🌲⚡ rapidtrees

Blazingly fast pairwise phylogenetic tree distance calculations — Robinson–Foulds, Weighted RF, and Kuhner–Felsenstein — powered by Rust

Overview • Installing • Usage • Python API • Presence Matrix • Benchmarks

🗺️ Overview

rapidtrees computes pairwise tree distances from BEAST/NEXUS .trees files or plain multi-tree Newick files, and writes a labeled distance matrix. Three metrics are supported:

Metric	Flag	Output	Description
Robinson–Foulds	`--metric rf`	integer	Symmetric difference of bipartitions
Weighted RF	`--metric weighted`	float	Branch-length-weighted bipartition difference
Kuhner–Felsenstein	`--metric kf`	float	Euclidean distance on branch lengths

✨ Why `rapidtrees`?

🦀 Rust core — splits as 128-bit fingerprints, compared as u32 IDs in a cache-friendly layout
🔀 Parallel by default — powered by rayon, automatically scales across all cores
🐍 Python bindings — drop into any Python/NumPy workflow via PyO3
📦 No Rust toolchain required — pre-built wheels on PyPI for Linux, macOS, and Windows
🗜️ Gzip output — stream directly to .tsv.gz without a separate compression step

🚀 Performance

Benchmarked on a ZIKA dataset (283 taxa · 4 000 trees · ~8 M comparisons) (non-gzipped output):

Metric	Total time	Throughput
Robinson-Foulds	~2.7 s	~3.0 M comparisons/sec
Weighted RF	~3.4 s	~2.3 M comparisons/sec
Kuhner-Felsenstein	~3.3 s	~2.3 M comparisons/sec

🔧 Installing

🐍 Python (PyPI) — recommended

Pre-built wheels for Linux, macOS, and Windows. No Rust toolchain needed.

pip install rapidtrees

🦀 CLI (crates.io)

Install the standalone command-line binary. Requires the Rust toolchain.

cargo install rapidtrees

🛠️ From source

Prerequisites

Rust toolchain — for building the Rust core
pixi — for managing Python and R dependencies

Setup

git clone https://github.com/Joon-Klaps/rapidtrees.git
cd rapidtrees
# Set up environment — installs Python, R (with phangorn), and builds the package
pixi install

Development tasks

# Run Python API tests (includes R/phangorn cross-validation)
pixi run test-python

# Run Rust unit tests
pixi run test-rust

💻 Usage

rapidtrees \
  --input <path/to/file.trees> \
  --output <path/to/output.tsv[.gz]> \
  [--burnin-trees <N>] \
  [--burnin-states <STATE>] \
  [--use-real-taxa] \
  [--metric rf|weighted|kf] \
  [--backend auto|dense|sparse] \
  [-q|--quiet]

Flag	Description
`-i, --input <INPUT>`	Path to a NEXUS `.trees` or plain Newick file (auto-detected)
`-o, --output <OUTPUT>`	Output path. Use `.gz` suffix for gzip compression; `-` for stdout
`-t, --burnin-trees <N>`	Drop the first N trees (default: `0`)
`-s, --burnin-states <STATE>`	Keep only trees with `STATE > STATE` (default: `0`)
`--use-real-taxa`	Map numeric taxon IDs via the TRANSLATE block
`--metric <rf\|weighted\|kf>`	Distance metric (default: `rf`)
`--backend <auto\|dense\|sparse>`	Per-pair kernel (default: `auto`). Both give identical results
`-q, --quiet`	Suppress progress messages (errors still go to stderr)

Input formats. --input takes either a NEXUS/BEAST trees file or a plain Newick file with one tree per line; the format is sniffed from the contents, not the extension. Newick files name nothing, carry no TRANSLATE block and no STATE_ labels, so their trees are named after the line they start on, and --burnin-states / --use-real-taxa have nothing to act on (--burnin-trees still works).

The output is a square TSV matrix where both the header row and first column contain tree names: <file_basename>_<tree_name> for NEXUS (e.g. hiv1_STATE_10000) and <file_basename>_line<n> for Newick (e.g. hiv1_line3). Use -o - to write to stdout for easy piping.

💡 Examples

Compute RF matrix → gzipped file

rapidtrees \
  -i tests/data/hiv1.trees \
  -o out/hiv1_rf.tsv.gz \
  --metric rf

# Reading in beast 0.003s
# Read in 162 taxons for 21 trees
# Creating tree bit snapshots 0.002s
# Determining distances using RF for 210 combinations
# Determining distances using RF 0.000s
# Writing to output 0.000s

Apply burn-in by tree count

rapidtrees \
  -i tests/data/hiv1.trees \
  -o out/hiv1_rf.tsv \
  -t 2

# Reading in beast 0.003s
# Read in 162 taxons for 19 trees
# Creating tree bit snapshots 0.001s
# Determining distances using RF for 171 combinations
# Determining distances using RF 0.000s
# Writing to output 0.000s

🐍 Python API

rapidtrees exposes four functions from its Rust core. All accept a Python iterator of newick strings, keeping memory constant regardless of tree count.

