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Jacob-Stevens-Haas
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A few quick suggestions where my comments overlapped with the other reviewer.
| "surd @ git+https://github.com/djpasseyjr/surd.git", | ||
| "pyclustering @ git+https://github.com/djpasseyjr/pyclustering", |
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I don't think this level of sophistication is a requirement for JOSS - just suggestion from someone else who's followed the pattern you suggested and run into difficulties keeping dependencies in sync when packages co-evolved.
| "surd @ git+https://github.com/djpasseyjr/surd.git", | |
| "pyclustering @ git+https://github.com/djpasseyjr/pyclustering", | |
| "surd @ git+https://github.com/djpasseyjr/surd.git@0b4fb1c", | |
| "pyclustering @ git+https://github.com/djpasseyjr/pyclustering.git@6bb2311", |
What you're essentially doing is vendoring forks of the original surd/pyclustering. This can be tricky to do correctly. If you're expecting to potentially reuse your forks in other projects (and potentially change them/keep them up to date with their upstream), it's helpful to pin dependencies here to a specific commit. If your development plan for interfere and the dependency forks has all evolving in tandem, it's better to vendor them as git submodules (and configured in pyproject.toml so pip install interfere also installs them), so that changes to interefere are always tested against the correct vendored version. There's other ways of handling vendoring, e.g. the vendoring package, but I haven't used them.
| fail-fast: false | ||
| matrix: | ||
| python-version: ["3.9", "3.10", "3.11"] | ||
| python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"] |
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Could you also specify a minimum python version in pyproject.toml?
README.md
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| - `pysindy` | ||
| - `scikit-learn` | ||
| - `statsmodels` | ||
| - `typing_extensions` |
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NIT: listing the dependencies on the readme is not required and I think clutters the page. If anything, just list the extra dependency groups available (dev, methods) and what they do - not their contents.
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Adding linter and formatting support. |
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#10 The commits above add a benchmark documentation page to address the issue. |
Clarified the description of hyperparameter optimization in forecasting methods and added context regarding observational data and interventions.
Clarified figure caption regarding the simulation of the quadratic Belozyorov system and the representation of stochastic noise.
See #3