lsst.pipe.tasks  21.0.0-131-g8cabc107+a69ba78d82
imageDifference.py
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21 
22 import math
23 import random
24 import numpy
25 
26 import lsst.utils
27 import lsst.pex.config as pexConfig
28 import lsst.pipe.base as pipeBase
29 import lsst.daf.base as dafBase
30 import lsst.geom as geom
31 import lsst.afw.math as afwMath
32 import lsst.afw.table as afwTable
33 from lsst.meas.astrom import AstrometryConfig, AstrometryTask
34 from lsst.meas.base import ForcedMeasurementTask, ApplyApCorrTask
35 from lsst.meas.algorithms import LoadIndexedReferenceObjectsTask, SkyObjectsTask
36 from lsst.pipe.tasks.registerImage import RegisterTask
37 from lsst.pipe.tasks.scaleVariance import ScaleVarianceTask
38 from lsst.meas.algorithms import SourceDetectionTask, SingleGaussianPsf, ObjectSizeStarSelectorTask
39 from lsst.ip.diffim import (DipoleAnalysis, SourceFlagChecker, KernelCandidateF, makeKernelBasisList,
40  KernelCandidateQa, DiaCatalogSourceSelectorTask, DiaCatalogSourceSelectorConfig,
41  GetCoaddAsTemplateTask, GetCalexpAsTemplateTask, DipoleFitTask,
42  DecorrelateALKernelSpatialTask, subtractAlgorithmRegistry)
43 import lsst.ip.diffim.diffimTools as diffimTools
44 import lsst.ip.diffim.utils as diUtils
45 import lsst.afw.display as afwDisplay
46 from lsst.skymap import BaseSkyMap
47 from lsst.obs.base import ExposureIdInfo
48 
49 __all__ = ["ImageDifferenceConfig", "ImageDifferenceTask"]
50 FwhmPerSigma = 2*math.sqrt(2*math.log(2))
51 IqrToSigma = 0.741
52 
53 
54 class ImageDifferenceTaskConnections(pipeBase.PipelineTaskConnections,
55  dimensions=("instrument", "visit", "detector", "skymap"),
56  defaultTemplates={"coaddName": "deep",
57  "skyMapName": "deep",
58  "warpTypeSuffix": "",
59  "fakesType": ""}):
60 
61  exposure = pipeBase.connectionTypes.Input(
62  doc="Input science exposure to subtract from.",
63  dimensions=("instrument", "visit", "detector"),
64  storageClass="ExposureF",
65  name="{fakesType}calexp"
66  )
67 
68  # TODO DM-22953
69  # kernelSources = pipeBase.connectionTypes.Input(
70  # doc="Source catalog produced in calibrate task for kernel candidate sources",
71  # name="src",
72  # storageClass="SourceCatalog",
73  # dimensions=("instrument", "visit", "detector"),
74  # )
75 
76  skyMap = pipeBase.connectionTypes.Input(
77  doc="Input definition of geometry/bbox and projection/wcs for template exposures",
78  name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
79  dimensions=("skymap", ),
80  storageClass="SkyMap",
81  )
82  coaddExposures = pipeBase.connectionTypes.Input(
83  doc="Input template to match and subtract from the exposure",
84  dimensions=("tract", "patch", "skymap", "band"),
85  storageClass="ExposureF",
86  name="{fakesType}{coaddName}Coadd{warpTypeSuffix}",
87  multiple=True,
88  deferLoad=True
89  )
90  dcrCoadds = pipeBase.connectionTypes.Input(
91  doc="Input DCR template to match and subtract from the exposure",
92  name="{fakesType}dcrCoadd{warpTypeSuffix}",
93  storageClass="ExposureF",
94  dimensions=("tract", "patch", "skymap", "band", "subfilter"),
95  multiple=True,
96  deferLoad=True
97  )
98  outputSchema = pipeBase.connectionTypes.InitOutput(
99  doc="Schema (as an example catalog) for output DIASource catalog.",
100  storageClass="SourceCatalog",
101  name="{fakesType}{coaddName}Diff_diaSrc_schema",
102  )
103  subtractedExposure = pipeBase.connectionTypes.Output(
