Expand testing

Currently the test suite is very small, most notably the juliacall suite: https://github.com/JuliaPy/PythonCall.jl/blob/main/pytest/test_all.py which is only 19 lines long. I am very worried about reliability issues from this and whether this might result in regressions.

I think expanding the test suite should be a priority, especially on the juliacall side, before new features are worked on.

Here are some suggestions. Feel free to check any that are already done or add more

- [ ] Unittests for features shown in the docs
- [ ] ALL JuliaCall guide examples (https://juliapy.github.io/PythonCall.jl/stable/juliacall/)
- [ ] ALL PythonCall guide examples (https://juliapy.github.io/PythonCall.jl/stable/pythoncall/)
- [ ] ALL conversion rules Python -> Julia (https://juliapy.github.io/PythonCall.jl/stable/conversion-to-julia/)
- [ ] ALL conversion rules Julia -> Python (https://juliapy.github.io/PythonCall.jl/stable/conversion-to-python/)
- [ ] Miscellaneous unittests
- [ ] GC tests (currently there are zero)
- [ ] Dummy package with pyjuliapkg and `newmodule` used
- [ ] Multithreading unittests (with PythonCall.GC.disable())
- Need to launch additional test suite in CI, with signals turned on
- [ ] Distributed compute unittests
- [ ] Julia's stdlib tests from Python via juliacall (copy the test suite; disable everything, and gradually turn on tests)
- [ ] Python's stdlib tests from Julia via PythonCall.jl (copy the test suite; disable everything, and gradually turn on tests)
- [ ] Any areas missing due to coverage milestones:
- [ ] Code coverage milestones
- [ ] Find way to track Python code generated from Julia code
- [ ] Reach 60% code coverage on Python source code
- [ ] Reach 80% code coverage on Python source code
- [ ] Reach 90% code coverage on Python source code
- [ ] Reach 100% code coverage on Python source code
- [ ] Set up code coverage for Julia source code (currently not tracked)
- [ ] Reach 60% code coverage on Julia source code
- [ ] Reach 80% code coverage on Julia source code
- [ ] Reach 90% code coverage on Julia source code
- [ ] Reach 100% code coverage on Julia source code
- [ ] Add integration tests
- [ ] Numpy
- [ ] PythonPlot.jl
- [ ] Pandas (https://juliapy.github.io/PythonCall.jl/stable/compat/#Tabular-data-/-Pandas)
- [ ] SymPy.jl (whenever PythonCall.jl version is made)
- [ ] Matplotlib interactive support (harder to test, but you would want to test the features explained here – https://juliapy.github.io/PythonCall.jl/stable/compat/#MatPlotLib-/-PyPlot-/-Seaborn)
- [ ] IPython support
- [ ] PySR
- [ ] diffeqpy
- [ ] PyTorch import issue (basically just verify the solution on https://juliapy.github.io/PythonCall.jl/stable/faq/)

Start with pytest/test_all.py and the linked JuliaCall and PythonCall guides, conversion rules, and compatibility pages to identify one specific testing gap. Review the existing test suite before selecting a narrowly scoped checklist item, such as documentation examples, garbage collection, or an integration case. Done should be defined by the selected item’s tests passing and any stated coverage or CI requirement being met.

索引モデルが issue の本文から書いたものです。