lsst.meas.algorithms  20.0.0-2-g92e20685+4
Public Member Functions | Static Public Member Functions | Public Attributes | Static Public Attributes | List of all members
lsst.meas.algorithms.dynamicDetection.DynamicDetectionTask Class Reference
Inheritance diagram for lsst.meas.algorithms.dynamicDetection.DynamicDetectionTask:
lsst.meas.algorithms.detection.SourceDetectionTask

Public Member Functions

def __init__ (self, *args, **kwargs)
 
def calculateThreshold (self, exposure, seed, sigma=None)
 
def detectFootprints (self, exposure, doSmooth=True, sigma=None, clearMask=True, expId=None)
 
def tweakBackground (self, exposure, bgLevel, bgList=None)
 
def run (self, table, exposure, doSmooth=True, sigma=None, clearMask=True, expId=None)
 
def makeSourceCatalog (self, *args, **kwargs)
 
def display (self, exposure, results, convolvedImage=None)
 
def applyTempLocalBackground (self, exposure, middle, results)
 
def clearMask (self, mask)
 
def calculateKernelSize (self, sigma)
 
def getPsf (self, exposure, sigma=None)
 
def convolveImage (self, maskedImage, psf, doSmooth=True)
 
def applyThreshold (self, middle, bbox, factor=1.0)
 
def finalizeFootprints (self, mask, results, sigma, factor=1.0)
 
def reEstimateBackground (self, maskedImage, backgrounds)
 
def clearUnwantedResults (self, mask, results)
 
def makeThreshold (self, image, thresholdParity, factor=1.0)
 
def updatePeaks (self, fpSet, image, threshold)
 
def tempWideBackgroundContext (self, exposure)
 

Static Public Member Functions

def setEdgeBits (maskedImage, goodBBox, edgeBitmask)
 

Public Attributes

 skySchema
 
 skyMeasurement
 
 negativeFlagKey
 

Static Public Attributes

 ConfigClass = DynamicDetectionConfig
 
 reason
 
 category
 

Detailed Description

Detection of sources on an image with a dynamic threshold

We first detect sources using a lower threshold than normal (see config
parameter ``prelimThresholdFactor``) in order to identify good sky regions
(configurable ``skyObjects``). Then we perform forced PSF photometry on
those sky regions. Using those PSF flux measurements and estimated errors,
we set the threshold so that the stdev of the measurements matches the
median estimated error.

Definition at line 36 of file dynamicDetection.py.

Constructor & Destructor Documentation

◆ __init__()

def lsst.meas.algorithms.dynamicDetection.DynamicDetectionTask.__init__ (   self,
args,
**  kwargs 
)
Constructor

Besides the usual initialisation of configurables, we also set up
the forced measurement which is deliberately not represented in
this Task's configuration parameters because we're using it as part
of the algorithm and we don't want to allow it to be modified.

Definition at line 49 of file dynamicDetection.py.

Member Function Documentation

◆ applyTempLocalBackground()

def lsst.meas.algorithms.detection.SourceDetectionTask.applyTempLocalBackground (   self,
  exposure,
  middle,
  results 
)
inherited
Apply a temporary local background subtraction

This temporary local background serves to suppress noise fluctuations
in the wings of bright objects.

Peaks in the footprints will be updated.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure for which to fit local background.
middle : `lsst.afw.image.MaskedImage`
    Convolved image on which detection will be performed
    (typically smaller than ``exposure`` because the
    half-kernel has been removed around the edges).
results : `lsst.pipe.base.Struct`
    Results of the 'detectFootprints' method, containing positive and
    negative footprints (which contain the peak positions that we will
    plot). This is a `Struct` with ``positive`` and ``negative``
    elements that are of type `lsst.afw.detection.FootprintSet`.

Definition at line 417 of file detection.py.

◆ applyThreshold()

def lsst.meas.algorithms.detection.SourceDetectionTask.applyThreshold (   self,
  middle,
  bbox,
  factor = 1.0 
)
inherited
Apply thresholds to the convolved image

Identifies ``Footprint``s, both positive and negative.

