class tensorstore.DimSelection(DimExpression)

Specifies a dimension selection, for starting a dimension expression.

A dimension selection specifies a sequence of dimensions, either by index or label.

Dimension selections may be used as part of a dimension expression to specify the dimensions to which an indexing operation applies.

A DimSelection may be constructed by subscripting tensorstore.d:

Examples

>>> ts.d[0, 1, 2]
d[0,1,2]
>>> ts.d[0:1, 2, "x"]
d[0:1,2,'x']
>>> ts.d[[0, 1], [2]]
d[0,1,2]
>>> ts.d[[0, 1], ts.d[2, 3]]
d[0,1,2,3]

Operations

__getitem__(indices: NumpyIndexingSpec) → DimExpression

Applies a NumPy-style indexing operation with default index array semantics.

oindex[indices: NumpyIndexingSpec] → DimExpression

Applies a NumPy-style indexing operation with outer indexing semantics.

vindex[indices: NumpyIndexingSpec] → DimExpression

Applies a NumPy-style indexing operation with vectorized indexing semantics.

translate_to[origins] → DimExpression

Translates the domains of the selected input dimensions to the specified origins without affecting the output range.

translate_by[offsets] → DimExpression

Translates (shifts) the domains of the selected input dimensions by the specified offsets, without affecting the output range.

translate_backward_by[offsets] → DimExpression

Translates (shifts) the domains of the selected input dimensions backward by the specified offsets, without affecting the output range.

stride[strides: Iterable[int | None] | int | None] → DimExpression

Strides the domains of the selected input dimensions by the specified amounts.

transpose[target: DimSelectionLike] → DimExpression

Transposes the selected dimensions to the specified target indices.

label[labels: str | Iterable[str]] → DimExpression

Sets (or changes) the labels of the selected dimensions.

property diagonal : DimExpression

Extracts the diagonal of the selected dimensions.

mark_bounds_implicit[implicit] → DimExpression

Marks the lower/upper bounds of the selected dimensions as implicit/explicit.