Improve the data type checking for 2-D arrays passed to the GMT C API (…

…#3563)

pygmt/clib/session.py

@@ -82,7 +82,8 @@
 
 REGISTRATIONS = ["GMT_GRID_NODE_REG", "GMT_GRID_PIXEL_REG"]
 
-DTYPES = {
+# Dictionary for mapping numpy dtypes to GMT data types.
+DTYPES_NUMERIC = {
     np.int8: "GMT_CHAR",
     np.int16: "GMT_SHORT",
     np.int32: "GMT_INT",
@@ -93,10 +94,14 @@
     np.uint64: "GMT_ULONG",
     np.float32: "GMT_FLOAT",
     np.float64: "GMT_DOUBLE",
+    np.timedelta64: "GMT_LONG",
+}
+DTYPES_TEXT = {
     np.str_: "GMT_TEXT",
     np.datetime64: "GMT_DATETIME",
-    np.timedelta64: "GMT_LONG",
 }
+DTYPES = DTYPES_NUMERIC | DTYPES_TEXT
+
 # Dictionary for storing the values of GMT constants.
 GMT_CONSTANTS = {}
 
@@ -879,63 +884,59 @@ def _parse_constant(
         integer_value = sum(self[part] for part in parts)
         return integer_value
 
-    def _check_dtype_and_dim(self, array, ndim):
+    def _check_dtype_and_dim(self, array: np.ndarray, ndim: int) -> int:
         """
         Check that a numpy array has the given number of dimensions and is a valid data
         type.
 
         Parameters
         ----------
-        array : numpy.ndarray
+        array
             The array to be tested.
-        ndim : int
+        ndim
             The desired number of array dimensions.
 
         Returns
         -------
-        gmt_type : int
+        gmt_type
             The GMT constant value representing this data type.
 
         Raises
         ------
         GMTInvalidInput
-            If the array has the wrong number of dimensions or
-            is an unsupported data type.
+            If the array has the wrong number of dimensions or is an unsupported data
+            type.
 
         Examples
         --------
-
         >>> import numpy as np
         >>> data = np.array([1, 2, 3], dtype="float64")
-        >>> with Session() as ses:
-        ...     gmttype = ses._check_dtype_and_dim(data, ndim=1)
-        ...     gmttype == ses["GMT_DOUBLE"]
+        >>> with Session() as lib:
+        ...     gmttype = lib._check_dtype_and_dim(data, ndim=1)
+        ...     gmttype == lib["GMT_DOUBLE"]
         True
         >>> data = np.ones((5, 2), dtype="float32")
-        >>> with Session() as ses:
-        ...     gmttype = ses._check_dtype_and_dim(data, ndim=2)
-        ...     gmttype == ses["GMT_FLOAT"]
+        >>> with Session() as lib:
+        ...     gmttype = lib._check_dtype_and_dim(data, ndim=2)
+        ...     gmttype == lib["GMT_FLOAT"]
         True
         """
-        # Check that the array has the given number of dimensions
+        # Check that the array has the given number of dimensions.
         if array.ndim != ndim:
-            raise GMTInvalidInput(
-                f"Expected a numpy {ndim}-D array, got {array.ndim}-D."
-            )
+            msg = f"Expected a numpy {ndim}-D array, got {array.ndim}-D."
+            raise GMTInvalidInput(msg)
 
-        # Check that the array has a valid/known data type
-        if array.dtype.type not in DTYPES:
-            try:
-                if array.dtype.type is np.object_:
-                    # Try to convert unknown object type to np.datetime64
-                    array = array_to_datetime(array)
-                else:
-                    raise ValueError
-            except ValueError as e:
-                raise GMTInvalidInput(
-                    f"Unsupported numpy data type '{array.dtype.type}'."
-                ) from e
-        return self[DTYPES[array.dtype.type]]
+        # For 1-D arrays, try to convert unknown object type to np.datetime64.
+        if ndim == 1 and array.dtype.type is np.object_:
+            with contextlib.suppress(ValueError):
+                array = array_to_datetime(array)
+
+        # 1-D arrays can be numeric or text, 2-D arrays can only be numeric.
+        valid_dtypes = DTYPES if ndim == 1 else DTYPES_NUMERIC
+        if (dtype := array.dtype.type) not in valid_dtypes:
+            msg = f"Unsupported numpy data type '{dtype}'."
+            raise GMTInvalidInput(msg)
+        return self[DTYPES[dtype]]
 
     def put_vector(self, dataset: ctp.c_void_p, column: int, vector: np.ndarray):
         r"""