@@ -14,7 +14,7 @@ Tutorial `~adaptive.Learner2D`

     :hide-code:

     import adaptive

-    adaptive.notebook_extension(_inline_js=False)

+    adaptive.notebook_extension()

     import numpy as np

     from functools import partial

@@ -10,8 +10,6 @@ Tutorial `~adaptive.Learner2D`

     The complete source code of this tutorial can be found in

     :jupyter-download:notebook:`tutorial.Learner2D`

-.. thebe-button:: Run the code live inside the documentation!

-

 .. jupyter-execute::

     :hide-code:

@@ -10,6 +10,8 @@ Tutorial `~adaptive.Learner2D`

     The complete source code of this tutorial can be found in

     :jupyter-download:notebook:`tutorial.Learner2D`

+.. thebe-button:: Run the code live inside the documentation!

+

 .. jupyter-execute::

     :hide-code:

@@ -14,7 +14,7 @@ Tutorial `~adaptive.Learner2D`

     :hide-code:

     import adaptive

-    adaptive.notebook_extension()

+    adaptive.notebook_extension(_inline_js=False)

     import numpy as np

     from functools import partial

@@ -8,11 +8,10 @@ Tutorial `~adaptive.Learner2D`

 .. seealso::

     The complete source code of this tutorial can be found in

-    :jupyter-download:notebook:`Learner2D`

+    :jupyter-download:notebook:`tutorial.Learner2D`

-.. execute::

+.. jupyter-execute::

     :hide-code:

-    :new-notebook: Learner2D

     import adaptive

     adaptive.notebook_extension()

@@ -23,7 +22,7 @@ Tutorial `~adaptive.Learner2D`

 Besides 1D functions, we can also learn 2D functions:

 :math:`\ f: ℝ^2 → ℝ`.

-.. execute::

+.. jupyter-execute::

     def ring(xy, wait=True):

         import numpy as np

@@ -37,20 +36,20 @@ Besides 1D functions, we can also learn 2D functions:

     learner = adaptive.Learner2D(ring, bounds=[(-1, 1), (-1, 1)])

-.. execute::

+.. jupyter-execute::

     runner = adaptive.Runner(learner, goal=lambda l: l.loss() < 0.01)

-.. execute::

+.. jupyter-execute::

     :hide-code:

     await runner.task  # This is not needed in a notebook environment!

-.. execute::

+.. jupyter-execute::

     runner.live_info()

-.. execute::

+.. jupyter-execute::

     def plot(learner):

         plot = learner.plot(tri_alpha=0.2)

@@ -58,7 +57,7 @@ Besides 1D functions, we can also learn 2D functions:

     runner.live_plot(plotter=plot, update_interval=0.1)

-.. execute::

+.. jupyter-execute::

     %%opts EdgePaths (color='w')

new file mode 100644

@@ -0,0 +1,75 @@

+Tutorial `~adaptive.Learner2D`

+------------------------------

+

+.. note::

+   Because this documentation consists of static html, the ``live_plot``

+   and ``live_info`` widget is not live. Download the notebook

+   in order to see the real behaviour.

+

+.. seealso::

+    The complete source code of this tutorial can be found in

+    :jupyter-download:notebook:`Learner2D`

+

+.. execute::

+    :hide-code:

+    :new-notebook: Learner2D

+

+    import adaptive

+    adaptive.notebook_extension()

+

+    import numpy as np

+    from functools import partial

+

+Besides 1D functions, we can also learn 2D functions:

+:math:`\ f: ℝ^2 → ℝ`.

+

+.. execute::

+

+    def ring(xy, wait=True):

+        import numpy as np

+        from time import sleep

+        from random import random

+        if wait:

+            sleep(random()/10)

+        x, y = xy

+        a = 0.2

+        return x + np.exp(-(x**2 + y**2 - 0.75**2)**2/a**4)

+

+    learner = adaptive.Learner2D(ring, bounds=[(-1, 1), (-1, 1)])

+

+.. execute::

+

+    runner = adaptive.Runner(learner, goal=lambda l: l.loss() < 0.01)

+

+.. execute::

+    :hide-code:

+

+    await runner.task  # This is not needed in a notebook environment!

+

+.. execute::

+

+    runner.live_info()

+

+.. execute::

+

+    def plot(learner):

+        plot = learner.plot(tri_alpha=0.2)

+        return (plot.Image + plot.EdgePaths.I + plot).cols(2)

+

+    runner.live_plot(plotter=plot, update_interval=0.1)

+

+.. execute::

+

+    %%opts EdgePaths (color='w')

+

+    import itertools

+

+    # Create a learner and add data on homogeneous grid, so that we can plot it

+    learner2 = adaptive.Learner2D(ring, bounds=learner.bounds)

+    n = int(learner.npoints**0.5)

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

+    xys = list(itertools.product(xs, ys))

+    learner2.tell_many(xys, map(partial(ring, wait=False), xys))

+

+    (learner2.plot(n).relabel('Homogeneous grid') + learner.plot().relabel('With adaptive') +

+     learner2.plot(n, tri_alpha=0.4) + learner.plot(tri_alpha=0.4)).cols(2)