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Automatic Computational Research Notebook
=========================================
``acorn`` uses the mutability of python objects, together with
decorators, to produce an automatic notebook for computational research.
Common libraries like ``numpy``, ``scipy``, ``sklearn`` and ``pandas``
are mutated with decorators that enable logging of calls to important
methods within those libraries.
This is really helpful for data science where experimenting with fits,
pipelines and pre-processing transformations can result in hundreds of
fits and predictions a day. At the end of the day, it is hard to
remember which set of parameters produced that one fit, which (of
course) you didn't realize was important at the time.
The library is `well
documented <https://rosenbrockc.github.io/acorn/>`__.
Basic Flow
----------
1. Depending on the logging level, every time a method/function is
called (whether bound or unbound), we log it into a JSON database.
2. The JSON database is analyzed using javascript by the browser to
produce nice sets of objects, separated by project, task, date and
specific object instances.
3. A nice UI using ``bootstrap`` populates the HTML dynamically.
Synchronization
---------------
We recommend that the JSON database directory be configured on a Dropbox
folder (later we will support Google Drive, etc.). The HTML notebook can
be authorized (per session) to have access to Dropbox so that the JSON
databases can be accessed from anywhere (and any device). Thi HTML and
javascript is completely standalone (i.e., no server backend required
outside of the web service requests).
Contribution
------------
If this sparks your interest, please message us. The project is still in
early development, so we can't say more up front.
Special Notes
=============
The ``matplotlib`` module is used frequently, but not in the typical
way. Most of the methods and objects are used internally unless a plot
is being tweaked for some special reason. The ``matplotlib.cfg`` file
prunes the number of objects that get decorate very aggressively so that
only the common calls are logged. You can adjust your own local config
file if you spend a lot of time actually coding ``matplotlib``
internals.
.. |Build Status| image:: https://travis-ci.org/rosenbrockc/acorn.svg?branch=master
:target: https://travis-ci.org/rosenbrockc/acorn
.. |Coverage Status| image:: https://coveralls.io/repos/github/rosenbrockc/acorn/badge.svg?branch=master
:target: https://coveralls.io/github/rosenbrockc/acorn?branch=master
.. |PyPI| image:: https://img.shields.io/pypi/v/acorn.svg
:target: https://pypi.python.org/pypi/acorn/
