Remove floor, ceiling for integer symbols

Original Intent

Allow optimizations by SymPy when we know that a TypedSymbol is_integer or is_real (e.g. drop rounding functions).

We can deduce some of those properties with Numpy's type system (https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.scalars.html). We have to be careful since all the is_* methods have ternary logic (True, False, None== we don't know).

Field.Access can take advantage of those optimizations by making it a subclass of TypedSymbol.

Extended Changes

By writing a test I realized that it would be handy to compare AssignmentCollections and use the functions find, match, subs, replace of SymPy.

pystencils/data_types.py

@@ -127,12 +127,30 @@ class TypedSymbol(sp.Symbol):
     def __getnewargs__(self):
         return self.name, self.dtype
 
+    # For reference: Numpy type hierarchy https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.scalars.html
     @property
     def is_integer(self):
         if hasattr(self.dtype, 'numpy_dtype'):
-            return np.issubdtype(self.dtype.numpy_dtype, np.integer)
+            return np.issubdtype(self.dtype.numpy_dtype, np.integer) or super().is_integer
         else:
-            return False
+            return super().is_integer
+
+    @property
+    def is_negative(self):
+        if hasattr(self.dtype, 'numpy_dtype'):
+            if np.issubdtype(self.dtype.numpy_dtype, np.unsignedinteger):
+                return False
+
+        return super().is_positive
+
+    @property
+    def is_real(self):
+        if hasattr(self.dtype, 'numpy_dtype'):
+            return np.issubdtype(self.dtype.numpy_dtype, np.integer) or \
+                np.issubdtype(self.dtype.numpy_dtype, np.floating) or \
+                super().is_real
+        else:
+            return super().is_real
 
 
 def create_type(specification):