论文
Replicated radiation of a plant clade along a cloud forest archipelago
https://www.nature.com/articles/s41559-022-01823-x#data-availability
数据代码链接
https://zenodo.org/record/5504439#.YtpqTXZBzic
https://github.com/eaton-lab/Oreinotinus-phylogeny
论文里提供了几乎所有数据和代码，要好好学习一下这篇论文
今天推文的主要内容是重复其中 Extended Data Fig. 1 ，不对绘图代码进行仔细研究了，知道python里有一个 toytree的模块可以展示进化树，看了代码，应该是不如ggtree好用 帮助文档链接 https://toytree.readthedocs.io/en/latest/3-installation.html
github主页链接https://github.com/eaton-lab/toytree
还有一个模块是ipyrad，用来做群体遗传分析，帮助文档链接 https://ipyrad.readthedocs.io/en/master/
完整的作图代码
import toytree
import pandas as pd
fulldata = pd.read_csv("../Oreinotinus-phylogeny-main/Raw_data/oreinotinus_samples_database.csv")
colors = pd.read_csv("../Oreinotinus-phylogeny-main/Raw_data/oreinotinus_color_codes.csv")
sdata = fulldata[["NameInAssembly","Lastest_SP_name", "Num_for_Publication", "UltimateName"]]
namedict = {}
for i in range(sdata.shape[0]):
if sdata.iloc[i, 2]:
number = " (" + str(sdata.iloc[i, 2]) + ")"
else:
number = ""
namedict[sdata.iloc[i, 0]] = f"V. {sdata.iloc[i, 1]}{number}"
# load color data and put in a dictionary
colordata = colors[["Species","Color"]]
colordict = {colordata.iloc[i, 0]: str(colordata.iloc[i, 1]) for i in range(colordata.shape[0])}
treeFile = "../Oreinotinus-phylogeny-main/Analyses/Individual-level-tree-reconstruction/analysis-raxml/RAxML_bipartitions.fulldataset_withAyava_10scaff_mcov025_rmcov01_mar2021"
tre = toytree.tree(treeFile)
rtre = tre.root(wildcard="dentatum")
# do some rotations to fit with geo
for i in [309,262,251,252,239,233]:
rtre.idx_dict[i].children.reverse()
rtre._coords.update()
# set new names
labels_updated = [namedict[i] for i in rtre.get_tip_labels()]
color_labels = []
# set color base on leaf form
for i in labels_updated:
result = "Black"
for key, item in colordict.items():
if i.find(key) > -1:
result = item
color_labels.append(result)
# collapse weak supported nodes
# rtre = rtre.collapse_nodes(min_support=75)
# define threshold
support_value_threshold = 84
# plot the tree
canvas, axes, marks = rtre.draw(
height=1800, width=600,
use_edge_lengths=True,
tip_labels_align=True,
tip_labels_style={"font-size": "12px"},
tip_labels=labels_updated,
# tip_labels_colors=color_labels,
node_sizes=[5 if i else 0 for i in rtre.get_node_values()],
node_colors=['black' if (i and int(i) > support_value_threshold) else 'white' for i in rtre.get_node_values('support', 1, 1)],
# node_colors=colors,
node_style={"stroke": "black", "stroke-width": 1},
# node_labels="support"
node_labels=['' if (i and int(i) > support_value_threshold) else i for i in rtre.get_node_values('support', 1, 0)],
node_labels_style= {
"-toyplot-anchor-shift": "10px",
"baseline-shift": "0px",
"text-shadow": "0.5px 0.5px #fff, -0.5px 0.5px #fff, 0.5px -0.5px #fff, -0.5px -0.5px #fff",
"fill": "#000",
"font-size": 8,
},
# node_labels="idx",
);
import toyplot.pdf
toyplot.svg.render(canvas, "./RAxML_bipartitions.fulldataset_withAyava_10scaff_mcov025_rmcov01_mar2021.svg")
保存的是svg格式，试了下保存pdf,遇到了报错，暂时不知道什么原因
最终结果
输出结果太长，就不在这里截图展示了
示例数据和代码可以自己到论文中获取
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