def gseaplot(rank_metric, term, hits_indices, nes, pval, fdr, RES,
              pheno_pos='', pheno_neg='', figsize=(6,5.5), 
              cmap='seismic', ofname=None, **kwargs):
    """This is the main function for reproducing the gsea plot.

    :param rank_metric: pd.Series for rankings, rank_metric.values.
    :param term: gene_set name
    :param hits_indices: hits indices of rank_metric.index presented in gene set S.
    :param nes: Normalized enrichment scores.
    :param pval: nominal p-value.
    :param fdr: false discovery rate.
    :param RES: running enrichment scores.
    :param pheno_pos: phenotype label, positive correlated.
    :param pheno_neg: phenotype label, negative correlated.
    :param figsize: matplotlib figsize.
    :param ofname: output file name. If None, don't save figure 

    """
    # plt.style.use('classic')
    # center color map at midpoint = 0
    norm = _MidpointNormalize(midpoint=0)

    #dataFrame of ranked matrix scores
    x = np.arange(len(rank_metric))
    rankings = rank_metric.values
    # figsize = (6,6)
    phenoP_label = pheno_pos + ' (Positively Correlated)'
    phenoN_label = pheno_neg + ' (Negatively Correlated)'
    zero_score_ind = np.abs(rankings).argmin()
    z_score_label = 'Zero score at ' + str(zero_score_ind)
    nes_label = 'NES: '+ "{:.3f}".format(float(nes))
    pval_label = 'Pval: '+ "{:.3f}".format(float(pval))
    fdr_label = 'FDR: '+ "{:.3f}".format(float(fdr))
    im_matrix = np.tile(rankings, (2,1))

    # output truetype
    plt.rcParams.update({'pdf.fonttype':42,'ps.fonttype':42})
    # in most case, we will have many plots, so do not display plots
    # It's also usefull to run this script on command line.

    # GSEA Plots
    gs = plt.GridSpec(16,1)
    if hasattr(sys, 'ps1') and (ofname is None):
        # working inside python console, show figure
        fig = plt.figure(figsize=figsize)
    else:
        # If working on commandline, don't show figure
        fig = Figure(figsize=figsize)
        canvas = FigureCanvas(fig)
    # Ranked Metric Scores Plot
    ax1 =  fig.add_subplot(gs[11:])
    module = 'tmp' if ofname is  None else ofname.split(".")[-2]
    if module == 'ssgsea':
        nes_label = 'ES: '+ "{:.3f}".format(float(nes))
        pval_label='Pval: '
        fdr_label='FDR: '
        ax1.fill_between(x, y1=np.log(rankings), y2=0, color='#C9D3DB')
        ax1.set_ylabel("log ranked metric", fontsize=14)
    else:
        ax1.fill_between(x, y1=rankings, y2=0, color='#C9D3DB')
        ax1.set_ylabel("Ranked list metric", fontsize=14)
    ax1.text(.05, .9, phenoP_label, color='red',
             horizontalalignment='left', verticalalignment='top',
             transform=ax1.transAxes)
    ax1.text(.95, .05, phenoN_label, color='Blue',
             horizontalalignment='right', verticalalignment='bottom',
             transform=ax1.transAxes)
    # the x coords of this transformation are data, and the y coord are axes
    trans1 = transforms.blended_transform_factory(ax1.transData, ax1.transAxes)
    if module != 'ssgsea':
        ax1.vlines(zero_score_ind, 0, 1, linewidth=.5, transform=trans1, linestyles='--', color='grey')
        ax1.text(zero_score_ind, 0.5, z_score_label,
                 horizontalalignment='center',
                 verticalalignment='center',
                 transform=trans1)
    ax1.set_xlabel("Rank in Ordered Dataset", fontsize=14)
    ax1.spines['top'].set_visible(False)
    ax1.tick_params(axis='both', which='both', top=False, right=False, left=False)
    ax1.locator_params(axis='y', nbins=5)
    ax1.yaxis.set_major_formatter(plt.FuncFormatter(lambda tick_loc,tick_num :  '{:.1f}'.format(tick_loc) ))

    # use round method to control float number
    # ax1.yaxis.set_major_formatter(plt.FuncFormatter(lambda tick_loc,tick_num :  round(tick_loc, 1) ))

    # gene hits
    ax2 = fig.add_subplot(gs[8:10], sharex=ax1)

    # the x coords of this transformation are data, and the y coord are axes
    trans2 = transforms.blended_transform_factory(ax2.transData, ax2.transAxes)
    ax2.vlines(hits_indices, 0, 1,linewidth=.5,transform=trans2)
    ax2.spines['bottom'].set_visible(False)
    ax2.tick_params(axis='both', which='both', bottom=False, top=False,
                    labelbottom=False, right=False, left=False, labelleft=False)
    # colormap
    ax3 =  fig.add_subplot(gs[10], sharex=ax1)
    ax3.imshow(im_matrix, aspect='auto', norm=norm, cmap=cmap, interpolation='none') # cm.coolwarm
    ax3.spines['bottom'].set_visible(False)
    ax3.tick_params(axis='both', which='both', bottom=False, top=False,
                    labelbottom=False, right=False, left=False,labelleft=False)

    # Enrichment score plot
    ax4 = fig.add_subplot(gs[:8], sharex=ax1)
    ax4.plot(x, RES, linewidth=4, color ='#88C544')
    ax4.text(.1, .1, fdr_label, transform=ax4.transAxes)
    ax4.text(.1, .2, pval_label, transform=ax4.transAxes)
    ax4.text(.1, .3, nes_label, transform=ax4.transAxes)

    # the y coords of this transformation are data, and the x coord are axes
    trans4 = transforms.blended_transform_factory(ax4.transAxes, ax4.transData)
    ax4.hlines(0, 0, 1, linewidth=.5, transform=trans4, color='grey')
    ax4.set_ylabel("Enrichment score (ES)", fontsize=14)
    ax4.set_xlim(min(x), max(x))
    ax4.tick_params(axis='both', which='both', bottom=False, top=False, labelbottom=False, right=False)
    ax4.locator_params(axis='y', nbins=5)
    # FuncFormatter need two argument, I don't know why. this lambda function used to format yaxis tick labels.
    ax4.yaxis.set_major_formatter(plt.FuncFormatter(lambda tick_loc,tick_num :  '{:.1f}'.format(tick_loc)) )

    # fig adjustment
    fig.suptitle(term, fontsize=16, fontweight='bold')
    fig.subplots_adjust(hspace=0)
    # fig.tight_layout()
    if ofname is not None: 
        # canvas.print_figure(ofname, bbox_inches='tight', dpi=300)
        fig.savefig(ofname, bbox_inches='tight', dpi=300)
    return
This is the main function for reproducing the gsea plot.

    :param rank_metric: pd.Series for rankings, rank_metric.values.
    :param term: gene_set name
    :param hits_indices: hits indices of rank_metric.index presented in gene set S.
    :param nes: Normalized enrichment scores.
    :param pval: nominal p-value.
    :param fdr: false discovery rate.
    :param RES: running enrichment scores.
    :param pheno_pos: phenotype label, positive correlated.
    :param pheno_neg: phenotype label, negative correlated.
    :param figsize: matplotlib figsize.
    :param ofname: output file name. If None, don't save figure