From d79e41eb7e2afd3f7132ca9e20b867a4fb7dea86 Mon Sep 17 00:00:00 2001
From: Jakob Jordan <jakobjordan@posteo.de>
Date: Mon, 19 Apr 2021 16:18:39 +0200
Subject: [PATCH] Apply changes from code review

---
 cgp/cartesian_graph.py | 39 +++++++++++++++++++--------------------
 1 file changed, 19 insertions(+), 20 deletions(-)

diff --git a/cgp/cartesian_graph.py b/cgp/cartesian_graph.py
index 0c48bbaf..a817da2d 100644
--- a/cgp/cartesian_graph.py
+++ b/cgp/cartesian_graph.py
@@ -234,9 +234,9 @@ def to_func(self) -> Callable[..., List[float]]:
         """Create a Python callable implementing the function described by
         this graph.
 
-        The function expects as many arguments as the number of inputs
-        defined in the genome. The function returns a tuple with
-        length equal to the number of outputs defined in the
+        The returned callable expects as many arguments as the number
+        of inputs defined in the genome. The function returns a tuple
+        with length equal to the number of outputs defined in the
         genome. For convenience, if only a single output is defined
         the function will *not* return a tuple but only its first
         element.
@@ -278,9 +278,9 @@ def to_numpy(self) -> Callable[..., List[np.ndarray]]:
         """Create a NumPy-array-compatible Python callable implementing the
         function described by this graph.
 
-        The function expects as many arguments as the number of inputs
-        defined in the genome. Every argument needs to be a NumPy
-        array of equal length. The function returns a tuple with
+        The returned callable expects as many arguments as the number
+        of inputs defined in the genome. Every argument needs to be a
+        NumPy array of equal length. The function returns a tuple with
         length equal to the number of outputs defined in the
         genome. Each element will have the same length as the input
         arrays. For convenience, if only a single output is defined
@@ -313,8 +313,8 @@ def _f(*x):
         return locals()["_f"]
 
     def to_torch(self) -> "torch.nn.Module":
-        """Create a Torch class instance implementing the function defined by
-        this graph.
+        """Create a Torch nn.Module instance implementing the function defined
+        by this graph.
 
         The generated instance will have a `forward` method accepting
         Torch tensor of dimension (<batch size>, n_inputs) and
@@ -403,14 +403,6 @@ def to_sympy(
 
         """
 
-        def possibly_unpack(
-            sympy_exprs: List["sympy_expr.Expr"],
-        ) -> Union["sympy_expr.Expr", List["sympy_expr.Expr"]]:
-            if len(sympy_exprs) == 1:
-                return sympy_exprs[0]
-            else:
-                return sympy_exprs
-
         if not sympy_available:
             raise ModuleNotFoundError("No module named 'sympy' (extra requirement)")
 
@@ -432,12 +424,19 @@ def possibly_unpack(
             # sympy should not automatically simplify the expression
             sympy_exprs.append(sympy.sympify(s, evaluate=False))
 
-        if not simplify:
-            return possibly_unpack(sympy_exprs)
-        else:  # simplify expression if desired and possible
+        if simplify:
             for i, expr in enumerate(sympy_exprs):
                 try:
                     sympy_exprs[i] = expr.simplify()
                 except TypeError:
                     RuntimeWarning(f"SymPy could not simplify expression: {expr}")
-            return possibly_unpack(sympy_exprs)
+
+        def possibly_unpack(
+            sympy_exprs: List["sympy_expr.Expr"],
+        ) -> Union["sympy_expr.Expr", List["sympy_expr.Expr"]]:
+            if len(sympy_exprs) == 1:
+                return sympy_exprs[0]
+            else:
+                return sympy_exprs
+
+        return possibly_unpack(sympy_exprs)