With scikit-build-core, the pyproject.toml is at the repository root,
not in wrappers/Python/. Updated cibuildwheel configuration:
- Changed package-dir from ./wrappers/Python/ to .
- Removed redundant CIBW_BEFORE_BUILD (dependencies are in pyproject.toml)
- Build dependencies are now automatically installed by pip from pyproject.toml
This fixes the "Multiple top-level packages discovered" error.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Changes:
- Replace --replace-setup-py with --replace-version flag
- Update sdist build to use 'python -m build --sdist' instead of deprecated prepare_pypi.py
- Workflows now work with scikit-build-core build system
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
1. Fetch full git history during checkout for accurate revision info
2. Set up git config earlier in the process
3. Call generate_headers.py explicitly to ensure gitrevision.h is generated
4. Remove duplicate git config
* Expansions are fully wrapped, looking good. Next step is the set of expansions that is the 1D approximation
* Get 1D approx working via cython
* Count solutions
* SuperAncillary class is working
>1000x speedup for water
Time for C++!
* Superancillaries are working!
In C++, speedup is more than 2000x. In Python, more like 150x because of Python <-> C++ overhead
* Add pmax check for PQ superancillary calls
* Update tests
* Allow T limits to be obtained
* Implement get_fluid_parameter_double for getting superanc value
* Add tests for getting parameters from superanc
* Script for testing superancillaries for sphinx
* Microoptimizations; don't help speed
The limiting factor remains the clear function, which takes about 30 ns
* Add R125 superancillary
* Use the release from fastchebpure for the files
* Drop a .gitignore in the unzipped folder
* Update superancillary injection script
* Turn on superancillaries by default
* Missing header
* Many int conversions in superancillary
* Another int cast
* More annoying solution for boost iter max
* Fix warnings
* One more warning
* Clear up the calculation of rho
* Update docs_docker-build.yml
Use arm64 since the containers were built on mac
* Superfluous ;
* Update backend.py
* Get the critical points working for superancillaries
* Fix wrapping changes of xmin&xmax methods
* squelch warnings
* Version 0 of jupyter notebook for docs
* Try to add the notebook to the docs
* Add jupyter notebook for superancillary
* Lots of updates to superancillary notebook
* More updates to docs
* Skip pseudo-pure for superancillary docs
* Fix output of superancillary figures
* Add superancillary plots to docs for the page for each fluid
* Make a placeholder figure for fluids without superancillary
* Add superancillary plots to task list
* Bump to release fixing m-xylene
* Relax the location of the REFPROP stuff
* Change default name for R-1336mzz(E)
* No need for figures to be so large
* Don't need REFPROP setting
* Bump to fastchebpure release with methanol
* Benchmark caching options
* Benchmark more granularly
* Add the fast methods to public API for HEOS class
* Back to memset - can memset with 0 but no other value
* Fix how caching is managed in Helmholtz class
* Close to final implementation
Perhaps a tiny bit more optimization possible?
* Update function name
* Make message more accurate
* Fix init order
* Expose update_QT_pure_superanc to Python
* Fix when _reducing is set for pures
* Fix the post_update
* Indent
* Notebook
* Notebook
* Make ln(p) construction lazy
Only really matters for debug builds
* Also make reference non-const
* Inject superancillary for methanol
* Make the superancillary loading entirely lazy in debug
* Fix PH bug for Nitrogen
Closes#2470
* Force the clear to be called on SatL and SatV
To invalidate them at start
* Default is non-lazy superancillary loading
* Add CMake option to have lazy-loading superancillaries [skip ci]
Not a good idea unless doing very narrow testing
