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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, numpy.generic, Sequence[Union[int, float, complex, str, bytes, numpy.generic]], Sequence[Sequence[Any]], numpy.typing._array_like._SupportsArray]], dtype: Union[numpy.dtype, None, type, numpy.typing._dtype_like._SupportsDType, str, Tuple[Any, int], Tuple[Any, Union[int, Sequence[int]]], List[Any], numpy.typing._dtype_like._DTypeDict, 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.

append(other: Mapping[str, Union[int, float, complex, str, bytes, numpy.generic, Sequence[Union[int, float, complex, str, bytes, numpy.generic]], Sequence[Sequence[Any]], numpy.typing._array_like._SupportsArray]]) → None

items() → a set-like object providing a view on D’s items

keys() → a set-like object providing a view on D’s keys

property n_events

select_events(selection: Union[int, slice]) → expertsystem.amplitude.data.DataSet

to_pandas(_: Union[numpy.dtype, None, type, numpy.typing._dtype_like._SupportsDType, str, Tuple[Any, int], Tuple[Any, Union[int, Sequence[int]]], List[Any], numpy.typing._dtype_like._DTypeDict, Tuple[Any, Any]] = None) → Dict[str, numpy.ndarray]

Converter for the data argument of pandas.DataFrame.

values() → an object providing a view on D’s values

class EventCollection(data: Mapping[int, Union[int, float, complex, str, bytes, numpy.generic, Sequence[Union[int, float, complex, str, bytes, numpy.generic]], Sequence[Sequence[Any]], numpy.typing._array_like._SupportsArray]])

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.

append(other: Mapping[int, Union[int, float, complex, str, bytes, numpy.generic, Sequence[Union[int, float, complex, str, bytes, numpy.generic]], Sequence[Sequence[Any]], numpy.typing._array_like._SupportsArray]]) → None

items() → a set-like object providing a view on D’s items

keys() → a set-like object providing a view on D’s keys

property n_events

select_events(selection: Union[int, slice]) → expertsystem.amplitude.data.EventCollection

sum(indices: Iterable[int]) → expertsystem.amplitude.data.FourMomentumSequence

to_pandas(_: Union[numpy.dtype, None, type, numpy.typing._dtype_like._SupportsDType, str, Tuple[Any, int], Tuple[Any, Union[int, Sequence[int]]], List[Any], numpy.typing._dtype_like._DTypeDict, Tuple[Any, Any]] = None) → Dict[Tuple[int, str], numpy.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() → an object providing a view on D’s values

class FourMomentumSequence(data: Union[int, float, complex, str, bytes, numpy.generic, Sequence[Union[int, float, complex, str, bytes, numpy.generic]], Sequence[Sequence[Any]], numpy.typing._array_like._SupportsArray])

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

mass() → expertsystem.amplitude.data.ScalarSequence

mass_squared() → expertsystem.amplitude.data.ScalarSequence

p_norm() → expertsystem.amplitude.data.ScalarSequence

Norm of three_momentum.

p_squared() → expertsystem.amplitude.data.ScalarSequence

Squared norm of three_momentum.

property p_x

property p_y

property p_z

phi() → expertsystem.amplitude.data.ScalarSequence

theta() → expertsystem.amplitude.data.ScalarSequence

property three_momentum

class MatrixSequence(data: Union[int, float, complex, str, bytes, numpy.generic, Sequence[Union[int, float, complex, str, bytes, numpy.generic]], Sequence[Sequence[Any]], numpy.typing._array_like._SupportsArray])

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

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

dot(vector: expertsystem.amplitude.data.FourMomentumSequence) → expertsystem.amplitude.data.FourMomentumSequence

class ScalarSequence(data: Union[int, float, complex, str, bytes, numpy.generic, Sequence[Union[int, float, complex, str, bytes, numpy.generic]], Sequence[Sequence[Any]], numpy.typing._array_like._SupportsArray], dtype: Union[numpy.dtype, None, type, numpy.typing._dtype_like._SupportsDType, str, Tuple[Any, int], Tuple[Any, Union[int, Sequence[int]]], List[Any], numpy.typing._dtype_like._DTypeDict, Tuple[Any, Any]] = None)

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

numpy.array data container of rank 1.

class ThreeMomentum(data: Union[int, float, complex, str, bytes, numpy.generic, Sequence[Union[int, float, complex, str, bytes, numpy.generic]], Sequence[Sequence[Any]], numpy.typing._array_like._SupportsArray], dtype: Union[numpy.dtype, None, type, numpy.typing._dtype_like._SupportsDType, str, Tuple[Any, int], Tuple[Any, Union[int, Sequence[int]]], List[Any], numpy.typing._dtype_like._DTypeDict, Tuple[Any, Any]] = None)

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

lineshape helicity