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Move to hatch for building the package, and test/style/doc build scripts. Update READMEs (#11)
* Moved build action to hatch, updated dev readme
* Set up hatch environments for tests, styling and docs.
* Updated the documentation build action to use checkout & python setup v3. Setup ReadTheDocs.
* Updated bumpversion config
* Updated README.md to include a reference to s2spy, along with some stray s2spy uses
* Updated readme and dev readme to include up to date hatch instructions
* Removed prospector. Linting is now handled by Flake8
* Set up Sonarcloud
Co-authored-by: Peter Kalverla <[email protected]>
A python package for generating calendars to resample timeseries into training and target data for machine learning. Named after [the inventor](https://en.wikipedia.org/wiki/Aloysius_Lilius) of the [Gregorian Calendar](https://en.wikipedia.org/wiki/Gregorian_calendar).
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A python package for generating calendars to resample timeseries into training and target data for machine learning.
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Lilio was originally designed for use in [`s2spy`](https://github.com/AI4S2S/s2spy), a high-level python package integrating expert knowledge and artificial intelligence to boost (sub) seasonal forecasting.
### Configure the package for development and testing
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A more extensive developer guide can be found [here](./docs/README.dev.md).
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The testing framework used here is [pytest](https://pytest.org). Before running the test, we get a local copy of the source code and install `lilio` via the command:
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```py
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git clone https://github.com/AI4S2S/lilio.git
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cd lilio
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python3 -m pip install -e .
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python3 -m pip install -e .[dev]
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```
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Then, run tests:
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```py
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python3 -m pytest
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hatch run test
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```
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## How the lilio calendars work
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In a typical ML-based timeseries analysis, the first step is always data processing. A calendar-based datetime module `time` is implemented for time operations. For instance, a user is looking for predictors for winter climate at seasonal timescales (~180 days). First, a `calendar` object is created using `AdventCalendar`:
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## Tutorials
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`lilio` supports operations that are common in a machine learning pipeline of sub-seasonal to seasonal forecasting research. Tutorials covering supported methods and functionalities are listed in [notebooks](https://github.com/AI4S2S/lilio/tree/main/notebooks). To check these notebooks, users need to install [`Jupyter lab`](https://jupyter.org/). More details about each method can be found in this [API reference documentation](https://ai4s2s.readthedocs.io/en/latest/autoapi/index.html).
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