AbstractArray

class kgpy.uncertainty.AbstractArray(nominal=<Quantity 0.>)

Bases: Copyable, NDArrayMethodsMixin, NDArrayOperatorsMixin, ABC, Generic[NominalT, DistributionT]

__init__(nominal=<Quantity 0.>)

Parameters

nominal (NominalT) –

Return type

None

Attributes

axis_distribution
distribution
distribution_normalized
nominal
nominal_normalized
num_samples
shape
type_array
type_array_auxiliary
unit

Methods

__init__([nominal])
copy()	rtype typing.TypeVar(CopyableT, bound= Copyable)
copy_shallow()	rtype typing.TypeVar(CopyableT, bound= Copyable)
max([axis, where])	rtype typing.TypeVar(NDArrayMethodsMixinT, bound= NDArrayMethodsMixin)
mean([axis, where])	rtype typing.TypeVar(NDArrayMethodsMixinT, bound= NDArrayMethodsMixin)
min([axis, where])	rtype typing.TypeVar(NDArrayMethodsMixinT, bound= NDArrayMethodsMixin)

Inheritance Diagram

type_array_primary

alias of AbstractArray

copy()

Return type

typing.TypeVar(CopyableT, bound= Copyable)

Parameters

self (CopyableT) –

copy_shallow()

Return type

typing.TypeVar(CopyableT, bound= Copyable)

Parameters

self (CopyableT) –

max(axis=None, where=<no value>)

Return type

typing.TypeVar(NDArrayMethodsMixinT, bound= NDArrayMethodsMixin)

Parameters

• self (NDArrayMethodsMixinT) –

• axis (Optional[Union[str, Sequence[str]]]) –

• where (NDArrayMethodsMixinT) –

mean(axis=None, where=<no value>)

Return type

typing.TypeVar(NDArrayMethodsMixinT, bound= NDArrayMethodsMixin)

Parameters

• self (NDArrayMethodsMixinT) –

• axis (Optional[Union[str, Sequence[str]]]) –

• where (NDArrayMethodsMixinT) –

min(axis=None, where=<no value>)

Return type

typing.TypeVar(NDArrayMethodsMixinT, bound= NDArrayMethodsMixin)

Parameters

• self (NDArrayMethodsMixinT) –

• axis (Optional[Union[str, Sequence[str]]]) –

• where (NDArrayMethodsMixinT) –

axis_distribution: typing.ClassVar[str] = '_distribution'

abstract property distribution: Optional[DistributionT]

property distribution_normalized: AbstractArray

nominal: typing.TypeVar(NominalT, bound= typing.Union[typing.Sequence[typing.Sequence[typing.Sequence[typing.Sequence[typing.Sequence[typing.Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype], typing.Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype]], typing.Sequence[typing.Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype]]], typing.Sequence[typing.Sequence[typing.Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype]]]], typing.Sequence[typing.Sequence[typing.Sequence[typing.Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype]]]]], bool, int, float, complex, str, bytes, typing.Sequence[typing.Union[bool, int, float, complex, str, bytes]], typing.Sequence[typing.Sequence[typing.Union[bool, int, float, complex, str, bytes]]], typing.Sequence[typing.Sequence[typing.Sequence[typing.Union[bool, int, float, complex, str, bytes]]]], typing.Sequence[typing.Sequence[typing.Sequence[typing.Sequence[typing.Union[bool, int, float, complex, str, bytes]]]]], astropy.units.Quantity, kgpy.labeled.AbstractArray]) = <Quantity 0.>

property nominal_normalized: AbstractArray[Quantity]

property num_samples: int

property shape: Dict[str, int]

type_array: typing.ClassVar[typing.Tuple[typing.Type, ...]] = (<class 'bool'>, <class 'int'>, <class 'float'>, <class 'complex'>, <class 'numpy.generic'>, <class 'numpy.ndarray'>, <class 'kgpy.labeled.AbstractArray'>)

type_array_auxiliary: typing.ClassVar[typing.Tuple[typing.Type, ...]] = (<class 'bool'>, <class 'int'>, <class 'float'>, <class 'complex'>, <class 'numpy.generic'>, <class 'numpy.ndarray'>)

property unit: Optional[Unit]