ZogyMapper

class lsst.ip.diffim.ZogyMapper(*args, **kwargs)

Bases: lsst.ip.diffim.ZogyTask, lsst.ip.diffim.ImageMapper

Task to be used as an ImageMapper for performing ZOGY image subtraction on a grid of subimages.

Methods Summary

computeDiffim([inImageSpace, padSize, …]) Wrapper method to compute ZOGY proper diffim
computeDiffimFourierSpace([debug, …]) Compute ZOGY diffim D as proscribed in ZOGY (2016) manuscript
computeDiffimImageSpace([padSize, debug]) Compute ZOGY diffim D using image-space convlutions
computeDiffimPsf([padSize, keepFourier, …]) Compute the ZOGY diffim PSF (ZOGY manuscript eq.
computePrereqs([psf1, psf2, padSize]) Compute standard ZOGY quantities used by (nearly) all methods.
computeScorr([xVarAst, yVarAst, …]) Wrapper method to compute ZOGY corrected likelihood image, optimal for source detection
computeScorrFourierSpace([xVarAst, yVarAst]) Compute corrected likelihood image, optimal for source detection
computeScorrImageSpace([xVarAst, yVarAst, …]) Compute corrected likelihood image, optimal for source detection
emptyMetadata() Empty (clear) the metadata for this Task and all sub-Tasks.
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.
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.
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.
run(subExposure, expandedSubExposure, …) Perform ZOGY proper image subtraction on sub-images
setup([templateExposure, scienceExposure, …]) Set up the ZOGY task.
timer(name[, logLevel]) Context manager to log performance data for an arbitrary block of code.

Methods Documentation

computeDiffim(inImageSpace=None, padSize=None, returnMatchedTemplate=False, **kwargs)

Wrapper method to compute ZOGY proper diffim

This method should be used as the public interface for computing the ZOGY diffim.

Parameters:
inImageSpace : bool

Override config inImageSpace parameter

padSize : int

Override config padSize parameter

returnMatchedTemplate : bool

Include the PSF-matched template in the results Struct

**kwargs

additional keyword arguments to be passed to computeDiffimFourierSpace or computeDiffimImageSpace.
Returns:
An lsst.pipe.base.Struct containing:
  • D : lsst.afw.Exposure

    the proper image difference, including correct variance, masks, and PSF

  • R : lsst.afw.Exposure

    If returnMatchedTemplate is True, the PSF-matched template exposure
computeDiffimFourierSpace(debug=False, returnMatchedTemplate=False, **kwargs)

Compute ZOGY diffim D as proscribed in ZOGY (2016) manuscript

Parameters:
debug : bool, optional

If set to True, filter the kernels by setting the edges to zero.

returnMatchedTemplate : bool, optional

Calculate the template image. If not set, the returned template will be None.
Returns:
result : lsst.pipe.base.Struct

Result struct with components:

Notes

In all functions, im1 is R (reference, or template) and im2 is N (new, or science) Compute the ZOGY eqn. (13):

\[\widehat{D} = \frac{Fr\widehat{Pr}\widehat{N} - F_n\widehat{Pn}\widehat{R}}{\sqrt{\sigma_n^2 Fr^2 \|\widehat{Pr}\|^2 + \sigma_r^2 F_n^2 \|\widehat{Pn}\|^2}}\]

where \(D\) is the optimal difference image, \(R\) and \(N\) are the reference and “new” image, respectively, \(Pr\) and \(P_n\) are their PSFs, \(Fr\) and \(Fn\) are their flux-based zero-points (which we will set to one here), \(\sigma_r^2\) and \(\sigma_n^2\) are their variance, and \(\widehat{D}\) denotes the FT of \(D\).

computeDiffimImageSpace(padSize=None, debug=False, **kwargs)

Compute ZOGY diffim D using image-space convlutions

This method is still being debugged as it results in artifacts when the PSFs are noisy (see module-level docstring). Thus there are several options still enabled by the debug flag, which are disabled by defult.