The threshold can be modified by the provided multiplication
``factor``.

Parameters
----------
middle : `lsst.afw.image.MaskedImage`
    Convolved image to threshold.
bbox : `lsst.geom.Box2I`
    Bounding box of unconvolved image.
factor : `float`
    Multiplier for the configured threshold.

Return Struct contents
----------------------
positive : `lsst.afw.detection.FootprintSet` or `None`
    Positive detection footprints, if configured.
negative : `lsst.afw.detection.FootprintSet` or `None`
    Negative detection footprints, if configured.
factor : `float`
    Multiplier for the configured threshold.

Definition at line 574 of file detection.py.

◆ calculateKernelSize()

def lsst.meas.algorithms.detection.SourceDetectionTask.calculateKernelSize (   self,
  sigma 
)
inherited
Calculate size of smoothing kernel

Uses the ``nSigmaForKernel`` configuration parameter. Note
that that is the full width of the kernel bounding box
(so a value of 7 means 3.5 sigma on either side of center).
The value will be rounded up to the nearest odd integer.

Parameters
----------
sigma : `float`
    Gaussian sigma of smoothing kernel.

Returns
-------
size : `int`
    Size of the smoothing kernel.

Definition at line 466 of file detection.py.

◆ calculateThreshold()

def lsst.meas.algorithms.dynamicDetection.DynamicDetectionTask.calculateThreshold (   self,
  exposure,
  seed,
  sigma = None 
)
Calculate new threshold

This is the main functional addition to the vanilla
`SourceDetectionTask`.

We identify sky objects and perform forced PSF photometry on
them. Using those PSF flux measurements and estimated errors,
we set the threshold so that the stdev of the measurements
matches the median estimated error.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure on which we're detecting sources.
seed : `int`
    RNG seed to use for finding sky objects.
sigma : `float`, optional
    Gaussian sigma of smoothing kernel; if not provided,
    will be deduced from the exposure's PSF.

Returns
-------
result : `lsst.pipe.base.Struct`
    Result struct with components:

    - ``multiplicative``: multiplicative factor to be applied to the
configured detection threshold (`float`).
    - ``additive``: additive factor to be applied to the background
level (`float`).

Definition at line 71 of file dynamicDetection.py.

◆ clearMask()

def lsst.meas.algorithms.detection.SourceDetectionTask.clearMask (   self,
  mask 
)
inherited
Clear the DETECTED and DETECTED_NEGATIVE mask planes

Removes any previous detection mask in preparation for a new
detection pass.

Parameters
----------
mask : `lsst.afw.image.Mask`
    Mask to be cleared.

Definition at line 453 of file detection.py.

◆ clearUnwantedResults()

def lsst.meas.algorithms.detection.SourceDetectionTask.clearUnwantedResults (   self,
  mask,
  results 
)
inherited
Clear unwanted results from the Struct of results

If we specifically want only positive or only negative detections,
drop the ones we don't want, and its associated mask plane.

Parameters
----------
mask : `lsst.afw.image.Mask`
    Mask image.
results : `lsst.pipe.base.Struct`
    Detection results, with ``positive`` and ``negative`` elements;
    modified.

Definition at line 714 of file detection.py.

◆ convolveImage()

def lsst.meas.algorithms.detection.SourceDetectionTask.convolveImage (   self,
  maskedImage,
  psf,
  doSmooth = True 
)
inherited
Convolve the image with the PSF

We convolve the image with a Gaussian approximation to the PSF,
because this is separable and therefore fast. It's technically a
correlation rather than a convolution, but since we use a symmetric
Gaussian there's no difference.

The convolution can be disabled with ``doSmooth=False``. If we do
convolve, we mask the edges as ``EDGE`` and return the convolved image
with the edges removed. This is because we can't convolve the edges
because the kernel would extend off the image.

