Introduce 'tell_pending' which replaces 'tell(x, None)'

and I also tackle

• lower_bound <= learner.ask()[0] <= upper_bound
• if we tell(x, y) with x outside the bounds, the point should be added to data, but not any of the other internal datastructures. This will mean that the point will be "invisible" to the learning algorithm, and only useful for plotting and data extraction

for both the Learner1D and Learner2D in that MR. I included this in here to avoid merge conflicts later.

adaptive/learner/average_learner.py

@@ -50,7 +50,7 @@ class AverageLearner(BaseLearner):
     def n_requested(self):
         return len(self.data) + len(self.pending_points)
 
-    def ask(self, n, add_data=True):
+    def ask(self, n, tell_pending=True):
         points = list(range(self.n_requested, self.n_requested + n))
 
         if any(p in self.data or p in self.pending_points for p in points):
@@ -60,8 +60,9 @@ class AverageLearner(BaseLearner):
                           - set(self.pending_points))[:n]
 
         loss_improvements = [self.loss_improvement(n) / n] * n
-        if add_data:
-            self.tell_many(points, itertools.repeat(None))
+        if tell_pending:
+            for p in points:
+                self.tell_pending(p)
         return points, loss_improvements
 
     def tell(self, n, value):
@@ -69,14 +70,14 @@ class AverageLearner(BaseLearner):
             # The point has already been added before.
             return
 
-        if value is None:
-            self.pending_points.add(n)
-        else:
-            self.data[n] = value
-            self.pending_points.discard(n)
-            self.sum_f += value
-            self.sum_f_sq += value**2
-            self.npoints += 1
+        self.data[n] = value
+        self.pending_points.discard(n)
+        self.sum_f += value
+        self.sum_f_sq += value**2
+        self.npoints += 1
+
+    def tell_pending(self, n):
+        self.pending_points.add(n)
 
     @property
     def mean(self):