clib.converison._to_numpy: Add tests for numpy arrays of numpy numeric dtypes#3583
clib.converison._to_numpy: Add tests for numpy arrays of numpy numeric dtypes#3583
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pygmt/tests/test_clib_to_numpy.py
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| (np.clongdouble, False), | ||
| ], | ||
| ) | ||
| def test_to_numpy_ndarray_numpy_dtypes_numeric(dtype, supported): |
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The test name test_to_numpy_ndarray_numpy_dtypes_numeric is in the format of
test_to_numpy_<data-structure-to-test>_<numpy/pandas/pyarrow>_dtypes_<numeric/str/bool/datetime>
pygmt/tests/test_clib_to_numpy.py
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| (np.clongdouble, False), | ||
| ], | ||
| ) | ||
| def test_to_numpy_pandas_series_numpy_dtypes_numeric(dtype, supported): |
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This test is almost the same as test_to_numpy_ndarray_numpy_dtypes_numeric and can be combined into one single test, but I feel two separate tests are more readable and maintainable.
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pygmt/tests/test_clib_to_numpy.py
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| @pytest.mark.parametrize( | ||
| ("dtype", "supported"), | ||
| [ | ||
| (np.int8, True), |
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Instead of checking if the result ndarray dtype is supported or not, I'm wondering if we should explicitly check the result ndarray dtype. The main reasons is to ensure that dtypes are converted to the expected numpy dtypes (e.g., pd.Int8Dtype is converted to np.int8, not np.int64).
pygmt/tests/test_clib_to_numpy.py
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| def test_to_numpy_ndarray_numpy_dtypes_numeric(dtype, expected_dtype): | ||
| """ | ||
| Test the _to_numpy function with NumPy arrays of NumPy numeric dtypes. | ||
|
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| Test both 1-D and 2-D arrays. | ||
| """ | ||
| # 1-D array | ||
| array = np.array([1, 2, 3], dtype=dtype) |
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This test feels a bit redundant, given that we are converting numpy C-order to numpy C-order which will obviously work. Should we test with order="F" instead?
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This is a good point. Instead of testing order="F" which is usually less used, I've used array slice which is not C-contiguous as input (see 933bc62).
Co-authored-by: Wei Ji <23487320+weiji14@users.noreply.github.com>
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| ("data", "expected_dtype"), | ||
| [ | ||
| pytest.param([1, 2, 3], np.int64, id="int"), | ||
| pytest.param([1.0, 2.0, 3.0], np.float64, id="float"), |
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Not sure if it's necessary, but Python does have built-in complex number types (https://docs.python.org/3/library/stdtypes.html#numeric-types-int-float-complex)
| pytest.param([1.0, 2.0, 3.0], np.float64, id="float"), | |
| pytest.param([1.0, 2.0, 3.0], np.float64, id="float"), | |
| pytest.param( | |
| [complex(+1), complex(-2j), complex("-Infinity+NaNj")], | |
| np.complex128, | |
| id="complex", | |
| ), |
The Python standard library also includes fractions.Fraction and decimal.Decimal, but I don't know if anyone really uses those.
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The Python standard library also includes fractions.Fraction and decimal.Decimal, but I don't know if anyone really uses those.
They can't be converted to a numpy array, so PyGMT can't support them anyway.
Co-authored-by: Wei Ji <23487320+weiji14@users.noreply.github.com>
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…c dtypes (#3583) Co-authored-by: Wei Ji <23487320+weiji14@users.noreply.github.com>
…c dtypes (#3583) Co-authored-by: Wei Ji <23487320+weiji14@users.noreply.github.com>
…c dtypes (#3583) Co-authored-by: Wei Ji <23487320+weiji14@users.noreply.github.com>
…c dtypes (#3583) Co-authored-by: Wei Ji <23487320+weiji14@users.noreply.github.com>
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This PR adds tests for
_to_numpyto ensure it works for the 17 NumPy numeric dtypes, in which 12 are supported by PyGMT.Related to #3581, #2848, #3513.