More efficient 'tell_many'

import adaptive

def f(x, offset=0):
    a = 0.01
    return x + a**2 / (a**2 + (x - offset)**2)

learner = adaptive.Learner1D(f, bounds=(-1, 1))
adaptive.runner.simple(learner, goal=lambda l: l.npoints > 200)

Timing new implementation

%%timeit
learner2 = adaptive.Learner1D(f, bounds=(-1, 1))
learner2.tell_many(*zip(*learner.data.items()))

1.17 ms ± 24.9 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

Timing old implementation

%%timeit
learner2 = adaptive.Learner1D(f, bounds=(-1, 1))
for x, y in learner.data.items():
    learner2.tell(x, y)

6.82 ms ± 447 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

This makes it ~6 times faster for functions that return scalars and is >10 times faster for vectors.

adaptive/learner/learner1D.py

@@ -439,8 +439,7 @@ class Learner1D(BaseLearner):
         elif not self.vdim > 1:
             p = hv.Scatter(self.data) * hv.Path([])
         else:
-            xs = list(self.data.keys())
-            ys = list(self.data.values())
+            xs, ys = zip(*sorted(self.data.items()))
             p = hv.Path((xs, ys)) * hv.Scatter([])
 
         # Plot with 5% empty margins such that the boundary points are visible