DataCoordinateSet¶

class lsst.daf.butler.DataCoordinateSet(dataIds: AbstractSet[lsst.daf.butler.core.dimensions._coordinate.DataCoordinate], graph: lsst.daf.butler.core.dimensions._graph.DimensionGraph, *, hasFull: Optional[bool, None] = None, hasRecords: Optional[bool, None] = None, check: bool = True)¶

Bases: lsst.daf.butler.core.dimensions._dataCoordinateIterable._DataCoordinateCollectionBase

Iterable iteration that is set-like.

A DataCoordinateIterable implementation that adds some set-like functionality, and is backed by a true set-like object.

Parameters:

dataIds : collections.abc.Set [ DataCoordinate ]: A set of DataCoordinate instances, with dimensions equal to graph. If this is a mutable object, the caller must be able to guarantee that it will not be modified by any other holders.
graph : DimensionGraph: Dimensions identified by all data IDs in the set.
hasFull : bool, optional: If True, the caller guarantees that DataCoordinate.hasFull returns True for all given data IDs. If False, no such guarantee is made, and DataCoordinateSet.hasFull will always return False. If None (default), DataCoordinateSet.hasFull will be computed from the given data IDs, immediately if check is True, or on first use if check is False.
hasRecords : bool, optional: If True, the caller guarantees that DataCoordinate.hasRecords returns True for all given data IDs. If False, no such guarantee is made and DataCoordinateSet.hasRecords will always return False. If None (default), DataCoordinateSet.hasRecords will be computed from the given data IDs, immediately if check is True, or on first use if check is False.
check: `bool`, optional: If True (default) check that all data IDs are consistent with the given graph and state flags at construction. If False, no checking will occur.

Notes

DataCoordinateSet does not formally implement the collections.abc.Set interface, because that requires many binary operations to accept any set-like object as the other argument (regardless of what its elements might be), and it’s much easier to ensure those operations never behave surprisingly if we restrict them to DataCoordinateSet or (sometimes) DataCoordinateIterable, and in most cases restrict that they identify the same dimensions. In particular:

a DataCoordinateSet will compare as not equal to any object that is not a DataCoordinateSet, even native Python sets containing the exact same elements;
subset/superset comparison _operators_ (<, >, <=, >=) require both operands to be DataCoordinateSet instances that have the same dimensions (i.e. graph attribute);
issubset, issuperset, and isdisjoint require the other argument to be a DataCoordinateIterable with the same dimensions;
operators that create new sets (&, |, ^, -) require both operands to be DataCoordinateSet instances that have the same dimensions _and_ the same dtype;
named methods that create new sets (intersection, union, symmetric_difference, difference) require the other operand to be a DataCoordinateIterable with the same dimensions _and_ the same dtype.

In addition, when the two operands differ in the return values of hasFull and/or hasRecords, we make no guarantees about what those methods will return on the new DataCoordinateSet (other than that they will accurately reflect what elements are in the new set - we just don’t control which elements are contributed by each operand).

Attributes Summary

`graph`	Dimensions identified by these data IDs (`DimensionGraph`).
`universe`	Universe that defines all known compatible dimensions.

Methods Summary

`difference`(other)	Return a new set with all data IDs in this that are not in other.
`fromScalar`(dataId)	Return a `DataCoordinateIterable` containing the single data ID.
`hasFull`()	Indicate if all data IDs in this iterable identify all dimensions.
`hasRecords`()	Return whether all data IDs in this iterable contain records.
`intersection`(other)	Return a new set that contains all data IDs from parameters.
`isdisjoint`(other)	Test whether there are no data IDs in both `self` and `other`.
`issubset`(other)	Test whether `self` contains all data IDs in `other`.
`issuperset`(other)	Test whether `other` contains all data IDs in `self`.
`subset`(graph)	Return a set whose data IDs identify a subset.
`symmetric_difference`(other)	Return a new set with all data IDs in either parameters, not both.
`toSequence`()	Transform this iterable into a `DataCoordinateSequence`.
`toSet`()	Transform this iterable into a `DataCoordinateSet`.
`union`(other)	Return a new set that contains all data IDs in either parameters.

Attributes Documentation

graph¶: Dimensions identified by these data IDs (DimensionGraph).

universe¶

Universe that defines all known compatible dimensions.

(DimensionUniverse).

Methods Documentation

difference(other: lsst.daf.butler.core.dimensions._dataCoordinateIterable.DataCoordinateIterable) → lsst.daf.butler.core.dimensions._dataCoordinateIterable.DataCoordinateSet¶

Return a new set with all data IDs in this that are not in other.

Parameters:	other : `DataCoordinateIterable` An iterable of data IDs with `other.graph == self.graph`.
Returns:	intersection : `DataCoordinateSet` A new `DataCoordinateSet` instance.

static fromScalar(dataId: lsst.daf.butler.core.dimensions._coordinate.DataCoordinate) → lsst.daf.butler.core.dimensions._dataCoordinateIterable._ScalarDataCoordinateIterable¶

Return a DataCoordinateIterable containing the single data ID.

