ParallelOverscanCorrectionTask¶

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

Bases: OverscanCorrectionTaskBase

Correction task for parallel overscan.

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[, fillMasked])	Collapse overscan array (and mask) to a 1-D vector of values.
`correctOverscan`(exposure, amp, imageBBox, ...)	Trim the exposure, fit the overscan, subtract the fit, and calculate statistics.
`debugView`(image, model[, amp, isTransposed])	Debug display for the final overscan solution.
`emptyMetadata`()	Empty (clear) the metadata for this Task and all sub-Tasks.
`fillMaskedPixels`(overscanVector)	Fill masked/NaN pixels in the overscan.
`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.
`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.
`maskParallelOverscan`(exposure, detector[, ...])	Mask parallel overscan, growing saturated pixels.
`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 parallel 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.

collapseArray(maskedArray, fillMasked=True)¶

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

Parameters:

maskedArraynumpy.ma.masked_array: Masked array of input overscan data.
fillMaskedbool, optional: If true, fill any pixels that are masked with a median of neighbors.

Returns:

collapsednumpy.ma.masked_array: Single dimensional overscan data, combined with the mean.

correctOverscan(exposure, amp, imageBBox, overscanBBox, isTransposed=True, leadingToSkip=0, trailingToSkip=0)¶

Trim the exposure, fit the overscan, subtract the fit, and calculate statistics.

Parameters:

exposurelsst.afw.image.Exposure: Exposure containing the data.
amplsst.afw.cameraGeom.Amplifier: The amplifier that is to be corrected.
imageBBox: `lsst.geom.Box2I`: Bounding box of the image data that will have the overscan subtracted. If parallel overscan will be performed, that area is added to the image bounding box during serial overscan correction.
overscanBBox: `lsst.geom.Box2I`: Bounding box for the overscan data.
isTransposed: `bool`: If true, then the data will be transposed before fitting the overscan.
leadingToSkipint, optional: Leading rows/columns to skip.
trailingToSkipint, optional: Leading rows/columns to skip.

Returns:

resultslsst.pipe.base.Struct

ampOverscanModel: Overscan model broadcast to the full image size. (lsst.afw.image.Exposure)
overscanOverscanModel: Overscan model broadcast to the full overscan image size. (lsst.afw.image.Exposure)
overscanImage: Overscan image with the overscan fit subtracted. (lsst.afw.image.Exposure)
overscanValue: Overscan model. (float or np.array)
overscanMean: Mean value of the overscan fit. (float)
overscanMedian: Median value of the overscan fit. (float)
overscanSigma: Standard deviation of the overscan fit. (float)
overscanMeanResidual: Mean value of the overscan region after overscan subtraction. (float)
overscanMedianResidual: Median value of the overscan region after overscan subtraction. (float)
overscanSigmaResidual: Standard deviation of the overscan region after overscan subtraction. (float)

debugView(image, model, amp=None, isTransposed=True)¶

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.
isTransposedbool, optional: Does the data need to be transposed before display?

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

fillMaskedPixels(overscanVector)¶

Fill masked/NaN pixels in the overscan.

Parameters:

overscanVectornp.array or np.ma.masked_array: Overscan vector to fill.

Returns:

overscanVectornp.ma.masked_array: Filled vector.

Notes

Each maskSlice is a section of overscan with contiguous masks. Ideally this adds 5 pixels from the left and right of that mask slice, and takes the median of those values to fill the slice. If this isn’t possible, the median of all non-masked values is used. The mask is removed for the pixels filled.

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: Get the full name of the task.

getTaskDict() → dict[str, weakref.ReferenceType[lsst.pipe.base.task.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.

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.

maskParallelOverscan(exposure, detector, saturationLevel=None)¶

Mask parallel overscan, growing saturated pixels.

This operates on the image in-place.

Parameters:

exposurelsst.afw.image.Exposure: An untrimmed raw exposure.
detectorlsst.afw.cameraGeom.Detector: The detetor to use for amplifier geometry.
saturationLevelfloat, optional: Saturation level to use for masking.

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 parallel overscan from an amplifier image.

This method assumes that serial overscan has already been removed from the amplifier.

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 parallel overscan image data (scalar or lsst.afw.image.Image).
overscanImage: Image of the parallel overscan region with the parallel overscan correction applied (lsst.afw.image.Image). This quantity is used to estimate the amplifier read noise empirically.
overscanMean: Mean of the fit parallel overscan region.
overscanMedian: Median of the fit parallel overscan region.
overscanSigma: Sigma of the fit parallel overscan region.
residualMean: Mean of the residual of the parallel overscan region after correction.
residualMedian: Median of the residual of the parallel overscan region after correction.
residualSigma: Mean of the residual of the parallel overscan region after correction.

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.
logLevelint: A logging level constant.

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ParallelOverscanCorrectionTask¶