ZogyTask¶

class lsst.ip.diffim.ZogyTask(templateExposure=None, scienceExposure=None, sig1=None, sig2=None, psf1=None, psf2=None, *args, **kwargs)¶

Bases: lsst.pipe.base.Task

Task to perform ZOGY proper image subtraction. See module-level documentation for additional details.

In all methods, im1 is R (reference, or template) and im2 is N (new, or science).

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.
`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: `D` : 2D `numpy.array`, the proper image difference `D_var` : 2D `numpy.array`, the variance image for `D`

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

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.

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 photometrically 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.

Navigation

ZogyTask¶