clib.conversion._to_numpy: Add tests for pyarrow.array with pyarrow n…

…umeric types (#3599)

pygmt/tests/test_clib_to_numpy.py

@@ -11,6 +11,13 @@
 from packaging.version import Version
 from pygmt.clib.conversion import _to_numpy
 
+try:
+    import pyarrow as pa
+
+    _HAS_PYARROW = True
+except ImportError:
+    _HAS_PYARROW = False
+
 
 def _check_result(result, expected_dtype):
     """
@@ -121,7 +128,7 @@ def test_to_numpy_ndarray_numpy_dtypes_numeric(dtype, expected_dtype):
 #
 # 1. NumPy dtypes (see above)
 # 2. pandas dtypes
-# 3. PyArrow dtypes
+# 3. PyArrow types (see below)
 #
 # pandas provides following dtypes:
 #
@@ -152,3 +159,82 @@ def test_to_numpy_pandas_series_numpy_dtypes_numeric(dtype, expected_dtype):
     result = _to_numpy(series)
     _check_result(result, expected_dtype)
     npt.assert_array_equal(result, series)
+
+
+########################################################################################
+# Test the _to_numpy function with PyArrow arrays.
+#
+# PyArrow provides the following types:
+#
+# - Numeric types:
+#   - int8, int16, int32, int64
+#   - uint8, uint16, uint32, uint64
+#   - float16, float32, float64
+#
+# In PyArrow, array types can be specified in two ways:
+#
+# - Using string aliases (e.g., "int8")
+# - Using pyarrow.DataType (e.g., ``pa.int8()``)
+#
+# Reference: https://arrow.apache.org/docs/python/api/datatypes.html
+########################################################################################
+@pytest.mark.skipif(not _HAS_PYARROW, reason="pyarrow is not installed")
+@pytest.mark.parametrize(
+    ("dtype", "expected_dtype"),
+    [
+        pytest.param("int8", np.int8, id="int8"),
+        pytest.param("int16", np.int16, id="int16"),
+        pytest.param("int32", np.int32, id="int32"),
+        pytest.param("int64", np.int64, id="int64"),
+        pytest.param("uint8", np.uint8, id="uint8"),
+        pytest.param("uint16", np.uint16, id="uint16"),
+        pytest.param("uint32", np.uint32, id="uint32"),
+        pytest.param("uint64", np.uint64, id="uint64"),
+        pytest.param("float16", np.float16, id="float16"),
+        pytest.param("float32", np.float32, id="float32"),
+        pytest.param("float64", np.float64, id="float64"),
+    ],
+)
+def test_to_numpy_pyarrow_array_pyarrow_dtypes_numeric(dtype, expected_dtype):
+    """
+    Test the _to_numpy function with PyArrow arrays of PyArrow numeric types.
+    """
+    data = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
+    if dtype == "float16":  # float16 needs special handling
+        # Example from https://arrow.apache.org/docs/python/generated/pyarrow.float16.html
+        data = np.array(data, dtype=np.float16)
+    array = pa.array(data, type=dtype)[::2]
+    result = _to_numpy(array)
+    _check_result(result, expected_dtype)
+    npt.assert_array_equal(result, array)
+
+
+@pytest.mark.skipif(not _HAS_PYARROW, reason="pyarrow is not installed")
+@pytest.mark.parametrize(
+    ("dtype", "expected_dtype"),
+    [
+        pytest.param("int8", np.float64, id="int8"),
+        pytest.param("int16", np.float64, id="int16"),
+        pytest.param("int32", np.float64, id="int32"),
+        pytest.param("int64", np.float64, id="int64"),
+        pytest.param("uint8", np.float64, id="uint8"),
+        pytest.param("uint16", np.float64, id="uint16"),
+        pytest.param("uint32", np.float64, id="uint32"),
+        pytest.param("uint64", np.float64, id="uint64"),
+        pytest.param("float16", np.float16, id="float16"),
+        pytest.param("float32", np.float32, id="float32"),
+        pytest.param("float64", np.float64, id="float64"),
+    ],
+)
+def test_to_numpy_pyarrow_array_pyarrow_dtypes_numeric_with_na(dtype, expected_dtype):
+    """
+    Test the _to_numpy function with PyArrow arrays of PyArrow numeric types and NA.
+    """
+    data = [1.0, 2.0, None, 4.0, 5.0, 6.0]
+    if dtype == "float16":  # float16 needs special handling
+        # Example from https://arrow.apache.org/docs/python/generated/pyarrow.float16.html
+        data = np.array(data, dtype=np.float16)
+    array = pa.array(data, type=dtype)[::2]
+    result = _to_numpy(array)
+    _check_result(result, expected_dtype)
+    npt.assert_array_equal(result, array)