- tensorstore.DimExpression.vindex[self, indices: NumpyIndexingSpec] DimExpression
Applies a NumPy-style indexing operation with vectorized indexing semantics.
This is similar to
DimExpression.__getitem__
, but differs in that ifindices
specifies any array indexing terms, the broadcasted array dimensions are unconditionally added as the first dimensions of the result domain:Examples
>>> transform = ts.IndexTransform(input_labels=['x', 'y', 'z']) >>> transform[ts.d['y', 'z'].vindex[[1, 2, 3], [4, 5, 6]]] Rank 2 -> 3 index space transform: Input domain: 0: [0, 3) 1: (-inf*, +inf*) "x" Output index maps: out[0] = 0 + 1 * in[1] 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 = {{4}, {5}, {6}}
- Returns:¶
Dimension expression with the indexing operation added.
See also