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Use pytest.mark.parametrize
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@seisman
seisman committed Nov 2, 2024
1 parent e75e894 commit 319880c
Showing 1 changed file with 93 additions and 129 deletions.
222 changes: 93 additions & 129 deletions pygmt/tests/test_clib_to_ndarray.py
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Expand Up @@ -7,6 +7,7 @@
import pandas as pd
import pytest
from pygmt.clib.conversion import _to_ndarray
from pygmt.helpers.testing import skip_if_no

try:
import pyarrow as pa
Expand All @@ -15,99 +16,25 @@
except ImportError:
_HAS_PYARROW = False


@pytest.fixture(scope="module", name="dtypes_numpy_numeric")
def fixture_dtypes_numpy_numeric():
"""
List of NumPy numeric dtypes.
Reference: https://numpy.org/doc/stable/reference/arrays.scalars.html
"""
return [
np.int8,
np.int16,
np.int32,
np.int64,
np.longlong,
np.uint8,
np.uint16,
np.uint32,
np.uint64,
np.ulonglong,
np.float16,
np.float32,
np.float64,
np.longdouble,
np.complex64,
np.complex128,
np.clongdouble,
]


@pytest.fixture(scope="module", name="dtypes_pandas_numeric")
def fixture_dtypes_pandas_numeric():
"""
List of pandas numeric dtypes.
Reference: https://pandas.pydata.org/docs/reference/arrays.html
"""
return [
pd.Int8Dtype(),
pd.Int16Dtype(),
pd.Int32Dtype(),
pd.Int64Dtype(),
pd.UInt8Dtype(),
pd.UInt16Dtype(),
pd.UInt32Dtype(),
pd.UInt64Dtype(),
pd.Float32Dtype(),
pd.Float64Dtype(),
]


@pytest.fixture(scope="module", name="dtypes_pandas_numeric_pyarrow_backend")
def fixture_dtypes_pandas_numeric_pyarrow_backend():
"""
List of pandas dtypes that use pyarrow as the backend.
Reference: https://pandas.pydata.org/docs/user_guide/pyarrow.html
"""
return [
"int8[pyarrow]",
"int16[pyarrow]",
"int32[pyarrow]",
"int64[pyarrow]",
"uint8[pyarrow]",
"uint16[pyarrow]",
"uint32[pyarrow]",
"uint64[pyarrow]",
"float32[pyarrow]",
"float64[pyarrow]",
]


@pytest.fixture(scope="module", name="dtypes_pyarrow_numeric")
def fixture_dtypes_pyarrow_numeric():
"""
List of pyarrow numeric dtypes.
Reference: https://arrow.apache.org/docs/python/api/datatypes.html
"""
if not _HAS_PYARROW:
return []
return [
pa.int8(),
pa.int16(),
pa.int32(),
pa.int64(),
pa.uint8(),
pa.uint16(),
pa.uint32(),
pa.uint64(),
# pa.float16(), # Need special handling.
pa.float32(),
pa.float64(),
]
dtypes_numpy = [
np.int8,
np.int16,
np.int32,
np.int64,
np.longlong,
np.uint8,
np.uint16,
np.uint32,
np.uint64,
np.ulonglong,
np.float16,
np.float32,
np.float64,
np.longdouble,
np.complex64,
np.complex128,
np.clongdouble,
]


def _check_result(result):
Expand All @@ -125,60 +52,97 @@ def _check_result(result):
assert result.dtype != np.object_


def test_to_ndarray_numpy_ndarray_numpy_numeric(dtypes_numpy_numeric):
@pytest.mark.parametrize("dtype", dtypes_numpy)
def test_to_ndarray_numpy_ndarray_numpy_numeric(dtype):
"""
Test the _to_ndarray function with 1-D NumPy arrays.
"""
# 1-D array
for dtype in dtypes_numpy_numeric:
array = np.array([1, 2, 3], dtype=dtype)
assert array.dtype == dtype
result = _to_ndarray(array)
_check_result(result)
npt.assert_array_equal(result, array)
array = np.array([1, 2, 3], dtype=dtype)
assert array.dtype == dtype
result = _to_ndarray(array)
_check_result(result)
npt.assert_array_equal(result, array)

