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.EuclideanVertices(default):inputhas shape(n, d)(vertex coordinates).repr=howzat.Representation.HomogeneousGenerators:inputhas shape(n, d+1)(homogeneous generator rows).repr=howzat.Representation.Inequality:inputhas shape(m, d+1)(H-rep rows(b, a...)).
howzat.solve(...) accepts float64 arrays.
howzat.solve_exact(...) accepts int64 arrays or dtype=object arrays containing exact rationals
(e.g. fractions.Fraction objects).
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:f64howzat-dd:f64[eps[1e-12],max]-resolve[gmprat]cddlib:gmprationalcddlib+hlbl:f64lrslib+hlbl(defaults tolrslib+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[...]: configurehowzat-ddpurifiers, e.g.purify[snap]orpurify[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.EuclideanVertices) -> SolveResultConvenience function which uses the default backend (howzat-dd[purify[snap]]:f64[eps[1e-12]]).howzat.solve_exact(input, repr=Representation.EuclideanVertices) -> SolveResultConvenience function which uses the default exact backend (howzat-dd:gmprat).howzat.Backend(spec: str | None = None)Loads the specified backend;Noneselects the default.Backend.solve(input, repr=Representation.EuclideanVertices) -> SolveResultRuns the backend.Backend.solve_exact(input, repr=Representation.EuclideanVertices) -> SolveResultRuns the backend in exact mode (int64); errors if the backend is not exact.
SolveResult
A SolveResult has the fields:
spec: strbackend spec actually useddimension: intambient dimensiondvertices: intnumber of verticesnfacets: intnumber of facetsridges: intnumber of ridges (edges in the facet adjacency / FR graph)total_seconds: floattime spent inside the backend (seconds)vertex_positions: list[list[float]] | Nonevertex coordinates if the backend returned baseline geometryvertex_adjacency: DenseGraph | AdjacencyListvertex adjacency graph (dense or sparse)facets_to_vertices: list[list[int]]for each facet, the incident vertex indicesfacet_adjacency: DenseGraph | AdjacencyListfacet adjacency graph (FR graph; dense or sparse)generators:numpy.ndarray[float64] | list[list[str]] | Noneinequalities:numpy.ndarray[float64] | list[list[str]] | Nonefails: intbackend-specific failure count (pipeline dependent)fallbacks: intbackend-specific fallback count (pipeline dependent)
For exact rational solves (solve_exact(...) with exact backend input), coefficient matrices are returned
as list[list[str]] (exact strings).
Both graph types support:
node_count() -> intdegree(node: int) -> intcontains(node: int, neighbor: int) -> boolneighbors(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).
Metadata
Release files for howzat 0.3.9
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| howzat-0.3.9.tar.gz | 17.0 MB | Details |
Built distributions (wheels)
Total release size: 178.9 MB
Release files / howzat-0.3.9.tar.gz
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| Tags | CPython 3.10 macOS 10.12+ x86-64 |
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