src package

Subpackages

• src.executors package
  • Submodules
  • src.executors.base module
    • BaseExecutor
      • BaseExecutor._execute()
      • BaseExecutor.execute()
      • BaseExecutor.get_n_workers()
      • BaseExecutor.wait_for_workers()
  • src.executors.dask module
    • DaskGatewayExecutor
      • DaskGatewayExecutor._execute()
      • DaskGatewayExecutor.get_n_workers()
    • DaskLocalExecutor
      • DaskLocalExecutor._execute()
      • DaskLocalExecutor.get_n_workers()
  • src.executors.futures module
    • FuturesExecutor
      • FuturesExecutor._execute()
      • FuturesExecutor.get_n_workers()
  • src.executors.sequential module
    • SequentialExecutor
      • SequentialExecutor._execute()
      • SequentialExecutor.get_n_workers()
  • Module contents

Submodules

src.benchmark module

src.data_loader module

src.time_profiler module

class src.time_profiler.TimeProfiler

Bases: object

enable(func)

@tp.enable decorator is used to indicate that we want to extract the cumulative timing measurement for the function to which it is applied.

print_stats()

profile(func)

@tp.profile decorator is applied to a function for which we want to run cProfile (ideally it should be the outermost function in the workflow). Rather than displaying all cProfile outputs, we find rows corresponding to the functions selected by @tp.enable, plus some extra rows interesting for us. The outputs are saved in a Pandas DataFrame.

reset()

src.uproot_processor module

class src.uproot_processor.UprootProcessor(config)

Bases: object

get_column_list(file)

load_columns_into_memory(column_data)

open_nanoaod(file_path, **kwargs)

process_column(tree, column, **kwargs)

run_processor(files, executor, **kwargs)

run_worker_operation()

worker_func(args, **kwargs)

Module contents