104  doc="Output AL difference or Zogy proper difference image",
105  dimensions=("instrument", "visit", "detector"),
106  storageClass="ExposureF",
107  name="{fakesType}{coaddName}Diff_differenceExp",
108  )
109  scoreExposure = pipeBase.connectionTypes.Output(
110  doc="Output AL likelihood or Zogy score image",
111  dimensions=("instrument", "visit", "detector"),
112  storageClass="ExposureF",
113  name="{fakesType}{coaddName}Diff_scoreExp",
114  )
115  warpedExposure = pipeBase.connectionTypes.Output(
116  doc="Warped template used to create `subtractedExposure`.",
117  dimensions=("instrument", "visit", "detector"),
118  storageClass="ExposureF",
119  name="{fakesType}{coaddName}Diff_warpedExp",
120  )
121  matchedExposure = pipeBase.connectionTypes.Output(
122  doc="Warped template used to create `subtractedExposure`.",
123  dimensions=("instrument", "visit", "detector"),
124  storageClass="ExposureF",
125  name="{fakesType}{coaddName}Diff_matchedExp",
126  )
127  diaSources = pipeBase.connectionTypes.Output(
128  doc="Output detected diaSources on the difference image",
129  dimensions=("instrument", "visit", "detector"),
130  storageClass="SourceCatalog",
131  name="{fakesType}{coaddName}Diff_diaSrc",
132  )
133 
134  def __init__(self, *, config=None):
135  super().__init__(config=config)
136  if config.coaddName == 'dcr':
137  self.inputs.remove("coaddExposures")
138  else:
139  self.inputs.remove("dcrCoadds")
140  if not config.doWriteSubtractedExp:
141  self.outputs.remove("subtractedExposure")
142  if not config.doWriteScoreExp:
143  self.outputs.remove("scoreExposure")
144  if not config.doWriteWarpedExp:
145  self.outputs.remove("warpedExposure")
146  if not config.doWriteMatchedExp:
147  self.outputs.remove("matchedExposure")
148  if not config.doWriteSources:
149  self.outputs.remove("diaSources")
150 
151  # TODO DM-22953: Add support for refObjLoader (kernelSourcesFromRef)
152  # Make kernelSources optional
153 
154 
155 class ImageDifferenceConfig(pipeBase.PipelineTaskConfig,
156  pipelineConnections=ImageDifferenceTaskConnections):
157  """Config for ImageDifferenceTask
158  """
159  doAddCalexpBackground = pexConfig.Field(dtype=bool, default=False,
160  doc="Add background to calexp before processing it. "
161  "Useful as ipDiffim does background matching.")
162  doUseRegister = pexConfig.Field(dtype=bool, default=False,
163  doc="Re-compute astrometry on the template. "
164  "Use image-to-image registration to align template with "
165  "science image (AL only).")
166  doDebugRegister = pexConfig.Field(dtype=bool, default=False,
167  doc="Writing debugging data for doUseRegister")
168  doSelectSources = pexConfig.Field(dtype=bool, default=False,
169  doc="Select stars to use for kernel fitting (AL only)")
170  doSelectDcrCatalog = pexConfig.Field(dtype=bool, default=False,
171  doc="Select stars of extreme color as part "
172  "of the control sample (AL only)")
173  doSelectVariableCatalog = pexConfig.Field(dtype=bool, default=False,
174  doc="Select stars that are variable to be part "
175  "of the control sample (AL only)")
176  doSubtract = pexConfig.Field(dtype=bool, default=True, doc="Compute subtracted exposure?")
177  doPreConvolve = pexConfig.Field(dtype=bool, default=False,
178  doc="Not in use. Superseded by useScoreImageDetection.",
179  deprecated="This option superseded by useScoreImageDetection."
