data¶
import ampform.data
Data containers for working with four-momenta.
See also
- class DataSet(data: Mapping[str, Union[int, float, complex, str, bytes, generic, Sequence[Union[int, float, complex, str, bytes, generic]], Sequence[Sequence[Any]], ArrayLike]], dtype: Union[dtype, None, type, DTypeLike, str, Tuple[Any, int], Tuple[Any, Union[SupportsIndex, Sequence[SupportsIndex]]], List[Any], DTypeLike, Tuple[Any, Any]] = None)[source]¶
Bases:
collections.abc.Mapping
A mapping of variable names to their
ScalarSequence
.The
keys
ofDataSet
represent variable names in aHelicityModel
, while itsvalues
are inserted in their place.- __getitem__(i: str) ScalarSequence [source]¶
- append(other: Mapping[str, Union[int, float, complex, str, bytes, generic, Sequence[Union[int, float, complex, str, bytes, generic]], Sequence[Sequence[Any]], ArrayLike]]) None [source]¶
- to_pandas(_: Union[dtype, None, type, DTypeLike, str, Tuple[Any, int], Tuple[Any, Union[SupportsIndex, Sequence[SupportsIndex]]], List[Any], DTypeLike, Tuple[Any, Any]] = None) Dict[str, ndarray] [source]¶
Converter for the
data
argument ofpandas.DataFrame
.
- values() ValuesView [source]¶
- class EventCollection(data: Mapping[int, Union[int, float, complex, str, bytes, generic, Sequence[Union[int, float, complex, str, bytes, generic]], Sequence[Sequence[Any]], ArrayLike]])[source]¶
Bases:
collections.abc.Mapping
A mapping of state IDs to their
FourMomentumSequence
data samples.An
EventCollection
has to be converted toDataSet
so that it can be used to evaluate aHelicityModel
.- __getitem__(i: int) FourMomentumSequence [source]¶
- append(other: Mapping[int, Union[int, float, complex, str, bytes, generic, Sequence[Union[int, float, complex, str, bytes, generic]], Sequence[Sequence[Any]], ArrayLike]]) None [source]¶
- select_events(selection: Union[int, slice]) EventCollection [source]¶
- sum(indices: Iterable[int]) FourMomentumSequence [source]¶
- to_pandas(_: Union[dtype, None, type, DTypeLike, str, Tuple[Any, int], Tuple[Any, Union[SupportsIndex, Sequence[SupportsIndex]]], List[Any], DTypeLike, Tuple[Any, Any]] = None) Dict[Tuple[int, str], ndarray] [source]¶
Converter for the
data
argument ofpandas.DataFrame
.The resulting
DataFrame
has multi-columns (see MultiIndex / advanced indexing) where the first column layer represents the state IDs and the second column layer represents each of the four-momentum entries (\(E, p_x, p_y, p_z\)).
- values() ValuesView [source]¶
- class FourMomentumSequence(data: Union[int, float, complex, str, bytes, generic, Sequence[Union[int, float, complex, str, bytes, generic]], Sequence[Sequence[Any]], ArrayLike])[source]¶
Bases:
numpy.lib.mixins.NDArrayOperatorsMixin
,collections.abc.Sequence
Container for a
numpy.array
of four-momentum tuples.The input data has to be of shape (N, 4) and the order of the items has to be \((E, p)\) (energy first).
- property energy: ScalarSequence¶
- mass() ScalarSequence [source]¶
- mass_squared(dtype: Union[dtype, None, type, DTypeLike, str, Tuple[Any, int], Tuple[Any, Union[SupportsIndex, Sequence[SupportsIndex]]], List[Any], DTypeLike, Tuple[Any, Any]] = None) ScalarSequence [source]¶
- p_norm() ScalarSequence [source]¶
Norm of
three_momentum
.
- p_squared() ScalarSequence [source]¶
Squared norm of
three_momentum
.
- property p_x: ScalarSequence¶
- property p_y: ScalarSequence¶
- property p_z: ScalarSequence¶
- phi() ScalarSequence [source]¶
- theta() ScalarSequence [source]¶
- property three_momentum: ThreeMomentum¶
- class MatrixSequence(data: Union[int, float, complex, str, bytes, generic, Sequence[Union[int, float, complex, str, bytes, generic]], Sequence[Sequence[Any]], ArrayLike])[source]¶
Bases:
numpy.lib.mixins.NDArrayOperatorsMixin
,collections.abc.Sequence
Safe data container for a sequence of 4x4-matrices.
- dot(vector: FourMomentumSequence) FourMomentumSequence [source]¶
- class ScalarSequence(data: Union[int, float, complex, str, bytes, generic, Sequence[Union[int, float, complex, str, bytes, generic]], Sequence[Sequence[Any]], ArrayLike], dtype: Union[dtype, None, type, DTypeLike, str, Tuple[Any, int], Tuple[Any, Union[SupportsIndex, Sequence[SupportsIndex]]], List[Any], DTypeLike, Tuple[Any, Any]] = None)[source]¶
Bases:
numpy.lib.mixins.NDArrayOperatorsMixin
,collections.abc.Sequence
numpy.array
data container of rank 1.
- class ThreeMomentum(data: Union[int, float, complex, str, bytes, generic, Sequence[Union[int, float, complex, str, bytes, generic]], Sequence[Sequence[Any]], ArrayLike], dtype: Union[dtype, None, type, DTypeLike, str, Tuple[Any, int], Tuple[Any, Union[SupportsIndex, Sequence[SupportsIndex]]], List[Any], DTypeLike, Tuple[Any, Any]] = None)[source]¶
Bases:
numpy.lib.mixins.NDArrayOperatorsMixin
,collections.abc.Sequence