使用猿来显示BEAST祖先状态重建

Question

我使用BEAST运行祖先状态重建，这给了我一个像这样的Nexus文件

#NEXUS

Begin taxa;
    Dimensions ntax=93;
        Taxlabels
            adan1251 
            blag1240-nule 
            wers1238-marit
            ;
End;
Begin trees;
    Translate
           1 adan1251,
           2 blag1240-nule,
           3 wers1238-marit
;
tree STATE_0 = ((1[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.02243609504948792,2[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.02243609504948792)[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.01067010801410265,3[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.03310620306359057)[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.022661511629175332;
tree STATE_1 = ((1[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:1.02243609504948792,2[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.02243609504948792)[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.01067010801410265,3[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.03310620306359057)[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.022661511629175332;
tree STATE_2 = ((1[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:2.02243609504948792,2[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.02243609504948792)[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.01067010801410265,3[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.03310620306359057)[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.022661511629175332;
tree STATE_3 = ((1[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:3.02243609504948792,2[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.02243609504948792)[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.01067010801410265,3[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.03310620306359057)[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.022661511629175332;
tree STATE_4 = ((1[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:4.02243609504948792,2[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.02243609504948792)[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.01067010801410265,3[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.03310620306359057)[&recon_lexicon:cooked rice="00000000000001",recon_lexicon:mountain="000000000001",recon_lexicon:to die="00001",recon_lexicon:wall="00000001"]:0.022661511629175332;
End;

（除了20倍的分类群，2000倍的树木和树木实际上有所不同。）

我想想想内心节点和tip节点中词汇项目的重建，似乎 ape 可能是一个很好的工具，因为它可以编写脚本，它可以阅读Nexus文件（我尝试使用read.nexus（＆＃34; filename.nex＆＃34;），似乎str是合理的）并且从http://ape-package.ird.fr/ape_screenshots.html判断它可以以一种很好的格式显示重建：

如何构建 ape ，根据10000个不同的Newick树的注释（thermo）中给出的数据构建类似此[&...]树的内容来自原始数据的共识树？