Project status and evidence#

SplineOps publishes maturity labels so users can choose APIs with appropriate expectations. This page records what has been verified, what remains under active stabilization, and how the evidence can be reproduced.

Status definitions#

Stable: The public contract is documented, edge cases and accelerated/reference parity are tested, and compatibility changes require migration guidance.
Stabilizing: The central use case is sound and useful, but some combinations of shapes, dtypes, boundaries, backends, or memory behavior still need a completed contract.
Experimental: The implementation is available for research and evaluation, but its API, numerical contract, provenance review, or coverage is not yet sufficient for a stability promise.

Current capability matrix#

Capability evidence#
Module	Status	Demonstrated capability	Remaining graduation work
Resize	Stable	N-D native and Python paths, explicit axes and output geometry, interpolation and direct projection, reusable plans, bounded caches, output buffers, concurrency and fork handling.	Continue cross-platform performance monitoring; validate the measured small-3-D scheduler policy on additional machines before generalizing it to other workload classes.
`TensorSpline`	Stabilizing	Multiple bases and per-axis modes, N-D and batched evaluation, real and complex data, tested singleton/short-periodic behavior, bounded-memory queries, separable grid contraction, reusable geometry plans across compatible sample arrays, explicit refitting/precomputed-coefficient paths, SciPy parity for B-spline degrees 0–5 through four dimensions, published-formula Keys and cubic O-MOMS references, and partial CuPy interoperability.	Complete dedicated CuPy CI before making a stable backend-wide promise; continue independent references for non-B-spline bases and modes.
Affine	Experimental	General 2-D/3-D matrix transforms, analytical rotation tests, explicit batch/channel axes, output buffers, bounded one-shot execution, cached fixed-geometry plans, vectorized batch coefficient evaluation, immutable compatibility-tagged coefficients reusable across affine geometries, atomic endian-portable coefficient persistence with schema-1 reading, immutable schema-1/schema-2 archive fixtures, repeated thread/process field reuse and corruption rejection, and equivalent SciPy parity for degrees 0–5; SplineOps also accepts its higher-order degrees 6 and 7.	Improve performance only with profile-backed changes. SciPy remains 8.96x–19.54x faster than the SplineOps one-shot path in the current standard matched benchmark, despite useful gains from cached geometry.
Differentials	Experimental	Raw repeatable outputs, preserved source arrays, vectorized coefficient filtering, physical spacing, 2-D/3-D gradient and packed Hessian components, explicit batch/channel axes through one batched multi-output workspace, independent output-family selection, exact structured output buffers prevalidated for writability and non-overlap, explicit mirror boundary/dtype checks, legacy-reference coverage, and polynomial and trigonometric invariants. Randomized explicit-axis and strided-buffer parity plus repeated caller-buffer reuse are checked.	Define additional boundary/dtype contracts before describing the module as a general N-D differential engine; angular maps intentionally remain 2-D and the legacy object remains scalar.
Smoothing splines	Experimental	Fractional FFT and recursive examples, reusable real-FFT half-spectrum plans with explicit batch/channel axes, real/finite parameter domains, constant preservation, periodic cosine-response checks, and an independent dense-system reference for the recursive formulation plus a one- and two-dimensional dense-DFT reference for the published fractional response.	Add broader published numerical fixtures while preserving the clear distinction between exact fractional, radial approximate, and recursive formulations.
Adaptive regression	Experimental	Deterministic denoising and piecewise-linear reconstruction tests, reusable fixed-sample sparse factorizations, prefix-sum spline evaluation, stateless warm-start lambda paths, non-mutating amplitude sparsification, sorted-input validation, opt-in ADMM convergence diagnostics, and a constrained-optimizer reference. Known sparse hinge models on nonuniform samples are recovered and checked between sample locations.	Add larger optimization-reference comparisons and systematic penalty and ADMM-parameter guidance.
Multiscale	Experimental	Explicit odd/singleton pyramid behavior, vectorized whole-axis pyramid, adaptive small-plane vectorization and large-plane wavelet dispatch with explicit batch/channel axes, perfect reconstruction for supported even rectangular Haar and cubic spline-wavelet shapes, randomized reversible shape/scale/dtype checks, and an inspectable bounded-approximation contract for the source-verified order-5 tap table.	Obtain higher-precision order-5 taps or continue labeling it approximate; expand supported shape and scale classes only with reversible evidence.

Initial execution baseline#

The roadmap execution began from commit a0bb350 on 2026-07-15. On the initial Linux development environment (Python 3.12.3, NumPy 2.4.6), the full suite completed with 953 passed in 109.74 seconds. A strict documentation build also succeeded after the roadmap update.

