Eskapade v0.7

Version 0.7 of Eskapade (February 2018) contains several major updates:

• The Eskapade code has been made pip friendly. One can now simply do:

  pip install Eskapade
  

  or check out the code from out github repository:

  git clone [email protected]:KaveIO/Eskapade.git
  pip install -e Eskapade/
  

  where in this example the code is installed in edit mode (option -e).

  You can now use Eskapade in Python with:

  import eskapade

  This change has resulted in some restructuring of the python directories, making the overall structure more transparent:
  all python code, including the tutorials, now fall under the (single) python directory. Additionally, thanks to the pip convention, our prior dependence on environment variables ($ESKAPADE) has now been fully stripped out of the code.

• There has been a cleanup of the core code, removing obsolete code and making it better maintainable. This has resulted in a (small) change in the api of the process manager, adding chains, and using the logger. All tutorials and example macro files have been updated accordingly. See the migration section from-version-0-6-to-0-7 of the documentation for detailed tips on migrating existing Eskapade code to version 0.7.

• All eskapade commands now start with the prefix eskapade_. All tutorials have been updated accordingly. We have the commands:

  • eskapade_bootstrap, for creating a new Eskapade analysis project. See the new tutorial tutorial-4-creating-a-new-analysis-project for all the details.
  • eskapade_run, for running the Eskapade macros.
  • eskapade_trail, for running the Eskapade unit and integration tests.
  • eskapade_generate_link, eskapade_generate_macro, eskapade_generate_notebook, for generating a new link, macro, or Jupyter notebook respectively.

Eskapade v0.6

The primary feature of version 0.6 (August 2017) is the inclusion of Spark, but this version
also includes several other new features and analyses.

We include multiple Spark links and 10 Spark examples on:

• The configuration of spark, reading, writing and converting spark dataframes, applying functions and queries to dataframes,
  filling histograms and (very useful!) applying arbitrary functions (e.g. pandas) to groupby calls.

In addition we hade added:

• A ROOT analysis for studying and quantifying between sets of (non-)categorical and observables.
  This is useful for finding outliers in arbitrary datasets (e.g. surveys), and we include a tutorial of how to do this.
• A ROOT analysis on predictive maintenance that decomposes a distribution of time difference between malfunctions
  by fitting this multiple Weibull distributions.
• New flexible features to create and chain analysis reports with several analysis and visualization links.

Eskapade v0.5

Eskapade v0.5 (May 2017) contains two new packages as well as updates to existing packages:

• 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!

new packages:

• root_analysis
• data_quality

Eskapade v0.4

Eskapade v0.4 (February 2017) contains the core framework and analysis code. Included packages:

• core
• core_ops
• analysis
• visualization