tensorstore_demo.IndexDomain(
    
rank: int | None = None,
    
*,
    
inclusive_min: collections.abc.Sequence[int] | None = None,
    
implicit_lower_bounds(
       
 collections.abc.Sequence[bool] | None = None
,
    
exclusive_max: collections.abc.Sequence[int] | None = None,
    
inclusive_max: collections.abc.Sequence[int] | None = None,
    
shape: collections.abc.Sequence[int] | None = None,
    
implicit_upper_bounds(
       
 collections.abc.Sequence[bool] | None = None
,
    
labels: collections.abc.Sequence[str | None] | None = None)

Constructs an index domain from component vectors.

Parameters:
rank: int | None = None

Number of dimensions. Only required if no other parameter is specified.

inclusive_min: collections.abc.Sequence[int] | None = None

Inclusive lower bounds for each dimension. If not specified, defaults to all zero if shape is specified, otherwise unbounded.

implicit_lower_bounds: collections.abc.Sequence[bool] | None = None

Indicates whether each lower bound is implicit or explicit. Defaults to all explicit if inclusive_min or shape is specified, otherwise defaults to all implicit.

exclusive_max: collections.abc.Sequence[int] | None = None

Exclusive upper bounds for each dimension. At most one of exclusive_max, inclusive_max, and shape may be specified.

inclusive_max: collections.abc.Sequence[int] | None = None

Inclusive upper bounds for each dimension.

shape: collections.abc.Sequence[int] | None = None

Size for each dimension.

implicit_upper_bounds: collections.abc.Sequence[bool] | None = None

Indicates whether each upper bound is implicit or explicit. Defaults to all explicit if exclusive_max, inclusive_max, or shape is specified, otherwise defaults to all implicit.

labels: collections.abc.Sequence[str | None] | None = None

Dimension labels. Defaults to all unlabeled.

Examples

>>> ts.IndexDomain(rank=5)
{ (-inf*, +inf*), (-inf*, +inf*), (-inf*, +inf*), (-inf*, +inf*), (-inf*, +inf*) }
>>> ts.IndexDomain(shape=[2, 3])
{ [0, 2), [0, 3) }

Last update: Nov 16, 2024