Coverage at 51%

[dbrakenhoff]

besselnumba.py and invlapnumba.py seem to be the cause of the low coverage at the moment:

---------- coverage: platform linux, python 3.11.10-final-0 ----------
Name                         Stmts   Miss  Cover
------------------------------------------------
ttim/__init__.py                10      0   100%
ttim/aquifer.py                146     26    82%
ttim/aquifer_parameters.py      99      6    94%
ttim/besselnumba.py           1035    898    13%
ttim/circareasink.py            98      7    93%
ttim/element.py                153     64    58%
ttim/equation.py               189     71    62%
ttim/fit.py                    139     16    88%
ttim/invlapnumba.py            143    132     8%
ttim/linedoublet.py            153      9    94%
ttim/linesink.py               322    127    61%
ttim/model.py                  339    103    70%
ttim/trace.py                  180     83    54%
ttim/util.py                    71      8    89%
ttim/version.py                  1      0   100%
ttim/well.py                   150     21    86%
------------------------------------------------
TOTAL                         3228   1571    51%

There is a test_bessel.py file in ttim/src/ but it compares fortran compiled bessel functions to their numba counterparts. Adapting this file to test the numba funcs against stored results would probably significantly improve coverage, and actually test the besselnumba code.

[mbakker7]

But all notebooks (all TTim models) call both the besselnumba and invlapnumba routines.
Wouldn't that be part of testing?

[vcantarella]

If these contain numba functions it could be that coverage is not recognizing them because they are compiled outside of python scope. I think a possible solution would be to use add the NUMBA_DISABLE_JIT=1 environment variable, but it might slow things down a bit.

[Huite]

If these contain numba functions it could be that coverage is not recognizing them because they are compiled outside of python scope. I think a possible solution would be to use add the NUMBA_DISABLE_JIT=1 environment variable, but it might slow things down a bit.

Indeed.

For a variety of packages I test twice, once for coverage with NUMBA_DISABLE_JIT=1, then another time to see whether everything works correctly with Numba enabled.

E.g. this project is 90% numba: https://github.com/Deltares/numba_celltree/blob/6092c456587c8f4ade1d3b19e87bc7278b3d379d/pyproject.toml#L85
But has 99% coverage.

Presumably the test cases are not too large, such that the slowdown from dynamic Python isn't too bad.

And otherwise, it could be worthwhile to split into (small) unit tests for coverage and bigger slow integration tests without coverage.