PhotoCalTask¶

class lsst.pipe.tasks.photoCal.PhotoCalTask(refObjLoader, schema=None, **kwds)¶

Bases: lsst.pipe.base.Task

Calculate an Exposure’s zero-point given a set of flux measurements of stars matched to an input catalogue.

Parameters:	refObjLoader : `lsst.meas.algorithms.ReferenceObjectLoader` An instance of LoadReferenceObjectsTasks that supplies an external reference catalog. schema : `lsst.afw.table.Schema`, optional The schema of the detection catalogs used as input to this task. **kwds Additional keyword arguments.

Notes

The type of flux to use is specified by PhotoCalConfig.fluxField.

The algorithm clips outliers iteratively, with parameters set in the configuration.

This task can adds fields to the schema, so any code calling this task must ensure that these columns are indeed present in the input match list; see pipe_tasks_photocal_Example.

Debugging:

The available lsstDebug variables in PhotoCalTask are:

display :

If True enable other debug outputs.

displaySources :

If True, display the exposure on ds9’s frame 1 and overlay the source catalogue.

red o :

Reserved objects.

green o :

Objects used in the photometric calibration.

scatterPlot :

Make a scatter plot of flux v. reference magnitude as a function of reference magnitude:

good objects in blue
rejected objects in red

(if scatterPlot is 2 or more, prompt to continue after each iteration)

Methods Summary

`displaySources`(exposure, matches, reserved)	Display sources we’ll use for photocal.
`emptyMetadata`()	Empty (clear) the metadata for this Task and all sub-Tasks.
`extractMagArrays`(matches, filterLabel, …)	Extract magnitude and magnitude error arrays from the given matches.
`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.
`getSourceKeys`(schema)	Return a struct containing the source catalog keys for fields used by PhotoCalTask.
`getTaskDict`()	Get a dictionary of all tasks as a shallow copy.
`getZeroPoint`(src, ref[, srcErr, zp0])	Flux calibration code, returning (ZeroPoint, Distribution Width, Number of stars).
`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.
`run`(exposure, sourceCat[, expId])	Do photometric calibration - select matches to use and (possibly iteratively) compute the zero point.
`timer`(name, logLevel)	Context manager to log performance data for an arbitrary block of code.

Methods Documentation

displaySources(exposure, matches, reserved, frame=1)¶

Display sources we’ll use for photocal.

Sources that will be actually used will be green. Sources reserved from the fit will be red.

Parameters:	exposure : `lsst.afw.image.ExposureF` Exposure to display. matches : `list` of `lsst.afw.table.RefMatch` Matches used for photocal. reserved : `numpy.ndarray` of type `bool` Boolean array indicating sources that are reserved. frame : `int`, optional Frame number for display.

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

extractMagArrays(matches, filterLabel, sourceKeys)¶

Extract magnitude and magnitude error arrays from the given matches.

Parameters:

matches : lsst.afw.table.ReferenceMatchVector: Reference/source matches.
filterLabel : str: Label of filter being calibrated.
sourceKeys : lsst.pipe.base.Struct: Struct of source catalog keys, as returned by getSourceKeys().

Returns:

result : lsst.pipe.base.Struct

Results as a struct with attributes:

srcMag: Source magnitude (np.array).
refMag: Reference magnitude (np.array).
srcMagErr: Source magnitude error (np.array).
refMagErr: Reference magnitude error (np.array).
magErr: An error in the magnitude; the error in srcMag - refMag. If nonzero, config.magErrFloor will be added to magErr only (not srcMagErr or refMagErr), as magErr is what is later used to determine the zero point (np.array).
refFluxFieldList: A list of field names of the reference catalog used for fluxes (1 or 2 strings) (list).

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.

getSourceKeys(schema)¶

Return a struct containing the source catalog keys for fields used by PhotoCalTask.

Parameters:	schema : `lsst.afw.table.schema` Schema of the catalog to get keys from.
Returns:	result : `lsst.pipe.base.Struct` Results as a struct with attributes: `instFlux` Instrument flux key. `instFluxErr` Instrument flux error key.

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.

getZeroPoint(src, ref, srcErr=None, zp0=None)¶

Flux calibration code, returning (ZeroPoint, Distribution Width, Number of stars).

Returns:	result : `lsst.pipe.base.Struct` Results as a struct with attributes: `zp` Photometric zero point (mag, `float`). `sigma` Standard deviation of fit of photometric zero point (mag, `float`). `ngood` Number of sources used to fit photometric zero point (`int`).

Notes

We perform nIter iterations of a simple sigma-clipping algorithm with a couple of twists: - We use the median/interquartile range to estimate the position to clip around, and the “sigma” to use. - We never allow sigma to go _above_ a critical value sigmaMax — if we do, a sufficiently large estimate will prevent the clipping from ever taking effect. - Rather than start with the median we start with a crude mode. This means that a set of magnitude residuals with a tight core and asymmetrical outliers will start in the core. We use the width of this core to set our maximum sigma (see second bullet).

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.

run(exposure, sourceCat, expId=0)¶

Do photometric calibration - select matches to use and (possibly iteratively) compute the zero point.

Parameters:	exposure : `lsst.afw.image.Exposure` Exposure upon which the sources in the matches were detected. sourceCat : `lsst.afw.image.SourceCatalog` A catalog of sources to use in the calibration (i.e. a `list` of `lsst.afw.table.Match` with first being of type `lsst.afw.table.SimpleRecord` and second type `lsst.afw.table.SourceRecord` the reference object and matched object respectively). Will not be modified except to set the outputField if requested. expId : `int`, optional Exposure ID.
Returns:	result : `lsst.pipe.base.Struct` Results as a struct with attributes: `photoCalib` Object containing the zero point (`lsst.afw.image.Calib`). `arrays` Magnitude arrays returned be `PhotoCalTask.extractMagArrays`. `matches` ReferenceMatchVector, as returned by `PhotoCalTask.selectMatches`. `zp` Photometric zero point (mag, `float`). `sigma` Standard deviation of fit of photometric zero point (mag, `float`). `ngood` Number of sources used to fit photometric zero point (`int`).
Raises:	RuntimeError Raised if any of the following occur: - No matches to use for photocal. - No matches are available (perhaps no sources/references were selected by the matcher). - No reference stars are available. - No matches are available from which to extract magnitudes.

Notes

The exposure is only used to provide the name of the filter being calibrated (it may also be used to generate debugging plots).

The reference objects: - Must include a field photometric; True for objects which should be considered as photometric standards. - Must include a field flux; the flux used to impose a magnitude limit and also to calibrate the data to (unless a color term is specified, in which case ColorTerm.primary is used; See https://jira.lsstcorp.org/browse/DM-933). - May include a field stargal; if present, True means that the object is a star. - May include a field var; if present, True means that the object is variable.

The measured sources: - Must include PhotoCalConfig.fluxField; the flux measurement to be used for calibration.

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

PhotoCalTask¶