OverscanCorrectionTask¶

class lsst.ip.isr.OverscanCorrectionTask(statControl=None, **kwargs)¶

Bases: lsst.pipe.base.Task

Correction task for overscan.

This class contains a number of utilities that are easier to understand and use when they are not embedded in nested if/else loops.

Parameters:	statControl : `lsst.afw.math.StatisticsControl`, optional Statistics control object.

Methods Summary

`broadcastFitToImage`(overscanValue, imageArray)	Broadcast 0 or 1 dimension fit to appropriate shape.
`collapseArray`(maskedArray)	Collapse overscan array (and mask) to a 1-D vector of values.
`collapseArrayMedian`(maskedArray)	Collapse overscan array (and mask) to a 1-D vector of using the correct integer median of row-values.
`correctOverscan`(exposure, amp, imageBBox, …)
`debugView`(image, model[, amp])	Debug display for the final overscan solution.
`emptyMetadata`()	Empty (clear) the metadata for this Task and all sub-Tasks.
`fitOverscan`(overscanImage[, isTransposed])
`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.
`getImageArray`(image)	Extract the numpy array from the input image.
`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.
`integerConvert`(image)	Return an integer version of the input image.
`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.
`maskExtrapolated`(collapsed)	Create mask if edges are extrapolated.
`maskOutliers`(imageArray)	Mask outliers in a row of overscan data from a robust sigma clipping procedure.
`measureConstantOverscan`(image)	Measure a constant overscan value.
`measureVectorOverscan`(image[, isTransposed])	Calculate the 1-d vector overscan from the input overscan image.
`run`(exposure, amp[, isTransposed])	Measure and remove an overscan from an amplifier image.
`splineEval`(indices, interp)	Wrapper function to match spline evaluation API to polynomial fit API.
`splineFit`(indices, collapsed, numBins)	Wrapper function to match spline fit API to polynomial fit API.
`timer`(name, logLevel)	Context manager to log performance data for an arbitrary block of code.
`trimOverscan`(exposure, amp, bbox, …[, …])	Trim overscan region to remove edges.

Methods Documentation

broadcastFitToImage(overscanValue, imageArray, transpose=False)¶

Broadcast 0 or 1 dimension fit to appropriate shape.

Parameters:	overscanValue : `numpy.ndarray`, (Nrows, ) or scalar Overscan fit to broadcast. imageArray : `numpy.ndarray`, (Nrows, Ncols) Image array that we want to match. transpose : `bool`, optional Switch order to broadcast along the other axis.
Returns:	overscanModel : `numpy.ndarray`, (Nrows, Ncols) or scalar Expanded overscan fit.
Raises:	RuntimeError Raised if no axis has the appropriate dimension.

static collapseArray(maskedArray)¶

Collapse overscan array (and mask) to a 1-D vector of values.

Parameters:	maskedArray : `numpy.ma.masked_array` Masked array of input overscan data.
Returns:	collapsed : `numpy.ma.masked_array` Single dimensional overscan data, combined with the mean.

collapseArrayMedian(maskedArray)¶

Collapse overscan array (and mask) to a 1-D vector of using the correct integer median of row-values.

Parameters:	maskedArray : `numpy.ma.masked_array` Masked array of input overscan data.
Returns:	collapsed : `numpy.ma.masked_array` Single dimensional overscan data, combined with the afwMath median.

correctOverscan(exposure, amp, imageBBox, overscanBBox, isTransposed=True)¶

debugView(image, model, amp=None)¶

Debug display for the final overscan solution.

Parameters:	image : `lsst.afw.image.Image` Input image the overscan solution was determined from. model : `numpy.ndarray` or `float` Overscan model determined for the image. amp : `lsst.afw.cameraGeom.Amplifier`, optional Amplifier to extract diagnostic information.

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

fitOverscan(overscanImage, isTransposed=False)¶

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”.

getImageArray(image)¶

Extract the numpy array from the input image.

Parameters:	image : `lsst.afw.image.Image` or `lsst.afw.image.MaskedImage` Image data to pull array from. calcImage : `numpy.ndarray` Image data array for numpy operating.

