columnflow.calibration#
Object and event calibration tools.
Classes:
|
Base class for all calibrators. |
Functions:
|
Decorator for creating a new |
- class TaskArrayFunctionWithCalibratorRequirements(*args, **kwargs)[source]#
Bases:
TaskArrayFunctionAttributes:
Methods:
requires_func(task, reqs, **kwargs)Default requires function.
setup_func(task, reqs, inputs, ...)Default setup function.
- require_calibrators: Sequence[str] | set[str] | None = None#
- setup_func(task, reqs, inputs, reader_targets, **kwargs)[source]#
Default setup function.
- Return type:
- cache_instances = True#
- class Calibrator(*args, **kwargs)[source]#
Bases:
TaskArrayFunctionWithCalibratorRequirementsBase class for all calibrators.
Attributes:
Methods:
calibrator([func, bases, mc_only, ...])Decorator for creating a new
Calibratorsubclass with additional, optional bases and attaching the decorated function to it ascall_func.- exposed = True#
- mc_only: bool = False#
- data_only: bool = False#
- classmethod calibrator(func=None, bases=(), mc_only=<object object>, data_only=<object object>, require_calibrators=<object object>, **kwargs)[source]#
Decorator for creating a new
Calibratorsubclass with additional, optional bases and attaching the decorated function to it ascall_func.When mc_only (data_only) is True, the calibrator is skipped and not considered by other calibrators, selectors and producers in case they are evalauted on a
order.Dataset(using thedataset_instattribute) whoseis_mc(is_data) attribute is False.All additional kwargs are added as class members of the new subclasses.
- Parameters:
func (Callable | None, default:
None) – Function to be wrapped and integrated into newCalibratorclass.bases (tuple, default:
()) – Additional bases for the newCalibrator.mc_only (bool | UNSET_TYPE, default:
<object object at 0x7d22b4d86760>) – Boolean flag indicating that thisCalibratorshould only run on Monte Carlo simulation and skipped for real data.data_only (bool | UNSET_TYPE, default:
<object object at 0x7d22b4d86760>) – Boolean flag indicating that thisCalibratorshould only run on real data and skipped for Monte Carlo simulation.require_calibrators (Sequence[str] | set[str] | None | UNSET_TYPE, default:
<object object at 0x7d22b4d86760>) – Sequence of names of other calibrators to add to the requirements.
- Return type:
DerivableMeta | Callable
- Returns:
New
Calibratorsubclass.
- cache_instances = True#
- calibrator(func=None, bases=(), mc_only=<object object>, data_only=<object object>, require_calibrators=<object object>, **kwargs)#
Decorator for creating a new
Calibratorsubclass with additional, optional bases and attaching the decorated function to it ascall_func.When mc_only (data_only) is True, the calibrator is skipped and not considered by other calibrators, selectors and producers in case they are evalauted on a
order.Dataset(using thedataset_instattribute) whoseis_mc(is_data) attribute is False.All additional kwargs are added as class members of the new subclasses.
- Parameters:
func (Callable | None, default:
None) – Function to be wrapped and integrated into newCalibratorclass.bases (tuple, default:
()) – Additional bases for the newCalibrator.mc_only (bool | UNSET_TYPE, default:
<object object at 0x7d22b4d86760>) – Boolean flag indicating that thisCalibratorshould only run on Monte Carlo simulation and skipped for real data.data_only (bool | UNSET_TYPE, default:
<object object at 0x7d22b4d86760>) – Boolean flag indicating that thisCalibratorshould only run on real data and skipped for Monte Carlo simulation.require_calibrators (Sequence[str] | set[str] | None | UNSET_TYPE, default:
<object object at 0x7d22b4d86760>) – Sequence of names of other calibrators to add to the requirements.
- Return type:
DerivableMeta | Callable
- Returns:
New
Calibratorsubclass.