Add tests for _to_ndarray with various numeric dtypes

Author: seisman

pygmt/tests/test_clib_to_ndarray.py

@@ -0,0 +1,185 @@
+"""
+Test the _to_ndarray function in the clib.conversion module.
+"""
+
+import numpy as np
+import numpy.testing as npt
+import pandas as pd
+import pytest
+from pygmt.clib.conversion import _to_ndarray
+
+try:
+    import pyarrow as pa
+
+    _HAS_PYARROW = True
+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(),
+    ]
+
+
+def _check_result(result):
+    """
+    A helper function to check the result of the _to_ndarray function.
+
+    Check the following:
+
+    1. The result is a NumPy array.
+    2. The result is C-contiguous.
+    3. The result dtype is not np.object_.
+    """
+    assert isinstance(result, np.ndarray)
+    assert result.flags.c_contiguous is True
+    assert result.dtype != np.object_
+
+
+def test_to_ndarray_numpy_ndarray_numpy_numeric(dtypes_numpy_numeric):
+    """
+    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)
+
+    # 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)
+
+
+def test_to_ndarray_pandas_series_numeric(
+    dtypes_numpy_numeric, dtypes_pandas_numeric, dtypes_pandas_numeric_pyarrow_backend
+):
+    """
+    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)
+
+
+@pytest.mark.skipif(not _HAS_PYARROW, reason="pyarrow is not installed")
+def test_to_ndarray_pandas_series_pyarrow_dtype(dtypes_pyarrow_numeric):
+    """
+    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(np.array([1.5, 2.5, 3.5], dtype=np.float16), type=pa.float16())
+    result = _to_ndarray(array)
+    _check_result(result)
+    npt.assert_array_equal(result, array)