ReserveSourcesTask¶
- class lsst.meas.algorithms.ReserveSourcesTask(columnName=None, schema=None, doc=None, **kwargs)¶
Bases:
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.
- columnName
Methods Summary
applySelectionPrior
(prior, selection)Apply selection to full catalog
Empty (clear) the metadata for this Task and all sub-Tasks.
Get metadata for all tasks.
Get the task name as a hierarchical name including parent task names.
getName
()Get the name of the task.
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 typebool
Prior selection of sources, identifying the subset from which the random selection has been made.
- selection
numpy.ndarray
of typebool
Selection of sources in subset identified by
prior
.
- prior
- Returns:
- full
numpy.ndarray
of typebool
Selection applied to full population.
- full
- getFullMetadata() 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.
- metadata
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”.
- fullName
- getName() str ¶
Get the name of the task.
- Returns:
- taskName
str
Name of the task.
- taskName
See also
getFullName
Get the full name of the task.
- 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.
- taskDict
- classmethod makeField(doc: str) ConfigurableField ¶
Make a
lsst.pex.config.ConfigurableField
for this task.- Parameters:
- doc
str
Help text for the field.
- doc
- Returns:
- configurableField
lsst.pex.config.ConfigurableField
A
ConfigurableField
for this task.
- configurableField
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: Any) 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
.
- name
Notes
The subtask must be defined by
Task.config.name
, an instance ofConfigurableField
orRegistryField
.
- 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 aCatalog
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
orlist
oflsst.afw.table.Record
Catalog in which to flag selected sources.
- selection
numpy.ndarray
of typebool
Selection of sources to mark.
- catalog
- 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 thereserved
array as an independent test of your fitting.- Parameters:
- sources
lsst.afw.table.Catalog
orlist
oflsst.afw.table.Record
Sources from which to select some to be reserved.
- prior
numpy.ndarray
of typebool
, optional Prior selection of sources. Should have the same length as
sources
. If set, we will only consider for reservation sources that are flaggedTrue
in this array.- expId
int
Exposure identifier; used for seeding the random number generator.
- sources
- 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 typebool
)use
Sources the user should use in analysis are flagged
True
. (numpy.ndarray
of typebool
)
- results
- 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:
- Returns:
- selection
numpy.ndarray
of typebool
Selected sources are flagged
True
in this array.
- selection