180  " Will be removed after v22.")
181  useScoreImageDetection = pexConfig.Field(
182  dtype=bool, default=False, doc="Calculate the pre-convolved AL likelihood or "
183  "the Zogy score image. Use it for source detection (if doDetection=True).")
184  doWriteScoreExp = pexConfig.Field(
185  dtype=bool, default=False, doc="Write AL likelihood or Zogy score exposure?")
186  doScaleTemplateVariance = pexConfig.Field(dtype=bool, default=False,
187  doc="Scale variance of the template before PSF matching")
188  doScaleDiffimVariance = pexConfig.Field(dtype=bool, default=True,
189  doc="Scale variance of the diffim before PSF matching. "
190  "You may do either this or template variance scaling, "
191  "or neither. (Doing both is a waste of CPU.)")
192  useGaussianForPreConvolution = pexConfig.Field(
193  dtype=bool, default=False, doc="Use a simple gaussian PSF model for pre-convolution "
194  "(oherwise use exposure PSF)? (AL and if useScoreImageDetection=True only)")
195  doDetection = pexConfig.Field(dtype=bool, default=True, doc="Detect sources?")
196  doDecorrelation = pexConfig.Field(dtype=bool, default=True,
197  doc="Perform diffim decorrelation to undo pixel correlation due to A&L "
198  "kernel convolution (AL only)? If True, also update the diffim PSF.")
199  doMerge = pexConfig.Field(dtype=bool, default=True,
200  doc="Merge positive and negative diaSources with grow radius "
201  "set by growFootprint")
202  doMatchSources = pexConfig.Field(dtype=bool, default=False,
203  doc="Match diaSources with input calexp sources and ref catalog sources")
204  doMeasurement = pexConfig.Field(dtype=bool, default=True, doc="Measure diaSources?")
205  doDipoleFitting = pexConfig.Field(dtype=bool, default=True, doc="Measure dipoles using new algorithm?")
206  doForcedMeasurement = pexConfig.Field(
207  dtype=bool,
208  default=True,
209  doc="Force photometer diaSource locations on PVI?")
210  doWriteSubtractedExp = pexConfig.Field(
211  dtype=bool, default=True, doc="Write difference exposure (AL and Zogy) ?")
212  doWriteWarpedExp = pexConfig.Field(
213  dtype=bool, default=False, doc="Write WCS, warped template coadd exposure?")
214  doWriteMatchedExp = pexConfig.Field(dtype=bool, default=False,
215  doc="Write warped and PSF-matched template coadd exposure?")
216  doWriteSources = pexConfig.Field(dtype=bool, default=True, doc="Write sources?")
217  doAddMetrics = pexConfig.Field(dtype=bool, default=False,
218  doc="Add columns to the source table to hold analysis metrics?")
219 
220  coaddName = pexConfig.Field(
221  doc="coadd name: typically one of deep, goodSeeing, or dcr",
222  dtype=str,
223  default="deep",
224  )
225  convolveTemplate = pexConfig.Field(
226  doc="Which image gets convolved (default = template)",
227  dtype=bool,
228  default=True
229  )
230  refObjLoader = pexConfig.ConfigurableField(
231  target=LoadIndexedReferenceObjectsTask,
232  doc="reference object loader",
233  )
234  astrometer = pexConfig.ConfigurableField(
235  target=AstrometryTask,
236  doc="astrometry task; used to match sources to reference objects, but not to fit a WCS",
237  )
238  sourceSelector = pexConfig.ConfigurableField(
239  target=ObjectSizeStarSelectorTask,
240  doc="Source selection algorithm",
241  )
242  subtract = subtractAlgorithmRegistry.makeField("Subtraction Algorithm", default="al")
243  decorrelate = pexConfig.ConfigurableField(
244  target=DecorrelateALKernelSpatialTask,
245  doc="Decorrelate effects of A&L kernel convolution on image difference, only if doSubtract is True. "
246  "If this option is enabled, then detection.thresholdValue should be set to 5.0 (rather than the "
247  "default of 5.5).",
248  )
249  # Old style ImageMapper grid. ZogyTask has its own grid option
250  doSpatiallyVarying = pexConfig.Field(
251  dtype=bool,
252  default=False,
253  doc="Perform A&L decorrelation on a grid across the "
254  "image in order to allow for spatial variations. Zogy does not use this option."
