🌲⚡ 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

🎛️ Tuning the kernels

Every metric keeps a dense column only for splits held by a large enough share of the trees, and gives the rarer ones posting lists. The defaults were tuned on simulated posteriors. RAPIDTREES_RF_DENSE_SHARE (default 0.03) and RAPIDTREES_WEIGHTED_DENSE_SHARE (default 0.25) override them, for re-tuning on other data or hardware. They change the speed only, never a distance.

🛠️ 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] \
  [-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`)
`-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.

Metadata

Release files for rapidtrees 0.11.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for rapidtrees 0.11.0
File	Size	Uploaded
rapidtrees-0.11.0.tar.gz	199.5 kB	Oct 2, 2026	Details

Built distributions (wheels)

Table of built distributions (wheels) for rapidtrees 0.11.0
File	Interpreter	ABI	Platform	Reset
rapidtrees-0.11.0-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 508.2 kB Oct 2, 2026	PyPy 3.11	PyPy 3.11 7.3	Linux glibc 2.17+ x86-64	Details
rapidtrees-0.11.0-pp311-pypy311_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 501.6 kB Oct 2, 2026	PyPy 3.11	PyPy 3.11 7.3	Linux glibc 2.17+ ARM64	Details
rapidtrees-0.11.0-cp315-cp315t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 502.9 kB Oct 2, 2026	CPython 3.15	CPython 3.15 free-threading	Linux glibc 2.17+ x86-64	Details
rapidtrees-0.11.0-cp315-cp315-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 502.7 kB Oct 2, 2026	CPython 3.15	CPython 3.15	Linux glibc 2.17+ x86-64	Details
rapidtrees-0.11.0-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 503.1 kB Oct 2, 2026	CPython 3.14	CPython 3.14 free-threading	Linux glibc 2.17+ x86-64	Details
rapidtrees-0.11.0-cp314-cp314t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 497.5 kB Oct 2, 2026	CPython 3.14	CPython 3.14 free-threading	Linux glibc 2.17+ ARM64	Details
rapidtrees-0.11.0-cp314-cp314-win_amd64.whl 326.4 kB Oct 2, 2026	CPython 3.14	CPython 3.14	Windows x86-64	Details
rapidtrees-0.11.0-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 502.3 kB Oct 2, 2026	CPython 3.14	CPython 3.14	Linux glibc 2.17+ x86-64	Details
rapidtrees-0.11.0-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 497.6 kB Oct 2, 2026	CPython 3.14	CPython 3.14	Linux glibc 2.17+ ARM64	Details
rapidtrees-0.11.0-cp314-cp314-macosx_11_0_arm64.whl 443.7 kB Oct 2, 2026	CPython 3.14	CPython 3.14	macOS 11.0+ ARM64	Details
rapidtrees-0.11.0-cp314-cp314-macosx_10_12_x86_64.whl 453.1 kB Oct 2, 2026	CPython 3.14	CPython 3.14	macOS 10.12+ x86-64	Details
rapidtrees-0.11.0-cp313-cp313-win_amd64.whl 325.8 kB Oct 2, 2026	CPython 3.13	CPython 3.13	Windows x86-64	Details
rapidtrees-0.11.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 501.9 kB Oct 2, 2026	CPython 3.13	CPython 3.13	Linux glibc 2.17+ x86-64	Details
rapidtrees-0.11.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 495.9 kB Oct 2, 2026	CPython 3.13	CPython 3.13	Linux glibc 2.17+ ARM64	Details
rapidtrees-0.11.0-cp313-cp313-macosx_11_0_arm64.whl 442.3 kB Oct 2, 2026	CPython 3.13	CPython 3.13	macOS 11.0+ ARM64	Details
rapidtrees-0.11.0-cp313-cp313-macosx_10_12_x86_64.whl 451.8 kB Oct 2, 2026	CPython 3.13	CPython 3.13	macOS 10.12+ x86-64	Details
rapidtrees-0.11.0-cp312-cp312-win_amd64.whl 326.0 kB Oct 2, 2026	CPython 3.12	CPython 3.12	Windows x86-64	Details
rapidtrees-0.11.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 502.4 kB Oct 2, 2026	CPython 3.12	CPython 3.12	Linux glibc 2.17+ x86-64	Details
rapidtrees-0.11.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 497.0 kB Oct 2, 2026	CPython 3.12	CPython 3.12	Linux glibc 2.17+ ARM64	Details
rapidtrees-0.11.0-cp312-cp312-macosx_11_0_arm64.whl 443.0 kB Oct 2, 2026	CPython 3.12	CPython 3.12	macOS 11.0+ ARM64	Details
rapidtrees-0.11.0-cp312-cp312-macosx_10_12_x86_64.whl 452.1 kB Oct 2, 2026	CPython 3.12	CPython 3.12	macOS 10.12+ x86-64	Details
rapidtrees-0.11.0-cp311-cp311-win_amd64.whl 328.5 kB Oct 2, 2026	CPython 3.11	CPython 3.11	Windows x86-64	Details
rapidtrees-0.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 506.0 kB Oct 2, 2026	CPython 3.11	CPython 3.11	Linux glibc 2.17+ x86-64	Details
rapidtrees-0.11.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 501.7 kB Oct 2, 2026	CPython 3.11	CPython 3.11	Linux glibc 2.17+ ARM64	Details
rapidtrees-0.11.0-cp311-cp311-macosx_11_0_arm64.whl 447.7 kB Oct 2, 2026	CPython 3.11	CPython 3.11	macOS 11.0+ ARM64	Details
rapidtrees-0.11.0-cp311-cp311-macosx_10_12_x86_64.whl 450.8 kB Oct 2, 2026	CPython 3.11	CPython 3.11	macOS 10.12+ x86-64	Details
rapidtrees-0.11.0-cp310-cp310-win_amd64.whl 328.4 kB Oct 2, 2026	CPython 3.10	CPython 3.10	Windows x86-64	Details
rapidtrees-0.11.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 505.9 kB Oct 2, 2026	CPython 3.10	CPython 3.10	Linux glibc 2.17+ x86-64	Details
rapidtrees-0.11.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 502.1 kB Oct 2, 2026	CPython 3.10	CPython 3.10	Linux glibc 2.17+ ARM64	Details

