Ridge plot

Plot histograms associated with tip trait values (ridge plot)¶

You can use the .hist() or .fill() functions of toytree to plot histograms. Here we will generate and plot a distribution of a data in order from top to bottom so that the histograms overlap in a "ridge plot" fashion. An analagous function in ggtree seems to have merited an entire publication: https://academic.oup.com/mbe/article/35/12/3041/5142656.

In [3]:

import toytree
import toyplot
import numpy as np
# we'll use scipy.stats to get prob. density func. of normal dist
import scipy.stats as sc

# generate a random tree with N tips
ntips = 40
tre = toytree.rtree.baltree(ntips)

# generate a distribution between -10 and 10 for each tip in the tree
points = np.linspace(-10, 10, 50)
dists = {}
for tip in tre.get_tip_labels():
    dists[tip] = sc.norm.pdf(points, loc=np.random.randint(-5, 5, 1), scale=2)

import toytree import toyplot import numpy as np # we'll use scipy.stats to get prob. density func. of normal dist import scipy.stats as sc # generate a random tree with N tips ntips = 40 tre = toytree.rtree.baltree(ntips) # generate a distribution between -10 and 10 for each tip in the tree points = np.linspace(-10, 10, 50) dists = {} for tip in tre.get_tip_labels(): dists[tip] = sc.norm.pdf(points, loc=np.random.randint(-5, 5, 1), scale=2)

In [4]:

# set up canvas for two panel plot
canvas = toyplot.Canvas(width=300, height=400)

# add tree to canvas
ax0 = canvas.cartesian(bounds=(50, 180, 50, 350), ymin=0, ymax=ntips, padding=15)
tre.draw(axes=ax0, tip_labels=False)
ax0.show = False

# add histograms to canvas
ax1 = canvas.cartesian(bounds=(200, 275, 50, 350), ymin=0, ymax=ntips, padding=15)

# iterate from top to bottom (ntips to 0)
for tip in range(tre.ntips)[::-1]:
    
    # select a color for hist
    color = "skyblue"
    
    # get tip name and get hist from dict
    tipname = tre.get_tip_labels()[tip]
    probs = dists[tipname]
    
    # fill histogram with slightly overlapping histograms
    ax1.fill(
        points, probs / probs.max() * 1.25,
        baseline=[tip] * len(points), 
        style={"fill": color, "stroke": "white", "stroke-width": 0.5},
        title=tipname,
    )
    
    # add horizontal line at base
    ax1.hlines(tip, opacity=0.5, color="grey", style={"stroke-width": 0.5})

# hide y axis, show x 
ax1.y.show = False
ax1.x.label.text = "Trait value"
ax1.x.ticks.show = True

# set up canvas for two panel plot canvas = toyplot.Canvas(width=300, height=400) # add tree to canvas ax0 = canvas.cartesian(bounds=(50, 180, 50, 350), ymin=0, ymax=ntips, padding=15) tre.draw(axes=ax0, tip_labels=False) ax0.show = False # add histograms to canvas ax1 = canvas.cartesian(bounds=(200, 275, 50, 350), ymin=0, ymax=ntips, padding=15) # iterate from top to bottom (ntips to 0) for tip in range(tre.ntips)[::-1]: # select a color for hist color = "skyblue" # get tip name and get hist from dict tipname = tre.get_tip_labels()[tip] probs = dists[tipname] # fill histogram with slightly overlapping histograms ax1.fill( points, probs / probs.max() * 1.25, baseline=[tip] * len(points), style={"fill": color, "stroke": "white", "stroke-width": 0.5}, title=tipname, ) # add horizontal line at base ax1.hlines(tip, opacity=0.5, color="grey", style={"stroke-width": 0.5}) # hide y axis, show x ax1.y.show = False ax1.x.label.text = "Trait value" ax1.x.ticks.show = True