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- Replaced `GeoPandas` functionality with `pyproj` and `Shapely` for coordinate reference system conversion and distance measurements. - Moved and renamed tests and updated the documentation accordingly.
- Switch to [semantic versioning](https://semver.org) from this release forward. - Efficiency improvements in AEP calculation - Energy Yield Analysis (EYA) added to Operational Assessment (OA) Gap Analysis method - Uncertainty quantification for electrical losses and longterm turbine gross energy - Implemented open source Engie example data - Complete update of example notebooks - Switch to standard BSD-3 Clause license - Automated quality control method to assist with data ingestion. Tools in this method include daylight savings time change detection and identification of the diurnal cycle. - Add electrical losses method - Method for estimating long-term turbine gross energy (excluding downtime and underperformance losses) - CI pipeline using Github Actions includes regression testing with Pytest, code coverage reporting via CodeCov, packaging and distribution via Pypi, and automatic documentation using ReadTheDocs.
- Python3 Support - Addition of reanalysis schemas to the Sphinx documentation - Easy import of EIA data using new module: Metadata_Fetch - Updated contributing.md document - Quality checks for reanalysis data - Improved installation instructions - Integration tests are now performed in CI - Performed PEP8 linting
- Refactor many analysis and toolkit modules to make them conform to a standard API (init, prepare, and run method). - Timeseries Table is now an integrated component, no sparkplug-datastructures dependency - Plant Level AEP method w/ Monte Carlo - Turbine / Scada level toolkits: Filtering, Imputing, Met, Pandas Plotting, Timeseries, Unit Conversion - Most toolkits and all methods are fully documented in Sphinx. - Two example notebooks: Operational AEP Analysis and Turbine Analysis - All toolkits except for Pandas Plotting have > 80% test coverage.