TMAP Documentation

TMAP builds a tree-shaped 2D map from high-dimensional data.

Most users only need one pattern:

Prepare data.
Call TMAP(...).fit(X).
Explore the result in a notebook, save it as HTML, or serve it locally.

Start Here

Molecule Tutorial Load cluster_65053.csv, compute fingerprints and molecular properties, fit a TMAP, and explore it with TmapViz.
Visualization Guide Learn when to use notebook widgets, HTML export, and serve().
API Reference Quick reference for TMAP, TmapViz, chemistry helpers, and layout functions.

Choose Your API

Estimator API

Use TMAP(...).fit(X) when you want the simplest path.

Lower-level pipeline

Use MinHash, LSHForest, layout_from_lsh_forest, and related functions when you want direct control over hashing, indexing, or graph construction.

Quick Example

import pandas as pd
from tmap import TMAP
from tmap.utils import fingerprints_from_smiles

df = pd.read_csv("../examples/cluster_65053.csv", nrows=3000)
smiles = df["smiles"].tolist()

fps = fingerprints_from_smiles(smiles, fp_type="morgan", radius=2, n_bits=2048)
model = TMAP(metric="jaccard", n_neighbors=20, seed=42).fit(fps)

viz = model.to_tmapviz()
viz.title = "Cluster 65053"
viz.add_smiles(smiles)
viz.write_html("cluster_65053.html")

Supported Input Paths

Input	Metric	Good for
Binary matrix	`jaccard`	Molecular fingerprints and other 0/1 features
Dense float matrix	`cosine`	Embeddings where direction matters
Dense float matrix	`euclidean`	Embeddings where magnitude matters
Distance matrix	`precomputed`	Distances you already computed elsewhere

Choose The Right Output

Goal	Method
Quick notebook exploration	`viz.to_widget(...)` or `model.plot(...)`
Share one file	`viz.write_html(...)` or `model.to_html(...)`
Browse a large map locally	`viz.serve(...)` or `model.serve(...)`

Advanced Guides

The files in docs/ that are not linked here are planning notes or historical material and may not track the current public API line by line.