empty

Empty selectors that still produce the minimal set of columns potentially required in downstream tasks.

Classes:

empty(*args, **kwargs)

class empty(*args, **kwargs)

Bases: Selector

Attributes:

cache_instances
category_ids
data_only
exposed
inclusive_category_name
mc_only
produces
uses

Methods:

call_func(events, stats, **kwargs)	Empty selector that only writes a minimal set of columns that are potentially required in downstream tasks, such as cutflow and plotting related tasks.
init_func(**kwargs)	Initializes the selector by finding the id of the inclusive category if no hard-coded category ids are given on class-level.
skip_func(**kwargs)
update_cls_dict(cls_name, cls_dict, get_attr)

cache_instances = True

call_func(events, stats, **kwargs)

Empty selector that only writes a minimal set of columns that are potentially required in downstream tasks, such as cutflow and plotting related tasks.

Parameters:

• events (Array) – The input events.

• stats (defaultdict) – The statistics dictionary.

• **kwargs –

  Additional keyword arguments that are passed to all other TaskArrayFunction’s.

Return type:

tuple[Array, SelectionResult]

Returns:

A tuple containing the original events and a SelectionResult object with a trivial event mask.

category_ids = None

data_only = False

exposed = True

inclusive_category_name = 'incl'

init_func(**kwargs)

Initializes the selector by finding the id of the inclusive category if no hard-coded category ids are given on class-level.

Raises:

ValueError – If the inclusive category cannot be found.

Return type:

None

mc_only = False

produces = {'category_ids', <class 'columnflow.production.cms.mc_weight.mc_weight'>, <class 'columnflow.production.processes.process_ids'>}

skip_func(**kwargs) → bool

Return type:

bool

static update_cls_dict(cls_name, cls_dict, get_attr)

uses = {<class 'columnflow.production.cms.mc_weight.mc_weight'>, <class 'columnflow.production.processes.process_ids'>, <class 'columnflow.selection.stats.increment_stats'>}