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.

static integerConvert(image)¶

Return an integer version of the input image.

Parameters:	image : `numpy.ndarray`, `lsst.afw.image.Image` or `MaskedImage` Image to convert to integers.
Returns:	outI : `numpy.ndarray`, `lsst.afw.image.Image` or `MaskedImage` The integer converted image.
Raises:	RuntimeError Raised if the input image could not be converted.

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.

static maskExtrapolated(collapsed)¶

Create mask if edges are extrapolated.

Parameters:	collapsed : `numpy.ma.masked_array` Masked array to check the edges of.
Returns:	maskArray : `numpy.ndarray` Boolean numpy array of pixels to mask.

maskOutliers(imageArray)¶

Mask outliers in a row of overscan data from a robust sigma clipping procedure.

Parameters:	imageArray : `numpy.ndarray` Image to filter along numpy axis=1.
Returns:	maskedArray : `numpy.ma.masked_array` Masked image marking outliers.

measureConstantOverscan(image)¶

Measure a constant overscan value.

Parameters:	image : `lsst.afw.image.Image` or `lsst.afw.image.MaskedImage` Image data to measure the overscan from.
Returns:	results : `lsst.pipe.base.Struct` Overscan result with entries: - `overscanValue`: Overscan value to subtract (`float`) - `isTransposed`: Orientation of the overscan (`bool`)

measureVectorOverscan(image, isTransposed=False)¶

Calculate the 1-d vector overscan from the input overscan image.

Parameters:	image : `lsst.afw.image.MaskedImage` Image containing the overscan data. isTransposed : `bool` If true, the image has been transposed.
Returns:	results : `lsst.pipe.base.Struct` Overscan result with entries: `overscanValue` Overscan value to subtract (`float`) `maskArray` List of rows that should be masked as `SUSPECT` when the overscan solution is applied. (`list` [ `bool` ]) `isTransposed` Indicates if the overscan data was transposed during calcuation, noting along which axis the overscan should be subtracted. (`bool`)

run(exposure, amp, isTransposed=False)¶

Measure and remove an overscan from an amplifier image.

Parameters:	exposure : `lsst.afw.image.Exposure` Image data that will have the overscan corrections applied. amp : `lsst.afw.cameraGeom.Amplifier` Amplifier to use for debugging purposes. isTransposed : `bool`, optional Is the image transposed, such that serial and parallel overscan regions are reversed? Default is False.
Returns:	overscanResults : `lsst.pipe.base.Struct` Result struct with components: `imageFit` Value or fit subtracted from the amplifier image data (scalar or `lsst.afw.image.Image`). `overscanFit` Value or fit subtracted from the serial overscan image data (scalar or `lsst.afw.image.Image`). `overscanImage` Image of the serial overscan region with the serial overscan correction applied (`lsst.afw.image.Image`). This quantity is used to estimate the amplifier read noise empirically. `parallelOverscanFit` Value or fit subtracted from the parallel overscan image data (scalar, `lsst.afw.image.Image`, or None). `parallelOverscanImage` Image of the parallel overscan region with the parallel overscan correction applied (`lsst.afw.image.Image` or None).
Raises:	RuntimeError Raised if an invalid overscan type is set.

static splineEval(indices, interp)¶

Wrapper function to match spline evaluation API to polynomial fit API.

Parameters:	indices : `numpy.ndarray` Locations to evaluate the spline. interp : `lsst.afw.math.interpolate` Interpolation object to use.
Returns:	values : `numpy.ndarray` Evaluated spline values at each index.

splineFit(indices, collapsed, numBins)¶

Wrapper function to match spline fit API to polynomial fit API.

Parameters:	indices : `numpy.ndarray` Locations to evaluate the spline. collapsed : `numpy.ndarray` Collapsed overscan values corresponding to the spline evaluation points. numBins : `int` Number of bins to use in constructing the spline.
Returns:	interp : `lsst.afw.math.Interpolate` Interpolation object for later evaluation.

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.

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OverscanCorrectionTask¶