Metadata-Version: 2.1
Name: hl-tables
Version: 1.0.0b2
Summary: Tables for structured data - universal backend
Home-page: https://github.com/gordonwatts/hep_tables
Author: G. Watts (IRIS-HEP/UW Seattle)
Author-email: gwatts@uw.edu
Maintainer: Gordon Watts (IRIS-HEP/UW Seattle)
Maintainer-email: gwatts@uw.edu
License: TBD
Description: # hl_tables
        
         A high level tables dispatcher for putting together multiple tables executors
        
        ## Examples
        
        ## Making a histogram
        
        ```python
        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.
        1. 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.
        
Platform: Any
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Software Development
Classifier: Topic :: Utilities
Requires-Python: >=3.6, <3.8
Description-Content-Type: text/markdown
Provides-Extra: complete
Provides-Extra: notebook
Provides-Extra: test