Total release size: 13.4 MB

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Workflow

Publishing configuration: github.com/Joon-Klaps/rapidtrees/blob/c235532d80c54160d021fbacc25f418e2abbed65/.github/workflows/publish-pypi.yml
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Release files / rapidtrees-0.11.0-cp315-cp315-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

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Publishing repository: github.com/Joon-Klaps/rapidtrees
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Release files / rapidtrees-0.11.0-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

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Release files / rapidtrees-0.11.0-cp314-cp314t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

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Release files / rapidtrees-0.11.0-cp314-cp314-win_amd64.whl

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Release files / rapidtrees-0.11.0-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

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Identity

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Publishing repository: github.com/Joon-Klaps/rapidtrees
Publishing commit: github.com/Joon-Klaps/rapidtrees/tree/c235532d80c54160d021fbacc25f418e2abbed65

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Release files / rapidtrees-0.11.0-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

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Publishing repository: github.com/Joon-Klaps/rapidtrees
Publishing commit: github.com/Joon-Klaps/rapidtrees/tree/c235532d80c54160d021fbacc25f418e2abbed65

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Release files / rapidtrees-0.11.0-cp314-cp314-macosx_11_0_arm64.whl

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Publishing repository: github.com/Joon-Klaps/rapidtrees
Publishing commit: github.com/Joon-Klaps/rapidtrees/tree/c235532d80c54160d021fbacc25f418e2abbed65

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Release files / rapidtrees-0.11.0-cp314-cp314-macosx_10_12_x86_64.whl

