hl-tables 1.0.0

Tables for structured data - universal backend

hl_tables

A high level tables dispatcher for putting together multiple tables executors

Examples

Making a histogram

dataset = EventDataset(f'localds://mc16_13TeV:{ds["RucioDSName"].values[0]}')
df = xaod_table(dataset)
truth = df.TruthParticles('TruthParticles')
llp_truth = truth[truth.pdgId == 35]
histogram(llp_truth.Count(), bins=3, range=(0,3))
plt.yscale('log')
plt.xlabel('Number of good LLPs in each event')
plt.ylabel('a MC Sample')

1. The histogram data will be calculated by the backend and returned to your local Jupyter instance.
2. Plots will be rendered!

Outstanding things

• Definitely need to decide on an approach to this whole thing. Reducers - and where should they be applied, at the outer most or inner most level? So seq.count() - should that mean seq.Select(a: a.count()), or seq.count() (number of events, or a list of objects inside the event)?

• Count needs to be changed to num or dimensions, etc.