255  )
256  detection = pexConfig.ConfigurableField(
257  target=SourceDetectionTask,
258  doc="Low-threshold detection for final measurement",
259  )
260  measurement = pexConfig.ConfigurableField(
261  target=DipoleFitTask,
262  doc="Enable updated dipole fitting method",
263  )
264  doApCorr = lsst.pex.config.Field(
265  dtype=bool,
266  default=True,
267  doc="Run subtask to apply aperture corrections"
268  )
269  applyApCorr = lsst.pex.config.ConfigurableField(
270  target=ApplyApCorrTask,
271  doc="Subtask to apply aperture corrections"
272  )
273  forcedMeasurement = pexConfig.ConfigurableField(
274  target=ForcedMeasurementTask,
275  doc="Subtask to force photometer PVI at diaSource location.",
276  )
277  getTemplate = pexConfig.ConfigurableField(
278  target=GetCoaddAsTemplateTask,
279  doc="Subtask to retrieve template exposure and sources",
280  )
281  scaleVariance = pexConfig.ConfigurableField(
282  target=ScaleVarianceTask,
283  doc="Subtask to rescale the variance of the template "
284  "to the statistically expected level"
285  )
286  controlStepSize = pexConfig.Field(
287  doc="What step size (every Nth one) to select a control sample from the kernelSources",
288  dtype=int,
289  default=5
290  )
291  controlRandomSeed = pexConfig.Field(
292  doc="Random seed for shuffing the control sample",
293  dtype=int,
294  default=10
295  )
296  register = pexConfig.ConfigurableField(
297  target=RegisterTask,
298  doc="Task to enable image-to-image image registration (warping)",
299  )
300  kernelSourcesFromRef = pexConfig.Field(
301  doc="Select sources to measure kernel from reference catalog if True, template if false",
302  dtype=bool,
303  default=False
304  )
305  templateSipOrder = pexConfig.Field(
306  dtype=int, default=2,
307  doc="Sip Order for fitting the Template Wcs (default is too high, overfitting)"
308  )
309  growFootprint = pexConfig.Field(
310  dtype=int, default=2,
311  doc="Grow positive and negative footprints by this amount before merging"
312  )
313  diaSourceMatchRadius = pexConfig.Field(
314  dtype=float, default=0.5,
315  doc="Match radius (in arcseconds) for DiaSource to Source association"
316  )
317  requiredTemplateFraction = pexConfig.Field(
318  dtype=float, default=0.1,
319  doc="Do not attempt to run task if template covers less than this fraction of pixels."
320  "Setting to 0 will always attempt image subtraction"
321  )
322  doSkySources = pexConfig.Field(
323  dtype=bool,
324  default=False,
325  doc="Generate sky sources?",
326  )
327  skySources = pexConfig.ConfigurableField(
328  target=SkyObjectsTask,
329  doc="Generate sky sources",
330  )
331 
332  def setDefaults(self):
333  # defaults are OK for catalog and diacatalog
334 
335  self.subtract['al'].kernel.name = "AL"
336  self.subtract['al'].kernel.active.fitForBackground = True
337  self.subtract['al'].kernel.active.spatialKernelOrder = 1
338  self.subtract['al'].kernel.active.spatialBgOrder = 2
339 
340  # DiaSource Detection
341  self.detection.thresholdPolarity = "both"
342  self.detection.thresholdValue = 5.0
343  self.detection.reEstimateBackground = False
344  self.detection.thresholdType = "pixel_stdev"
345 
346  # Add filtered flux measurement, the correct measurement for pre-convolved images.
