Prior art and credit¶

This page names the projects specsolve is derived from and says how to cite them, for anyone comparing specsolve with either project or citing it.

Calliope (Apache-2.0) is where this surface comes from. The surface is the YAML you write, and it is their design: solver-ready math declared as a reviewable file, a block per component, dims: for the list of dimensions, a where: string over AND/OR/NOT, bounds:, active:. So is parsing the strings with pyparsing rather than eval. Our expressions is their global_expressions, and our piecewise is their piecewise_constraints. What is ours is the semantics underneath: one expression per block, macros that take arguments, a schema closed at every level, and the absence and degree-1 laws (the ten rules). Their math is the corpus we score coverage against, not a specification we match (the limits). File portability is not a goal. A Calliope model does not load here, and operation parity with xarray or pandas is not a goal either.

linopy (MIT) is the vocabulary, the oracle and the denominator. Where a concept is already theirs, we copy the spelling. Every language feature is differentially tested against a linopy build, and every ratio on the benchmarks page is specsolve ÷ linopy. The three relationships are one page. The ported models in the gallery and their reference optima are PyPSA's.

No code from any of them is vendored, so none of this is a licence obligation. It is stated because a debt only the author knows about is one the next reader has to rediscover. Neither project has reviewed this one, and mistakes in the comparisons above are ours.

If you cite this project, cite the two it is built on:

@article{Pfenninger2018,
  doi = {10.21105/joss.00825}, year = {2018}, publisher = {The Open Journal},
  volume = {3}, number = {29}, pages = {825},
  author = {Stefan Pfenninger and Bryn Pickering},
  title = {Calliope: a multi-scale energy systems modelling framework},
  journal = {Journal of Open Source Software}
}

@article{Hofmann2023,
  doi = {10.21105/joss.04823}, year = {2023}, publisher = {The Open Journal},
  volume = {8}, number = {84}, pages = {4823},
  author = {Fabian Hofmann},
  title = {Linopy: Linear optimization with n-dimensional labeled variables},
  journal = {Journal of Open Source Software}
}