Executing notebooks
===================

.. module:: nbconvert.preprocessors

Jupyter notebooks are often saved with output cells that have been cleared.
nbconvert provides a convenient way to execute the input cells of an
.ipynb notebook file and save the results, both input and output cells,
as a .ipynb file.

In this section we show how to execute a ``.ipynb`` notebook
document saving the result in notebook format. If you need to export
notebooks to other formats, such as reStructured Text or Markdown (optionally
executing them) see section :doc:`nbconvert_library`.

Executing notebooks can be very helpful, for example, to run all notebooks
in Python library in one step, or as a way to automate the data analysis in
projects involving more than one notebook.

Executing notebooks from the command line
-----------------------------------------
The same functionality of executing notebooks is exposed through a
:doc:`command line interface <usage>` or a Python API interface.
As an example, a notebook can be executed from the command line with::

    jupyter nbconvert --to notebook --execute mynotebook.ipynb

Executing notebooks using the Python API interface
--------------------------------------------------
This section will illustrate the Python API interface.

Example
~~~~~~~

Let's start with a complete quick example, leaving detailed explanations
to the following sections.

**Import**: First we import nbconvert and the :class:`ExecutePreprocessor`
class::

    import nbformat
    from nbconvert.preprocessors import ExecutePreprocessor

**Load**: Assuming that ``notebook_filename`` contains the path of a notebook,
we can load it with::

    with open(notebook_filename) as f:
        nb = nbformat.read(f, as_version=4)

**Configure**: Next, we configure the notebook execution mode::

    ep = ExecutePreprocessor(timeout=600, kernel_name='python3')

We specified two (optional) arguments ``timeout`` and ``kernel_name``, which
define respectively the cell execution timeout and the execution kernel.

    The option to specify **kernel_name** is new in nbconvert 4.2.
    When not specified or when using nbconvert <4.2,
    the default Python kernel is chosen.

**Execute/Run (preprocess)**: To actually run the notebook we call the method
``preprocess``::

    ep.preprocess(nb, {'metadata': {'path': 'notebooks/'}})

Hopefully, we will not get any errors during the notebook execution
(see the last section for error handling). Note that ``path`` specifies
in which folder to execute the notebook.

**Save**: Finally, save the resulting notebook with::

    with open('executed_notebook.ipynb', 'w', encoding='utf-8') as f:
        nbformat.write(nb, f)

That's all. Your executed notebook will be saved in the current folder
in the file ``executed_notebook.ipynb``.

Execution arguments (traitlets)
-------------------------------

The arguments passed to :class:`ExecutePreprocessor` are configuration options
called `traitlets <https://traitlets.readthedocs.io/en/stable>`_.
There are many cool things about traitlets. For example,
they enforce the input type, and they can be accessed/modified as
class attributes. Moreover, each traitlet is automatically exposed
as command-line options. For example, we can pass the timeout from the
command-line like this::

    jupyter nbconvert --ExecutePreprocessor.timeout=600 --to notebook --execute mynotebook.ipynb

Let's now discuss in more detail the two traitlets we used.

The ``timeout`` traitlet defines the maximum time (in seconds) each notebook
cell is allowed to run, if the execution takes longer an exception will be
raised. The default is 30 s, so in cases of long-running cells you may want to
specify an higher value. The ``timeout`` option can also be set to ``None``
or ``-1`` to remove any restriction on execution time.

The second traitlet, ``kernel_name``, allows specifying the name of the kernel
to be used for the execution. By default, the kernel name is obtained from the
notebook metadata. The traitlet ``kernel_name`` allows specifying a
user-defined kernel, overriding the value in the notebook metadata. A common
use case is that of a Python 2/3 library which includes documentation/testing
notebooks. These notebooks will specify either a python2 or python3 kernel in
their metadata (depending on the kernel used the last time the notebook was
saved). In reality, these notebooks will work on both Python 2 and Python 3,
and, for testing, it is important to be able to execute them programmatically
on both versions. Here the traitlet ``kernel_name`` helps simplify and
maintain consistency: we can just run a notebook twice, specifying first
"python2" and then "python3" as the kernel name.

Handling errors and exceptions
------------------------------

In the previous sections we saw how to save an executed notebook, assuming
there are no execution errors. But, what if there are errors?

Execution until first error
~~~~~~~~~~~~~~~~~~~~~~~~~~~
An error during the notebook execution, by default, will stop the execution
and raise a ``CellExecutionError``. Conveniently, the source cell causing
the error and the original error name and message are also printed.
After an error, we can still save the notebook as before::

    with open('executed_notebook.ipynb', mode='w', encoding='utf-8') as f:
        nbformat.write(nb, f)

The saved notebook contains the output up until the failing cell,
and includes a full stack-trace and error (which can help debugging).

Handling errors
~~~~~~~~~~~~~~~
A useful pattern to execute notebooks while handling errors is the following::

    from nbconvert.preprocessors import CellExecutionError

    try:
        out = ep.preprocess(nb, {'metadata': {'path': run_path}})
    except CellExecutionError:
        out = None
        msg = 'Error executing the notebook "%s".\n\n' % notebook_filename
        msg += 'See notebook "%s" for the traceback.' % notebook_filename_out
        print(msg)
        raise
    finally:
        with open(notebook_filename_out, mode='w', encoding='utf-8') as f:
            nbformat.write(nb, f)

This will save the executed notebook regardless of execution errors.
In case of errors, however, an additional message is printed and the
``CellExecutionError`` is raised. The message directs the user to
the saved notebook for further inspection.

Execute and save all errors
~~~~~~~~~~~~~~~~~~~~~~~~~~~
As a last scenario, it is sometimes useful to execute notebooks which raise
exceptions, for example to show an error condition. In this case, instead of
stopping the execution on the first error, we can keep executing the notebook
using the traitlet ``allow_errors`` (default is False). With
``allow_errors=True``, the notebook is executed until the end, regardless of
any error encountered during the execution. The output notebook, will contain
the stack-traces and error messages for **all** the cells raising exceptions.

Widget state
------------

If your notebook contains any
`Jupyter Widgets <https://github.com/jupyter-widgets/ipywidgets/>`_,
the state of all the widgets can be stored in the notebook's metadata.
This allows rendering of the live widgets on for instance nbviewer, or when
converting to html.

We can tell nbconvert to not store the state using the `store_widget_state`
argument::

    jupyter nbconvert --ExecutePreprocessor.store_widget_state=False --to notebook --execute mynotebook.ipynb

This widget rendering is not performed against a browser during execution, so
only widget default states or states manipulated via user code will be
calculated during execution. `%%javascript` cells will execute upon notebook
rendering, enabling complex interactions to function as expected when viewed by
a UI.

If you can't view widget results after execution, you may need to select
`Trust Notebook` under the `File` menu.
