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]¶
-
property
n_events
¶
-
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]¶
-
property
n_events
¶
-
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
¶
-
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
¶
-
property
p_y
¶
-
property
p_z
¶
-
phi
() → ScalarSequence[source]¶
-
theta
() → ScalarSequence[source]¶
-
property
three_momentum
¶
-
property
-
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