akriti 0.2.0

Statistically grounded topological data analysis for Python.
akriti.io · Apache-2.0 · built on GUDHI and Ripser

Persistence diagrams tell you what shape your data has. Akriti tells you whether the answer is significant.

It provides the statistical layer that Python's TDA stack lacks — hypothesis tests, effect sizes, per-region significance, and sample-size calculation for persistence diagrams — while delegating persistence computation to the established engines rather than reimplementing them.

Status

Early development. The API is unstable and much of what is described below is not written yet. akriti 0.1.0 is on PyPI as of 2026-08-27 — one working module, akriti.diagrams, and nothing else. Star the repository to follow progress, or watch akriti.io.

We would rather be accurate than impressive, so:

Why

Three gaps, stated as precisely as we can:

1. Statistical inference for diagrams lives in R, not Python. The TDA, TDAstats and tdaverse packages have offered permutation tests and bootstrap confidence sets for years. Python users have had essentially nothing.
2. No library, in any language, calculates sample size for a topological effect. "How many samples do I need to detect a bottleneck-distance difference of size δ?" is a question applied statistics answers routinely, and topology has never answered at all.
3. Python's general-purpose TDA layer has gone quiet. giotto-tda has had no commits since 2024 while still being installed thousands of times a month. Its users deserve somewhere maintained to land.

Design commitments

• We delegate computation. Persistence, bottleneck and Wasserstein distances go to GUDHI, Ripser and Hera. We do not reimplement them, and we will not.
• Backend-agnostic input. Bring diagrams from any library, or none.
• Honest defaults. Where our theory supports a principled choice of filtration, scale or descriptor, the library makes it and explains why. Where it provably does not — the landmark budget, placement, bandwidth, and the concatenation rules — the library says so and points you at cross-validation instead of pretending.
• Permissive by default. Apache-2.0, and the default install closure is permissive-only — verified in CI, not asserted. Every backend has a copyleft dependency somewhere in its closure, so every backend is an opt-in extra.

Specifications

Before the code, the contract. RFC-0001 — Persistence Diagram Interchange pins down what a persistence diagram is across Python's backends: infinite bars, ordering, precision, equality, metadata and serialization. It is open for comment, and it is useful whether or not you ever install this library — the R ecosystem solved interchange first, and Python has not.

Every convention in it was measured against GUDHI, Ripser, persim and giotto-tda rather than recalled. rfcs/evidence/probe_backends.py reproduces every number. Three findings you may want regardless of Akriti:

• giotto-tda silently drops the essential H0 bar — under every infinity_values setting. 40 points, 40 components, 39 reported.
• giotto-tda's batch padding is indistinguishable from real bars. The same point cloud yields 2 one-dimensional bars alone and 11 when batched with another; the padding is written with a genuine birth value.
• persim.bottleneck returns a finite distance between diagrams that are infinitely far apart — 0.5 where the answer is ∞. It does warn that it is dropping the infinite bars, but the warning describes the mechanism rather than the consequence, and it fires more often on the case it gets right than on the case it gets wrong.

Install

pip install akriti              # interchange layer — zero dependencies
pip install akriti[rips]        # + Ripser    (MIT, GPLv3 transitively)
pip install akriti[alpha]       # + GUDHI     (GPLv3)
pip install akriti[distances]   # + persim    (MIT, GPLv3 transitively)
pip install akriti[numpy]       # + NumPy namespace / Python-row fallback
pip install akriti[parquet]     # + PyArrow   (Apache-2.0)
pip install akriti[torch]       # + torch and array-api-compat
pip install akriti[jax]         # + JAX       (Apache-2.0); see the note below
pip install akriti[bio]         # + anndata   (BSD-3)

Currently a placeholder release; real functionality is coming.

Nothing is a required dependency — no persistence backend, and no NumPy either. Native array inputs retain their Python array API namespace. Accepted Python-row adapter inputs lazily use akriti[numpy]; torch tensors use the compatibility resolver supplied by akriti[torch]; and Parquet imports PyArrow only when requested through akriti[parquet]. JAX arrays need no such boundary -- they expose the namespace natively, so akriti[jax] is a convenience that installs JAX for you and nothing more. "Bring your own diagrams" remains the primary path by design. The licence consequences above are stated here rather than in a footnote because they are real: persim depends on hopcroftkarp, which is GPLv3 and has had no release since 2019, and the gudhi wheel bundles CGAL-dependent modules and ships no licence metadata at all. See DEPENDENCIES.md for the verified closure, and tools/check_license_closure.py for the CI gate that keeps it honest.

JAX needs a 64-bit configuration that you set, not us. A diagram stores float64 births and deaths, and a default JAX install truncates both to float32 -- so building one raises a ValueError naming the dtype it got. Either lever fixes it, and the first is narrower and preferred, changing only what akriti asks for rather than every default dtype in your process:

import jax

jax.config.update("jax_explicit_x64_dtypes", "allow")  # preferred
jax.config.update("jax_enable_x64", True)  # heavier alternative

akriti will not set either for you. Both are process-global, so a library setting one would silently change the numerics of unrelated JAX code in your program. See RFC-0001 §3.3 and D23.

The research behind it

CASTLE is the practitioner-facing product; the others are the machinery that makes its guarantees possible.

Contributing

Contributions are welcome, including — especially — from maintainers of the projects we build on. If you maintain a TDA library and something here does not interoperate cleanly with yours, that is a bug and we would like to hear about it.

Please read CONTRIBUTING.md and CODE_OF_CONDUCT.md. Security reports go to SECURITY.md.

Team

• Sushovan Majhi — Data Science, GW · lead, library architecture
• Pramita Bagchi — biostatistics · practitioner statistics
• Atish Mitra — Mathematics, Montana Tech · theoretical foundations
• Žiga Virk — Mathematics, Ljubljana · theory advisor
• Alexander Silberman — GW · library development
• Edward Bae — GW · library development

Built on GUDHI (INRIA), Ripser, Hera and persim, and on the landmark embedding of Mitra & Virk (2024). With thanks to the wider TDA community, including scikit-tda, giotto-tda and the R tdaverse.

Licence

Apache-2.0 — see LICENSE. The explicit patent grant is deliberate: it is what makes the library usable inside institutions whose legal review would otherwise block adoption.

Contact

hello@akriti.io · @akritihq · github.com/akritihq