CalculationRowIterable

class lsst.daf.relation.iteration.CalculationRowIterable(target: RowIterable, tag: ColumnTag, callable: Callable[[Mapping[ColumnTag, Any]], Any])

Bases: RowIterable

A RowIterable implementation that implements a calculation operation.

Parameters:

targetRowIterable

Original iterable.

tagColumnTag

Key for the new column in result-row mappings.

callableCallable

Callable that takes a single mapping argument and returns a new column value.

Methods Summary

materialized()	Convert this iterable to one that holds its rows in a Python collection of some kind, instead of generating them lazily.
sliced(start, stop)	Apply a slice operation to this RowIterable.
to_mapping(unique_key)	Convert this iterable to a RowMapping, unless it already is one.
to_sequence()	Convert this iterable to a RowSequence, unless it already is one.

Methods Documentation

materialized() → MaterializedRowIterable

Convert this iterable to one that holds its rows in a Python collection of some kind, instead of generating them lazily.

Returns:

rowsMaterializedRowIterable

A RowIterable that isn’t lazy.

sliced(start: int, stop: int | None) → RowIterable

Apply a slice operation to this RowIterable.

Parameters:

startint

Start index.

stopint or None

Stop index (one-past-the-end), or None to include up through the last row.

Returns:

rowsRowIterable

Iterable representing the slice. May or may not be lazy.

to_mapping(unique_key: Sequence[ColumnTag]) → RowMapping

Convert this iterable to a RowMapping, unless it already is one.

Parameters:

unique_keySequence [ ColumnTag ]

Sequence of columns to extract into a tuple to use as keys in the mapping, guaranteeing uniqueness over these columns.

Returns:

rowsRowMapping

A RowIterable backed by a mapping.

to_sequence() → RowSequence

Convert this iterable to a RowSequence, unless it already is one.

Returns:

rowsRowSequence

A RowIterable backed by a sequence.