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

Author: seisman

pygmt/tests/test_clib_to_numpy.py

@@ -0,0 +1,82 @@
+"""
+Tests for the _to_numpy function in the clib.conversion module.
+"""
+
+import numpy as np
+import numpy.testing as npt
+import pytest
+from pygmt.clib.conversion import _to_numpy
+from pygmt.clib.session import DTYPES
+
+
+def _check_result(result, supported):
+    """
+    Check the result of the _to_numpy function.
+    """
+    # Check that the result is a NumPy array and is C-contiguous.
+    assert isinstance(result, np.ndarray)
+    assert result.flags.c_contiguous
+    # Check that the dtype is supported by PyGMT (or the GMT C API).
+    assert (result.dtype.type in DTYPES) == supported
+
+
+########################################################################################
+# Test the _to_numpy function with NumPy dtypes.
+#
+# There are 24 fundamental dtypes in NumPy. Not all of them are supported by PyGMT.
+# Reference: https://numpy.org/doc/2.1/reference/arrays.scalars.html
+#
+# - 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
+########################################################################################
+@pytest.mark.parametrize(
+    ("dtype", "supported"),
+    [
+        (np.int8, True),
+        (np.int16, True),
+        (np.int32, True),
+        (np.int64, True),
+        (np.longlong, True),
+        (np.uint8, True),
+        (np.uint16, True),
+        (np.uint32, True),
+        (np.uint64, True),
+        (np.ulonglong, True),
+        (np.float16, False),
+        (np.float32, True),
+        (np.float64, True),
+        (np.longdouble, False),
+        (np.complex64, False),
+        (np.complex128, False),
+        (np.clongdouble, False),
+    ],
+)
+def test_to_numpy_ndarray_numpy_dtypes_numeric(dtype, supported):
+    """
+    Test the _to_numpy function with NumPy arrays of NumPy numeric dtypes.
+
+    "dtype" is the NumPy dtype to be tested and "supported" is a boolean value
+    indicating whether the dtype is supported by PyGMT (or the GMT C API).
+    """
+    # 1-D array
+    array = np.array([1, 2, 3], dtype=dtype)
+    assert array.dtype == dtype
+    result = _to_numpy(array)
+    _check_result(result, supported)
+    npt.assert_array_equal(result, array)
+
+    # 2-D array
+    array = np.array([[1, 2, 3], [4, 5, 6]], dtype=dtype)
+    assert array.dtype == dtype
+    result = _to_numpy(array)
+    _check_result(result, supported)
+    npt.assert_array_equal(result, array)