tensorstore.IndexDomain.__getitem__(domain)

tensorstore.IndexDomain.__getitem__(self, other: IndexDomain) → IndexDomain

Slices this domain by another domain.

The result is determined by matching dimensions of other to dimensions of self either by label or by index, according to one of the following three cases:

`other` is entirely unlabeled	Result is `self[ts.d[:][other.inclusive_min:other.exclusive_max]`. It is an error if `self.rank != other.rank`.
`self` is entirely unlabeled	Result is `self[ts.d[:][other.inclusive_min:other.exclusive_max].labels[other.labels]`. It is an error if `self.rank != other.rank`.
Both `self` and `other` have at least one labeled dimension.	Result is `self[ts.d[dims][other.inclusive_min:other.exclusive_max]`, where the sequence of `other.rank` dimension identifiers `dims` is determined as follows: If `other.labels[i]` is specified (i.e. non-empty), `dims[i] = self.labels.index(other.labels[i])`. It is an error if no such dimension exists. Otherwise, `i` is the `j`th unlabeled dimension of `other` (left to right), and `dims[i] = k`, where `k` is the `j`th unlabeled dimension of `self` (left to right). It is an error if no such dimension exists. If any dimensions of `other` are unlabeled, then it is an error if `self.rank != other.rank`. This condition is not strictly necessary but serves to avoid a discrepancy in behavior with normal domain alignment.

Example with all unlabeled dimensions

>>> a = ts.IndexDomain(inclusive_min=[0, 1], exclusive_max=[5, 7])
>>> b = ts.IndexDomain(inclusive_min=[2, 3], exclusive_max=[4, 6])
>>> a[b]
{ [2, 4), [3, 6) }

Example with fully labeled dimensions

>>> a = ts.IndexDomain(inclusive_min=[0, 1, 2],
...                    exclusive_max=[5, 7, 8],
...                    labels=["x", "y", "z"])
>>> b = ts.IndexDomain(inclusive_min=[2, 3],
...                    exclusive_max=[6, 4],
...                    labels=["y", "x"])
>>> a[b]
{ "x": [3, 4), "y": [2, 6), "z": [2, 8) }

Example with mixed labeled and unlabeled dimensions

>>> a = ts.IndexDomain(inclusive_min=[0, 0, 0, 0],
...                    exclusive_max=[10, 10, 10, 10],
...                    labels=["x", "", "", "y"])
>>> b = ts.IndexDomain(inclusive_min=[1, 2, 3, 4],
...                    exclusive_max=[6, 7, 8, 9],
...                    labels=["y", "", "x", ""])
>>> a[b]
{ "x": [3, 8), [2, 7), [4, 9), "y": [1, 6) }

Note

On other, implicit bounds indicators have no effect.