if args.D3:
            views.pca_projection(prj, pca, X, y, args.D3)
        views.pca_explained_variance(prj, pca, args.img_only)

    if args.stats:
        log.info("computing features stats")
        print_stats_table(X)

    inertia = False
    if args.cluster:
        if args.cluster_alg == 'kmeans':
            cluster_alg = kmeans_clustering
            if not args.nclusters:
                args.nclusters = len(set(np.argmax(y, axis=1)))
            args.nclusters = int(args.nclusters)
            if args.nmaxclusters: