The enb Python (>= 3.7) library is a table-based framework designed to define, run and report computer-based experiments.

• Your can create and run any type of (computer-based) experiment. Quickly.
• You can analyze and plot results produced with your enb experiments. Clearly. You can also reuse previously existing data (e.g., in CSV format).
• You can easily create reproducible, redistributable software to be shared with others, e.g., as supplementary materials in your publication or project.
• It runs on Linux, Windows and MacOS, in parallel. You can use clusters of Linux or MacOS computers.

The latest stable version of enb is available via pip, e.g.,

pip install enb

You can use this library in your python scripts by adding:

import enb

Several project demos and templates for your experiments are provided with enb. For a list of documentation templates, you can run:

enb plugin list documentation

For example, you can try the distributed (although not really accurate) pi approximation project:

enb plugin install montecarlo-pi ./mp
./mp/montecarlo_pi_experiment.py

Or check out the most basic working examples with the basic workflow example

enb plugin install basic-workflow ./bw
./bw/basic_workflow.py

• A tutorial-like user manual is available at https://miguelinux314.github.io/experiment-notebook.

• You can browse the detailed installation instructions.

• A gallery of plots produced (semi-)automatically produced from enb experiment results and from external CSV files is also available.

• Please refer to the changelog for the main differences between consecutive enb versions.