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jlogan03 merged 1 commit into from
Nov 14, 2025
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Update readme #56

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20 changes: 11 additions & 9 deletions README.md
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Original file line number Diff line number Diff line change
@@ -1,5 +1,11 @@
# InterpN

<style type="text/css">
svg { filter: invert(100%) hue-rotate(180deg); background-color: transparent !important; }
image { filter: invert(100%) hue-rotate(180deg) saturate(1.25); }
</style>

[Writeup](https://jlogan.dev/blog/#2025-11-10-interpn-fast-interpolation) |
[Repo](https://github.com/jlogan03/interpn) |
[Python Docs](https://interpnpy.readthedocs.io/en/latest/) |
[Rust Docs](https://docs.rs/interpn/latest/interpn/)
Expand All @@ -9,11 +15,6 @@ prioritizing correctness, performance, and compatiblity with memory-constrained

Available as a rust crate and python library.

These methods perform zero allocation when evaluated (except, optionally, for the output).
Because of this, they have minimal per-call overhead, and are particularly
effective when examining small numbers of observation points. See the
[performance](https://interpnpy.readthedocs.io/en/latest/perf/) page for detailed benchmarks.

## Features

| Feature →<br>↓ Interpolant Method | Regular<br>Grid | Rectilinear<br>Grid | Json<br>Serialization |
Expand All @@ -28,10 +29,12 @@ The methods provided here, while more limited in scope than scipy's,
* use almost no RAM (and perform no heap allocations at all)
* produce significantly improved floating-point error (by several orders of magnitude)
* are json-serializable using Pydantic
* can also be used easily in web and embedded applications via the Rust library
* can also be used easily in web, embedded and gpu applications via the Rust library
* are permissively licensed

![ND throughput 1000 obs](./docs/throughput_vs_dims_1000_obs.svg)
<br>

<img style="filter: invert(100%) hue-rotate(180deg)" src="./docs/speedup_vs_dims_1_obs_linear.svg" alt="">

See [here](https://interpnpy.readthedocs.io/en/latest/perf/) for more info about quality-of-fit, throughput, and memory usage.

Expand All @@ -43,8 +46,7 @@ pip install interpn

## Profile-Guided Optimization

To build the extension with profile-guided optimization using pre-built profiles, do `sh ./scripts/distr_pgo_install.sh`.
You can also generate your own PGO profiles like `sh ./scripts/distr_pgo_profile.sh`.
To build the extension with profile-guided optimization, do `sh ./scripts/distr_pgo.sh`
after installing this extra compiler dependency:

```bash
Expand Down