pileup

Column production methods related to pileup weights.

Classes:

pu_weight(*args[, requires_func, ...])
pu_weights_from_columnflow(*args[, ...])

class pu_weight(*args, requires_func=law.util.no_value, setup_func=law.util.no_value, sandbox=law.util.no_value, call_force=law.util.no_value, max_chunk_size=law.util.no_value, pick_cached_result=law.util.no_value, inst_dict=None, **kwargs)

Bases: Producer

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(reqs)	Adds the requirements needed the underlying task to derive the pileup weights into reqs.
setup_func(reqs, inputs, reader_targets)	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()
update_cls_dict(cls_name, cls_dict, get_attr)

Attributes:

data_only
mc_only
nominal_only
produces
shifts_only
uses

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

nominal_only = False

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

requires_func(reqs)

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

Return type:

None

setup_func(reqs, inputs, reader_targets)

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

shifts_only = None

skip_func()

static update_cls_dict(cls_name, cls_dict, get_attr)

uses = {'Pileup.nTrueInt'}

class pu_weights_from_columnflow(*args, requires_func=law.util.no_value, setup_func=law.util.no_value, sandbox=law.util.no_value, call_force=law.util.no_value, max_chunk_size=law.util.no_value, pick_cached_result=law.util.no_value, inst_dict=None, **kwargs)

Bases: Producer

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(reqs)	Adds the requirements needed the underlying task to derive the pileup weights into reqs.
setup_func(reqs, inputs, reader_targets)	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()
update_cls_dict(cls_name, cls_dict, get_attr)

Attributes:

data_only
mc_only
nominal_only
produces
shifts_only
uses

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

nominal_only = False

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

requires_func(reqs)

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

Return type:

None

setup_func(reqs, inputs, reader_targets)

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

shifts_only = None

skip_func()

static update_cls_dict(cls_name, cls_dict, get_attr)

uses = {'Pileup.nTrueInt'}