Allow np.object dtypes into virtualfile_from_vectors (#684)

Loosen the check in `virtualfile_from_vectors` to allow for
any string-like dtype (np.str, np.object) by performing the
check using `pd.api.types.is_string_dtype()`. The array is
then converted (if needed) to a proper `np.str` dtype
before giving it to `put_strings`.

pygmt/clib/session.py

@@ -9,6 +9,7 @@
 
 from packaging.version import Version
 import numpy as np
+import pandas as pd
 
 from ..exceptions import (
     GMTCLibError,
@@ -1160,7 +1161,7 @@ def virtualfile_from_vectors(self, *vectors):
         # Assumes that first 2 columns contains coordinates like longitude
         # latitude, or datetime string types.
         for col, array in enumerate(arrays[2:]):
-            if np.issubdtype(array.dtype, np.str_):
+            if pd.api.types.is_string_dtype(array.dtype):
                 columns = col + 2
                 break
 
@@ -1189,6 +1190,7 @@ def virtualfile_from_vectors(self, *vectors):
                 strings = np.apply_along_axis(
                     func1d=" ".join, axis=0, arr=string_arrays
                 )
+            strings = np.asanyarray(a=strings, dtype=np.str)
             self.put_strings(
                 dataset, family="GMT_IS_VECTOR|GMT_IS_DUPLICATE", strings=strings
             )

pygmt/tests/test_clib.py

@@ -397,12 +397,16 @@ def test_virtualfile_from_vectors():
             assert output == expected
 
 
-def test_virtualfile_from_vectors_one_string_column():
-    "Test passing in one column with string dtype into virtual file dataset"
+@pytest.mark.parametrize("dtype", [np.str, np.object])
+def test_virtualfile_from_vectors_one_string_or_object_column(dtype):
+    """
+    Test passing in one column with string or object dtype into virtual file
+    dataset
+    """
     size = 5
     x = np.arange(size, dtype=np.int32)
     y = np.arange(size, size * 2, 1, dtype=np.int32)
-    strings = np.array(["a", "bc", "defg", "hijklmn", "opqrst"], dtype=np.str)
+    strings = np.array(["a", "bc", "defg", "hijklmn", "opqrst"], dtype=dtype)
     with clib.Session() as lib:
         with lib.virtualfile_from_vectors(x, y, strings) as vfile:
             with GMTTempFile() as outfile:
@@ -412,13 +416,17 @@ def test_virtualfile_from_vectors_one_string_column():
         assert output == expected
 
 
-def test_virtualfile_from_vectors_two_string_columns():
-    "Test passing in two columns of string dtype into virtual file dataset"
+@pytest.mark.parametrize("dtype", [np.str, np.object])
+def test_virtualfile_from_vectors_two_string_or_object_columns(dtype):
+    """
+    Test passing in two columns of string or object dtype into virtual file
+    dataset
+    """
     size = 5
     x = np.arange(size, dtype=np.int32)
     y = np.arange(size, size * 2, 1, dtype=np.int32)
-    strings1 = np.array(["a", "bc", "def", "ghij", "klmno"], dtype=np.str)
-    strings2 = np.array(["pqrst", "uvwx", "yz!", "@#", "$"], dtype=np.str)
+    strings1 = np.array(["a", "bc", "def", "ghij", "klmno"], dtype=dtype)
+    strings2 = np.array(["pqrst", "uvwx", "yz!", "@#", "$"], dtype=dtype)
     with clib.Session() as lib:
         with lib.virtualfile_from_vectors(x, y, strings1, strings2) as vfile:
             with GMTTempFile() as outfile: