collider-dashboard 1.4.2.0

A Dash application to visualize the observables and parameters of a collider built and configured with Xsuite.

Collider Dashboard

A Dash application to visualize the observables and parameters of a collider built and configured with Xsuite.

Installation

The dashboard can be installed from PyPI with pip:

pip install collider-dashboard

This will install the required packages and build the application. If you haven't done it already, it is recommended to prebuild the Xsuite kernel to gain some computation time:

xsuite-prebuild

Usage

For personal usage, the simplest way to use the dashboard is to run the package as a development server from the command line, providing a few arguments:

python -m collider_dashboard --collider-path path_to_collider.json --filling-path path_to_scheme.json --port 8080 --force-reload --ignore-footprint --full-twiss --type-particles proton --debug

• --collider-path, or -c, sets the path to the collider configuration file. Default value to the path of a dummy collider used for testing.
• --filling-path, or -f, sets the path to the filling scheme, instead of using the one in the collider configuration file. Optional.
• --port, or -p, sets the port on which the dashboard will be deployed. Default value to `8080``.
• --force-reload, or -r, sets a boolean indicating whether the collider dashboard data should be reloaded if already existing. Optional.
• --ignore-footprint, or -i, sets a boolean indicating whether the footprint should be ignored to gain computation time. Optional.
• --full-twiss, or -t, sets a boolean indicating whether the Twiss/Survey tables should be computed fully (not removing duplicates and entry/exit elements), at the expense of computation time. Optional.
• --type-particles, or -a, sets the type of particles to be used for the collider. Default value to proton.
• --debug, or -d, sets a boolean indicating whether the dashboard should be run in debug mode. Optional.

After computing some temporary variables (this may take a while the first time), this will deploy a local server and open the dashboard in a browser window.

Alternatively, one can import the dashboard as a module and use it in a custom script:

# my-awesome-dashboard.py

from collider_dashboard import build_app
app, server = build_app(path_to_collider.json, 
                        path_scheme=path_to_scheme.json, 
                        port=8080, 
                        force_reload=False, 
                        ignore_footprint=False, 
                        debug = False, 
                        simplify_tw=True
                        type_particles='proton'
                )

The dashboard can then be deployed e.g. with gunicorn:

gunicorn my-awesome-dashboard:server -b :8080

Note that, as the dashboard deals with global variables, it is not thread-safe. It is therefore recommended to run it with a single worker (it's the case by default).