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Publishing repository: github.com/Joon-Klaps/rapidtrees
Publishing commit: github.com/Joon-Klaps/rapidtrees/tree/c235532d80c54160d021fbacc25f418e2abbed65

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Release files / rapidtrees-0.11.0-cp313-cp313-win_amd64.whl

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Publishing repository: github.com/Joon-Klaps/rapidtrees
Publishing commit: github.com/Joon-Klaps/rapidtrees/tree/c235532d80c54160d021fbacc25f418e2abbed65

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Release files / rapidtrees-0.11.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

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Identity

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Publishing repository: github.com/Joon-Klaps/rapidtrees
Publishing commit: github.com/Joon-Klaps/rapidtrees/tree/c235532d80c54160d021fbacc25f418e2abbed65

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Release files / rapidtrees-0.11.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

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Provenance

Identity

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Publishing repository: github.com/Joon-Klaps/rapidtrees
Publishing commit: github.com/Joon-Klaps/rapidtrees/tree/c235532d80c54160d021fbacc25f418e2abbed65

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Release files / rapidtrees-0.11.0-cp313-cp313-macosx_11_0_arm64.whl

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Provenance

Identity

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Publishing repository: github.com/Joon-Klaps/rapidtrees
Publishing commit: github.com/Joon-Klaps/rapidtrees/tree/c235532d80c54160d021fbacc25f418e2abbed65

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Release files / rapidtrees-0.11.0-cp313-cp313-macosx_10_12_x86_64.whl

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Provenance

Identity

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Publishing repository: github.com/Joon-Klaps/rapidtrees
Publishing commit: github.com/Joon-Klaps/rapidtrees/tree/c235532d80c54160d021fbacc25f418e2abbed65

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Release files / rapidtrees-0.11.0-cp312-cp312-win_amd64.whl

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Provenance

Identity

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Publishing repository: github.com/Joon-Klaps/rapidtrees
Publishing commit: github.com/Joon-Klaps/rapidtrees/tree/c235532d80c54160d021fbacc25f418e2abbed65

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Release files / rapidtrees-0.11.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

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Uploaded using Trusted Publishing? What is trusted publishing?	Yes
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Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Joon-Klaps/rapidtrees
Publishing commit: github.com/Joon-Klaps/rapidtrees/tree/c235532d80c54160d021fbacc25f418e2abbed65

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SHA-256 checksum: b1e05858e19e01f198abaa29cb89bca1ceb5388563a3c94717e5554a8db1915a Verifying attestations

Release files / rapidtrees-0.11.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

Download URL	rapidtrees-0.11.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
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Tags	CPython 3.12 Linux glibc 2.17+ ARM64
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Upload date	Oct 2, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Joon-Klaps/rapidtrees
Publishing commit: github.com/Joon-Klaps/rapidtrees/tree/c235532d80c54160d021fbacc25f418e2abbed65

Workflow

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Artifact

Subject: rapidtrees-0.11.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
SHA-256 checksum: ee69dec5a396e452bbddc59b97846fbd66c22ca9205d1b587aa2f54834e0afea Verifying attestations

Release files / rapidtrees-0.11.0-cp312-cp312-macosx_11_0_arm64.whl

Download URL	rapidtrees-0.11.0-cp312-cp312-macosx_11_0_arm64.whl
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Upload date	Oct 2, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Joon-Klaps/rapidtrees
Publishing commit: github.com/Joon-Klaps/rapidtrees/tree/c235532d80c54160d021fbacc25f418e2abbed65

Workflow

Publishing configuration: github.com/Joon-Klaps/rapidtrees/blob/c235532d80c54160d021fbacc25f418e2abbed65/.github/workflows/publish-pypi.yml
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Artifact

Subject: rapidtrees-0.11.0-cp312-cp312-macosx_11_0_arm64.whl
SHA-256 checksum: 5c4b835dca520ad79b7d11d093fa8a072d0c94202e54053f88accd2d815301cb Verifying attestations

