MeasureDefectsTask¶

class lsst.cp.pipe.MeasureDefectsTask(*, config: Optional[PipelineTaskConfig] = None, log: Optional[Union[logging.Logger, LsstLogAdapter]] = None, initInputs: Optional[Dict[str, Any]] = None, **kwargs)¶

Bases: lsst.pipe.base.PipelineTask

Measure the defects from one exposure.

Attributes Summary

canMultiprocess

Methods Summary

`debugHistogram`(stepname, ampImage, …)	Make a histogram of the distribution of pixel values for each amp.
`debugView`(stepname, ampImage, defects, detector)	Plot the defects found by the task.
`emptyMetadata`()	Empty (clear) the metadata for this Task and all sub-Tasks.
`findHotAndColdPixels`(exp, nSigma)	Find hot and cold pixels in an image.
`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.
`getResourceConfig`()	Return resource configuration for this 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.
`maskBlocksIfIntermitentBadPixelsInColumn`(defects)	Mask blocks in a column if there are on-and-off bad pixels
`run`(inputExp, camera)	Measure one exposure for defects.
`runQuantum`(butlerQC, inputRefs, outputRefs)	Method to do butler IO and or transforms to provide in memory objects for tasks run method
`timer`(name, logLevel)	Context manager to log performance data for an arbitrary block of code.

Attributes Documentation

canMultiprocess = True¶

Methods Documentation

debugHistogram(stepname, ampImage, nSigmaUsed, exp)¶

Make a histogram of the distribution of pixel values for each amp.

The main image data histogram is plotted in blue. Edge pixels, if masked, are in red. Note that masked edge pixels do not contribute to the underflow and overflow numbers.

Note that this currently only supports the 16-amp LSST detectors.

Parameters:	stepname : `str` Debug frame to request. ampImage : `lsst.afw.image.MaskedImage` Amplifier image to display. nSigmaUsed : `float` The number of sigma used for detection exp : `lsst.afw.image.exposure.Exposure` The exposure in which the defects were found.

debugView(stepname, ampImage, defects, detector)¶

Plot the defects found by the task.

Parameters:	stepname : `str` Debug frame to request. ampImage : `lsst.afw.image.MaskedImage` Amplifier image to display. defects : `lsst.ip.isr.Defects` The defects to plot. detector : `lsst.afw.cameraGeom.Detector` Detector holding camera geometry.

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

findHotAndColdPixels(exp, nSigma)¶

Find hot and cold pixels in an image.

Using config-defined thresholds on a per-amp basis, mask pixels that are nSigma above threshold in dark frames (hot pixels), or nSigma away from the clipped mean in flats (hot & cold pixels).

Parameters:	exp : `lsst.afw.image.exposure.Exposure` The exposure in which to find defects. nSigma : `list` [`float`] Detection threshold to use. Positive for DETECTED pixels, negative for DETECTED_NEGATIVE pixels.
Returns:	defects : `lsst.ip.isr.Defects` The defects found in the image.

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.

maskBlocksIfIntermitentBadPixelsInColumn(defects)¶

Mask blocks in a column if there are on-and-off bad pixels

If there’s a column with on and off bad pixels, mask all the pixels in between, except if there is a large enough gap of consecutive good pixels between two bad pixels in the column.

Parameters:	defects : `lsst.ip.isr.Defects` The defects found in the image so far
Returns:	defects : `lsst.ip.isr.Defects` If the number of bad pixels in a column is not larger or equal than self.config.badPixelColumnThreshold, the input list is returned. Otherwise, the defects list returned will include boxes that mask blocks of on-and-of pixels.

run(inputExp, camera)¶

Measure one exposure for defects.

Parameters:	inputExp : `lsst.afw.image.Exposure` Exposure to examine. camera : `lsst.afw.cameraGeom.Camera` Camera to use for metadata.
Returns:	results : `lsst.pipe.base.Struct` Results struct containing: `outputDefects` The defects measured from this exposure (`lsst.ip.isr.Defects`).

runQuantum(butlerQC: lsst.pipe.base.butlerQuantumContext.ButlerQuantumContext, inputRefs: lsst.pipe.base.connections.InputQuantizedConnection, outputRefs: lsst.pipe.base.connections.OutputQuantizedConnection) → None¶

Method to do butler IO and or transforms to provide in memory objects for tasks run method

Parameters:

butlerQC : ButlerQuantumContext: A butler which is specialized to operate in the context of a lsst.daf.butler.Quantum.
inputRefs : InputQuantizedConnection: Datastructure whose attribute names are the names that identify connections defined in corresponding PipelineTaskConnections class. The values of these attributes are the lsst.daf.butler.DatasetRef objects associated with the defined input/prerequisite connections.
outputRefs : OutputQuantizedConnection: Datastructure whose attribute names are the names that identify connections defined in corresponding PipelineTaskConnections class. The values of these attributes are the lsst.daf.butler.DatasetRef objects associated with the defined output connections.

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

MeasureDefectsTask¶