ReserveSourcesTask¶

class lsst.meas.algorithms.ReserveSourcesTask(columnName=None, schema=None, doc=None, **kwargs)¶

Bases: lsst.pipe.base.Task

Reserve sources from analysis

We randomly select a fraction of sources that will be reserved from analysis. This allows evaluation of the quality of model fits using sources that were not involved in the fitting process.

Methods Summary

`applySelectionPrior`(prior, selection)	Apply selection to full catalog
`emptyMetadata`()	Empty (clear) the metadata for this Task and all sub-Tasks.
`getAllSchemaCatalogs`()	Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.
`getFullMetadata`()	Get metadata for all tasks.
`getFullName`()	Get the task name as a hierarchical name including parent task names.
`getName`()	Get the name of the task.
`getSchemaCatalogs`()	Get the schemas generated by this task.
`getTaskDict`()	Get a dictionary of all tasks as a shallow copy.
`makeField`(doc)	Make a `lsst.pex.config.ConfigurableField` for this task.
`makeSubtask`(name, **keyArgs)	Create a subtask as a new instance as the `name` attribute of this task.
`markSources`(sources, selection)	Mark sources in a list or catalog
`run`(sources[, prior, expId])	Select sources to be reserved
`select`(numSources[, expId])	Randomly select some sources
`timer`(name, logLevel)	Context manager to log performance data for an arbitrary block of code.

Methods Documentation

applySelectionPrior(prior, selection)¶

Apply selection to full catalog

The select method makes a random selection of sources. If those sources don’t represent the full population (because a sub-selection has already been made), then we need to generate a selection covering the entire population.

Parameters:	prior : `numpy.ndarray` of type `bool` Prior selection of sources, identifying the subset from which the random selection has been made. selection : `numpy.ndarray` of type `bool` Selection of sources in subset identified by `prior`.
Returns:	full : `numpy.ndarray` of type `bool` Selection applied to full population.

emptyMetadata() → None¶: Empty (clear) the metadata for this Task and all sub-Tasks.

getAllSchemaCatalogs() → Dict[str, Any]¶

Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.

Returns:	schemacatalogs : `dict` Keys are butler dataset type, values are a empty catalog (an instance of the appropriate `lsst.afw.table` Catalog type) for all tasks in the hierarchy, from the top-level task down through all subtasks.

Notes

This method may be called on any task in the hierarchy; it will return the same answer, regardless.

The default implementation should always suffice. If your subtask uses schemas the override Task.getSchemaCatalogs, not this method.

getFullMetadata() → lsst.pipe.base._task_metadata.TaskMetadata¶

Get metadata for all tasks.

Returns:	metadata : `TaskMetadata` The keys are the full task name. Values are metadata for the top-level task and all subtasks, sub-subtasks, etc.

Notes

The returned metadata includes timing information (if @timer.timeMethod is used) and any metadata set by the task. The name of each item consists of the full task name with . replaced by :, followed by . and the name of the item, e.g.:

topLevelTaskName:subtaskName:subsubtaskName.itemName

using : in the full task name disambiguates the rare situation that a task has a subtask and a metadata item with the same name.

getFullName() → str¶

Get the task name as a hierarchical name including parent task names.

Returns:	fullName : `str` The full name consists of the name of the parent task and each subtask separated by periods. For example: The full name of top-level task “top” is simply “top”. The full name of subtask “sub” of top-level task “top” is “top.sub”. The full name of subtask “sub2” of subtask “sub” of top-level task “top” is “top.sub.sub2”.

getName() → str¶

Get the name of the task.

Returns:	taskName : `str` Name of the task.

See also

Task.getAllSchemaCatalogs

Notes

Warning

Subclasses that use schemas must override this method. The default implementation returns an empty dict.

This method may be called at any time after the Task is constructed, which means that all task schemas should be computed at construction time, not when data is actually processed. This reflects the philosophy that the schema should not depend on the data.

Returning catalogs rather than just schemas allows us to save e.g. slots for SourceCatalog as well.

getTaskDict() → Dict[str, weakref]¶

Get a dictionary of all tasks as a shallow copy.

Returns:	taskDict : `dict` Dictionary containing full task name: task object for the top-level task and all subtasks, sub-subtasks, etc.

classmethod makeField(doc: str) → lsst.pex.config.configurableField.ConfigurableField¶

Make a lsst.pex.config.ConfigurableField for this task.

Parameters:	doc : `str` Help text for the field.
Returns:	configurableField : `lsst.pex.config.ConfigurableField` A `ConfigurableField` for this task.

Examples

Provides a convenient way to specify this task is a subtask of another task.

Here is an example of use:

class OtherTaskConfig(lsst.pex.config.Config):
    aSubtask = ATaskClass.makeField("brief description of task")

makeSubtask(name: str, **keyArgs) → None¶

Create a subtask as a new instance as the name attribute of this task.

Parameters:	name : `str` Brief name of the subtask. keyArgs Extra keyword arguments used to construct the task. The following arguments are automatically provided and cannot be overridden: “config”. “parentTask”.

Notes

The subtask must be defined by Task.config.name, an instance of ConfigurableField or RegistryField.

markSources(sources, selection)¶

Mark sources in a list or catalog

This requires iterating through the list and setting the flag in each source individually. Even if the sources is a Catalog with contiguous records, it’s not currently possible to set a boolean column (DM-6981) so we need to iterate.

Parameters:	catalog : `lsst.afw.table.Catalog` or `list` of `lsst.afw.table.Record` Catalog in which to flag selected sources. selection : `numpy.ndarray` of type `bool` Selection of sources to mark.

run(sources, prior=None, expId=0)¶

Select sources to be reserved

Reserved sources will be flagged in the catalog, and we will return boolean arrays that identify the sources to be reserved from and used in the analysis. Typically you’ll want to use the sources from the use array in your fitting, and use the sources from the reserved array as an independent test of your fitting.

Parameters:

sources : lsst.afw.table.Catalog or list of lsst.afw.table.Record: Sources from which to select some to be reserved.
prior : numpy.ndarray of type bool, optional: Prior selection of sources. Should have the same length as sources. If set, we will only consider for reservation sources that are flagged True in this array.
expId : int: Exposure identifier; used for seeding the random number generator.

select(numSources, expId=0)¶

Randomly select some sources

We return a boolean array with a random selection. The fraction of sources selected is specified by the config parameter fraction.

Parameters:	numSources : `int` Number of sources in catalog from which to select. expId : `int` Exposure identifier; used for seeding the random number generator.
Returns:	selection : `numpy.ndarray` of type `bool` Selected sources are flagged `True` in this array.

timer(name: str, logLevel: int = 10) → Iterator[None]¶

Context manager to log performance data for an arbitrary block of code.

Parameters:	name : `str` Name of code being timed; data will be logged using item name: `Start` and `End`. logLevel A `logging` level constant.

Navigation

ReserveSourcesTask¶