# 2-D array
for dtype in dtypes_numpy_numeric:
array = np.array([[1, 2, 3], [4, 5, 6]], dtype=dtype)
assert array.dtype == dtype
result = _to_ndarray(array)
_check_result(result)
npt.assert_array_equal(result, array)
array = np.array([[1, 2, 3], [4, 5, 6]], dtype=dtype)
assert array.dtype == dtype
result = _to_ndarray(array)
_check_result(result)
npt.assert_array_equal(result, array)


def test_to_ndarray_pandas_series_numeric(
dtypes_numpy_numeric, dtypes_pandas_numeric, dtypes_pandas_numeric_pyarrow_backend
):
@pytest.mark.parametrize(
"dtype",
[
*dtypes_numpy,
pytest.param(pd.Int8Dtype(), id="Int8"),
pytest.param(pd.Int16Dtype(), id="Int16"),
pytest.param(pd.Int32Dtype(), id="Int32"),
pytest.param(pd.Int64Dtype(), id="Int64"),
pytest.param(pd.UInt8Dtype(), id="UInt8"),
pytest.param(pd.UInt16Dtype(), id="UInt16"),
pytest.param(pd.UInt32Dtype(), id="UInt32"),
pytest.param(pd.UInt64Dtype(), id="UInt64"),
pytest.param(pd.Float32Dtype(), id="Float32"),
pytest.param(pd.Float64Dtype(), id="Float64"),
pytest.param("int8[pyarrow]", marks=skip_if_no(package="pyarrow")),
pytest.param("int16[pyarrow]", marks=skip_if_no(package="pyarrow")),
pytest.param("int32[pyarrow]", marks=skip_if_no(package="pyarrow")),
pytest.param("int64[pyarrow]", marks=skip_if_no(package="pyarrow")),
pytest.param("uint8[pyarrow]", marks=skip_if_no(package="pyarrow")),
pytest.param("uint16[pyarrow]", marks=skip_if_no(package="pyarrow")),
pytest.param("uint32[pyarrow]", marks=skip_if_no(package="pyarrow")),
pytest.param("uint64[pyarrow]", marks=skip_if_no(package="pyarrow")),
pytest.param("float32[pyarrow]", marks=skip_if_no(package="pyarrow")),
pytest.param("float64[pyarrow]", marks=skip_if_no(package="pyarrow")),
],
)
def test_to_ndarray_pandas_series_numeric(dtype):
"""
Test the _to_ndarray function with pandas Series with NumPy dtypes, pandas dtypes,
and pandas dtypes with pyarrow backend.
"""
for dtype in (
dtypes_numpy_numeric
+ dtypes_pandas_numeric
+ dtypes_pandas_numeric_pyarrow_backend
):
series = pd.Series([1, 2, 3], dtype=dtype)
assert series.dtype == dtype
result = _to_ndarray(series)
_check_result(result)
npt.assert_array_equal(result, series)
series = pd.Series([1, 2, 3], dtype=dtype)
assert series.dtype == dtype
result = _to_ndarray(series)
_check_result(result)
npt.assert_array_equal(result, series)


@pytest.mark.skipif(not _HAS_PYARROW, reason="pyarrow is not installed")
def test_to_ndarray_pandas_series_pyarrow_dtype(dtypes_pyarrow_numeric):
@pytest.mark.parametrize(
"dtype",
[
"int8",
"int16",
"int32",
"int64",
"uint8",
"uint16",
"uint32",
"uint64",
"float32",
"float64",
],
)
def test_to_ndarray_pyarrow_array(dtype):
"""
Test the _to_ndarray function with pandas Series with pyarrow dtypes.
"""
for dtype in dtypes_pyarrow_numeric:
array = pa.array([1, 2, 3], type=dtype)
assert array.type == dtype
result = _to_ndarray(array)
_check_result(result)
npt.assert_array_equal(result, array)

# Special handling for float16.
# Example from https://arrow.apache.org/docs/python/generated/pyarrow.float16.html
array = pa.array([1, 2, 3], type=dtype)
assert array.type == dtype
result = _to_ndarray(array)
_check_result(result)
npt.assert_array_equal(result, array)


def test_to_ndarray_pyarrow_array_float16():
"""
Test the _to_ndarray function with pyarrow float16 array.
Example from https://arrow.apache.org/docs/python/generated/pyarrow.float16.html
"""
array = pa.array(np.array([1.5, 2.5, 3.5], dtype=np.float16), type=pa.float16())
result = _to_ndarray(array)
_check_result(result)
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