treeweave Python bindings

Python (nanobind) wrapper for the treeweave piecewise-polynomial function approximator.

Installation

pip install scikit-build-core nanobind numpy
pip install .          # regular install
# or for development:
pip install -e . --no-build-isolation

Quick start

import math
import numpy as np
import treeweave

# Fit exp(x) on [0, 1] to 1e-10 relative tolerance.
def func(x):
    return math.exp(x[0])   # x is a (dim,) ndarray

approx = treeweave.fit(func, 0.0, 1.0, tol=1e-10)   # dim & out_dim inferred

# Scalar eval
print(approx(0.5))          # ≈ exp(0.5)

# Batch eval
xs = np.linspace(0, 1, 1000)
ys = approx(xs)                  # shape (1000,)
ys = approx(xs, sorted=True)     # 1-D ascending fast path

API

treeweave.fit(f, a, b, tol, *, dim=None, out_dim=None, dtype="f64", ...)

Fits callable f and returns a callable TreeweaveFunction.

  • a, b: domain corners (scalars for 1D, length-dim lists otherwise).
  • tol: approximation tolerance (drives adaptive tree refinement).
  • dim: input dimension; inferred from len(a) (scalar corners ⇒ 1) when omitted.
  • out_dim: number of output components; inferred by probing f once at the box midpoint (np.asarray(f(mid)).size, scalar ⇒ 1) when omitted.
  • dtype: "f64" (default) or "f32".

treeweave.fit(a, b, tol, ...) as a decorator

Omitting f returns a decorator, the functools.cache spelling:

@treeweave.fit(0.0, 1.0, tol=1e-10)
def expensive(x):
    return math.exp(x[0])

expensive(0.5)          # evaluates the approximation
expensive.__wrapped__   # the original callable

fit(a, b, tol) and fit(a, b, tol=...) both work, and all keyword options apply unchanged. The result is a plain TreeweaveFunction carrying the original __name__ / __doc__. Fitting happens at decoration time.

TreeweaveFunction

Call the fitted object. There are no named eval methods. A point gives a point result and a batch gives a batch result. Two optional keyword flags select the other batch modes.

Call / property Description
fn(x) Evaluate. Point: scalar (dim==1) or (dim,) → scalar / (out_dim,). Batch: (N,) (dim==1) or (N, dim) → (N,) / (N, out_dim)
fn(x, sorted=True) 1-D ascending-batch fast path (requires dim == 1)
fn(x, transposed=True) Batch returning (out_dim, N) (requires out_dim > 1)
.dim Input dimensionality
.out_dim Output dimensionality
.dtype "f64" or "f32"
.memory_usage Bytes used by the approximation
.print_stats() Print internal tree statistics

A point whose length != dim, a batch whose column count != dim, sorted=True with dim != 1, or transposed=True with out_dim == 1 each raise ValueError rather than silently mis-shaping the result.

Running tests

pip install pytest
pytest tests/ -q

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for DiamonDinoia from gravatar.com DiamonDinoia

License expression

BSD-3-Clause
View SPDX License List

Requires

Python >=3.9

Provides Extra

test

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0.0.6 This release

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