Update tensors.py

Author: JakeKitchen

mrmustard/math/tensor_networks/tensors.py

@@ -55,9 +55,10 @@ class Wire:
     is_input: bool
     is_ket: bool
 
-    _contraction_id: int = field(default_factory=random_int, init=False)
-    _dim: int | None = field(default=None, init=False)
-    _is_connected: bool = field(default=False, init=False)
+    def __post_init__(self):
+        self._contraction_id: int = random_int()
+        self._dim = None
+        self._is_connected = False
 
     @property
     def contraction_id(self) -> int:
@@ -79,7 +80,7 @@ def dim(self):
 
     @dim.setter
     def dim(self, value: int):
-        if self._dim is not None:
+        if self._dim:
             raise ValueError("Cannot change the dimension of wire with specified dimension.")
         self._dim = value
 
@@ -186,16 +187,18 @@ def _update_modes(
         self._modes_in_bra = modes_in_bra if modes_in_bra else []
         self._modes_out_bra = modes_out_bra if modes_out_bra else []
 
-        # initialize ket and bra wire dicts using dictionary comprehensions for better performance
-        self._input = WireGroup(
-            ket={mode: Wire(random_int(), mode, True, True) for mode in self._modes_in_ket},
-            bra={mode: Wire(random_int(), mode, True, False) for mode in self._modes_in_bra},
-        )
+        # initialize ket and bra wire dicts
+        self._input = WireGroup()
+        for mode in self._modes_in_ket:
+            self._input.ket |= {mode: Wire(random_int(), mode, True, True)}
+        for mode in self._modes_in_bra:
+            self._input.bra |= {mode: Wire(random_int(), mode, True, False)}
 
-        self._output = WireGroup(
-            ket={mode: Wire(random_int(), mode, False, True) for mode in self._modes_out_ket},
-            bra={mode: Wire(random_int(), mode, False, False) for mode in self._modes_out_bra},
-        )
+        self._output = WireGroup()
+        for mode in self._modes_out_ket:
+            self._output.ket |= {mode: Wire(random_int(), mode, False, True)}
+        for mode in self._modes_out_bra:
+            self._output.bra |= {mode: Wire(random_int(), mode, False, False)}
 
     @property
     def adjoint(self) -> AdjointView:
@@ -351,25 +354,30 @@ def shape(self, default_dim: int | None = None, out_in=False):
         Returns the shape of the underlying tensor, as inferred from the dimensions of the individual
         wires.
 
-        If ``out_in`` is ``False``, the shape returned is in the order ``(in_ket, in_bra, out_ket, out_bra)``
+        If ``out_in`` is ``False``, the shape returned is in the order ``(in_ket, in_bra, out_ket, out_bra)``.
         Otherwise, it is in the order ``(out_ket, out_bra, in_ket, in_bra)``.
 
         Args:
             default_dim: The default dimension of wires with unspecified dimension.
             out_in: Whether to return output shapes followed by input shapes or viceversa.
         """
 
+        def _sort_shapes(*args):
+            for arg in args:
+                if arg:
+                    yield arg
+
         shape_in_ket = [w.dim if w.dim else default_dim for w in self.input.ket.values()]
         shape_out_ket = [w.dim if w.dim else default_dim for w in self.output.ket.values()]
         shape_in_bra = [w.dim if w.dim else default_dim for w in self.input.bra.values()]
         shape_out_bra = [w.dim if w.dim else default_dim for w in self.output.bra.values()]
 
         if out_in:
-            combined_shape = shape_out_ket + shape_out_bra + shape_in_ket + shape_in_bra
-        else:
-            combined_shape = shape_in_ket + shape_in_bra + shape_out_ket + shape_out_bra
+            ret = _sort_shapes(shape_out_ket, shape_out_bra, shape_in_ket, shape_in_bra)
+        ret = _sort_shapes(shape_in_ket, shape_in_bra, shape_out_ket, shape_out_bra)
 
-        return tuple(combined_shape)
+        # pylint: disable=consider-using-generator
+        return tuple([item for sublist in ret for item in sublist])
 
 
 class AdjointView(Tensor):
@@ -381,10 +389,10 @@ def __init__(self, tensor):
         self._original = tensor
         super().__init__(
             name=self._original.name,
-            modes_in_ket=list(self._original.input.bra.keys()),
-            modes_out_ket=list(self._original.output.bra.keys()),
-            modes_in_bra=list(self._original.input.ket.keys()),
-            modes_out_bra=list(self._original.output.ket.keys()),
+            modes_in_ket=self._original.input.bra.keys(),
+            modes_out_ket=self._original.output.bra.keys(),
+            modes_in_bra=self._original.input.ket.keys(),
+            modes_out_bra=self._original.output.ket.keys(),
         )
 
     def value(self, shape: tuple[int]):
@@ -397,7 +405,12 @@ def value(self, shape: tuple[int]):
             ComplexTensor: the unitary matrix in Fock representation
         """
         # converting the given shape into a shape for the original tensor
-        shape_in_ket, shape_out_ket, shape_in_bra, shape_out_bra = self._original.unpack_shape(shape)
+        (
+            shape_in_ket,
+            shape_out_ket,
+            shape_in_bra,
+            shape_out_bra,
+        ) = self._original.unpack_shape(shape)
         shape_ret = shape_in_bra + shape_out_bra + shape_in_ket + shape_out_ket
 
         ret = math.conj(math.astensor(self._original.value(shape_ret)))
@@ -413,10 +426,10 @@ def __init__(self, tensor):
         self._original = tensor
         super().__init__(
             name=self._original.name,
-            modes_in_ket=list(self._original.output.ket.keys()),
-            modes_out_ket=list(self._original.input.ket.keys()),
-            modes_in_bra=list(self._original.output.bra.keys()),
-            modes_out_bra=list(self._original.input.bra.keys()),
+            modes_in_ket=self._original.output.ket.keys(),
+            modes_out_ket=self._original.input.ket.keys(),
+            modes_in_bra=self._original.output.bra.keys(),
+            modes_out_bra=self._original.input.bra.keys(),
         )
 
     def value(self, shape: tuple[int]):
@@ -430,6 +443,6 @@ def value(self, shape: tuple[int]):
         """
         # converting the given shape into a shape for the original tensor
         shape_in_ket, shape_out_ket, shape_in_bra, shape_out_bra = self.unpack_shape(shape)
-        shape_ret = shape_out_ket + shape_in_ket + shape_out_bra + shape_in_bra
+        shape_ret = shape_out_ket + shape_in_ket + shape_out_bra, shape_in_bra
 
         return math.conj(self._original.value(shape_ret))