Fix handling of int dtype

- Seems like some versions of numpy behave inconsistently

Author: smathot

datamatrix/_datamatrix/_numericcolumn.py

@@ -321,6 +321,7 @@ def _tosequence(self, value, length=None):
                 pass
             else:
                 return super(NumericColumn, self)._tosequence(value, length)
+                return super(NumericColumn, self)._tosequence(value, length)
         value = self._checktype(value)
         return super(NumericColumn, self)._tosequence(value, length)
 
@@ -370,7 +371,7 @@ def _operate(self, other, number_op, str_op=None, flip=False):
     def __eq__(self, other):
 
         if isinstance(other, type):
-            if other is self.dtype:
+            if other == self.dtype:
                 return self._datamatrix
             return self._datamatrix._selectrowid(Index(0))
         if self._issequence(other):
@@ -387,7 +388,7 @@ def __eq__(self, other):
     def __ne__(self, other):
 
         if isinstance(other, type):
-            if other is not self.dtype:
+            if other != self.dtype:
                 return self._datamatrix
             return self._datamatrix._selectrowid(Index(0))
         if self._issequence(other):

testcases/test_basics.py

@@ -80,7 +80,9 @@ def _test_numericcolumn(cls):
     dm.col = 1, 2, 3, 4, 5
     # int -> float
     val = dm.col[2]
-    assert isinstance(val, (int, float))
+    # In some versions of numpy, np.int32/64 don't match regular ints. For float
+    # this isn't an issue.
+    assert isinstance(val, (np.int64, np.int32, int, float))
     assert val == 3
     # (int, int) -> FloatColumn
     val = dm.col[1, 3]

testcases/test_typing.py

@@ -116,7 +116,7 @@ def check_intcolumn_typing():
 
     dm = DataMatrix(length=4, default_col_type=IntColumn)
     dm.f = 1.1, '1.8', 2, '2'
-    assert all(isinstance(v, int) for v in dm.f)
+    assert all(isinstance(v, (int, np.int32, np.int64)) for v in dm.f)
     def _():
         with pytest.raises(TypeError):
             dm.inf = INF, -INF, 'inf', '-inf'