lauraser / tpcqcvis

Framework for the developing and deploying interactive visulization tools to be used by experts investigating the Quality-Control status of ALICE TPC operations.

TPCQCVis

Index:

1. Introduction
2. Setting up
3. User Guide
4. Demos

Introduction:

The monitoring of the ALICE TPC quality control data in RUN3 is planned to be done using the Jupyter environment, where libraries e.g. Bokeh plotter can be used which enables very user-friendly interactivity capabilities. We have PyRoot based notebooks for visualizing the Root Object outputs from central sync and async QC. And, we have expert dashboards made with RootInteractive using skimmed data.

Setting up:

1. Install Quality Control
2. Install RootInteractive

Exporting notebooks:

As report:

Command to run:

jupyter nbconvert myNotebook.ipynb --to html --template classic --no-input

Remove the --no-input to have the code also in the report

As slides:

Command to run:

jupyter nbconvert myNotebook.ipynb --to slides --no-input --SlidesExporter.reveal_scroll=True

Then open the html file with text editor and change the initializer of reveal to this:

Reveal.initialize({
            controls: true,
            progress: true,
            history: false,
            transition: "slide",
            slideNumber: "true",
            viewDistance: 50,
            mobileViewDistance: 20,
            preloadIframes: true,
            autoPlayMedia:true,
            plugins: [RevealNotes]
        });

This makes it possible that all the plots in the slides will be loaded automatically when file is opened on browser. (works on Chrome, still didn't get the expected behavior in Firefox)