Tutorial `~adaptive.DataSaver`
 ------------------------------
 
 .. 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:`tutorial.DataSaver`

 .. jupyter-execute::

     :hide-code:
 
     import adaptive

     adaptive.notebook_extension(_inline_js=False)

 
 If the function that you want to learn returns a value along with some
 metadata, you can wrap your learner in an `adaptive.DataSaver`.
 
 In the following example the function to be learned returns its result
 and the execution time in a dictionary:
 

 .. jupyter-execute::

 
     from operator import itemgetter
 
     def f_dict(x):
         """The function evaluation takes roughly the time we `sleep`."""
         import random
         from time import sleep
 
         waiting_time = random.random()
         sleep(waiting_time)
         a = 0.01
         y = x + a**2 / (a**2 + x**2)
         return {'y': y, 'waiting_time': waiting_time}
 
     # Create the learner with the function that returns a 'dict'
     # This learner cannot be run directly, as Learner1D does not know what to do with the 'dict'
     _learner = adaptive.Learner1D(f_dict, bounds=(-1, 1))
 
     # Wrapping the learner with 'adaptive.DataSaver' and tell it which key it needs to learn
     learner = adaptive.DataSaver(_learner, arg_picker=itemgetter('y'))
 
 ``learner.learner`` is the original learner, so
 ``learner.learner.loss()`` will call the correct loss method.
 

 .. jupyter-execute::

 
     runner = adaptive.Runner(learner, goal=lambda l: l.learner.loss() < 0.1)
 

 .. jupyter-execute::

     :hide-code:
 
     await runner.task  # This is not needed in a notebook environment!
 

 .. jupyter-execute::

 
     runner.live_info()
 

 .. jupyter-execute::

 
     runner.live_plot(plotter=lambda l: l.learner.plot(), update_interval=0.1)
 
 Now the ``DataSavingLearner`` will have an dictionary attribute
 ``extra_data`` that has ``x`` as key and the data that was returned by
 ``learner.function`` as values.
 

 .. jupyter-execute::

 
     learner.extra_data