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Fix test for _fit_sandia_cocontent #2615

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Merged
kandersolar merged 4 commits into from
Dec 9, 2025
Merged

Fix test for _fit_sandia_cocontent #2615

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fdbaec3
correct argument and value order
cwhanse Dec 8, 2025
40d141c
whatsnew
cwhanse Dec 8, 2025
06d8abb
move note to Testing section
cwhanse Dec 8, 2025
6642d62
Update docs/sphinx/source/whatsnew/v0.13.2.rst
cwhanse Dec 8, 2025
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3 changes: 2 additions & 1 deletion docs/sphinx/source/whatsnew/v0.13.2.rst
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Expand Up @@ -18,7 +18,6 @@ Deprecations
Bug fixes
~~~~~~~~~


Enhancements
~~~~~~~~~~~~
* Add :py:func:`~pvlib.ivtools.sdm.fit_desoto_batzelis`, a function to estimate
Expand Down Expand Up @@ -56,6 +55,8 @@ Testing
* Add Python 3.14 to test suite. (:pull:`2590`)
* Update pytest configuration in ``pyproject.toml`` to work with pytest 9.0.
(:pull:`2596`)
* Corrected argument and value order in :py:func:`~pvlib.tests.ivtools.test_sde`,
in tests of :py:func:`~pvlib.ivtools.sde._fit_sandia_cocontent`. (:issue:`2613`, :pull:`2615`)


Benchmarking
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6 changes: 3 additions & 3 deletions tests/ivtools/test_sde.py
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Expand Up @@ -57,18 +57,18 @@ def test_fit_sandia_simple_bad_iv(get_bad_iv_curves):
(np.array([3., 2.9, 2.8, 2.7, 2.6, 2.5, 2.4, 1.7, 0.8, 0.]),
np.array([0., 0.2, 0.4, 0.6, 0.8, 1., 1.2, 1.4, 1.45, 1.5]),
10.,
(2.3392, 11.6865, -.232, -.2596, -.7119)),
(2.3392, 11.6865, -.2596, -.232, -.7119)),
(np.array(
[5., 4.9, 4.8, 4.7, 4.6, 4.5, 4.4, 4.3, 4.2, 4.1, 4., 3.8, 3.5, 1.7,
0.]),
np.array(
[0., .1, .2, .3, .4, .5, .6, .7, .8, .9, 1., 1.1, 1.18, 1.2, 1.22]),
15.,
(-22.0795, 27.1196, -4.2076, -.0056, -.0498))])
(-22.0795, 27.1196, -.0056, -4.2076, -.0498))])
def test__fit_sandia_cocontent(i, v, nsvth, expected):
# test confirms agreement with Matlab code. The returned parameters
# are nonsense
iph, io, rsh, rs, n = sde._fit_sandia_cocontent(v, i, nsvth)
iph, io, rs, rsh, n = sde._fit_sandia_cocontent(v, i, nsvth)
np.testing.assert_allclose(iph, np.array(expected[0]), atol=.0001)
np.testing.assert_allclose(io, np.array([expected[1]]), atol=.0001)
np.testing.assert_allclose(rs, np.array([expected[2]]), atol=.0001)
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