misc: Improve readability

Author: EdCaunt

devito/finite_differences/differentiable.py

@@ -546,9 +546,26 @@ def _gather_for_diff(self):
     def _eval_is_even(self):
         return None
 
-    _eval_is_odd = _eval_is_integer = _eval_is_negative = _eval_is_even
-    _eval_is_extended_negative = _eval_is_positive = _eval_is_even
-    _eval_is_extended_positive = _eval_is_zero = _eval_is_even
+    def _eval_is_odd(self):
+        return None
+
+    def _eval_is_integer(self):
+        return None
+
+    def _eval_is_negative(self):
+        return None
+
+    def _eval_is_extended_negative(self):
+        return None
+
+    def _eval_is_positive(self):
+        return None
+
+    def _eval_is_extended_positive(self):
+        return None
+
+    def _eval_is_zero(self):
+        return None
 
 
 class DifferentiableFunction(DifferentiableOp):
@@ -827,9 +844,11 @@ def __new__(cls, expr, dimensions, **kwargs):
         if not dimensions:
             return expr
         for d in dimensions:
-            with suppress(AttributeError):
+            try:
                 if d.is_Dimension and is_integer(d.symbolic_size):
                     continue
+            except AttributeError:
+                pass
             raise ValueError("Expected Dimension with numeric size, "
                              f"got `{d}` instead")
 
@@ -886,8 +905,11 @@ def _evaluate(self, **kwargs):
             return self._rebuild(expr)
 
         values = product(*[list(d.range) for d in self.dimensions])
-        return sum([expr.xreplace(dict(zip(self.dimensions, i, strict=True)))
-                    for i in values])
+        terms = []
+        for i in values:
+            mapper = dict(zip(self.dimensions, i, strict=True))
+            terms.append(expr.xreplace(mapper))
+        return sum(terms)
 
     @property
     def bound_symbols(self):

devito/tools/data_structures.py

@@ -744,9 +744,14 @@ class UnboundTuple(tuple):
     """
 
     def __new__(cls, *items, **kwargs):
-        nitems = [UnboundTuple(*i)
-                  if isinstance(i, Iterable) and not isinstance(i, UnboundTuple) else i
-                  for i in as_tuple(items)]
+        nitems = []
+        for i in as_tuple(items):
+            if isinstance(i, UnboundTuple):
+                nitems.append(i)
+            elif isinstance(i, Iterable):
+                nitems.append(UnboundTuple(*i))
+            else:
+                nitems.append(i)
 
         obj = super().__new__(cls, tuple(nitems))
         obj.last = len(nitems)

devito/types/basic.py

@@ -1166,6 +1166,7 @@ def dmap(self):
             return DeviceMap(f'd_{self.name}', shape=self._shape, function=self.function)
         elif self._mem_local:
             return self.indexed
+        return None
 
     @property
     def size(self):

devito/types/dense.py

@@ -1593,9 +1593,9 @@ def _halo_exchange(self):
 
     def _arg_values(self, estimate_memory=False, **kwargs):
         if self._parent is not None and self.parent.name not in kwargs:
-            return self._parent._arg_defaults(alias=self._parent,
-                                              estimate_memory=estimate_memory
-                                              ).reduce_all()
+            return self._parent._arg_defaults(
+                alias=self._parent, estimate_memory=estimate_memory
+            ).reduce_all()
         elif self.name in kwargs:
             raise RuntimeError(f"`{self.name}` is a SubFunction, so it can't be assigned "
                                "a value dynamically")

devito/types/dimension.py

@@ -1335,7 +1335,11 @@ def _rebuild_hierarchy(self, callback=None, step=None):
 
         name0 = pp.name
 
-        name1 = p.name if callback is None else callback(f'{callback(name0)}_blk')
+        if callback is None:
+            name1 = p.name
+        else:
+            base = callback(name0)
+            name1 = callback(f'{base}_blk')
 
         bd = p._rebuild(name1, pp, step=step or p.step)
 

devito/types/sparse.py

@@ -2065,8 +2065,10 @@ def manual_scatter(self, *, data_all_zero=False):
 
         # now all ranks can allocate the buffers to receive into
         if distributor.myrank != 0:
-            scattered_data = (np.zeros if data_all_zero else np.empty)([nt, npoint],
-                                                                       dtype=self.dtype)
+            if data_all_zero:
+                scattered_data = np.zeros([nt, npoint], dtype=self.dtype)
+            else:
+                scattered_data = np.empty([nt, npoint], dtype=self.dtype)
             scattered_gp = np.empty([nloc, ndim], dtype=np.int32)
             scattered_coeffs = [np.empty([nloc, r_tuple_no_none[idim]], dtype=self.dtype)
                                 for idim in range(ndim)]