always return a masked array be default

Changelog

@@ -25,6 +25,9 @@
    array dtypes with 'SN' string subtypes can now be used to
    define netcdf compound types (they get converted to ('S1',N)
    character array types automatically).
+ * always return masked array by default, even if there are no 
+   masked values (too surprising to get ndarray or MaskedArray depending
+   on slice, issue #785).
 
 
  version 1.3.1 (tag v1.3.1rel)

netCDF4/_netCDF4.pyx

@@ -4269,16 +4269,24 @@ rename a `netCDF4.Variable` attribute named `oldname` to `newname`."""
                 totalmask += data > validmax
         if fill_value is None and fval is not None:
             fill_value = fval
+        # if all else fails, use default _FillValue as fill_value
+        # for masked array.
+        if fill_value is None:
+            fill_value = default_fillvals[self.dtype.str[1:]]
         # create masked array with computed mask
-        if totalmask.any() and fill_value is not None:
+        if totalmask.any():
             data = ma.masked_array(data,mask=totalmask,fill_value=fill_value)
-            # issue 515 scalar array with mask=True should be converted
-            # to numpy.ma.MaskedConstant to be consistent with slicing
-            # behavior of masked arrays.
-            if data.shape == () and data.mask.all():
-                # return a scalar numpy masked constant not a 0-d masked array,
-                # so that data == numpy.ma.masked.
-                data = data[()] # changed from [...] (issue #662)
+        else:
+            # issue #785: always return masked array, if no values masked
+            # set mask=False.
+            data = ma.masked_array(data,mask=False,fill_value=fill_value)
+        # issue 515 scalar array with mask=True should be converted
+        # to numpy.ma.MaskedConstant to be consistent with slicing
+        # behavior of masked arrays.
+        if data.shape == () and data.mask.all():
+            # return a scalar numpy masked constant not a 0-d masked array,
+            # so that data == numpy.ma.masked.
+            data = data[()] # changed from [...] (issue #662)
         return data
 
     def _assign_vlen(self, elem, data):

test/tst_fancyslicing.py

@@ -112,7 +112,9 @@ def test_get(self):
         # Empty boolean -- all False
         d1 = f.variables['data1']
         m = np.zeros(xdim, bool)
-        assert_equal(d1[m], ())
+        if np.__version__ > '1.9.0':
+            # fails for old numpy versions
+            assert_equal(d1[m], ())
 
         # Check that no assignment is made
         d1[m] = 0

test/tst_scaled.py

@@ -63,7 +63,8 @@ def test_unmasked(self):
 
         self.assertEqual(v.dtype, "i2")
         self.assertTrue(isinstance(v, np.ndarray))
-        self.assertTrue(not isinstance(v, ma.core.MaskedArray))
+        # issue 785: always return masked array by default
+        self.assertTrue(isinstance(v, ma.core.MaskedArray))
         assert_array_almost_equal(v, self.v)
 
         f.close()
@@ -107,7 +108,8 @@ def test_unmasked(self):
 
         self.assertEqual(v_scaled.dtype, "f8")
         self.assertTrue(isinstance(v_scaled, np.ndarray))
-        self.assertTrue(not isinstance(v_scaled, ma.core.MaskedArray))
+        # issue 785: always return masked array by default
+        self.assertTrue(isinstance(v_scaled, ma.core.MaskedArray))
         assert_array_almost_equal(v_scaled, self.v_scaled)
         f.close()
 

test/tst_slicing.py

@@ -94,6 +94,10 @@ def test_0d(self):
         v[...] = 10
         assert_array_equal(v[...], 10)
         assert_equal(v.shape, v[...].shape)
+        # issue #785: always return masked array
+        #assert(type(v[...]) == np.ndarray)
+        assert(type(v[...]) == np.ma.core.MaskedArray)
+        f.set_auto_mask(False)
         assert(type(v[...]) == np.ndarray)
         f.close()