added ot_base.is_singlet to get rid of lattice.otype.data_otype() == …

…gpt.ot_singlet

lib/gpt/core/coordinates.py

@@ -131,7 +131,7 @@ def mat_backward(dst, src):
 
 def coordinate_mask(field, mask):
     assert isinstance(mask, numpy.ndarray)
-    assert field.otype.data_otype() == gpt.ot_singlet
+    assert field.otype.is_singlet
 
     x = gpt.coordinates(field)
     field[x] = mask.astype(field.grid.precision.complex_dtype).reshape((len(mask), 1))

lib/gpt/core/expr.py

@@ -113,7 +113,7 @@ def __mul__(self, l):
                 if uf & gpt.factor_unary.BIT_CONJ != 0:
                     lhs = lhs.conj()
                 res = gpt.tensor(np.tensordot(lhs, l.array, axes=mt[1]), mt[0]())
-                if res.otype == gpt.ot_singlet:
+                if res.otype.is_singlet:
                     res = complex(res.array)
                 return res
             assert 0

lib/gpt/core/lattice.py

@@ -288,8 +288,8 @@ def __itruediv__(self, expr):
         return self
 
     def __lt__(self, other):
-        assert self.otype.data_otype() == gpt.ot_singlet
-        assert other.otype.data_otype() == gpt.ot_singlet
+        assert self.otype.is_singlet
+        assert other.otype.is_singlet
         res = gpt.lattice(self)
         params = {"operator": "<"}
         cgpt.binary(res.v_obj[0], self.v_obj[0], other.v_obj[0], params)

lib/gpt/core/object_type/base.py

@@ -36,6 +36,7 @@ class ot_base:
 
     # list of object types to which I can convert and converter function
     ctab = {}
+    is_singlet = False
 
     # safe cast of data_alias
     def data_otype(self):

lib/gpt/core/object_type/container.py

@@ -67,25 +67,7 @@ class ot_singlet(ot_base):
     mtab = {
         "ot_singlet": (lambda: ot_singlet(), None),
     }
-
-    # FIXME: I am not sure if this is a good idea wrt. parellelism.
-    # Make singlet and all its derived subclasses singleton.
-    _instance = None
-    def __new__(cls, *args, **kwargs):
-        if not isinstance(cls._instance, cls):
-            cls._instance = object.__new__(cls, *args, **kwargs)
-        return cls._instance
-
-    # There are many LoCs like
-    #   res.otype == g.ot_singlet
-    # This will allow this behaviour to continue.
-    def __eq__(self, o):
-        if isinstance(o, type):
-            return type(self) == o
-        # This works because singleton.
-        if self is o:
-            return True
-        return o.__eq__(self)
+    is_singlet = True
 
     def data_otype(self):
         return ot_singlet()

lib/gpt/core/tensor.py

@@ -75,7 +75,7 @@ def adj(self):
         return tensor(np.transpose(self.array.conj(), self.otype.transposed), self.otype)
 
     def reduced(self):
-        if self.otype.data_otype() == gpt.ot_singlet:
+        if self.otype.is_singlet:
             return complex(self.array)
         return self
 
@@ -92,7 +92,7 @@ def trace(self, t):
             if ct[0] is not None:
                 res = tensor(np.trace(res.array, offset=0, axis1=ct[0], axis2=ct[1]), ct[2]())
 
-        if res.otype == gpt.ot_singlet:
+        if res.otype.is_singlet:
             res = complex(res.array)
         return res
 

lib/gpt/core/util.py

@@ -52,7 +52,7 @@ def to_num(x):
 
 # tensor
 def value_to_tensor(val, otype):
-    if otype.data_otype() == gpt.ot_singlet:
+    if otype.is_singlet:
         # this is not ideal, can we do a subclass of complex that preserves otype info?
         return complex(val)
     return gpt.tensor(val, otype)