OverscanCorrectionTask¶

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

Bases: 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:

statControllsst.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:

overscanValuenumpy.ndarray, (Nrows, ) or scalar: Overscan fit to broadcast.
imageArraynumpy.ndarray, (Nrows, Ncols): Image array that we want to match.
transposebool, optional: Switch order to broadcast along the other axis.

Returns:

overscanModelnumpy.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:

maskedArraynumpy.ma.masked_array: Masked array of input overscan data.

Returns:

collapsednumpy.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:

maskedArraynumpy.ma.masked_array: Masked array of input overscan data.

Returns:

collapsednumpy.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:

imagelsst.afw.image.Image: Input image the overscan solution was determined from.
modelnumpy.ndarray or float: Overscan model determined for the image.
amplsst.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() → TaskMetadata¶

Get metadata for all tasks.

Returns:

metadataTaskMetadata: 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:

fullNamestr

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:

imagelsst.afw.image.Image or lsst.afw.image.MaskedImage: Image data to pull array from.
calcImagenumpy.ndarray: Image data array for numpy operating.

getName() → str¶

Get the name of the task.

Returns:

taskNamestr: Name of the task.

See also

getFullName

getTaskDict() → Dict[str, ReferenceType[Task]]¶

Get a dictionary of all tasks as a shallow copy.

Returns:

taskDictdict: 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:

imagenumpy.ndarray, lsst.afw.image.Image or MaskedImage: Image to convert to integers.

Returns:

outInumpy.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) → ConfigurableField¶

Make a lsst.pex.config.ConfigurableField for this task.

Parameters:

docstr: Help text for the field.

Returns:

configurableFieldlsst.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: Any) → None¶

Create a subtask as a new instance as the name attribute of this task.

Parameters:

namestr

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:

collapsednumpy.ma.masked_array: Masked array to check the edges of.

Returns:

maskArraynumpy.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:

imageArraynumpy.ndarray: Image to filter along numpy axis=1.

Returns:

maskedArraynumpy.ma.masked_array: Masked image marking outliers.

measureConstantOverscan(image)¶

Measure a constant overscan value.

Parameters:

imagelsst.afw.image.Image or lsst.afw.image.MaskedImage: Image data to measure the overscan from.

Returns:

resultslsst.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:

imagelsst.afw.image.MaskedImage: Image containing the overscan data.
isTransposedbool: If true, the image has been transposed.

Returns:

resultslsst.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:

exposurelsst.afw.image.Exposure: Image data that will have the overscan corrections applied.
amplsst.afw.cameraGeom.Amplifier: Amplifier to use for debugging purposes.
isTransposedbool, optional: Is the image transposed, such that serial and parallel overscan regions are reversed? Default is False.

Returns:

overscanResultslsst.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:

indicesnumpy.ndarray: Locations to evaluate the spline.
interplsst.afw.math.interpolate: Interpolation object to use.

Returns:

valuesnumpy.ndarray: Evaluated spline values at each index.

splineFit(indices, collapsed, numBins)¶

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

Parameters:

indicesnumpy.ndarray: Locations to evaluate the spline.
collapsednumpy.ndarray: Collapsed overscan values corresponding to the spline evaluation points.
numBinsint: Number of bins to use in constructing the spline.

Returns:

interplsst.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:

namestr: 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¶