Function	Returns
`pairwise_rf_from_newick_iter`	`(names, bytes)` — RF matrix as flat `uint32` bytes, row-major
`pairwise_rf_with_snapshots_from_newick_iter`	`(names, bytes, leaf_names, n_bip, bytes, bytes)` — RF matrix + presence matrix + clade bitmasks
`pairwise_wrf_from_newick_iter`	`(names, list[float])` — Weighted RF, flat row-major
`pairwise_wrf_with_snapshots_from_newick_iter`	`(names, bytes, leaf_names, n_bip, bytes, bytes)` — wRF matrix + branch-length matrix + clade bitmasks
`pairwise_kf_from_newick_iter`	`(names, list[float])` — Kuhner-Felsenstein, flat row-major
`pairwise_kf_with_snapshots_from_newick_iter`	`(names, bytes, leaf_names, n_bip, bytes, bytes)` — KF matrix + branch-length matrix + clade bitmasks

import rapidtrees as rtd
import numpy as np

trees = [
    "(A:0.1,(B:0.1,C:0.1):0.1);",
    "(A:0.1,(C:0.1,B:0.1):0.1);",
    "((A:0.1,B:0.1):0.1,C:0.1);",
]
names = ["t1", "t2", "t3"]

tree_names, rf_bytes = rtd.pairwise_rf_from_newick_iter(
    names, iter(trees), [{}], [0, 0, 0]
)
rf = np.frombuffer(rf_bytes, dtype=np.uint32).reshape(len(tree_names), -1)

For BEAST .trees files, translate maps, the snapshot API, and multi-file usage see docs/python-api.md.

📦 Presence Matrix

Alongside a distance matrix, rapidtrees can hand back the presence matrix: which bipartition appears in which tree. That is everything the convergence diagnostics need (ESS, ASDSF, split frequencies), and it comes out of the same single parse as the distances.

Splits are identified by a 128-bit fingerprint, not by comparing leaf sets — that is what keeps building a tree linear in its taxon count rather than quadratic. Two distinct splits are merged if their fingerprints collide, with probability about e²/2¹²⁹ for e distinct splits in the run: 1.5 × 10⁻²³ at a hundred million splits, some nineteen orders of magnitude below the rate at which the machine's own RAM flips a bit unnoticed. Every run prints its own e and bound.

Bipartition column order is deterministic: columns are sorted in ascending Bitset order (lexicographic over u64 words, i.e. by leaf-index bit pattern), so the same tree set always produces the same column indices regardless of parse order.

What the presence matrix gives you

The presence matrix is sufficient for all major convergence diagnostics:

Diagnostic	What you need
Pseudo ESS	`presence.mean(axis=0)` per chain
ASDSF	Per-chain split frequencies
Fréchet ESS	RF distances via XOR of rows
WRF / KF distances	Branch lengths — use `pairwise_wrf_with_snapshots_from_newick_iter` or `pairwise_kf_with_snapshots_from_newick_iter`

Note: sum(presence[i] XOR presence[j]) == RF(tree_i, tree_j) exactly.

Presence matrix from Python

Use pairwise_rf_with_snapshots_from_newick_iter to get the presence matrix alongside the RF distances, from one parse — see the Python API section above.

import rapidtrees as rtd
import numpy as np

import math

tree_names, rf_bytes, leaf_names, n_bip, pres_bytes, bip_clade_bytes = (
    rtd.pairwise_rf_with_snapshots_from_newick_iter(
        names, iter(newicks), translate_maps, map_indices
    )
)
n = len(tree_names)
presence = np.frombuffer(pres_bytes, dtype=np.uint8).reshape(n, n_bip).copy()

# Global split frequencies (for Pseudo ESS / ASDSF)
global_freq = presence.mean(axis=0)

# RF distance between any two trees — no recomputation needed
rf_01 = int((presence[0].astype(int) ^ presence[1].astype(int)).sum())

# Named presence matrix — decode clade bitmasks for column labels
bytes_per_bip = math.ceil(len(leaf_names) / 8)
bip_arr  = np.frombuffer(bip_clade_bytes, dtype=np.uint8).reshape(n_bip, bytes_per_bip)
bip_bool = np.unpackbits(bip_arr, axis=1, bitorder="little")[:, :len(leaf_names)]
col_labels = ["|".join(leaf_names[i] for i in np.where(bip_bool[j])[0]) for j in range(n_bip)]
import pandas as pd
df = pd.DataFrame(presence, index=tree_names, columns=col_labels)

⏱️ Benchmarks

Benchmarks were run on a MacBook Pro M1. Trees are parsed once and their interned split IDs are reused across all pairwise comparisons. Parallelism is provided by rayon — no manual thread management needed.

