added __repr__ methods to _FunctionApplier, _Amplitude and _LognormalMomentMatching

nifty6/library/correlated_fields.py

@@ -129,6 +129,7 @@ class _LognormalMomentMatching(Operator):
         op = _normal(logmean, logsig, key, N_copies).ptw("exp")
         self._domain, self._target = op.domain, op.target
         self.apply = op.apply
+        self._repr_str = f"_LognormalMomentMatching: " + op.__repr__()
 
     @property
     def mean(self):
@@ -138,6 +139,9 @@ class _LognormalMomentMatching(Operator):
     def std(self):
         return self._sig
 
+    def __repr__(self):
+        return self._repr_str
+
 
 class _SlopeRemover(EndomorphicOperator):
     def __init__(self, domain, space=0):
@@ -346,11 +350,15 @@ class _Amplitude(Operator):
         self.apply = op.apply
         self._domain, self._target = op.domain, op.target
         self._space = space
+        self._repr_str = "_Amplitude: " + op.__repr__()
 
     @property
     def fluctuation_amplitude(self):
         return self._fluc
 
+    def __repr__(self):
+        return self._repr_str
+
 
 class CorrelatedFieldMaker:
     """Constrution helper for hirarchical correlated field models.

nifty6/operators/operator.py

@@ -362,6 +362,9 @@ class _FunctionApplier(Operator):
         self._check_input(x)
         return x.ptw(self._funcname, *self._args, **self._kwargs)
 
+    def __repr__(self):
+        return f"_FunctionApplier ('{self._funcname}')"
+
 
 class _CombinedOperator(Operator):
     def __init__(self, ops, _callingfrommake=False):