adaptive/learner/balancing_learner.py

@@ -335,13 +335,13 @@ class BalancingLearner(BaseLearner):
         -------
         >>> def combo_fname(learner):
         ...     val = learner.function.keywords  # because functools.partial
-        ...     fname = '__'.join([f'{k}_{v}.pickle' for k, v in val])
+        ...     fname = '__'.join([f'{k}_{v}.pickle' for k, v in val.items()])
         ...     return 'data_folder/' + fname
         >>>
         >>> def f(x, a, b): return a * x**2 + b
         >>>
         >>> learners = [Learner1D(functools.partial(f, **combo), (-1, 1))
-        ...             for combo in adaptive.utils.named_product(a=[1, 2], b=[1]]
+        ...             for combo in adaptive.utils.named_product(a=[1, 2], b=[1])]
         >>>
         >>> learner = BalancingLearner(learners)
         >>> # Run the learner

adaptive/learner/learnerND.py

@@ -506,7 +506,7 @@ class LearnerND(BaseLearner):
         if self.vdim > 1:
             raise NotImplementedError('holoviews currently does not support',
                                       '3D surface plots in bokeh.')
-        if len(self.ndim) != 2:
+        if self.ndim != 2:
             raise NotImplementedError("Only 2D plots are implemented: You can "
                                       "plot a 2D slice with 'plot_slice'.")
         x, y = self._bbox
@@ -516,7 +516,9 @@ class LearnerND(BaseLearner):
             if n is None:
                 # Calculate how many grid points are needed.
                 # factor from A=√3/4 * a² (equilateral triangle)
-                n = int(0.658 / np.sqrt(np.min(self.tri.volumes())))
+                scale_factor = np.product(np.diag(self._transform))
+                a_sq = np.sqrt(np.min(self.tri.volumes()) * scale_factor)
+                n = max(10, int(0.658 / a_sq))
 
             xs = ys = np.linspace(0, 1, n)
             xs = xs * (x[1] - x[0]) + x[0]
@@ -585,7 +587,9 @@ class LearnerND(BaseLearner):
             if n is None:
                 # Calculate how many grid points are needed.
                 # factor from A=√3/4 * a² (equilateral triangle)
-                n = int(0.658 / np.sqrt(np.min(self.tri.volumes())))
+                scale_factor = np.product(np.diag(self._transform))
+                a_sq = np.sqrt(np.min(self.tri.volumes()) * scale_factor)
+                n = max(10, int(0.658 / a_sq))
 
             xs = ys = np.linspace(0, 1, n)
             xys = [xs[:, None], ys[None, :]]
@@ -780,7 +784,7 @@ class LearnerND(BaseLearner):
                 plot = plot * self.plot_isoline(level=l, n=-1)
             return plot
 
-        vertices, lines = self.self._get_iso(level, which='line')
+        vertices, lines = self._get_iso(level, which='line')
         paths = [[vertices[i], vertices[j]] for i, j in lines]
         contour = hv.Path(paths)