Show full benchmark table

Output of cargo bench --bench memory_time_benchmark

Taxa (N)	Trees (T)	Combinations	Est. Memory	Actual Memory	Wall Time	CPU Time
10	100	5.0K	13.96 KB	17.87 KB	172.29 µs	733.00 µs
10	1000	500.0K	139.65 KB	168.95 KB	5.94 ms	12.99 ms
10	10000	50.0M	1.36 MB	1.64 MB	717.43 ms	1.36 s
10	100000	5.0B	13.64 MB	16.40 MB	3.53 min	4.04 min
100	100	5.0K	133.59 KB	136.09 KB	244.42 µs	1.75 ms
100	1000	500.0K	1.30 MB	1.21 MB	17.49 ms	50.99 ms
100	10000	50.0M	13.05 MB	11.95 MB	1.08 s	4.81 s
100	100000	5.0B	130.46 MB	119.41 MB	4.29 min	9.86 min
500	100	5.0K	706.84 KB	713.93 KB	2.02 ms	3.08 ms
500	1000	500.0K	6.90 MB	5.89 MB	27.77 ms	216.87 ms
500	10000	50.0M	69.03 MB	57.84 MB	2.82 s	21.30 s
500	100000	5.0B	690.27 MB	577.28 MB	7.44 min	37.13 min
1000	100	5.0K	1.50 MB	1.51 MB	873.75 µs	5.63 ms
1000	1000	500.0K	15.03 MB	11.86 MB	51.22 ms	420.94 ms
1000	10000	50.0M	150.32 MB	115.30 MB	5.12 s	42.92 s
1000	100000	5.0B	1.47 GB	1.12 GB	11.26 min	74.74 min
2000	100	5.0K	3.50 MB	3.51 MB	1.14 ms	9.92 ms
2000	1000	500.0K	35.01 MB	24.15 MB	93.73 ms	838.01 ms
2000	10000	50.0M	350.06 MB	230.59 MB	9.42 s	1.38 min
2000	100000	5.0B	3.42 GB	2.24 GB	18.57 min	141.11 min
5000	100	5.0K	14.30 MB	12.43 MB	61.36 ms	83.85 ms
5000	1000	500.0K	143.02 MB	63.97 MB	224.40 ms	2.09 s
5000	10000	50.0M	1.40 GB	579.40 MB	22.45 s	3.44 min

Note: Weighted RF and KF produce floating-point matrices; RF produces integer matrices.

🔍 Troubleshooting

No trees parsed? Verify the input is a valid NEXUS .trees or Newick file and adjust --burnin-* settings.
Piping to other tools? Use -q to suppress timing messages on stdout.
Gzipped output not working? Ensure the output filename ends with .gz.

⚖️ License

rapidtrees is provided under the MIT License.

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Algorithm	Hash digest
SHA256	`a83fd2abde040cf46fd2ea6b792a534c9df5bdb3b71294a76cb89982647b62b1`
MD5	`07c66c44a37facf4ce35443556c09ee4`
BLAKE2b-256	`190cbb200fae1504b3eff44083073e26b08f938f5f570a5a478fd5315aaf0d3f`

Algorithm	Hash digest
SHA256	`b3b6ce4f976f58ecf625fabafe61684f442b304cd40c9dd5c28412acfce4f24f`
MD5	`8b71628e075bbb96ce3c8d01ea5e9486`
BLAKE2b-256	`54828b0270d3b84dadbf5c29220f22c77bc3bd52aab1a07698063c9bd21ff2a0`

Algorithm	Hash digest
SHA256	`365a8f3a87bbe58f20a25f4571d134b31547332be3e1838ae74203459eb74fdd`
MD5	`b492b8ef863f9f577cbaf77c8c4457e0`
BLAKE2b-256	`54541e40d59ac9793fc17795912778600191400f27447a7805c528b1a4ba2913`

Algorithm	Hash digest
SHA256	`dbe5ec00e8e416a924bdfc420f16116b8f294f0c4ecdfd1d8b19a71d2dd9d046`
MD5	`b46703b5fa3e6e94c20fcf92898b0ffd`
BLAKE2b-256	`b581482137178591659d881d6a9fd3f7211df79b4a29e773cbeb778726406684`

Algorithm	Hash digest
SHA256	`fa6aabd0982980492e9d746f5e89cfdd9d7565b924d1d41375c8dd2d12e752aa`
MD5	`9cee69b3175864864317da9e7d374d1d`
BLAKE2b-256	`c872bd6713f93e31765d17c270ad760a5a18bf8857d7e559ea81624dfb5ac26b`

rapidtrees 0.9.0

🌲⚡ rapidtrees

🗺️ Overview

✨ Why rapidtrees?

