refactor: simplify forceflat logic

pylops/linearoperator.py

@@ -95,9 +95,10 @@ class LinearOperator(_LinearOperator):
     forceflat : :obj:`bool`, optional
         .. versionadded:: 2.2.0
 
-        Force an array to be flattened after matvec/rmatvec if the input is ambiguous (i.e., is a 1D array both when
-        operating with ND arrays and with 1D arrays. Defaults to ``None`` for operators that have no ambiguity (and
-        to ``False`` for those with ambiguity)
+        Force an array to be flattened after matvec/rmatvec if the input is ambiguous
+        (i.e., is a 1D array both when operating with ND arrays and with 1D arrays).
+        Defaults to ``None`` for operators that have no ambiguity or to ``False``
+        for those with ambiguity.
     name : :obj:`str`, optional
         .. versionadded:: 2.0.0
 
@@ -361,25 +362,26 @@ def __add__(self, x: LinearOperator) -> LinearOperator:
             )
             Op.clinear = Op.clinear and Opx.clinear
             Op.explicit = False
-            # Define forceflat (if differing, raise error)
-            if isinstance(self.forceflat, bool) and isinstance(Opx.forceflat, bool):
+            if self.forceflat is None and Opx.forceflat is None:
+                Op.forceflat = None
+            elif self.forceflat is not None and Opx.forceflat is not None:
+                # Define forceflat only if differing, otherwise raise error
                 if self.forceflat != Opx.forceflat:
                     raise ValueError(
-                        f"the two operators have contrasting forceflat {Op.forceflat}-{Opx.forceflat}"
+                        f"Operators have conflicting forceflat {Op.forceflat} != {Opx.forceflat}"
                     )
-                else:
-                    Op.forceflat = self.forceflat
-            if isinstance(self.forceflat, bool) or isinstance(Opx.forceflat, bool):
+                Op.forceflat = self.forceflat
+            else:  # Only one of them is None
                 Op.forceflat = (
                     self.forceflat if self.forceflat is not None else Opx.forceflat
                 )
-            else:
-                Op.forceflat = None
+
             # Replace if shape-like
             if len(self.dims) == 1:
                 Op.dims = Opx.dims
             if len(self.dimsd) == 1:
                 Op.dimsd = Opx.dimsd
+
             return Op
         else:
             return NotImplemented
@@ -627,20 +629,19 @@ def dot(self, x: NDArray) -> NDArray:
             self._copy_attributes(Op, exclude=["dims", "explicit", "forceflat", "name"])
             Op.clinear = Op.clinear and Opx.clinear
             Op.explicit = False
-            # Define forceflat (if differing, raise error)
-            if isinstance(self.forceflat, bool) and isinstance(Opx.forceflat, bool):
+            if self.forceflat is None and Opx.forceflat is None:
+                Op.forceflat = None
+            elif self.forceflat is not None and Opx.forceflat is not None:
+                # Define forceflat only if differing, otherwise raise error
                 if self.forceflat != Opx.forceflat:
                     raise ValueError(
-                        f"the two operators have contrasting forceflat {Op.forceflat}-{Opx.forceflat}"
+                        f"Operators have conflicting forceflat {Op.forceflat} != {Opx.forceflat}"
                     )
-                else:
-                    Op.forceflat = self.forceflat
-            if isinstance(self.forceflat, bool) or isinstance(Opx.forceflat, bool):
+                Op.forceflat = self.forceflat
+            else:  # Only one of them is None
                 Op.forceflat = (
                     self.forceflat if self.forceflat is not None else Opx.forceflat
                 )
-            else:
-                Op.forceflat = None
             Op.dims = Opx.dims
             return Op
         elif np.isscalar(x):
@@ -840,7 +841,6 @@ def tosparse(self) -> NDArray:
 
         # loop through columns of self
         for i in range(n):
-
             # make a unit vector for the ith column
             unit_i = np.zeros(n)
             unit_i[i] = 1