ftag#

atlas-ftag-tools - Common tools for ATLAS flavour tagging software.

Submodules#

Attributes#

`__version__`
`Flavours`

Classes#

`Cuts`
`Label`
`LabelContainer`
`Sample`
`Transform`

Functions#

`get_mock_file`(→ tuple[str, h5py.File])
`get_working_points`(→ dict \| None)	Calculate the working points.

Package Contents#

ftag.__version__ = 'v0.2.13'#

class ftag.Cuts#

cuts: tuple[Cut, Ellipsis]#

classmethod from_list(cuts: list) → Cuts#

classmethod empty() → Cuts#

__post_init__()#

property variables: list[str]#

ignore(variables: list[str])#

__call__(array: numpy.ndarray) → CutsResult#

__add__(other: Cuts)#

__len__() → int#

__iter__() → collections.abc.Iterator#

__getitem__(variable)#

__repr__() → str#

ftag.Flavours#

class ftag.Label#

name: str#

label: str#

cuts: ftag.cuts.Cuts#

colour: str#

category: str#

_px: str | None = None#

property px: str#

property eff_str: str#

property rej_str: str#

property frac_str: str#

__str__() → str#

__lt__(other) → bool#

class ftag.LabelContainer#

labels: dict[str, Label]#

__iter__() → collections.abc.Iterator#

__getitem__(key) → Label#

__len__() → int#

__getattr__(name) → Label#

__contains__(label: str | Label) → bool#

__eq__(other) → bool#

__repr__() → str#

property categories: list[str]#

by_category(category: str) → LabelContainer#

from_cuts(cuts: list | ftag.cuts.Cuts) → Label#

classmethod from_yaml(yaml_path: pathlib.Path | None = None) → LabelContainer#

classmethod from_list(labels: list[Label]) → LabelContainer#

backgrounds(signal: Label, only_signals: bool = True) → LabelContainer#

ftag.get_mock_file(num_jets=1000, fname: str | None = None, tracks_name: str = 'tracks', num_tracks: int = 40) → tuple[str, h5py.File]#

class ftag.Sample#

pattern: pathlib.Path | str | tuple[pathlib.Path | str, Ellipsis]#

ntuple_dir: pathlib.Path | str | None = None#

name: str | None = None#

weights: list[float] | None = None#

__post_init__() → None#

property path: tuple[pathlib.Path, Ellipsis]#

property files: list[str]#

property num_files: int#

property dsid: list[str]#

property sample_id: list[str]#

property tags: list[str]#

property ptag: list[str]#

property rtag: list[str]#

property dumper_tag: list[str]#

virtual_file(**kwargs) → list[pathlib.Path | str]#

__str__()#

__lt__(other)#

__eq__(other)#

class ftag.Transform#

variable_map: dict[str, dict[str, str]] | None = None#

ints_map: dict[str, dict[str, dict[int, int]]] | None = None#

floats_map: dict[str, dict[str, str | Callable]] | None = None#

__post_init__()#

__call__(batch: Batch) → Batch#

map_variables(batch: Batch) → Batch#

Rename variables in a batch of data.

Parameters:: batch (Batch) – Dict of structured numpy arrays.
Returns:: Dict of structured numpy arrays with renamed variables.
Return type:: Batch

map_ints(batch: Batch) → Batch#

Map integer values to new values.

Parameters:: batch (Batch) – Dict of structured numpy arrays.
Returns:: Dict of structured numpy arrays with mapped integer values.
Return type:: Batch

map_floats(batch: Batch) → Batch#

Transform float values.

Parameters:: batch (Batch) – Dict of structured numpy arrays.
Returns:: Dict of structured numpy arrays with transformed float values.
Return type:: Batch

map_dtype(name: str, dtype: numpy.dtype) → numpy.dtype#

map_variable_names(name: str, variables: list[str], inverse=False) → list[str]#

ftag.get_working_points(args: argparse.Namespace) → dict | None#

Calculate the working points.

Parameters:: args (argparse.Namespace) – Input arguments from the argparser
Returns:: Dict with the working points. If args.outfile is given, the function returns None and stored the resulting dict in a yaml file in args.outfile.
Return type:: dict | None