Parameters:	dataId : `DataCoordinate` Data ID to adapt. Must be a true `DataCoordinate` instance, not an arbitrary mapping. No runtime checking is performed.
Returns:	iterable : `DataCoordinateIterable` A `DataCoordinateIterable` instance of unspecified (i.e. implementation-detail) subclass. Guaranteed to implement the `collections.abc.Sized` (i.e. `__len__`) and `collections.abc.Container` (i.e. `__contains__`) interfaces as well as that of `DataCoordinateIterable`.

hasFull() → bool¶

Indicate if all data IDs in this iterable identify all dimensions.

Not just required dimensions.

Returns:	state : `bool` If `True`, `all(d.hasFull() for d in iterable)` is guaranteed. If `False`, no guarantees are made.

hasRecords() → bool¶

Return whether all data IDs in this iterable contain records.

Returns:	state : `bool` If `True`, `all(d.hasRecords() for d in iterable)` is guaranteed. If `False`, no guarantees are made.

intersection(other: lsst.daf.butler.core.dimensions._dataCoordinateIterable.DataCoordinateIterable) → lsst.daf.butler.core.dimensions._dataCoordinateIterable.DataCoordinateSet¶

Return a new set that contains all data IDs from parameters.

Parameters:	other : `DataCoordinateIterable` An iterable of data IDs with `other.graph == self.graph`.
Returns:	intersection : `DataCoordinateSet` A new `DataCoordinateSet` instance.

isdisjoint(other: lsst.daf.butler.core.dimensions._dataCoordinateIterable.DataCoordinateIterable) → bool¶

Test whether there are no data IDs in both self and other.

Parameters:	other : `DataCoordinateIterable` An iterable of data IDs with `other.graph == self.graph`.
Returns:	isdisjoint : `bool` `True` if there are no data IDs in both `self` and `other`, and `False` otherwise.

issubset(other: lsst.daf.butler.core.dimensions._dataCoordinateIterable.DataCoordinateIterable) → bool¶

Test whether self contains all data IDs in other.

Parameters:	other : `DataCoordinateIterable` An iterable of data IDs with `other.graph == self.graph`.
Returns:	issubset : `bool` `True` if all data IDs in `self` are also in `other`, and `False` otherwise.

issuperset(other: lsst.daf.butler.core.dimensions._dataCoordinateIterable.DataCoordinateIterable) → bool¶

Test whether other contains all data IDs in self.

Parameters:	other : `DataCoordinateIterable` An iterable of data IDs with `other.graph == self.graph`.
Returns:	issuperset : `bool` `True` if all data IDs in `other` are also in `self`, and `False` otherwise.

subset(graph: lsst.daf.butler.core.dimensions._graph.DimensionGraph) → lsst.daf.butler.core.dimensions._dataCoordinateIterable.DataCoordinateSet¶

Return a set whose data IDs identify a subset.

Parameters:	graph : `DimensionGraph` Dimensions to be identified by the data IDs in the returned iterable. Must be a subset of `self.graph`.
Returns:	set : `DataCoordinateSet` A `DataCoordinateSet` with `set.graph == graph`. Will be `self` if `graph == self.graph`. Elements are equivalent to those that would be created by calling `DataCoordinate.subset` on all elements in `self`, with deduplication but and in arbitrary order.

symmetric_difference(other: lsst.daf.butler.core.dimensions._dataCoordinateIterable.DataCoordinateIterable) → lsst.daf.butler.core.dimensions._dataCoordinateIterable.DataCoordinateSet¶

Return a new set with all data IDs in either parameters, not both.

Parameters:	other : `DataCoordinateIterable` An iterable of data IDs with `other.graph == self.graph`.
Returns:	intersection : `DataCoordinateSet` A new `DataCoordinateSet` instance.

toSequence() → lsst.daf.butler.core.dimensions._dataCoordinateIterable.DataCoordinateSequence¶

Transform this iterable into a DataCoordinateSequence.

Returns:	seq : `DataCoordinateSequence` A new `DatasetCoordinateSequence` with the same elements as `self`, in the same order. May be `self` if it is already a `DataCoordinateSequence`.

toSet() → lsst.daf.butler.core.dimensions._dataCoordinateIterable.DataCoordinateSet¶

Transform this iterable into a DataCoordinateSet.

Returns:	set : `DataCoordinateSet` A `DatasetCoordinateSet` instance with the same elements as `self`, after removing any duplicates. May be `self` if it is already a `DataCoordinateSet`.

union(other: lsst.daf.butler.core.dimensions._dataCoordinateIterable.DataCoordinateIterable) → lsst.daf.butler.core.dimensions._dataCoordinateIterable.DataCoordinateSet¶

Return a new set that contains all data IDs in either parameters.

Parameters:	other : `DataCoordinateIterable` An iterable of data IDs with `other.graph == self.graph`.
Returns:	intersection : `DataCoordinateSet` A new `DataCoordinateSet` instance.

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DataCoordinateSet¶