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clib.conversion._to_numpy: Shorten test names #3657

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Nov 28, 2024
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clib.conversion._to_numpy: Shorten test names #3657

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34 changes: 17 additions & 17 deletions pygmt/tests/test_clib_to_numpy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -115,9 +115,9 @@ def test_to_numpy_python_types(data, expected_dtype):


@pytest.mark.parametrize(("dtype", "expected_dtype"), np_dtype_params)
def test_to_numpy_ndarray_numpy_dtypes_numeric(dtype, expected_dtype):
def test_to_numpy_numpy_numeric(dtype, expected_dtype):
"""
Test the _to_numpy function with NumPy arrays of NumPy numeric dtypes.
Test the _to_numpy function with NumPy arrays of numeric dtypes.

Test both 1-D and 2-D arrays which are not C-contiguous.
"""
Expand All @@ -137,9 +137,9 @@ def test_to_numpy_ndarray_numpy_dtypes_numeric(dtype, expected_dtype):


@pytest.mark.parametrize("dtype", [None, np.str_, "U10"])
def test_to_numpy_ndarray_numpy_dtypes_string(dtype):
def test_to_numpy_numpy_string(dtype):
"""
Test the _to_numpy function with NumPy arrays of string types.
Test the _to_numpy function with NumPy arrays of string dtypes.
"""
array = np.array(["abc", "defg", "12345"], dtype=dtype)
result = _to_numpy(array)
Expand Down Expand Up @@ -177,9 +177,9 @@ def test_to_numpy_ndarray_numpy_dtypes_string(dtype):
# 3. https://pandas.pydata.org/docs/user_guide/pyarrow.html
########################################################################################
@pytest.mark.parametrize(("dtype", "expected_dtype"), np_dtype_params)
def test_to_numpy_pandas_series_numpy_dtypes_numeric(dtype, expected_dtype):
def test_to_numpy_pandas_numeric(dtype, expected_dtype):
"""
Test the _to_numpy function with pandas.Series of NumPy numeric dtypes.
Test the _to_numpy function with pandas.Series of numeric dtypes.
"""
series = pd.Series([1, 2, 3, 4, 5, 6], dtype=dtype)[::2] # Not C-contiguous
result = _to_numpy(series)
Expand Down Expand Up @@ -207,9 +207,9 @@ def test_to_numpy_pandas_series_numpy_dtypes_numeric(dtype, expected_dtype):
),
],
)
def test_to_numpy_pandas_series_pandas_dtypes_string(dtype):
def test_to_numpy_pandas_string(dtype):
"""
Test the _to_numpy function with pandas.Series of pandas string types.
Test the _to_numpy function with pandas.Series of string dtypes.

In pandas, string arrays can be specified in multiple ways.

Expand All @@ -229,7 +229,7 @@ def test_to_numpy_pandas_series_pandas_dtypes_string(dtype):
pytest.param("date64[ms][pyarrow]", "datetime64[ms]", id="date64[ms]"),
],
)
def test_to_numpy_pandas_series_pyarrow_dtypes_date(dtype, expected_dtype):
def test_to_numpy_pandas_date(dtype, expected_dtype):
"""
Test the _to_numpy function with pandas.Series of PyArrow date32/date64 types.
"""
Expand Down Expand Up @@ -282,9 +282,9 @@ def test_to_numpy_pandas_series_pyarrow_dtypes_date(dtype, expected_dtype):
pytest.param("float64", np.float64, id="float64"),
],
)
def test_to_numpy_pyarrow_array_pyarrow_dtypes_numeric(dtype, expected_dtype):
def test_to_numpy_pyarrow_numeric(dtype, expected_dtype):
"""
Test the _to_numpy function with PyArrow arrays of PyArrow numeric types.
Test the _to_numpy function with PyArrow arrays of numeric types.
"""
data = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
if dtype == "float16": # float16 needs special handling
Expand Down Expand Up @@ -313,9 +313,9 @@ def test_to_numpy_pyarrow_array_pyarrow_dtypes_numeric(dtype, expected_dtype):
pytest.param("float64", np.float64, id="float64"),
],
)
def test_to_numpy_pyarrow_array_pyarrow_dtypes_numeric_with_na(dtype, expected_dtype):
def test_to_numpy_pyarrow_numeric_with_na(dtype, expected_dtype):
"""
Test the _to_numpy function with PyArrow arrays of PyArrow numeric types and NA.
Test the _to_numpy function with PyArrow arrays of numeric types and NA.
"""
data = [1.0, 2.0, None, 4.0, 5.0, 6.0]
if dtype == "float16": # float16 needs special handling
Expand All @@ -339,9 +339,9 @@ def test_to_numpy_pyarrow_array_pyarrow_dtypes_numeric_with_na(dtype, expected_d
"string_view",
],
)
def test_to_numpy_pyarrow_array_pyarrow_dtypes_string(dtype):
def test_to_numpy_pyarrow_string(dtype):
"""
Test the _to_numpy function with PyArrow arrays of PyArrow string types.
Test the _to_numpy function with PyArrow arrays of string types.
"""
array = pa.array(["abc", "defg", "12345"], type=dtype)
result = _to_numpy(array)
Expand All @@ -357,9 +357,9 @@ def test_to_numpy_pyarrow_array_pyarrow_dtypes_string(dtype):
pytest.param("date64[ms]", "datetime64[ms]", id="date64[ms]"),
],
)
def test_to_numpy_pyarrow_array_pyarrow_dtypes_date(dtype, expected_dtype):
def test_to_numpy_pyarrow_date(dtype, expected_dtype):
"""
Test the _to_numpy function with PyArrow arrays of PyArrow date types.
Test the _to_numpy function with PyArrow arrays of date32/date64 types.

date32[day] and date64[ms] are stored as 32-bit and 64-bit integers, respectively,
representing the number of days and milliseconds since the UNIX epoch (1970-01-01).
Expand Down