Metadata-Version: 2.1
Name: enb
Version: 0.4.1
Summary: Experiment notebook (enb): efficient and reproducible science.
Home-page: https://github.com/miguelinux314/experiment-notebook
Download-URL: https://github.com/miguelinux314/experiment-notebook/archive/v0.4.1.tar.gz
Author: Miguel Hernandez Cabronero, et al.
Author-email: miguel.hernandez@uab.cat
License: MIT License
Platform: any
Classifier: Programming Language :: Python
Classifier: Development Status :: 4 - Beta
Classifier: Natural Language :: English
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: pathos
Requires-Dist: appdirs
Requires-Dist: deprecation
Requires-Dist: jinja2
Requires-Dist: matplotlib
Requires-Dist: numpngw
Requires-Dist: numpy
Requires-Dist: pandas (>=1.4.1)
Requires-Dist: imageio
Requires-Dist: pdf2image
Requires-Dist: psutil
Requires-Dist: requests
Requires-Dist: scipy
Requires-Dist: sortedcontainers
Requires-Dist: astropy
Requires-Dist: alive-progress
Requires-Dist: natsort
Requires-Dist: ray[default] (>=1.11.0) ; platform_system != "Windows"

# Experiment Notebook (`enb`)

The `enb` Python (>= 3.6) 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.

## Quick start

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](https://github.com/miguelinux314/experiment-notebook/blob/dev/enb/plugins/template_montecarlo_pi/montecarlo_pi_experiment.py):

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

Or check out the most basic working examples with
the [basic workflow example](https://github.com/miguelinux314/experiment-notebook/blob/dev/enb/plugins/template_basic_workflow_example/basic_workflow.py)

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

## Resources

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

- You can browse
  the [detailed installation instructions](https://miguelinux314.github.io/experiment-notebook/installation.html).

- A [gallery of plots](https://miguelinux314.github.io/experiment-notebook/analyzing_data.html)
  produced (semi-)automatically produced from enb experiment results and from external CSV files is also available.

- Please refere tp the [changelog](https://github.com/miguelinux314/experiment-notebook/blob/master/CHANGELOG.md)
  for the main differences between consecutive `enb` versions.


    
