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])
`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.
`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)¶

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

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.

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¶