Release files / rapidtrees-0.11.0-cp312-cp312-macosx_10_12_x86_64.whl

Download URL	rapidtrees-0.11.0-cp312-cp312-macosx_10_12_x86_64.whl
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Upload date	Oct 2, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Joon-Klaps/rapidtrees
Publishing commit: github.com/Joon-Klaps/rapidtrees/tree/c235532d80c54160d021fbacc25f418e2abbed65

Workflow

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Artifact

Subject: rapidtrees-0.11.0-cp312-cp312-macosx_10_12_x86_64.whl
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Release files / rapidtrees-0.11.0-cp311-cp311-win_amd64.whl

Download URL	rapidtrees-0.11.0-cp311-cp311-win_amd64.whl
Size	328.5 kB
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Upload date	Oct 2, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Joon-Klaps/rapidtrees
Publishing commit: github.com/Joon-Klaps/rapidtrees/tree/c235532d80c54160d021fbacc25f418e2abbed65

Workflow

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Publishing logs: github.com/Joon-Klaps/rapidtrees/actions/runs/36977604221/attempts/1

Artifact

Subject: rapidtrees-0.11.0-cp311-cp311-win_amd64.whl
SHA-256 checksum: 8af13714db7ba2ecd17c4aa03ff0b0a585d63787427ff743c950eeaab1e23039 Verifying attestations

Release files / rapidtrees-0.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL	rapidtrees-0.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
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Tags	CPython 3.11 Linux glibc 2.17+ x86-64
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Upload date	Oct 2, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Joon-Klaps/rapidtrees
Publishing commit: github.com/Joon-Klaps/rapidtrees/tree/c235532d80c54160d021fbacc25f418e2abbed65

Workflow

Publishing configuration: github.com/Joon-Klaps/rapidtrees/blob/c235532d80c54160d021fbacc25f418e2abbed65/.github/workflows/publish-pypi.yml
Publishing logs: github.com/Joon-Klaps/rapidtrees/actions/runs/36977604221/attempts/1

Artifact

Subject: rapidtrees-0.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
SHA-256 checksum: 84e889885a80017a2c514296a085a2c4c6a42e4a12091999afbb03f8d05f87ad Verifying attestations

Release files / rapidtrees-0.11.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

Download URL	rapidtrees-0.11.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Size	501.7 kB
Tags	CPython 3.11 Linux glibc 2.17+ ARM64
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Upload date	Oct 2, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Joon-Klaps/rapidtrees
Publishing commit: github.com/Joon-Klaps/rapidtrees/tree/c235532d80c54160d021fbacc25f418e2abbed65

Workflow

Publishing configuration: github.com/Joon-Klaps/rapidtrees/blob/c235532d80c54160d021fbacc25f418e2abbed65/.github/workflows/publish-pypi.yml
Publishing logs: github.com/Joon-Klaps/rapidtrees/actions/runs/36977604221/attempts/1

Artifact

Subject: rapidtrees-0.11.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
SHA-256 checksum: 092a564950fa1ed5bf36dc6876acb8d46485b61d295cf2541aed5fd7a2278aac Verifying attestations

Release files / rapidtrees-0.11.0-cp311-cp311-macosx_11_0_arm64.whl

Download URL	rapidtrees-0.11.0-cp311-cp311-macosx_11_0_arm64.whl
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Upload date	Oct 2, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Joon-Klaps/rapidtrees
Publishing commit: github.com/Joon-Klaps/rapidtrees/tree/c235532d80c54160d021fbacc25f418e2abbed65

Workflow

Publishing configuration: github.com/Joon-Klaps/rapidtrees/blob/c235532d80c54160d021fbacc25f418e2abbed65/.github/workflows/publish-pypi.yml
Publishing logs: github.com/Joon-Klaps/rapidtrees/actions/runs/36977604221/attempts/1

