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-dimlists otherwise).tol: approximation tolerance (drives adaptive tree refinement).dim: input dimension; inferred fromlen(a)(scalar corners ⇒ 1) when omitted.out_dim: number of output components; inferred by probingfonce 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
1 maintainer
Metadata
1 maintainer
Release files for treeweave 0.0.6
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
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Built distributions (wheels)
Total release size: 12.7 MB
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