rename 'total_samples' -> 'nsamples', inline with 'npoints'

Author: basnijholt

adaptive/learner/average_learner1D.py

@@ -94,12 +94,14 @@ def __init__(
         self.rescaled_error = decreasing_dict()
 
     @property
-    def total_samples(self):
+    def nsamples(self):
         """Returns the total number of samples"""
         return sum(self._number_samples.values())
 
     @property
     def min_samples_per_point(self):
+        if not self._number_samples:
+            return 0
         return min(self._number_samples.values())
 
     def ask(self, n, tell_pending=True):

adaptive/notebook_integration.py

@@ -263,7 +263,7 @@ def _info_html(runner):
         info.append(("# of points", runner.learner.npoints))
 
     with suppress(Exception):
-        info.append(("# of samples", runner.learner.total_samples))
+        info.append(("# of samples", runner.learner.nsamples))
 
     with suppress(Exception):
         info.append(("latest loss", f'{runner.learner._cache["loss"]:.3f}'))

docs/source/tutorial/tutorial.AverageLearner1D.rst

@@ -65,10 +65,9 @@ second condition ensures that we have at least 20 samples at each point):
 
 .. jupyter-execute::
 
-    def goal(total_samples):
+    def goal(nsamples):
         def _goal(learner):
-            min_samples = min(learner._number_samples.values())
-            return learner.total_samples >= total_samples and min_samples >= 20
+            return learner.nsamples >= nsamples and learner.min_samples_per_point >= 20
         return _goal
 
     runner = adaptive.Runner(learner, goal=goal(10_000))

example-notebook.ipynb

@@ -370,7 +370,7 @@
     "\n",
     "\n",
     "def goal(l):\n",
-    "    return l.total_samples >= 10_000 and l.min_samples_per_point >= 20\n",
+    "    return l.nsamples >= 10_000 and l.min_samples_per_point >= 20\n",
     "\n",
     "\n",
     "runner = adaptive.Runner(learner, goal=goal)\n",