howzat 0.3.4

High-performance polytope backends via Rust FFI bindings to howzat

howzat

User-friendly Python bindings for the Rust crates howzat and howzat-kit via PyO3.

Usage

import numpy as np
import howzat

verts = np.asarray([[0.0, 0.0], [1.0, 0.0], [0.0, 1.0]], dtype=np.float64)

res = howzat.solve(verts)  # cached default backend: "howzat-dd[purify[snap]]:f64[eps[1e-12]]"
# res = howzat.Backend().solve(verts)  # same as above
# res = howzat.Backend("howzat-dd[purify[snap],adj[sparse]]:f64").solve(verts)  # force sparse adjacency graphs

verts_i64 = np.asarray([[0, 0], [1, 0], [0, 1]], dtype=np.int64)
res_exact = howzat.solve_exact(verts_i64)  # cached exact backend: "howzat-dd:gmprat"

print(res.facets, res.ridges)
print(res.facet_adjacency)
print(res.generators)    # numpy.ndarray[float64] (row-major, shape=(n, d+1))
print(res.inequalities)  # numpy.ndarray[float64] (row-major, shape=(m, d+1))

input must be a contiguous (C-order) 2D numpy.ndarray.

• repr=howzat.Representation.Generator (default): input has shape (n, d) (vertex coordinates).
• repr=howzat.Representation.Inequality: input has shape (m, d+1) (H-rep rows (b, a...)).

Backends

The class howzat.Backend(spec) accepts backend spec strings, which specify the algorithm and parameters of the computation pipeline.

At a high level, the syntax is: KIND[OPTIONS][:SPEC]. Some examples:

• howzat-dd[purify[snap]]:f64[eps[1e-12]] (default)
• howzat-dd:f64
• howzat-dd:f64[eps[1e-12],max]-resolve[gmprat]
• cddlib:gmprational
• cddlib+hlbl:f64
• lrslib+hlbl (defaults to lrslib+hlbl:gmpint)

Backend options ([OPTIONS]) are comma-separated and may include:

• adj[dense] / adj[sparse]: force dense bitset graphs vs adjacency lists (if supported natively)
• purify[...]: configure howzat-dd purifiers, e.g. purify[snap] or purify[upsnap[gmprat]]

If adj[...] is omitted and the backend supports both adjacency representations, howzat-kit chooses dense unless the dense bitset representation would exceed 128MiB (for either the vertex graph, or the facet graph upper bound), in which case it chooses sparse.

Supported KIND[:SPEC] forms:

• cddlib[:f64|gmpfloat|gmprational]
• cddlib+hlbl[:f64|gmpfloat|gmprational]
• howzat-dd[:PIPELINE]
• howzat-lrs[:rug|dashu]
• lrslib+hlbl[:gmpint]
• ppl+hlbl[:gmpint]

API

• howzat.solve(input, repr=Representation.Generator) -> SolveResult Convenience function which uses the default backend (howzat-dd[purify[snap]]:f64[eps[1e-12]]).
• howzat.solve_exact(input, repr=Representation.Generator) -> SolveResult Convenience function which uses the default exact backend (howzat-dd:gmprat).
• howzat.Backend(spec: str | None = None) Loads the specified backend; None selects the default.
• Backend.solve(input, repr=Representation.Generator) -> SolveResult Runs the backend.
• Backend.solve_exact(input, repr=Representation.Generator) -> SolveResult Runs the backend in exact mode (int64); errors if the backend is not exact.

SolveResult

A SolveResult has the fields:

• spec: str backend spec actually used
• dimension: int ambient dimension d
• vertices: int number of vertices n
• facets: int number of facets
• ridges: int number of ridges (edges in the facet adjacency / FR graph)
• total_seconds: float time spent inside the backend (seconds)
• vertex_positions: list[list[float]] | None vertex coordinates if the backend returned baseline geometry
• vertex_adjacency: DenseGraph | AdjacencyList vertex adjacency graph (dense or sparse)
• facets_to_vertices: list[list[int]] for each facet, the incident vertex indices
• facet_adjacency: DenseGraph | AdjacencyList facet adjacency graph (FR graph; dense or sparse)
• generators: V-representation coefficients (row-major, shape (n, d+1))
• inequalities: H-representation coefficients (row-major, shape (m, d+1))
• fails: int backend-specific failure count (pipeline dependent)
• fallbacks: int backend-specific fallback count (pipeline dependent)

For solve_exact(...), the coefficient matrices are returned as list[list[str]] (exact strings).

Both graph types support:

• node_count() -> int
• degree(node: int) -> int
• contains(node: int, neighbor: int) -> bool
• neighbors(node: int) -> list[int]

Install

From PyPI:

python -m pip install howzat

Force a local source build, and enabling CPU-native codegen:

RUSTFLAGS="-C target-cpu=native" python -m pip install --no-binary howzat howzat

Prebuilt wheels are compiled for a portable baseline CPU (not target-cpu=native).