@@ -32,7 +32,7 @@ class AverageLearner(BaseLearner):

         self.function = function

         self.atol = atol

         self.rtol = rtol

-        self.n = 0

+        self.npoints = 0

         self.sum_f = 0

         self.sum_f_sq = 0

@@ -56,25 +56,25 @@ class AverageLearner(BaseLearner):

             self.sum_f += value

             self.sum_f_sq += value**2

             if value_is_new:

-                self.n += 1

+                self.npoints += 1

             else:

                 self.sum_f -= value_old

                 self.sum_f_sq -= value_old**2

     @property

     def mean(self):

-        return self.sum_f / self.n

+        return self.sum_f / self.npoints

     @property

     def std(self):

-        n = self.n

+        n = self.npoints

         if n < 2:

             return np.inf

         return sqrt((self.sum_f_sq - n * self.mean**2) / (n - 1))

     def loss(self, real=True, *, n=None):

         if n is None:

-            n = self.n if real else self.n_requested

+            n = self.npoints if real else self.n_requested

         else:

             n = n

         if n < 2:

@@ -86,7 +86,7 @@ class AverageLearner(BaseLearner):

     def loss_improvement(self, n):

         loss = self.loss()

         if np.isfinite(loss):

-            return loss - self.loss(n=self.n + n)

+            return loss - self.loss(n=self.npoints + n)

         else:

             return np.inf

@@ -99,6 +99,6 @@ class AverageLearner(BaseLearner):

         vals = [v for v in self.data.values() if v is not None]

         if not vals:

             return hv.Histogram([[], []])

-        num_bins = int(max(5, sqrt(self.n)))

+        num_bins = int(max(5, sqrt(self.npoints)))

         vals = hv.Points(vals)

         return hv.operation.histogram(vals, num_bins=num_bins, dimension=1)

@@ -15,7 +15,7 @@ class BaseLearner(metaclass=abc.ABCMeta):

         'function' evaluated at certain points.

         The values can be 'None', which indicates that the point

         will be evaluated, but that we do not have the result yet.

-    n : int, optional

+    npoints : int, optional

         The number of evaluated points that have been added to the learner.

         Subclasses do not *have* to implement this attribute.

@@ -100,7 +100,7 @@ class Learner1D(BaseLearner):

         return {**self.data, **self.data_interp}

     @property

-    def n(self):

+    def npoints(self):

         return len(self.data)

     def loss(self, real=True):

@@ -178,7 +178,7 @@ class Learner2D(BaseLearner):

         self.stack_size = 10

     @property

-    def n(self):

+    def npoints(self):

         return len(self.data)

     @property

@@ -177,7 +177,7 @@ def _info_html(runner):

     ]

     try:

-        info.append(('# of points', runner.learner.n))

+        info.append(('# of points', runner.learner.npoints))

     except Exception:

         pass

@@ -208,7 +208,7 @@

    "source": [

     "def plot(learner):\n",

     "    plot = learner.plot(tri_alpha=0.2)\n",

-    "    title = f'loss={learner._loss:.3f}, n_points={learner.n}'\n",

+    "    title = f'loss={learner._loss:.3f}, n_points={learner.npoints}'\n",

     "    opts = dict(plot=dict(title_format=title))\n",

     "    return plot.Image + plot.EdgePaths.I.clone().opts(**opts) + plot\n",

     "\n",

@@ -223,7 +223,7 @@

    "source": [

     "import itertools\n",

     "learner2 = adaptive.Learner2D(ring, bounds=learner.bounds)\n",

-    "n = int(learner.n**0.5)\n",

+    "n = int(learner.npoints**0.5)\n",

     "xs, ys = [np.linspace(*bounds, n) for bounds in learner.bounds]\n",

     "xys = list(itertools.product(xs, ys))\n",

     "learner2.add_data(xys, map(partial(ring, wait=False), xys))\n",