/ Data Visualization

Visualize Python code execution in Jupyter Notebook cells

Visualize Python code execution in Jupyter Notebook cells

Nbtutor

Visualize Python code execution (line-by-line) in Jupyter Notebook cells. Inspired by Online Python Tutor.

Nbtutor

Install

Note: installing directly off this repo won't work, as we don't ship the built
JavaScript and CSS assets. See more about developing <#develop>__ below.

pip


.. code:: shell

    pip install nbtutor
    jupyter nbextension install --overwrite --py nbtutor
    jupyter nbextension enable --py nbtutor

``conda``

.. code:: shell

conda install -c conda-forge nbtutor

Usage (Jupyter Notebook)

First load the nbtutor IPython kernel extension at top of the Notebook by
executing the following magic in a CodeCell:

.. code:: python

%load_ext nbtutor

Then to visualize the execute of code in a CodeCell add the
following magic to the top of the CodeCell and execute it again:

.. code:: python

%%nbtutor

Optional arguments


There are also optional arguments that can be used with the cell magic:

-  Reset the IPython user namespace

   .. code:: python

       %%nbtutor -r/--reset

-  Suppress the confirmation message from ``-r/--reset``

   .. code:: python

       %%nbtutor -r/--reset -f/--force

-  Render primitive objects inline

   .. code:: python

       %%nbtutor -i/--inline

-  Specify the maximum frame depth to visualize (default: 1)

   .. code:: python

       %%nbtutor -d/--depth N

-  Specify the number of significant digits for floats (default: 3)

   .. code:: python

       %%nbtutor --digits D

-  Specify the maximum number of elements to visualize for "sequence"
   type objects (default: 5)

   .. code:: python

       %%nbtutor --max_size S

-  Step through all frames (including frames from other cells and other
   global scopes altogether)

   .. code:: python

       %%nbtutor --step_all

-  Expand ``numpy`` arrays to show underlying data

   .. code:: python

       %%nbtutor --expand_arrays

-  No inlined keys, attributes, or primitive objects

   .. code:: python

       %%nbtutor --nolies


Notes
-----

-  Visualizing ``numpy`` arrays is somewhat experimental. Simple
   ``ndarray``\ s and simple slicing should work, but anything beyond
   that is un-tested.
-  If you find a problem please feel free to submit an
   `issue <https://github.com/lgpage/nbtutor/issues>`__


Develop
-------

This assumes you have cloned this repository locally:

.. code:: shell

    git clone https://github.com/lgpage/nbtutor.git
    cd nbtutor


Repo architecture
~~~~~~~~~~~~~~~~~

The ``nbtutor`` nbextension is built from ``./src`` into
``./nbtutor/static/nbtutor`` with: - ``less`` for style - ``es6`` (via
``babel``) for javascript - ``browserify`` for packaging

The ``nbtutor`` ipython kernel extension (magics) is stored in the
``./nbtutor/ipython`` folder

Build tools are stored in the ``./tools`` folder.


Getting started
~~~~~~~~~~~~~~~

You'll need conda installed, either from
`Anaconda <https://www.continuum.io/downloads>`__ or
`miniconda <http://conda.pydata.org/miniconda.html>`__. You can create a
Python development environment named ``nbtutor`` from
``./environment.yml``.

.. code:: shell

    conda create -n nbtutor python=YOUR_FAVORITE_PYTHON
    conda env update
    source activate nbtutor

We use ``npm`` for ``node.js`` dependencies, so then run:

.. code:: shell

    npm install

Finally, you are ready to build the assets with:

.. code:: shell

    npm run build


Installing the nbextension

To ensure that you always get the right assets (for development),
install the nbextension with the symlink options:

.. code:: shell

python setup.py develop
jupyter nbextension install --overwrite --symlink --sys-prefix --py nbtutor
jupyter nbextension enable --sys-prefix --py nbtutor

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