Parameters
----------
maskedImage : `lsst.afw.image.MaskedImage`
    Image to convolve.
psf : `lsst.afw.detection.Psf`
    PSF to convolve with (actually with a Gaussian approximation
    to it).
doSmooth : `bool`
    Actually do the convolution? Set to False when running on
    e.g. a pre-convolved image, or a mask plane.

Return Struct contents
----------------------
middle : `lsst.afw.image.MaskedImage`
    Convolved image, without the edges.
sigma : `float`
    Gaussian sigma used for the convolution.

Definition at line 513 of file detection.py.

◆ detectFootprints()

def lsst.meas.algorithms.dynamicDetection.DynamicDetectionTask.detectFootprints (   self,
  exposure,
  doSmooth = True,
  sigma = None,
  clearMask = True,
  expId = None 
)
Detect footprints with a dynamic threshold

This varies from the vanilla ``detectFootprints`` method because we
do detection twice: one with a low threshold so that we can find
sky uncontaminated by objects, then one more with the new calculated
threshold.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure to process; DETECTED{,_NEGATIVE} mask plane will be
    set in-place.
doSmooth : `bool`, optional
    If True, smooth the image before detection using a Gaussian
    of width ``sigma``.
sigma : `float`, optional
    Gaussian Sigma of PSF (pixels); used for smoothing and to grow
    detections; if `None` then measure the sigma of the PSF of the
    ``exposure``.
clearMask : `bool`, optional
    Clear both DETECTED and DETECTED_NEGATIVE planes before running
    detection.
expId : `int`, optional
    Exposure identifier, used as a seed for the random number
    generator. If absent, the seed will be the sum of the image.

Return Struct contents
----------------------
positive : `lsst.afw.detection.FootprintSet`
    Positive polarity footprints (may be `None`)
negative : `lsst.afw.detection.FootprintSet`
    Negative polarity footprints (may be `None`)
numPos : `int`
    Number of footprints in positive or 0 if detection polarity was
    negative.
numNeg : `int`
    Number of footprints in negative or 0 if detection polarity was
    positive.
background : `lsst.afw.math.BackgroundList`
    Re-estimated background.  `None` if
    ``reEstimateBackground==False``.
factor : `float`
    Multiplication factor applied to the configured detection
    threshold.
prelim : `lsst.pipe.base.Struct`
    Results from preliminary detection pass.

Reimplemented from lsst.meas.algorithms.detection.SourceDetectionTask.

Definition at line 140 of file dynamicDetection.py.

◆ display()

def lsst.meas.algorithms.detection.SourceDetectionTask.display (   self,
  exposure,
  results,
  convolvedImage = None 
)
inherited
Display detections if so configured

Displays the ``exposure`` in frame 0, overlays the detection peaks.

Requires that ``lsstDebug`` has been set up correctly, so that
``lsstDebug.Info("lsst.meas.algorithms.detection")`` evaluates `True`.

If the ``convolvedImage`` is non-`None` and
``lsstDebug.Info("lsst.meas.algorithms.detection") > 1``, the
``convolvedImage`` will be displayed in frame 1.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure to display, on which will be plotted the detections.
results : `lsst.pipe.base.Struct`
    Results of the 'detectFootprints' method, containing positive and
    negative footprints (which contain the peak positions that we will
    plot). This is a `Struct` with ``positive`` and ``negative``
    elements that are of type `lsst.afw.detection.FootprintSet`.
convolvedImage : `lsst.afw.image.Image`, optional
    Convolved image used for thresholding.

Definition at line 363 of file detection.py.

◆ finalizeFootprints()

def lsst.meas.algorithms.detection.SourceDetectionTask.finalizeFootprints (   self,
  mask,
  results,
  sigma,
  factor = 1.0 
)
inherited
Finalize the detected footprints

Grows the footprints, sets the ``DETECTED`` and ``DETECTED_NEGATIVE``
mask planes, and logs the results.

``numPos`` (number of positive footprints), ``numPosPeaks`` (number
of positive peaks), ``numNeg`` (number of negative footprints),
``numNegPeaks`` (number of negative peaks) entries are added to the
detection results.

