complex

nutils/evaluable.py

@@ -852,10 +852,6 @@ def _node(self, cache, subgraph, times):
 
     # simplifications
     _eig = lambda self, symmetric: None
-    _loopsum = lambda self, loop_index: None  # NOTE: type of `loop_index` is `_LoopIndex`
-    _real = lambda self: None
-    _imag = lambda self: None
-    _conjugate = lambda self: None
 
     representations = ()
 
@@ -1108,9 +1104,6 @@ def evalf(func, length):
     def _derivative(self, var, seen):
         return insertaxis(derivative(self.func, var, seen), self.ndim-1, self.length)
 
-    def _loopsum(self, index):
-        return InsertAxis(loop_sum(self.func, index), self.length)
-
     @cached_property
     def _assparse(self):
         return tuple((*(InsertAxis(idx, self.length) for idx in indices), prependaxes(Range(self.length), values.shape), InsertAxis(values, self.length)) for *indices, values in self.func._assparse)
@@ -1278,9 +1271,6 @@ def _axes_for(self, ndim, axis):
         axes[axis:axis] = range(funcaxis, funcaxis + ndim)
         return tuple(axes)
 
-    def _loopsum(self, index):
-        return Transpose(loop_sum(self.func, index), self.axes)
-
     @cached_property
     def _assparse(self):
         return tuple((*(indices[i] for i in self.axes), values) for *indices, values in self.func._assparse)
@@ -2573,9 +2563,8 @@ def return_type(T):
         return complex
 
     def _simplified(self):
-        retval = self.args[0]._conjugate()
-        if retval is not None:
-            return retval
+        for f, in self.args[0]._as(FloatToComplex):
+            return FloatToComplex(f)
         return super()._simplified()
 
 
@@ -2588,9 +2577,8 @@ def return_type(T):
         return float
 
     def _simplified(self):
-        retval = self.args[0]._real()
-        if retval is not None:
-            return retval
+        for f, in self.args[0]._as(FloatToComplex):
+            return f
         return super()._simplified()
 
 
@@ -2603,9 +2591,8 @@ def return_type(T):
         return float
 
     def _simplified(self):
-        retval = self.args[0]._imag()
-        if retval is not None:
-            return retval
+        for f, in self.args[0]._as(FloatToComplex):
+            return zeros_like(self)
         return super()._simplified()
 
 
@@ -2652,15 +2639,6 @@ def return_type(T):
             raise TypeError(f'Expected an array with dtype float but got {T.__name__}.')
         return complex
 
-    def _real(self):
-        return self.args[0]
-
-    def _imag(self):
-        return zeros_like(self.args[0])
-
-    def _conjugate(self):
-        return self
-
     def _derivative(self, var, seen):
         if var.dtype == complex:
             raise ValueError('The complex derivative does not exist.')
@@ -3101,9 +3079,6 @@ def evalf(arr):
     def _derivative(self, var, seen):
         return diagonalize(derivative(self.func, var, seen), self.ndim-2, self.ndim-1)
 
-    def _loopsum(self, index):
-        return Diagonalize(loop_sum(self.func, index))
-
     @cached_property
     def _assparse(self):
         return tuple((*indices, indices[-1], values) for *indices, values in self.func._assparse)
@@ -3343,9 +3318,6 @@ def evalf(f):
     def _derivative(self, var, seen):
         return ravel(derivative(self.func, var, seen), axis=self.ndim-1)
 
-    def _loopsum(self, index):
-        return Ravel(loop_sum(self.func, index))
-
     @cached_property
     def _assparse(self):
         return tuple((*indices[:-2], indices[-2]*self.func.shape[-1]+indices[-1], values) for *indices, values in self.func._assparse)
@@ -4320,9 +4292,10 @@ def _node_loop_body(self, cache, subgraph, times):
     def _simplified(self):
         if isinstance(self.func, Zeros):
             return zeros_like(self)
-        elif self.index not in self.func.arguments:
+        if self.index not in self.func.arguments:
             return self.func * astype(self.index.length, self.func.dtype)
-        return self.func._loopsum(self.index)
+        if simple := self._as_any(insertaxis, diagonalize, ravel):
+            return simple
 
     @cached_property
     def _assparse(self):