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.

Parameters:
columnName : str, required

Name of flag column to add; we will suffix this with “_reserved”.

schema : lsst.afw.table.Schema, required

Catalog schema.

doc : str

Documentation for column to add.

config : ReserveSourcesConfig

Configuration.

Methods Summary

applySelectionPrior(prior, selection) Apply selection to full catalog
emptyMetadata() Empty (clear) the metadata for this Task and all sub-Tasks.
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.
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.

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

getFullName
getTaskDict() → Dict[str, weakref.ReferenceType[lsst.pipe.base.task.Task]]

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.

Returns:
results : lsst.pipe.base.Struct

The results in a Struct:

reserved

Sources to be reserved are flagged True in this array. (numpy.ndarray of type bool)

use

Sources the user should use in analysis are flagged True. (numpy.ndarray of type bool)

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.

See also

timer.logInfo

Examples

Creating a timer context:

with self.timer("someCodeToTime"):
    pass  # code to time