Artifact

Subject: rapidtrees-0.11.0-cp311-cp311-macosx_11_0_arm64.whl
SHA-256 checksum: fcc384bb0924f0d516c6ff8712cb69d950ab0e0d8ecf52fa4ca42a4c8f4cc9ba Verifying attestations

Release files / rapidtrees-0.11.0-cp311-cp311-macosx_10_12_x86_64.whl

Download URL	rapidtrees-0.11.0-cp311-cp311-macosx_10_12_x86_64.whl
Size	450.8 kB
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Upload date	Oct 2, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Joon-Klaps/rapidtrees
Publishing commit: github.com/Joon-Klaps/rapidtrees/tree/c235532d80c54160d021fbacc25f418e2abbed65

Workflow

Publishing configuration: github.com/Joon-Klaps/rapidtrees/blob/c235532d80c54160d021fbacc25f418e2abbed65/.github/workflows/publish-pypi.yml
Publishing logs: github.com/Joon-Klaps/rapidtrees/actions/runs/36977604221/attempts/1

Artifact

Subject: rapidtrees-0.11.0-cp311-cp311-macosx_10_12_x86_64.whl
SHA-256 checksum: 0d22239ef1ee36f865297735da620e5b16981cfc518ffd57592f397d9e9a9320 Verifying attestations

Release files / rapidtrees-0.11.0-cp310-cp310-win_amd64.whl

Download URL	rapidtrees-0.11.0-cp310-cp310-win_amd64.whl
Size	328.4 kB
Tags	CPython 3.10 Windows x86-64
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Upload date	Oct 2, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Joon-Klaps/rapidtrees
Publishing commit: github.com/Joon-Klaps/rapidtrees/tree/c235532d80c54160d021fbacc25f418e2abbed65

Workflow

Publishing configuration: github.com/Joon-Klaps/rapidtrees/blob/c235532d80c54160d021fbacc25f418e2abbed65/.github/workflows/publish-pypi.yml
Publishing logs: github.com/Joon-Klaps/rapidtrees/actions/runs/36977604221/attempts/1

Artifact

Subject: rapidtrees-0.11.0-cp310-cp310-win_amd64.whl
SHA-256 checksum: be3074febe9a9a6e9341f2ddf543c72debe9731a369b99f0cf48fff37f6a8d4c Verifying attestations

Release files / rapidtrees-0.11.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL	rapidtrees-0.11.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
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Upload date	Oct 2, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Joon-Klaps/rapidtrees
Publishing commit: github.com/Joon-Klaps/rapidtrees/tree/c235532d80c54160d021fbacc25f418e2abbed65

Workflow

Publishing configuration: github.com/Joon-Klaps/rapidtrees/blob/c235532d80c54160d021fbacc25f418e2abbed65/.github/workflows/publish-pypi.yml
Publishing logs: github.com/Joon-Klaps/rapidtrees/actions/runs/36977604221/attempts/1

Artifact

Subject: rapidtrees-0.11.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
SHA-256 checksum: d894f75e98046b894a7d7ab12885d9d70636beea37d338c9aa25a4e4ac13e2a1 Verifying attestations

Release files / rapidtrees-0.11.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

Download URL	rapidtrees-0.11.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Size	502.1 kB
Tags	CPython 3.10 Linux glibc 2.17+ ARM64
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Upload date	Oct 2, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Joon-Klaps/rapidtrees
Publishing commit: github.com/Joon-Klaps/rapidtrees/tree/c235532d80c54160d021fbacc25f418e2abbed65

Workflow

Publishing configuration: github.com/Joon-Klaps/rapidtrees/blob/c235532d80c54160d021fbacc25f418e2abbed65/.github/workflows/publish-pypi.yml
Publishing logs: github.com/Joon-Klaps/rapidtrees/actions/runs/36977604221/attempts/1

Artifact

Subject: rapidtrees-0.11.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
SHA-256 checksum: 77e638a4061548596af47eaa22a446eddb1e4ecfb0962073b9d91e90f777b241 Verifying attestations