🚀 Performance

🔧 Installing

🐍 Python (PyPI) — recommended

🦀 CLI (crates.io)

🛠️ From source

Prerequisites

Setup

Development tasks

💻 Usage

💡 Examples

🐍 Python API

📦 Presence Matrix

What the presence matrix gives you

Presence matrix from Python

⏱️ Benchmarks

🔍 Troubleshooting

⚖️ License

Project links

Key dates

1 maintainer

GitHub Statistics

License

Requires

Provides Extra

Tags

Classifiers

Download files

Source Distribution

Built Distributions

File details

File metadata

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Provenance

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File hashes

✨ Why `rapidtrees`?

Algorithm	Hash digest
SHA256	`4aedc87bd3353ee98e4fc07c09bcee3f9f4197085787b21eb0e45f2ec623fcf2`
MD5	`77ed5e1ddc3e4ee2b8059f8683758d21`
BLAKE2b-256	`d36c27befb4c2d5276b0a99a1747b543de85c5bcca15a686738c5f0795913d7a`

Algorithm	Hash digest
SHA256	`f96cc081ef2b7a385fbb3d0e04303e77275bc56fa4306d2a2d1a952fec87ef5b`
MD5	`1a9d4b10520197a5b8dde5141b99ee45`
BLAKE2b-256	`f24b3f037f3a6ff45a9fe14bbc10ba123cbc316027da9363c715ec3ef1a4a4de`

Algorithm	Hash digest
SHA256	`99e86ad7f2399a638ca567ee86892dd131b47ac60133bef2e19885b7c86bfabf`
MD5	`133fd34e7a0b03b53055482537e9fbed`
BLAKE2b-256	`ac1482a0b76e9219e0fe7da19c41ca39c51e52877700a4abb85eb9c64fdd5072`

Algorithm	Hash digest
SHA256	`eccc1e4834c9b727ca884a5fff0193c1730345529d6e6ad73d751704341aa581`
MD5	`f40406e6919629e52b928d6d54e4a0be`
BLAKE2b-256	`5fca6c78d354eb7eabac7cf7c5249cc4ac87684a92825e498f2229bcbe7b7e6a`

Algorithm	Hash digest
SHA256	`2f2a5af4b666b0c4ccc7ebe534b6fbc1c56226123a7ce4f82588e53f55410f94`
MD5	`10d056012125ffd7a6eebad4bfe06fc4`
BLAKE2b-256	`2997dae6a20a01a98dc3ad7495382e9e003fe156c87322f86ced7fe7dd6edf55`

Algorithm	Hash digest
SHA256	`e50ec94f4f298671c7d2de53e137730db3b4b0ed28be367adcbeff133f53b878`
MD5	`db239d2f8176f881fbe8c52c573d7ca7`
BLAKE2b-256	`c7ddbc5f1aa87f819e65177849800acdb39f5a08dc074d08fd4d43e0c8190e54`

Algorithm	Hash digest
SHA256	`a486ff9a4a450cf2b71ae53b70d58a8f02d30a58bb2dd69321d459937f09ea7b`
MD5	`a1cc556686767c6b1257df0f3425f9dc`
BLAKE2b-256	`37ffcc8e94727b92e08abe2e5321ec12b32869881f5787447d72074cb13b4ad0`

Algorithm	Hash digest
SHA256	`6d314b4268047a5a51fa2a7f303b877707f8941cd430d5f26a48be4c8b3341f1`
MD5	`03f1528dd4e56c663e603b4b541bff6b`
BLAKE2b-256	`f1ae5f09b5acc54d0fc8b169e2cb1c4a6f6c0723eaf28dbb38acde484cb414ab`

Algorithm	Hash digest
SHA256	`d6348090ef084e695178b91b38dafc85a274c82cc7da1f7e54df21baddc9e58c`
MD5	`b647f7dddac3566c1786e0e945ea00bb`
BLAKE2b-256	`390555577df53a8157828ae8c817d70a5acf3b649063b28babadd9096833ee8d`