v0.2.2

Bug-fix release, mostly fixing the data loader for Daymet point data

Added

• Automated publishing to PyPI upon release. PyPI is now a trusted publisher. Now you don't
  need to be a maintainer on PyPI anymore to publish a release.

Fixed

• Fixed issue with loading data got from the Daymet point dowloader.
• Fixed issue with GitHub action to push Docker image to GHCR.

Full Changelog: v0.2.1...v0.2.2

v0.2.1

The Docker image is updated, minor fixes are included in documentation and docstrings.

Added

• GitHub Action to build and push Docker image

Changed

• Small fixes in documentation and docstrings
• Dockerfile includes extra packages

Full Changelog: v0.2.0...v0.2.1

v0.2.0

Substantial update of the package with more standardization.

Added

• User guide
• Download data through Appeears
• Dockerfile

Changed

• More standardization of data classes
• R scripts run through command line call, no longer direct dependencies
• Simplified license
• Updated structure of documentation

Removed

• Direct R calls through Rpy2
• Executing models no longer part of workflow / package

Full Changelog: v0.1.0...v0.2.0

Initial release

The Springtime Python package helps to streamline workflows for doing machine learning with phenological datasets.

Phenology is the scientific discipline in which we study the lifecycle of plants and animals. A common objective is to develop (Machine Learning) models that can explain or predict the occurrence of phenological events, such as the blooming of plants. Since there is a variety of data sources and existing tools to retrieve and analyse phenology data, it is easy to get lost and disorganized.

With Springtime, we aim to provide a more streamlined workflow for working with a variety of datasets and (ML) models. You can run Springtime as a command line tool in a terminal or use it as a Python library e.g. in a Jupyter notebook.