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Changelog

All notable changes to this project are documented in this file.

The format is based on Keep a Changelog, and this project follows Semantic Versioning.

[0.6.3] - 2026-03-19

Added

  • Public power-fit problem export via pyvoro2.powerfit.build_power_fit_problem(...), including stable PowerFitProblem, PowerFitBounds, and PowerFitPredictions objects for external evaluation or solver experiments.
  • Public result packaging via pyvoro2.powerfit.build_power_fit_result(...), so externally computed candidate weights can be turned back into a normal PowerWeightFitResult with standard residuals, reports, and algebraic diagnostics.
  • PowerFitObjectiveBreakdown and PowerWeightFitResult.objective_breakdown, including explicit hard-bound satisfaction and per-penalty summaries for debugging or research workflows.
  • A small advanced problem-API regression suite covering problem export, read-only arrays, result repackaging, and status-detail reporting.

Changed

  • Shared public power-fit dataclasses now live in pyvoro2.powerfit.types, while pure public weight/radius conversions live in pyvoro2.powerfit.transforms.
  • Mathematical formulas for prediction, algebraic diagnostics, hard-bound conversion, gauge canonicalization, and objective evaluation are now centralized in pyvoro2.powerfit.problem instead of being split across solver/report helpers.
  • The standalone native solver, active-set layer, and report layer now consume that shared problem/evaluation machinery instead of maintaining their own private copies of the same formulas.
  • PairBisectorConstraints and the new problem-definition objects now expose read-only NumPy arrays to match their frozen-dataclass semantics more honestly.
  • PowerWeightFitResult.status is now open-ended and pairs with status_detail so externally packaged results can preserve solver-specific termination text without expanding the native status vocabulary.

Fixed

  • Internal formula drift risks between low-level fitting, active-set post-processing, and report serialization were removed by routing them all through one public problem/evaluation layer.

[0.6.2] - 2026-03-19

Added

  • PowerWeightFitResult.edge_diagnostics with theorem-facing edge-space arrays and summaries: alpha, beta, z_obs, z_fit, algebraic residuals, and weighted/unweighted algebraic inconsistency metrics.
  • Fit reports and per-constraint record exporters now serialize the new algebraic edge diagnostics alongside the existing measurement-space predictions and residuals.
  • A medium-size robust-fit regression test that checks the native Huber/ADMM path on a deterministic sparse-outlier benchmark rather than only on tiny two-point cases.

Changed

  • The iterative solver='admm' backend now splits on the predicted measurement variable itself (y = beta + alpha (w_i - w_j)) instead of on raw weight differences, so the linear solve uses the scientifically natural alpha^2 scaling of the graph Laplacian.
  • The ADMM backend now warm-starts from the quadratic analytic fit when that warm start is identifiable, improving convergence on robust Huber fits without expanding the public optimizer API.

Fixed

  • Native Huber fitting via FitModel(mismatch=HuberLoss(...)) and solver='admm' no longer stalls or blows up on the medium-size sparse-outlier benchmark family used during the JCAM paper work; the corrected native path now matches the expected measurement-space residual quality and converges in a small number of iterations on the representative failure case.

[0.6.1] - 2026-03-16

Added

  • Explicit realized-but-unaccounted pair diagnostics in both 3D and planar 2D power-fit realization, including public UnaccountedRealizedPair / UnaccountedRealizedPairError types and JSON/report export support.
  • Structured connectivity diagnostics for low-level fits and self-consistent active-set solves, covering unconstrained points, isolated points, connected components of candidate and active graphs, and whether relative offsets are identified by the data or only by gauge policy.
  • Active-set path diagnostics via result.path_summary and richer per-iteration history rows, so downstream code can distinguish final disconnectedness from transient component splits or transient candidate-absent realized pairs during optimization.
  • Repo-root notebook sources plus notebook-export / notebook-check tooling, distribution-content checks, and a one-shot tools/release_check.py helper for local publishability validation.

