heattree-py 0.1.0

Interactive phylogenetic tree visualization for Jupyter notebooks using the heat-tree JavaScript widget

heattree_py

Interactive phylogenetic tree visualisation for Jupyter notebooks.

heattree-py embeds the JavaScript heat-tree widget into Jupyter notebooks using IPython.display.HTML. The output is fully self-contained and works in live Jupyter sessions and in offline HTML exports (no running Python kernel required).

This is the Python counterpart of the R heattree package.

Installation

pip install heattree_py

Documentation

Full documentation with interactive examples is available at: https://grunwaldlab.github.io/heattree_py/

Quick start

from heattree_py import heat_tree

# Minimal example with a Newick string
heat_tree("(A:0.1,B:0.2,(C:0.3,D:0.4):0.5);")

Usage

Tree input

heat_tree() accepts trees in any of the common Python formats:

# Newick string
heat_tree("(A:0.1,B:0.2,(C:0.3,D:0.4):0.5);")

# Path to a Newick file
heat_tree("path/to/tree.nwk")

# ete3 TreeNode
from ete3 import Tree
tree = Tree("(A:0.1,B:0.2,(C:0.3,D:0.4):0.5);")
heat_tree(tree)

# dendropy Tree
import dendropy
tree = dendropy.Tree.get(data="(A:0.1,B:0.2,(C:0.3,D:0.4):0.5);", schema="newick")
heat_tree(tree)

# Biopython Phylo tree
from Bio import Phylo
from io import StringIO
tree = Phylo.read(StringIO("(A:0.1,B:0.2,(C:0.3,D:0.4):0.5);"), "newick")
heat_tree(tree)

# scikit-bio TreeNode
import skbio
tree = skbio.TreeNode.read(StringIO("(A:0.1,B:0.2,(C:0.3,D:0.4):0.5);"))
heat_tree(tree)

Metadata

Pass a pandas.DataFrame to colour or size tree elements by metadata columns. The DataFrame must include a column whose values match node labels in the tree (the JavaScript widget detects the matching column automatically).

import pandas as pd
from heattree_py import heat_tree

tree = "(A:0.1,B:0.2,(C:0.3,D:0.4):0.5);"

metadata = pd.DataFrame({
    "node_id": ["A", "B", "C", "D"],
    "group":   ["x", "x", "y", "y"],
    "size":    [10, 20, 30, 40],
})

heat_tree(tree, metadata=metadata, aesthetics={"tipLabelColor": "group"})

If the DataFrame index contains node IDs (rather than a column), it is automatically included as a column and used for matching:

metadata = pd.DataFrame(
    {"group": ["x", "x", "y", "y"]},
    index=["A", "B", "C", "D"],
)
heat_tree(tree, metadata=metadata, aesthetics={"tipLabelColor": "group"})

File paths to TSV/CSV files are also accepted:

heat_tree("tree.nwk", metadata="metadata.tsv")

Aesthetics

The aesthetics parameter maps visual properties to metadata columns:

Aesthetic	Description	Data type
tipLabelText	Text content of tip labels	any
tipLabelColor	Colour of tip labels	categorical/continuous
tipLabelSize	Size of tip labels	continuous
tipLabelFont	Font family for tip labels	categorical
tipLabelStyle	Font style (normal, bold, italic)	categorical

heat_tree(
    tree,
    metadata=metadata,
    aesthetics={
        "tipLabelColor": "group",
        "tipLabelSize": "size",
    },
)

Widget options

Additional keyword arguments are forwarded to the JavaScript widget as configuration options:

heat_tree(
    tree,
    layout="circular",          # "rectangular" (default) or "circular"
    branchLengthScale=1.5,      # scale factor for branch lengths
    treeHeightScale=2.0,        # scale factor for spacing between tips
    transitionDuration=750,     # animation duration in ms
)

See the heat-tree documentation for the full list of options.

Multiple trees

Pass a list of trees (and optionally matching lists of metadata and aesthetics) to initialise the widget with multiple trees:

heat_tree(
    tree=["(A,B,(C,D));", "(E,F,(G,H));"],
    metadata=[meta1, meta2],
    aesthetics=[{"tipLabelColor": "group"}, None],
)

Widget sizing

heat_tree(tree, width="80%", height="600px")

How it works

heat_tree() generates a self-contained HTML document that loads the @grunwaldlab/heat-tree JavaScript library from a CDN and initialises the interactive widget. This document is embedded in an <iframe> and returned as an IPython.display.HTML object. The iframe approach ensures the JavaScript executes correctly across all Jupyter front-ends (classic Notebook, JupyterLab, VS Code, Google Colab) and in static HTML exports.

Development

pip install hatch

# Run all checks (lint + test)
hatch run all

# Run tests only
hatch run test

# Auto-fix lint/format issues
hatch run fix

License

MIT