-
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
orshape
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
, andshape
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
, orshape
is specified, otherwise defaults to all implicit.- labels: collections.abc.Sequence[str | None] | None =
None
¶ Dimension labels. Defaults to all unlabeled.
- rank: int | None =
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:
Dec 16, 2024