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Changelog

[0.2.0] - 2026-03-26

Added

  • New CarpStudy public API as the primary entrypoint for CARP study analysis
  • Modular service layout under carp.core, participants, records, schema, export, frames, types, plotting, and commandline
  • Self-contained pytest suite with committed multi-phase fixtures and optional sleep-data smoke coverage
  • 100% line and branch coverage enforcement for src/carp
  • Sphinx documentation site with autodoc and Napoleon support
  • GitHub Actions CI for linting, type-checking, tests, and docs builds
  • Tag-driven CD workflow that validates version tags, publishes to PyPI, and creates GitHub releases
  • Dedicated test and docs dependency groups

Changed

  • Replaced the legacy method-heavy design with a thin CarpStudy composition root and focused services
  • Kept the carp CLI command set stable while rewriting the implementation behind modular handlers
  • Switched plotting defaults to dk.cachet.carp.location
  • Made parquet filenames namespace-aware to avoid same-name type collisions
  • Added Google-style docstrings and expanded type annotations across the package
  • Refreshed the README, example scripts, generated type example, and notebook to use the new API
  • Normalized Ruff, MyPy, coverage, and documentation build configuration in pyproject.toml

Removed

  • Legacy carp.reader monolith
  • Legacy carp.plotting.map_viz module
  • Old CarpDataStream-centric example usage and stale plotting/type-generation references

0.1.0

Added

  • Initial release of CARP Analytics Python library
  • CarpDataStream class for loading and processing CARP data streams
  • Streaming JSON parsing with ijson for memory-efficient processing
  • Schema discovery and inference from data
  • Parquet export for faster subsequent analysis
  • ParticipantManager for tracking participants across study phases
  • ParticipantAccessor fluent API for accessing individual participant data
  • ParticipantInfo dataclass for participant metadata
  • Data grouping by field, participant, email, SSN, or name
  • DataFrame integration with pandas
  • Location visualization with Folium heatmaps
  • Rich terminal output with progress bars and formatted tables
  • CLI entry point (carp command) for command-line usage
  • Support for Python 3.10, 3.11, 3.12, and 3.13
  • Optional dependencies for pandas, visualization, and scientific computing
  • Type hints with PEP 561 py.typed marker

Dependencies

  • Core: ijson, rich, tqdm
  • Optional pandas: pandas, pyarrow
  • Optional viz: matplotlib, folium
  • Optional science: numpy, scipy, scikit-learn