Parameters
----------
mask : `lsst.afw.image.Mask`
    Mask image on which to flag detected pixels.
results : `lsst.pipe.base.Struct`
    Struct of detection results, including ``positive`` and
    ``negative`` entries; modified.
sigma : `float`
    Gaussian sigma of PSF.
factor : `float`
    Multiplier for the configured threshold.

Definition at line 623 of file detection.py.

◆ getPsf()

def lsst.meas.algorithms.detection.SourceDetectionTask.getPsf (   self,
  exposure,
  sigma = None 
)
inherited
Retrieve the PSF for an exposure

If ``sigma`` is provided, we make a ``GaussianPsf`` with that,
otherwise use the one from the ``exposure``.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure from which to retrieve the PSF.
sigma : `float`, optional
    Gaussian sigma to use if provided.

Returns
-------
psf : `lsst.afw.detection.Psf`
    PSF to use for detection.

Definition at line 486 of file detection.py.

◆ makeSourceCatalog()

def lsst.meas.algorithms.detection.SourceDetectionTask.makeSourceCatalog (   self,
args,
**  kwargs 
)
inherited

Definition at line 360 of file detection.py.

◆ makeThreshold()

def lsst.meas.algorithms.detection.SourceDetectionTask.makeThreshold (   self,
  image,
  thresholdParity,
  factor = 1.0 
)
inherited
Make an afw.detection.Threshold object corresponding to the task's
configuration and the statistics of the given image.

Parameters
----------
image : `afw.image.MaskedImage`
    Image to measure noise statistics from if needed.
thresholdParity: `str`
    One of "positive" or "negative", to set the kind of fluctuations
    the Threshold will detect.
factor : `float`
    Factor by which to multiply the configured detection threshold.
    This is useful for tweaking the detection threshold slightly.

Returns
-------
threshold : `lsst.afw.detection.Threshold`
    Detection threshold.

Definition at line 806 of file detection.py.

◆ reEstimateBackground()

def lsst.meas.algorithms.detection.SourceDetectionTask.reEstimateBackground (   self,
  maskedImage,
  backgrounds 
)
inherited
Estimate the background after detection

Parameters
----------
maskedImage : `lsst.afw.image.MaskedImage`
    Image on which to estimate the background.
backgrounds : `lsst.afw.math.BackgroundList`
    List of backgrounds; modified.

Returns
-------
bg : `lsst.afw.math.backgroundMI`
    Empirical background model.

Definition at line 685 of file detection.py.

◆ run()

def lsst.meas.algorithms.detection.SourceDetectionTask.run (   self,
  table,
  exposure,
  doSmooth = True,
  sigma = None,
  clearMask = True,
  expId = None 
)
inherited
Run source detection and create a SourceCatalog of detections.

Parameters
----------
table : `lsst.afw.table.SourceTable`
    Table object that will be used to create the SourceCatalog.
exposure : `lsst.afw.image.Exposure`
    Exposure to process; DETECTED mask plane will be set in-place.
doSmooth : `bool`
    If True, smooth the image before detection using a Gaussian of width
    ``sigma``, or the measured PSF width. Set to False when running on
    e.g. a pre-convolved image, or a mask plane.
sigma : `float`
    Sigma of PSF (pixels); used for smoothing and to grow detections;
    if None then measure the sigma of the PSF of the exposure
clearMask : `bool`
    Clear DETECTED{,_NEGATIVE} planes before running detection.
expId : `int`
    Exposure identifier; unused by this implementation, but used for
    RNG seed by subclasses.

Returns
-------
result : `lsst.pipe.base.Struct`
  ``sources``
      The detected sources (`lsst.afw.table.SourceCatalog`)
  ``fpSets``
      The result resturned by `detectFootprints`
      (`lsst.pipe.base.Struct`).

Raises
------
ValueError
    If flags.negative is needed, but isn't in table's schema.
lsst.pipe.base.TaskError
    If sigma=None, doSmooth=True and the exposure has no PSF.

