Awesome

NBFlow

A tool that supports one-button reproducible workflows with the Jupyter Notebook and Scons. Note: this currently only supports Python kernels.

UPDATE: Scons >= 3.0.0 now supports Python 3 so Python 2 isn't needed anymore! Now Nbflow and Scons are both Python 2 and 3 compatible so you can choose whichever you want.

The actual version of Scons (3.0.1) currently supports Python >= 3.5, which is the default in Ubuntu 16.04

Installation

To install, run:

Linux:

pip3 install git+git://github.com/jhamrick/nbflow.git

Windows:

pip install git+git://github.com/jhamrick/nbflow.git

Usage

For a complete example of how to use nbflow, check out the example in this repository.

You can now you Binder to check the example online:

1. Entre in Binder here or through the badge above
2. Open a terminal
3. Run cd nbflow/example
4. Run scons
5. Check the results in the results directory

Optionally you can modify the notebook in this online environment and check how the results change.

Analysis notebooks

For each notebook that you want executed, you MUST include two special variables in the first code cell:

• __depends__ -- a list of relative paths to files that the notebook depends on
• __dest__ -- either a relative path, or list of relative paths, to files that the notebook produces

For example, the first cell in one of the example notebooks is:

__depends__ = ["../results/data.json"]
__dest__ = "../results/stats.json"

SConstruct file

You need a SConstruct file in the root of you analysis directory. In this SConstruct file you will need to import nbflow and use it to setup your scons environment, e.g.:

import os
from nbflow.scons import setup

env = Environment(ENV=os.environ)
setup(env, ["analyses"])

The second argument of the setup command takes a list of folder names that contain analysis notebooks.

Running nbflow

Once you have setup your analysis notebooks and your SConstruct file, you can run your notebooks by just running the scons command from the root of your analysis directory.