The initial resize smoke comparison used:

python scripts/benchmark_resize_pr.py \
  --profile smoke \
  --output-dir /tmp/splineops-analysis-smoke

On that environment, SplineOps was faster than SciPy on the three tested rows with close numerical agreement on the interpolation rows. OpenCV was faster on all three generic 2-D rows, and PyTorch was faster on two. These libraries do not share one resize contract, so the result supports a focused claim—fast, explicit spline semantics—not a claim of universal image-resize leadership.

Current execution result#

The dated accounts below preserve the wording used when each pre-release evidence pass completed. They are an engineering record, not the current distribution status; see the project changelog.

After the first roadmap implementation pass, the same Linux development environment completed 1134 passed in 125.37 seconds. Black formatting, the scoped MyPy check, a strict Sphinx build, wheel and source-distribution builds, and a clean-environment wheel smoke test all passed. The wheel smoke also exercised the native resize extension and an experimental repeated-query TensorSpline plan.

After the second optimization pass, a recreated Python 3.12 tox environment built the native extension from this worktree and completed 1167 passed in 138.32 seconds. The strict documentation build, Black check, scoped MyPy check, standalone native scheduler test, benchmark smoke tests, and package build are the accompanying branch-level gates. This is pre-release validation, not a claim that a new distribution has been published.

The development benchmarks from this pass are recorded in Performance evidence. Their central conclusions are intentionally mixed: TensorSpline has bounded query overhead and a useful reusable-geometry plan, vectorized differentials substantially outperform their scalar oracle, and affine transforms match SciPy numerically while remaining slower. Cached affine geometry and vectorized multiscale passes are useful workload-specific gains. These are development-machine measurements, not release promises.

After the consolidation pass, the recreated Python 3.12 environment completed 1179 passed in 145.02 seconds. The focused 453-test contract set, Black, scoped MyPy, strict Sphinx, source and wheel builds, and the stored benchmark regression policy also passed. The standard workflow profile found a 4.01x repeated-coordinate TensorSpline gain, a 1.18x affine coefficient-sharing gain, and approximately neutral smoothing, denoising-path, and explicit-axis wavelet timings. Exact operations agreed exactly; the independently converged denoising paths differed by at most 5.6e-9. No distribution was released from this validation pass.

After the fourth batch-execution pass, the full suite completed 1180 passed in 140.13 seconds. Black, scoped MyPy, a warning-fatal Sphinx build, and source/wheel builds also passed. The standard explicit-axis workflow rows measured 1.03x affine and 1.26x differential speedups with exact output parity; the four-plane Haar round trip measured 0.90x and is documented as a bounded regression rather than a win. The smoke Haar workload measured 1.73x. This pass also repairs the macOS FFT test’s overly strict bitwise comparison and improves CI failure annotations. It remains intentionally unreleased.

After the fifth hardening and profiling pass, the full suite completed 1189 passed in 140.51 seconds. Black, scoped MyPy, a warning-fatal Sphinx build, the stored smoke benchmark policy, source and native-wheel builds, and a clean wheel import smoke test also passed. The standard batch-memory sweep agreed with scalar dispatch for every measured affine, Laplacian-only, and Haar row; its worst normalized traced-memory growth was 1.08. Direct NumPy support contraction improved the measured affine workloads, while adaptive wavelet dispatch restored the representative four-plane Haar round trip to approximately neutral performance. The subsequent cross-platform smoke run 29424000663 passed on Linux, macOS, and Windows and uploaded one complete benchmark artifact per runner. The nine-job Python/OS library matrix and quality workflow also passed for commit 32b9d8c. This remains pre-release validation and no distribution was published.

After the sixth stability-soak pass, the full suite completed 1208 passed in 137.33 seconds. Black, scoped MyPy, the stored smoke benchmark policy, a warning-fatal Sphinx build, source and native-wheel builds, and a clean wheel smoke covering resize, coefficient persistence, and buffered differentials all passed. The isolated Linux standard profile found exact agreement for both downstream workloads: persisted registration fan-out measured 1.75x and buffered volume features 1.49x against their explicit references. Affine phase instrumentation identifies coefficient evaluation as the dominant in-call phase, but matched SciPy affine execution remains faster. This remains an unreleased development pass.

The subsequent standard development run 29429054363 passed on Linux, macOS, and Windows for commit 2b84cbe and published one complete ten-benchmark artifact bundle per runner. The accompanying nine-job library matrix 29429054290 and quality workflow 29429054326 also passed. No release or tag was created.

