pileup

Column production methods related to pileup weights.

Classes:

pu_weight(*args, **kwargs)
pu_weights_from_columnflow(*args, **kwargs)

class pu_weight(*args, **kwargs)

Bases: Producer

Attributes:

cache_instances
data_only
mc_only
produces
uses

Methods:

call_func(events, **kwargs)	Based on the number of primary vertices, assigns each event pileup weights using correctionlib.
get_pileup_file(external_files)
requires_func(task, reqs, **kwargs)	Adds the requirements needed the underlying task to derive the pileup weights into reqs.
setup_func(task, reqs, inputs, ...)	Loads the pileup calculator from the external files bundle and saves them in the py:attr:pileup_corrector attribute for simpler access in the actual callable.
skip_func(**kwargs)
update_cls_dict(cls_name, cls_dict, get_attr)

cache_instances = True

call_func(events, **kwargs)

Based on the number of primary vertices, assigns each event pileup weights using correctionlib.

Return type:

Array

data_only = False

get_pileup_file(external_files)

mc_only = True

produces = {'pu_weight{,_minbias_xs_up,_minbias_xs_down}'}

requires_func(task, reqs, **kwargs)

Adds the requirements needed the underlying task to derive the pileup weights into reqs.

Return type:

None

setup_func(task, reqs, inputs, reader_targets, **kwargs)

Loads the pileup calculator from the external files bundle and saves them in the py:attr:pileup_corrector attribute for simpler access in the actual callable.

Return type:

None

skip_func(**kwargs) → bool

Return type:

bool

static update_cls_dict(cls_name, cls_dict, get_attr)

uses = {'Pileup.nTrueInt'}

class pu_weights_from_columnflow(*args, **kwargs)

Bases: Producer

Attributes:

cache_instances
data_only
mc_only
produces
uses

Methods:

call_func(events, **kwargs)	Based on the number of primary vertices, assigns each event pileup weights using the profile of pileup ratios at the py:attr:pu_weights attribute provided by the requires and setup functions below.
requires_func(task, reqs, **kwargs)	Adds the requirements needed the underlying task to derive the pileup weights into reqs.
setup_func(task, reqs, inputs, ...)	Loads the pileup weights added through the requirements and saves them in the py:attr:pu_weights attribute for simpler access in the actual callable.
skip_func(**kwargs)
update_cls_dict(cls_name, cls_dict, get_attr)

cache_instances = True

call_func(events, **kwargs)

Based on the number of primary vertices, assigns each event pileup weights using the profile of pileup ratios at the py:attr:pu_weights attribute provided by the requires and setup functions below.

Return type:

Array

data_only = False

mc_only = True

produces = {'pu_weight{,_minbias_xs_up,_minbias_xs_down}'}

requires_func(task, reqs, **kwargs)

Adds the requirements needed the underlying task to derive the pileup weights into reqs.

Return type:

None

setup_func(task, reqs, inputs, reader_targets, **kwargs)

Loads the pileup weights added through the requirements and saves them in the py:attr:pu_weights attribute for simpler access in the actual callable.

Return type:

None

skip_func(**kwargs) → bool

Return type:

bool

static update_cls_dict(cls_name, cls_dict, get_attr)

uses = {'Pileup.nTrueInt'}