Version: 0.5

Eskapade is a light-weight, python-based data analysis framework, meant for all sorts of data analysis problems.

Our 0.5 release (May 2017) contains multiple new features, in particular:

• Support for ROOT, including multiple examples on using data analysis, fitting and simulation examples using RooFit.
• Histogram conversion and filling support, using ROOT, numpy, Histogrammar and Eskapade-internal histograms.
• Automated data-quality fixes for buggy columns datasets, including data type fixing and NaN conversion.
• New visualization utilities, e.g. plotting multiple types of (non-linear) correlation matrices and dendograms.
• And most importantly, many new and interesting example macros illustrating the new features above!

In our 0.4 release (Feb 2017) we are releasing the core code to run the framework. It is written in python 3. Anyone can use this to learn Eskapade, build data analyses with the link-chain methodology, and start experiencing its advantages.

The focus of the provided documentation is on constructing a data analysis setup in Eskapade. Machine learning interfaces will be included in an upcoming release. Stay tuned!

The repository is hosted on github, clone it to your machine with:

$ git clone git@github.com:KaveIO/Eskapade.git

See the readme's in other parts of the repository for specific requirements and usage.

Eskapade requires Python 3 and Anaconda version 4.3 (or greater), which can be found here.

To get started, source Eskapade in the root of the repository:

$ source setup.sh

You can now call the path of Eskapade with:

$ echo $ESKAPADE

or in python with

import os
os.environ['ESKAPADE']

The entire documentation including tutorials can be found here.

Contact us at: kave [at] kpmg [dot] com

Please note that the KPMG Eskapade group provides support only on a best-effort basis.