Parameters:
padSize : int

The amount to pad the PSFs by

debug : bool

Flag to enable debugging tests and options
Returns:
D : lsst.afw.Exposure

the proper image difference, including correct variance, masks, and PSF
computeDiffimPsf(padSize=0, keepFourier=False, psf1=None, psf2=None)

Compute the ZOGY diffim PSF (ZOGY manuscript eq. 14)

Parameters:
padSize : int

Override config padSize parameter

keepFourier : bool

Return the FFT of the diffim PSF (do not inverse-FFT it)

psf1 : 2D numpy.array

(Optional) Input psf of template, override if already padded

psf2 : 2D numpy.array

(Optional) Input psf of science image, override if already padded
Returns:
Pd : 2D numpy.array

The diffim PSF (or FFT of PSF if keepFourier=True)
computePrereqs(psf1=None, psf2=None, padSize=0)

Compute standard ZOGY quantities used by (nearly) all methods.

Many of the ZOGY calculations require similar quantities, including FFTs of the PSFs, and the “denominator” term (e.g. in eq. 13 of ZOGY manuscript (2016). This function consolidates many of those operations.

Parameters:
psf1 : 2D numpy.array

(Optional) Input psf of template, override if already padded

psf2 : 2D numpy.array

(Optional) Input psf of science image, override if already padded

padSize : int, optional

Number of pixels to pad the image on each side with zeroes.
Returns:
A `lsst.pipe.base.Struct` containing:
- Pr : 2D numpy.array, the (possibly zero-padded) template PSF
- Pn : 2D numpy.array, the (possibly zero-padded) science PSF
- Pr_hat : 2D numpy.array, the FFT of Pr
- Pn_hat : 2D numpy.array, the FFT of Pn
- denom : 2D numpy.array, the denominator of equation (13) in ZOGY (2016) manuscript
- Fd : float, the relative flux scaling factor between science and template
computeScorr(xVarAst=0.0, yVarAst=0.0, inImageSpace=None, padSize=0, **kwargs)

Wrapper method to compute ZOGY corrected likelihood image, optimal for source detection

This method should be used as the public interface for computing the ZOGY S_corr.

Parameters:
xVarAst, yVarAst : float

estimated astrometric noise (variance of astrometric registration errors)

inImageSpace : bool

Override config inImageSpace parameter

padSize : int

Override config padSize parameter
Returns:
S : lsst.afw.image.Exposure

The likelihood exposure S (eq. 12 of ZOGY (2016)), including corrected variance, masks, and PSF
computeScorrFourierSpace(xVarAst=0.0, yVarAst=0.0, **kwargs)

Compute corrected likelihood image, optimal for source detection

Compute ZOGY S_corr image. This image can be thresholded for detection without optimal filtering, and the variance image is corrected to account for astrometric noise (errors in astrometric registration whether systematic or due to effects such as DCR). The calculations here are all performed in Fourier space, as proscribed in ZOGY (2016).

Parameters:
xVarAst, yVarAst : float

estimated astrometric noise (variance of astrometric registration errors)
Returns:
result : lsst.pipe.base.Struct

Result struct with components:

  • S : numpy.array, the likelihood image S (eq. 12 of ZOGY (2016))
  • S_var : the corrected variance image (denominator of eq. 25 of ZOGY (2016))
  • Dpsf : the PSF of the diffim D, likely never to be used.
computeScorrImageSpace(xVarAst=0.0, yVarAst=0.0, padSize=None, **kwargs)

Compute corrected likelihood image, optimal for source detection

Compute ZOGY S_corr image. This image can be thresholded for detection without optimal filtering, and the variance image is corrected to account for astrometric noise (errors in astrometric registration whether systematic or due to effects such as DCR). The calculations here are all performed in Real (image) space.

Parameters:
xVarAst, yVarAst : float

estimated astrometric noise (variance of astrometric registration errors)
Returns:
A `lsst.pipe.base.Struct` containing:
- S : lsst.afw.image.Exposure, the likelihood exposure S (eq. 12 of ZOGY (2016)),

including corrected variance, masks, and PSF

- D : lsst.afw.image.Exposure, the proper image difference, including correct

variance, masks, and PSF
emptyMetadata()

Empty (clear) the metadata for this Task and all sub-Tasks.

getAllSchemaCatalogs()

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()

Get metadata for all tasks.