Notes
-----
If you want to avoid dealing with Sources and Tables, you can use
detectFootprints() to just get the `lsst.afw.detection.FootprintSet`s.

Definition at line 298 of file detection.py.

◆ setEdgeBits()

def lsst.meas.algorithms.detection.SourceDetectionTask.setEdgeBits (   maskedImage,
  goodBBox,
  edgeBitmask 
)
staticinherited
Set the edgeBitmask bits for all of maskedImage outside goodBBox

Parameters
----------
maskedImage : `lsst.afw.image.MaskedImage`
    Image on which to set edge bits in the mask.
goodBBox : `lsst.geom.Box2I`
    Bounding box of good pixels, in ``LOCAL`` coordinates.
edgeBitmask : `lsst.afw.image.MaskPixel`
    Bit mask to OR with the existing mask bits in the region
    outside ``goodBBox``.

Definition at line 884 of file detection.py.

◆ tempWideBackgroundContext()

def lsst.meas.algorithms.detection.SourceDetectionTask.tempWideBackgroundContext (   self,
  exposure 
)
inherited
Context manager for removing wide (large-scale) background

Removing a wide (large-scale) background helps to suppress the
detection of large footprints that may overwhelm the deblender.
It does, however, set a limit on the maximum scale of objects.

The background that we remove will be restored upon exit from
the context manager.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure on which to remove large-scale background.

Returns
-------
context : context manager
    Context manager that will ensure the temporary wide background
    is restored.

Definition at line 914 of file detection.py.

◆ tweakBackground()

def lsst.meas.algorithms.dynamicDetection.DynamicDetectionTask.tweakBackground (   self,
  exposure,
  bgLevel,
  bgList = None 
)
Modify the background by a constant value

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure for which to tweak background.
bgLevel : `float`
    Background level to remove
bgList : `lsst.afw.math.BackgroundList`, optional
    List of backgrounds to append to.

Returns
-------
bg : `lsst.afw.math.BackgroundMI`
    Constant background model.

Definition at line 254 of file dynamicDetection.py.

◆ updatePeaks()

def lsst.meas.algorithms.detection.SourceDetectionTask.updatePeaks (   self,
  fpSet,
  image,
  threshold 
)
inherited
Update the Peaks in a FootprintSet by detecting new Footprints and
Peaks in an image and using the new Peaks instead of the old ones.

Parameters
----------
fpSet : `afw.detection.FootprintSet`
    Set of Footprints whose Peaks should be updated.
image : `afw.image.MaskedImage`
    Image to detect new Footprints and Peak in.
threshold : `afw.detection.Threshold`
    Threshold object for detection.

Input Footprints with fewer Peaks than self.config.nPeaksMaxSimple
are not modified, and if no new Peaks are detected in an input
Footprint, the brightest original Peak in that Footprint is kept.

Definition at line 841 of file detection.py.

Member Data Documentation

◆ category

lsst.meas.algorithms.detection.SourceDetectionTask.category
staticinherited

Definition at line 359 of file detection.py.

◆ ConfigClass

lsst.meas.algorithms.dynamicDetection.DynamicDetectionTask.ConfigClass = DynamicDetectionConfig
static

Definition at line 46 of file dynamicDetection.py.

◆ negativeFlagKey

lsst.meas.algorithms.detection.SourceDetectionTask.negativeFlagKey
inherited

Definition at line 281 of file detection.py.

◆ reason

lsst.meas.algorithms.detection.SourceDetectionTask.reason
staticinherited

Definition at line 358 of file detection.py.

◆ skyMeasurement

lsst.meas.algorithms.dynamicDetection.DynamicDetectionTask.skyMeasurement

Definition at line 68 of file dynamicDetection.py.

◆ skySchema

lsst.meas.algorithms.dynamicDetection.DynamicDetectionTask.skySchema

Definition at line 67 of file dynamicDetection.py.


The documentation for this class was generated from the following file: