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data

Deprecated since version 0.7.3: Use QRules and AmpForm instead

import expertsystem.amplitude.data

Data containers for working with four-momenta.

See also

Writing custom array containers

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[int, Sequence[int]]], List[Any], DTypeLike, Tuple[Any, Any]] = None)

Bases: collections.abc.Mapping

A mapping of variable names to their ScalarSequence.

The keys of DataSet represent variable names in a HelicityModel, while its values are inserted in their place.

__getitem__(i: str) → ScalarSequence

append(other: Mapping[str, Union[int, float, complex, str, bytes, generic, Sequence[Union[int, float, complex, str, bytes, generic]], Sequence[Sequence[Any]], ArrayLike]]) → None

items() → ItemsView

keys() → KeysView

property n_events

select_events(selection: Union[int, slice]) → DataSet

to_pandas(_: Union[dtype, None, type, DTypeLike, str, Tuple[Any, int], Tuple[Any, Union[int, Sequence[int]]], List[Any], DTypeLike, Tuple[Any, Any]] = None) → Dict[str, ndarray]

Converter for the data argument of pandas.DataFrame.

values() → ValuesView

class EventCollection(data: Mapping[int, Union[int, float, complex, str, bytes, generic, Sequence[Union[int, float, complex, str, bytes, generic]], Sequence[Sequence[Any]], ArrayLike]])

Bases: collections.abc.Mapping

A mapping of state IDs to their FourMomentumSequence data samples.

An EventCollection has to be converted to DataSet so that it can be used to evaluate a HelicityModel.

__getitem__(i: int) → FourMomentumSequence

append(other: Mapping[int, Union[int, float, complex, str, bytes, generic, Sequence[Union[int, float, complex, str, bytes, generic]], Sequence[Sequence[Any]], ArrayLike]]) → None

items() → ItemsView

keys() → KeysView

property n_events

select_events(selection: Union[int, slice]) → EventCollection

sum(indices: Iterable[int]) → FourMomentumSequence

to_pandas(_: Union[dtype, None, type, DTypeLike, str, Tuple[Any, int], Tuple[Any, Union[int, Sequence[int]]], List[Any], DTypeLike, Tuple[Any, Any]] = None) → Dict[Tuple[int, str], ndarray]

Converter for the data argument of pandas.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

class FourMomentumSequence(data: Union[int, float, complex, str, bytes, generic, Sequence[Union[int, float, complex, str, bytes, generic]], Sequence[Sequence[Any]], ArrayLike])

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).

__getitem__(i: Union[Tuple[Union[int, slice], Union[int, slice]], int, slice]) → ndarray

property energy

mass() → ScalarSequence

mass_squared() → ScalarSequence

p_norm() → ScalarSequence

Norm of three_momentum.

p_squared() → ScalarSequence

Squared norm of three_momentum.

property p_x

property p_y

property p_z

phi() → ScalarSequence

theta() → ScalarSequence

property three_momentum

class MatrixSequence(data: Union[int, float, complex, str, bytes, generic, Sequence[Union[int, float, complex, str, bytes, generic]], Sequence[Sequence[Any]], ArrayLike])

Bases: numpy.lib.mixins.NDArrayOperatorsMixin, collections.abc.Sequence

Safe data container for a sequence of 4x4-matrices.

__getitem__(i: Union[int, slice]) → ndarray

dot(vector: FourMomentumSequence) → FourMomentumSequence

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[int, Sequence[int]]], List[Any], DTypeLike, Tuple[Any, Any]] = None)

Bases: numpy.lib.mixins.NDArrayOperatorsMixin, collections.abc.Sequence

numpy.array data container of rank 1.

__getitem__(i: Union[int, slice]) → ndarray

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[int, Sequence[int]]], List[Any], DTypeLike, Tuple[Any, Any]] = None)

Bases: numpy.lib.mixins.NDArrayOperatorsMixin, collections.abc.Sequence

__getitem__(i: Union[Tuple[Union[int, slice], Union[int, slice]], int, slice]) → ndarray

lineshape helicity