Changed

  • Disconnected standalone fits no longer inherit arbitrary anchor-order gauges: each effective component is centered to mean zero by default, or aligned to the regularization-reference mean when a zero-strength reference is supplied.
  • Self-consistent active-set fitting now preserves offsets per connected component of the current active effective graph by aligning each component to the previous iterate, including the final recomputed fit returned to the user.
  • weights_to_radii(...) and the fitting APIs now support an explicit weight_shift= gauge, while keeping r_min= as a backward-compatible convenience rather than the primary convention.
  • Power-fit reports now serialize connectivity diagnostics, unaccounted realized pairs, realized-diagnostics warnings, and active-set path summaries through the plain-Python report helpers.
  • The example notebooks now live in a repo-root notebooks/ directory and are exported into generated docs pages, while README.md and docs deployment are checked for sync in CI.
  • The package metadata now includes a convenience pyvoro2[all] extra for contributors who want the full optional notebook/docs/release-check stack.
  • The optional planar plot_tessellation(...) helper now accepts domain= and show_sites= to match the published guide examples.

Fixed

  • Active-set reports now nest the final low-level fit against the final active constraint subset rather than the full candidate table.
  • Periodic self-image boundaries are excluded from the new unaccounted-pair diagnostics, so wrong-shift reporting does not misclassify self-adjacencies as missing candidate pairs.

[0.6.0] - 2026-03-16

Added

  • New pyvoro2.planar namespace with the first 2D public surface: Box, RectangularCell, compute, locate, ghost_cells, duplicate checking, edge-property annotation, and optional matplotlib visualization helpers.
  • Vendored legacy Voro++ 2D backend is now wired into the build as a separate _core2d extension target.
  • New planar edge-shift reconstruction helper and pre-wheel integration tests that skip cleanly until _core2d wheels are available.
  • New planar tessellation diagnostics and strict validation helpers: analyze_tessellation(...) and validate_tessellation(...).
  • New planar normalization helpers: normalize_vertices(...), normalize_topology(...), and validate_normalized_topology(...).
  • New pyvoro2.planar.PlanarComputeResult for structured wrapper-level compute results carrying raw cells, optional tessellation diagnostics, and optional normalized outputs.
  • pyvoro2.powerfit realized-boundary matching and self-consistent active-set refinement now support planar 2D domains in addition to the original 3D path.

Changed

  • pyvoro2.planar.compute(...) now supports wrapper-level tessellation diagnostics (return_diagnostics=..., tessellation_check=...) and structured normalization convenience (normalize='vertices'|'topology', return_result=True), automatically computing temporary periodic edge shifts/geometry when needed and stripping the temporary fields back out of the raw returned cells unless they were explicitly requested.
  • tools/install_wheel_overlay.py now understands both _core and _core2d, so the editable-style wheel-overlay workflow can carry planar support once new wheels are built.
  • Package metadata, release notes, and top-level documentation now describe the frozen 0.6.0 release rather than the earlier development snapshot.
  • resolve_pair_bisector_constraints(...) now accepts both planar (2D) and spatial (3D) point sets, with dimension-aware shift validation and nearest-image resolution.
  • Power-fit reports now serialize both 2D and 3D tessellation diagnostics through a shared measure-oriented schema while preserving the existing area/volume-specific fields.

Fixed

  • Periodic 2D edge reconstruction now resolves hidden periodic adjacencies that the legacy backend can surface as negative neighbor ids, so fully periodic planar tessellations expose consistent neighbor/shift data to diagnostics and normalization utilities.

[0.5.1] - 2026-03-15

Added

  • tools/install_wheel_overlay.py to support a wheel-core + repository-source development workflow, so the compiled extension can come from an installed wheel while Python imports resolve to src/pyvoro2.
  • DEV_PLAN.md in the repository root with the planned 0.6.x refactoring and 2D implementation roadmap, including the current decision to ship planar 2D against the existing dedicated 2D backend before considering a later voro-dev migration.

Changed

  • Public API validation and block-grid resolution are now routed through shared internal helpers (_inputs.py, _domain_geometry.py) so 3D wrappers and the power-fit layer no longer duplicate the same coercion and geometry logic.
  • Project status metadata is now consistently marked as beta across the package metadata and top-level documentation.

Fixed

  • Power-fit input validation now rejects non-finite point coordinates, constraint values, confidence weights, and non-finite radius/weight conversion inputs.
  • resolve_pair_bisector_constraints(...) now validates external ids consistently, including shape/length and uniqueness checks.
  • The quadratic/analytic power-fit solver no longer crashes on zero-confidence constraints that would otherwise create singular gauge coupling.
  • Empty resolved constraint sets now respect L2 regularization and return the regularization-only solution instead of silently dropping the reference.
  • fit_power_weights(...) and the active-set driver now return the documented numerical_failure status for linear-algebra and non-finite-iterate failures instead of surfacing them as uncaught exceptions or misclassified active-set infeasibility.
  • Triclinic nearest-image resolution now warns when a chosen image touches the image_search boundary, making the search-window sensitivity explicit for skewed periodic cells.

[0.5.0] - 2026-03-14

Added

  • New pyvoro2.powerfit API for inverse power fitting from generic pairwise bisector constraints.
  • Power-fitting results now export plain-Python record rows for downstream reporting and diagnostics.
  • Hard infeasibility reporting is simplified around explicit contradiction witnesses.
  • resolve_pair_bisector_constraints(...) as a reusable low-level constraint-resolution primitive.
  • fit_power_weights(...) with configurable mismatch, hard feasibility, soft penalties, and explicit infeasibility reporting.
  • match_realized_pairs(...) for purely geometric realized-face matching with optional tessellation diagnostics.
  • solve_self_consistent_power_weights(...) for hysteretic active-set refinement driven by realized faces.
  • Rich per-constraint diagnostics, marginal-pair reporting, and optional final tessellation diagnostics.

Changed

  • The inverse-fitting surface is now math-oriented and chemistry-agnostic.
  • Documentation and examples now describe the unified power-fitting workflow.
  • The 0.5.x objective-model scope is explicitly documented around the current built-in convex model family.

[0.4.2] - 2026-03-04

Changed

  • Vendored Voro++: updated the vendored snapshot to include the upstream numeric robustness fix for power/Laguerre (radical) pruning (fixes rare cross-platform edge cases in fully periodic power tessellations).
  • Removed the previously vendored local nextafter-based max_radius inflation patch (no longer needed).

[0.4.1] - 2026-02-16

Fixed

  • Vendored Voro++: inflate the stored global max_radius by 1 ULP (via nextafter) in power/Laguerre mode to make radical pruning robust across platforms.
  • Removed a Python-side workaround that recomputed fully periodic orthorhombic power tessellations via the periodic (triclinic) backend when periodic face-shift assignment failed.
  • Updated documentation to reflect the patched vendored Voro++ snapshot.

[0.4.0] - 2026-02-15

Initial public release.

pyvoro2 wraps unmodified Voro++ and provides a Python-first interface for 3D Voronoi and power/Laguerre (radical Voronoi) tessellations, with practical utilities for periodic boundary conditions and topology/graph workflows.

Added

  • Tessellation computation:
    • compute(..., mode='standard') for standard Voronoi tessellations.
    • compute(..., mode='power') for power/Laguerre tessellations (per-site radii).
  • Domains (containers):
    • Box: finite axis-aligned bounding box.
    • OrthorhombicCell: axis-aligned cell with per-axis periodicity (useful for 1D/2D periodic systems such as wires and slabs).
    • PeriodicCell: fully periodic triclinic cell, including PeriodicCell.from_params(...) for Voro++ lower-triangular parameters.
  • Periodic neighbor-image support:
    • return_face_shifts=True to annotate faces with adjacent_shift=(na, nb, nc) lattice-image indices (for PeriodicCell and periodic OrthorhombicCell).
  • Point-query operations:
    • locate(...): batched ownership queries via Voro++ find_voronoi_cell.
    • ghost_cells(...): batched probe cells via Voro++ compute_ghost_cell.
  • Pre-processing utilities:
    • duplicate_check(...) near-duplicate point detection (Python-side).
    • compute(...), locate(...), and ghost_cells(...) can optionally run the near-duplicate pre-check via duplicate_check='raise'|'warn'.
    • PeriodicCell performs validation of lattice vectors, including near-degeneracy detection (ill-conditioned bases warn; nearly degenerate cells raise).
    • Additional input validation: non-finite coordinates are rejected; user ids must be unique and non-negative; power-mode radii are required to be finite and non-negative.
  • Post-processing utilities:
    • analyze_tessellation(...) sanity checks.
    • validate_tessellation(...) strict validation (raises on failure).
    • annotate_face_properties(...) helpers.
    • normalize.* helpers for reproducible periodic topology work.
    • validate_normalized_topology(...) strict validation for normalized topology.
  • Inverse fitting utilities (pyvoro2.inverse) to fit power weights/radii from desired separating plane positions.
  • Optional visualization helpers (pyvoro2.viz3d, extra pyvoro2[viz]) based on py3Dmol.
  • Documentation site (MkDocs Material) with a narrative guide, API reference, and example notebooks.
  • Test suite with deterministic unit tests, opt-in fuzz/property tests, and opt-in cross-checks against pyvoro.