tensorstore.IndexTransform.vindex

tensorstore.IndexTransform.vindex[self, indices: NumpyIndexingSpec] → IndexTransform

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

This is similar to IndexTransform.__getitem__(indices), but differs in that if indices specifies any array indexing terms, the broadcasted array dimensions are unconditionally added as the first dimensions of the result domain:

Example

>>> transform = ts.IndexTransform(3)
>>> transform.vindex[2, [1, 2, 3], [6, 7, 8]]
Rank 1 -> 3 index space transform:
  Input domain:
    0: [0, 3)
  Output index maps:
    out[0] = 2
    out[1] = 0 + 1 * bounded((-inf, +inf), array(in)), where array =
      {1, 2, 3}
    out[2] = 0 + 1 * bounded((-inf, +inf), array(in)), where array =
      {6, 7, 8}