clib.conversion._to_numpy: Add tests for pyarrow.array with pyarrow string types

pygmt/clib/conversion.py

@@ -156,8 +156,13 @@ def _to_numpy(data: Any) -> np.ndarray:
     array
         The C contiguous NumPy array.
     """
-    # Mapping of unsupported dtypes to the expected NumPy dtype.
+    # Mapping of unsupported dtypes to expected NumPy dtypes.
     dtypes: dict[str, str | type] = {
+        # For string dtypes.
+        "large_string": np.str_,  # pa.large_string and pa.large_utf8
+        "string": np.str_,  # pa.string and pa.utf8
+        "string_view": np.str_,  # pa.string_view
+        # For datetime dtypes.
         "date32[day][pyarrow]": "datetime64[D]",
         "date64[ms][pyarrow]": "datetime64[ms]",
     }
@@ -173,7 +178,7 @@ def _to_numpy(data: Any) -> np.ndarray:
         # we can remove the workaround in PyGMT v0.17.0.
         array = np.ascontiguousarray(data.astype(float))
     else:
-        vec_dtype = str(getattr(data, "dtype", ""))
+        vec_dtype = str(getattr(data, "dtype", getattr(data, "type", "")))
         array = np.ascontiguousarray(data, dtype=dtypes.get(vec_dtype))
     return array
 

pygmt/tests/test_clib_to_numpy.py

@@ -205,6 +205,7 @@ def test_to_numpy_pandas_series_pyarrow_dtypes_date(dtype, expected_dtype):
 #   - int8, int16, int32, int64
 #   - uint8, uint16, uint32, uint64
 #   - float16, float32, float64
+# - String types: string/utf8, large_string/large_utf8, string_view
 # - Date types:
 #   - date32[day]
 #   - date64[ms]
@@ -278,6 +279,28 @@ def test_to_numpy_pyarrow_array_pyarrow_dtypes_numeric_with_na(dtype, expected_d
     npt.assert_array_equal(result, array)
 
 
+@pytest.mark.skipif(not _HAS_PYARROW, reason="pyarrow is not installed")
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        None,
+        "string",
+        "utf8",  # alias for string
+        "large_string",
+        "large_utf8",  # alias for large_string
+        "string_view",
+    ],
+)
+def test_to_numpy_pyarrow_array_pyarrow_dtypes_string(dtype):
+    """
+    Test the _to_numpy function with PyArrow arrays of PyArrow string types.
+    """
+    array = pa.array(["abc", "defg", "12345"], type=dtype)
+    result = _to_numpy(array)
+    _check_result(result, np.str_)
+    npt.assert_array_equal(result, array)
+
+
 @pytest.mark.skipif(not _HAS_PYARROW, reason="pyarrow is not installed")
 @pytest.mark.parametrize(
     ("dtype", "expected_dtype"),