After the seventh evidence pass, the full suite completed 1222 passed in 146.33 seconds. Black, scoped MyPy, a warning-fatal documentation build, and source/native-wheel builds also passed. The new Linux standard API soak completed 12 cycles with 36 archive replacements, 108 threaded applications, 36 fresh spawned-process restorations, 12 corruption rejections, and 12 exact buffer reuses with zero numerical drift. Parent profiling was dominated by the deliberately fresh process lifecycle, so it did not justify another spline kernel change. Affine and Differentials remain experimental, CuPy remains an experimental TensorSpline path, and no release was made.

The corresponding API stability soak run 29479718318 then passed on Linux, macOS, and Windows and published one JSON artifact per runner. The nine-job Test Library run 29479718365 and Quality gates run 29479718142 also passed for commit df94733. The consolidated snapshot is maintained in Progress so far.

Stability-soak and graduation review#

The added capabilities do not automatically promote a module. TensorSpline remains stabilizing until dedicated CuPy CI and broader independent references cover the advertised backend and non-B-spline surface. Affine and differentials are the closest experimental modules to a future stability review, but this review deliberately does not promote them. Their new coefficient-persistence, output-selection, and output-buffer contracts need a real API-soak period. Two representative persisted-registration and buffered volume-feature workloads now exercise those APIs locally. Linux/macOS/Windows smoke artifacts have been collected and reviewed successfully; a standard profile has now also been reviewed. It confirms numerical portability while rejecting a portable speedup claim for explicit-axis affine and differential orchestration. Smoothing, adaptive regression, and multiscale remain experimental while their published-reference, parameter-guidance, and reconstruction gates are open.

This deliberately separates implementation quality from compatibility promises. Users can rely on the documented functionality today without the project pretending that one Linux validation run establishes a stable public contract on every platform.

Promotion from experimental requires all of the following evidence:

successful test and development-benchmark artifacts on Linux, macOS, and Windows, with numerical equivalence checked before timing ratios;
at least two representative downstream workloads exercising the new APIs without contract changes or unresolved correctness reports;
reviewed batch-memory scaling and concurrency behavior at the documented sizes; and
migration guidance for any naming or buffer-contract change discovered during the soak.

Until those gates close, changes may still refine these experimental APIs and no release is implied by implementation completeness.

Reproducing validation#

python -m pytest -q
python -m sphinx -b html docs /tmp/splineops-docs -W --keep-going \
  -D sphinx_gallery_conf.plot_gallery=0
python -m build
python scripts/benchmark_tensorspline_query_plan.py \
  --output-json /tmp/splineops-bench/tensorspline-query-plan.json
python scripts/benchmark_affine.py --profile standard \
  --output-json /tmp/splineops-bench/affine.json
python scripts/profile_affine_phases.py --profile standard \
  --output-json /tmp/splineops-bench/affine-phases.json
python scripts/benchmark_differentials.py --profile standard \
  --output-json /tmp/splineops-bench/differentials.json
python scripts/benchmark_multiscale.py --profile standard \
  --output-json /tmp/splineops-bench/multiscale.json
python scripts/benchmark_workflows.py --profile standard \
  --output-json /tmp/splineops-bench/workflows.json
python scripts/benchmark_batch_scaling.py --profile standard \
  --output-json /tmp/splineops-bench/batch-scaling.json
python scripts/benchmark_downstream_workflows.py --profile standard \
  --output-json /tmp/splineops-bench/downstream-workflows.json
python scripts/check_benchmark_thresholds.py \
  --policy benchmarks/consolidation-thresholds.json \
  --artifacts-dir /tmp/splineops-bench
python scripts/benchmark_resize_pr.py --profile smoke \
  --output-dir /tmp/splineops-smoke

Performance reports must record the environment, threads, runtime, output shape and dtype, numerical differences, and whether another library implements equivalent coordinates, boundaries, interpolation, and antialiasing.

Backend support#

NumPy: Supported throughout the package on the Python versions tested in CI.
Native C++ resize: Required and exercised in the main test matrix and published wheels.
CuPy: Experimental and limited to portions of TensorSpline. It is not a package-wide GPU backend; the zero-boundary coefficient path can transfer through a CPU solve, and no CuPy configuration is advertised as supported until dedicated GPU CI covers the promised combinations.

The precise matrix is in Backend support contract. Graduation gates and execution order are maintained in Affine and Differentials graduation audits and Development roadmap.