Returns:
metadata : lsst.daf.base.PropertySet

The PropertySet 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()

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”.
getName()

Get the name of the task.

Returns:
taskName : str

Name of the task.

See also

getFullName

getSchemaCatalogs()

Get the schemas generated by this task.

Returns:
schemaCatalogs : dict

Keys are butler dataset type, values are an empty catalog (an instance of the appropriate lsst.afw.table Catalog type) for this task.

See also

Task.getAllSchemaCatalogs

Notes

Warning

Subclasses that use schemas must override this method. The default implemenation 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()

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..
classmethod makeField(doc)

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("a brief description of what this task does")
makeSubtask(name, **keyArgs)

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 pex_config ConfigurableField or RegistryField.

run(subExposure, expandedSubExposure, fullBBox, template, **kwargs)

Perform ZOGY proper image subtraction on sub-images

This method performs ZOGY proper image subtraction on subExposure using local measures for image variances and PSF. subExposure is a sub-exposure of the science image. It also requires the corresponding sub-exposures of the template (template). The operations are actually performed on expandedSubExposure to allow for invalid edge pixels arising from convolutions, which are then removed.

Parameters:
subExposure : lsst.afw.image.Exposure

the sub-exposure of the diffim

expandedSubExposure : lsst.afw.image.Exposure

the expanded sub-exposure upon which to operate

fullBBox : lsst.geom.Box2I

the bounding box of the original exposure

template : lsst.afw.image.Exposure

the template exposure, from which a corresponding sub-exposure is extracted

**kwargs

additional keyword arguments propagated from ImageMapReduceTask.run. These include:

doScorr : bool

Compute and return the corrected likelihood image S_corr rather than the proper image difference

inImageSpace : bool

Perform all convolutions in real (image) space rather than in Fourier space. This option currently leads to artifacts when using real (measured and noisy) PSFs, thus it is set to False by default. These kwargs may also include arguments to be propagated to ZogyTask.computeDiffim and ZogyTask.computeScorr.
Returns:
result : lsst.pipe.base.Struct

Result struct with components:

subExposure: Either the subExposure of the proper image difference D,

or (if doScorr==True) the corrected likelihood exposure S.

Notes

This run method accepts parameters identical to those of ImageMapper.run, since it is called from the ImageMapperTask. See that class for more information.

setup(templateExposure=None, scienceExposure=None, sig1=None, sig2=None, psf1=None, psf2=None, correctBackground=False, *args, **kwargs)

Set up the ZOGY task.

Parameters:
templateExposure : lsst.afw.image.Exposure

Template exposure (“Reference image” in ZOGY (2016)).

scienceExposure : lsst.afw.image.Exposure

Science exposure (“New image” in ZOGY (2016)). Must have already been registered and photmetrically matched to template.

sig1 : float

(Optional) sqrt(variance) of templateExposure. If None, it is computed from the sqrt(mean) of the templateExposure variance image.

sig2 : float

(Optional) sqrt(variance) of scienceExposure. If None, it is computed from the sqrt(mean) of the scienceExposure variance image.

psf1 : 2D numpy.array

(Optional) 2D array containing the PSF image for the template. If None, it is extracted from the PSF taken at the center of templateExposure.

psf2 : 2D numpy.array

(Optional) 2D array containing the PSF image for the science img. If None, it is extracted from the PSF taken at the center of scienceExposure.

correctBackground : bool

(Optional) subtract sigma-clipped mean of exposures. Zogy doesn’t correct nonzero backgrounds (unlike AL) so subtract them here.

*args

additional arguments to be passed to lsst.pipe.base.Task

**kwargs

additional keyword arguments to be passed to lsst.pipe.base.Task
timer(name, logLevel=10000)

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 lsst.log level constant.

See also

timer.logInfo

Examples

Creating a timer context:

with self.timer("someCodeToTime"):
    pass  # code to time