Ensure numpy masked arrays stay masked after projection

As noted in pyproj4#1102, projecting numpy masked arrays returned numpy masked
arrays in the 2.x series of pyproj.  This behaviour changed in commit
4ab3ff7, where a "plain" numpy ndarray was returned.  The change
implemented here ensures that projecting numpy masked arrays returns
masked arrays as was previously the case.

pyproj/utils.py

@@ -108,6 +108,11 @@ def _copytobuffer(xxx: Any, inplace: bool = False) -> Tuple[Any, DataType]:
     Tuple[Any, DataType]
         The copy of the data prepared for the PROJ API & Python Buffer API.
     """
+    # handle numpy masked Arrays; note that pandas.Series also has a "mask"
+    # attribute, hence checking for simply the "mask" attr isn't sufficient
+    if hasattr(xxx, "hardmask"):
+        return xxx, DataType.ARRAY
+
     # check for pandas.Series, xarray.DataArray or dask.array.Array
     if hasattr(xxx, "__array__") and callable(xxx.__array__):
         xxx = xxx.__array__()

test/test_utils.py

@@ -42,6 +42,13 @@ def test__copytobuffer__numpy_array(in_arr):
     )
 
 
+def test__copytobuffer__numpy_masked_array():
+    in_arr = numpy.ma.array([1])
+    out_arr, dtype = _copytobuffer(in_arr)
+
+    assert isinstance(out_arr, numpy.ma.MaskedArray)
+
+
 def test__copytobuffer__fortran_order():
     data = numpy.ones((2, 4), dtype=numpy.float64, order="F")
     converted_data, dtype = _copytobuffer(data)