Changelog

v0.5.0 - 2026-04-08

Features

  • High-Precision Derivatives: New vodv module for computing relative vorticity and divergence using spin-1 vector harmonics.

  • Planetary Constants: Standardized Earth radius to 6,371,220 m across derivatives and tracking geometry.

  • pst-convert & JSON Support: New utility for format conversion and a GPU-optimized JSON format for track data.

  • Interactive Track Explorer: Web-based visualization with real-time filtering and animations.

  • Hodges (TRACK) Parity: Full implementation of the Hodges algorithm with TRACK parity.

  • Preprocessing & Performance: Improved spherical harmonic filtering and backend auto-detection.

  • Remote Zarr Support: Added support for remote Zarr datasets via HTTP, S3, and GS protocols.

  • Enhanced DataLoader: Refactored io.loader to io.data_loader with automatic format detection for NetCDF, GRIB, and Zarr.

  • Improved UX: Added friendly error messages with installation instructions when optional dependencies (cfgrib, zarr) are missing.

  • Expanded Sample Data: Integrated ERA5 UV850 sample datasets and Zarr-formatted alternatives in utils.data.

Testing

  • NCL Validation: New integration test suite validated against NCL 6.6.2 reference data.

  • Format Auto-detection Tests: Added comprehensive tests for NetCDF, GRIB, and Zarr auto-detection in DataLoader.

CI/CD & Testing

  • Verification: Enhanced documentation builds and expanded test coverage.

Maintenance

  • Spectral Backend Consolidation: Evaluated pyshtools, SHTns, ducc0, and jax for performance, accuracy, and portability. Selected ducc0 as the default backend due to its superior multi-frame performance (6.3x faster than SHTns), self-contained architecture (no external C dependencies), and near bit-wise parity with NCL for kinematics. Integrated JAX as an experimental alternative backend for GPU-accelerated spherical harmonic transforms.

  • Strict Typing: Achieved 100% mypy compliance in core I/O modules and removed usage of Any.

  • Dependency Refinement: Introduced a dedicated zarr optional dependency group and updated the all extra.

v0.4.2 - 2026-03-19

Performance

  • Transitioned Dask backend to threaded scheduling for improved efficiency.

  • Decoupled chunk processing to reduce memory overhead.

  • Optimized default worker count to match CPU core availability (max 4).

Features

  • Added --chunk-size (or -c) CLI argument to control processing granularity.

  • Refined Docker publishing logic with improved tag prioritization.

Maintenance

  • Enhanced CI/CD concurrency rules to reduce redundant build noise.

v0.4.1 - 2026-03-18

Note: First release available on Conda-Forge.

Testing

  • Registered integration markers to resolve warnings in Conda-forge CI pipelines.

v0.4.0 - 2026-03-14

Architecture (Modernization)

  • Vectorized Data Model: Massive refactor from Python-object trajectories to a high-performance, array-backed structure.

  • JIT-Optimized Kernels: Replaced core mathematical loops with Numba-compiled kernels, achieving C-level performance.

  • Dask Integration: Implemented end-to-end multi-processing with tree reduction for large-scale datasets.

Features

  • GRIB Support: Introduced support for GRIB files via the cfgrib engine.

  • Multi-Variable Tracking: Support for tracking multiple variables per center using a flexible dictionary structure.

Performance

  • Vectorized Linker: Re-engineered the SimpleLinker using NumPy broadcasting for near-instant distance calculations.

  • Memory Efficiency: Implemented slots=True for dataclasses and flat-array extraction for centers.

v0.3.3 - 2026-03-10

Security

  • Integrated Trivy vulnerability scanning into the Docker build pipeline.

  • Added SBOM (Software Bill of Materials) and provenance attestations to all releases.

Maintenance

  • Refined Docker image tagging and unified caching scopes.

  • Simplified CI test matrix for faster verification.

v0.3.2 - 2026-03-09

CI/CD

  • Added support for ARM64 Docker images.

  • Migrated project management to uv for deterministic builds and faster dependency resolution.

  • Optimized Docker layer caching for significantly faster image deployments.

Maintenance

  • Updated project homepage and refined repository metadata.

v0.3.1 - 2026-03-08

Maintenance

  • Synchronized documentation versions and updated dependency lockfiles.

v0.3.0 - 2026-03-08

Features

  • IMILAST Export: Added support for exporting cyclone trajectories in the standard IMILAST intercomparison format.

  • Automated Data Fetching: Integrated pooch for automatic retrieval of ERA5 test datasets.

Refactoring

  • Xarray Native: Migrated detection pipeline to use Xarray for coordinate-aware processing.

  • Strict Typing: Achieved 100% mypy compliance and dropped support for Python 3.10 (enforced Python 3.11+ standards).

Infrastructure

  • Added Read the Docs documentation scaffolding.

  • Implemented tiered integration testing (Short vs. Full variants).

v0.2.2 - 2026-03-04

Note: First release available on PyPI.

Distribution

  • Established automated publishing to PyPI via GitHub Actions.

  • Added Zenodo DOI integration for scientific citation.

Documentation

  • Initial Read the Docs configuration.

  • Standardized PyPI installation instructions in README.

v0.2.1 - 2026-03-04

Maintenance

  • Metadata fixes and version synchronization.

v0.2.0 - 2026-03-01

Features

  • Dask Backend: Introduced task-parallel execution with automatic worker detection.

  • CSV Output: Transitioned from pickle to user-friendly CSV as the default output format.

Architecture (Modernization)

  • Python 3 Migration: Complete refactor from Python 2.7 to Python 3.10+, including type hints and modern syntax.

  • NetCDF4 Migration: Switched from the legacy Nio library to netCDF4 for robust data handling.

Refactoring

  • Extracted core logic into simple/ and models/ modules for better maintainability.

v0.0.2 - 2018-10-25

  • Added minmaxmode support for ERA-Interim Mean Sea Level Pressure (MSL) and Vorticity (VO) tracking.

v0.0.1 - 2016-01-11

  • Initial release.

  • Core cyclone tracking logic based on local extrema detection.