clib.converison._to_numpy: Add tests for numpy arrays of numpy numeric dtypes

pygmt/tests/test_clib_to_numpy.py

@@ -0,0 +1,154 @@
+"""
+Tests for the _to_numpy function in the clib.conversion module.
+"""
+
+import sys
+
+import numpy as np
+import numpy.testing as npt
+import pandas as pd
+import pytest
+from packaging.version import Version
+from pygmt.clib.conversion import _to_numpy
+
+
+def _check_result(result, expected_dtype):
+    """
+    A helper function to check if the result of the _to_numpy function is a C-contiguous
+    NumPy array with the expected dtype.
+    """
+    assert isinstance(result, np.ndarray)
+    assert result.flags.c_contiguous
+    assert result.dtype.type == expected_dtype
+
+
+########################################################################################
+# Test the _to_numpy function with Python built-in types.
+########################################################################################
+@pytest.mark.parametrize(
+    ("data", "expected_dtype"),
+    [
+        pytest.param(
+            [1, 2, 3],
+            np.int32
+            if sys.platform == "win32" and Version(np.__version__) < Version("2.0")
+            else np.int64,
+            id="int",
+        ),
+        pytest.param([1.0, 2.0, 3.0], np.float64, id="float"),
+        pytest.param(
+            [complex(+1), complex(-2j), complex("-Infinity+NaNj")],
+            np.complex128,
+            id="complex",
+        ),
+    ],
+)
+def test_to_numpy_python_types_numeric(data, expected_dtype):
+    """
+    Test the _to_numpy function with Python built-in numeric types.
+    """
+    result = _to_numpy(data)
+    _check_result(result, expected_dtype)
+    npt.assert_array_equal(result, data)
+
+
+########################################################################################
+# Test the _to_numpy function with NumPy arrays.
+#
+# There are 24 fundamental dtypes in NumPy. Not all of them are supported by PyGMT.
+#
+# - Numeric dtypes:
+#   - int8, int16, int32, int64, longlong
+#   - uint8, uint16, uint32, uint64, ulonglong
+#   - float16, float32, float64, longdouble
+#   - complex64, complex128, clongdouble
+# - bool
+# - datetime64, timedelta64
+# - str_
+# - bytes_
+# - object_
+# - void
+#
+# Reference: https://numpy.org/doc/2.1/reference/arrays.scalars.html
+########################################################################################
+np_dtype_params = [
+    pytest.param(np.int8, np.int8, id="int8"),
+    pytest.param(np.int16, np.int16, id="int16"),
+    pytest.param(np.int32, np.int32, id="int32"),
+    pytest.param(np.int64, np.int64, id="int64"),
+    pytest.param(np.longlong, np.longlong, id="longlong"),
+    pytest.param(np.uint8, np.uint8, id="uint8"),
+    pytest.param(np.uint16, np.uint16, id="uint16"),
+    pytest.param(np.uint32, np.uint32, id="uint32"),
+    pytest.param(np.uint64, np.uint64, id="uint64"),
+    pytest.param(np.ulonglong, np.ulonglong, id="ulonglong"),
+    pytest.param(np.float16, np.float16, id="float16"),
+    pytest.param(np.float32, np.float32, id="float32"),
+    pytest.param(np.float64, np.float64, id="float64"),
+    pytest.param(np.longdouble, np.longdouble, id="longdouble"),
+    pytest.param(np.complex64, np.complex64, id="complex64"),
+    pytest.param(np.complex128, np.complex128, id="complex128"),
+    pytest.param(np.clongdouble, np.clongdouble, id="clongdouble"),
+]
+
+
+@pytest.mark.parametrize(("dtype", "expected_dtype"), np_dtype_params)
+def test_to_numpy_ndarray_numpy_dtypes_numeric(dtype, expected_dtype):
+    """
+    Test the _to_numpy function with NumPy arrays of NumPy numeric dtypes.
+
+    Test both 1-D and 2-D arrays which are not C-contiguous.
+    """
+    # 1-D array that is not C-contiguous
+    array = np.array([1, 2, 3, 4, 5, 6], dtype=dtype)[::2]
+    assert array.flags.c_contiguous is False
+    result = _to_numpy(array)
+    _check_result(result, expected_dtype)
+    npt.assert_array_equal(result, array, strict=True)
+
+    # 2-D array that is not C-contiguous
+    array = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=dtype)[::2, ::2]
+    assert array.flags.c_contiguous is False
+    result = _to_numpy(array)
+    _check_result(result, expected_dtype)
+    npt.assert_array_equal(result, array, strict=True)
+
+
+########################################################################################
+# Test the _to_numpy function with pandas.Series.
+#
+# In pandas, dtype can be specified by
+#
+# 1. NumPy dtypes (see above)
+# 2. pandas dtypes
+# 3. PyArrow dtypes
+#
+# pandas provides following dtypes:
+#
+# - Numeric dtypes:
+#   - Int8, Int16, Int32, Int64
+#   - UInt8, UInt16, UInt32, UInt64
+#   - Float32, Float64
+# - DatetimeTZDtype
+# - PeriodDtype
+# - IntervalDtype
+# - StringDtype
+# - CategoricalDtype
+# - SparseDtype
+# - BooleanDtype
+# - ArrowDtype: a special dtype used to store data in the PyArrow format.
+#
+# References:
+# 1. https://pandas.pydata.org/docs/reference/arrays.html
+# 2. https://pandas.pydata.org/docs/user_guide/basics.html#basics-dtypes
+# 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):
+    """
+    Test the _to_numpy function with pandas.Series of NumPy numeric dtypes.
+    """
+    series = pd.Series([1, 2, 3, 4, 5, 6], dtype=dtype)[::2]  # Not C-contiguous
+    result = _to_numpy(series)
+    _check_result(result, expected_dtype)
+    npt.assert_array_equal(result, series)