347  # Enable all measurements, regardless of doPreConvolve, as it makes data harvesting easier.
348  # To change that you must modify algorithms.names in the task's applyOverrides method,
349  # after the user has set doPreConvolve.
350  self.measurement.algorithms.names.add('base_PeakLikelihoodFlux')
351  self.measurement.plugins.names |= ['base_LocalPhotoCalib',
352  'base_LocalWcs']
353 
354  self.forcedMeasurement.plugins = ["base_TransformedCentroid", "base_PsfFlux"]
355  self.forcedMeasurement.copyColumns = {
356  "id": "objectId", "parent": "parentObjectId", "coord_ra": "coord_ra", "coord_dec": "coord_dec"}
357  self.forcedMeasurement.slots.centroid = "base_TransformedCentroid"
358  self.forcedMeasurement.slots.shape = None
359 
360  # For shuffling the control sample
361  random.seed(self.controlRandomSeed)
362 
363  def validate(self):
364  pexConfig.Config.validate(self)
365  if not self.doSubtract and not self.doDetection:
366  raise ValueError("Either doSubtract or doDetection must be enabled.")
367  if self.doMeasurement and not self.doDetection:
368  raise ValueError("Cannot run source measurement without source detection.")
369  if self.doMerge and not self.doDetection:
370  raise ValueError("Cannot run source merging without source detection.")
371  if self.doSkySources and not self.doDetection:
372  raise ValueError("Cannot run sky source creation without source detection.")
373  if self.doUseRegister and not self.doSelectSources:
374  raise ValueError("doUseRegister=True and doSelectSources=False. "
375  "Cannot run RegisterTask without selecting sources.")
376  if hasattr(self.getTemplate, "coaddName"):
377  if self.getTemplate.coaddName != self.coaddName:
378  raise ValueError("Mis-matched coaddName and getTemplate.coaddName in the config.")
379  if self.doScaleDiffimVariance and self.doScaleTemplateVariance:
380  raise ValueError("Scaling the diffim variance and scaling the template variance "
381  "are both set. Please choose one or the other.")
382  # We cannot allow inconsistencies that would lead to None or not available output products
383  if self.subtract.name == 'zogy':
384  if self.doWriteMatchedExp:
385  raise ValueError("doWriteMatchedExp=True Matched exposure is not "
386  "calculated in zogy subtraction.")
387  if self.doAddMetrics:
388  raise ValueError("doAddMetrics=True Kernel metrics does not exist in zogy subtraction.")
389  if self.doDecorrelation:
390  raise ValueError(
391  "doDecorrelation=True The decorrelation afterburner does not exist in zogy subtraction.")
392  if self.doSelectSources:
393  raise ValueError(
394  "doSelectSources=True Selecting sources for PSF matching is not a zogy option.")
395  if self.useGaussianForPreConvolution:
396  raise ValueError(
397  "useGaussianForPreConvolution=True This is an AL subtraction only option.")
398  else:
399  # AL only consistency checks
400  if self.doWriteSubtractedExp and self.useScoreImageDetection:
401  raise ValueError(
402  "doWriteSubtractedExp=True and useScoreImageDetection=True "
403  "Regular difference image is not calculated. "
404  "AL subtraction calculates either the regular difference image or the score image.")
405  if self.doWriteScoreExp and not self.useScoreImageDetection:
406  raise ValueError(
407  "doWriteScoreExp=True and useScoreImageDetection=False "
408  "Score image is not calculated. "
409  "AL subtraction calculates either the regular difference image or the score image.")
410  if self.doAddMetrics and not self.doSubtract:
411  raise ValueError("Subtraction must be enabled for kernel metrics calculation.")
412  if self.useScoreImageDetection and self.doDecorrelation:
413  raise NotImplementedError(
414  "doDecorrelation=True and useScoreImageDetection=True "
415  "The decorrelation afterburner for AL likelihood images is not implemented.")
416  if self.useGaussianForPreConvolution and not self.useScoreImageDetection:
417  raise ValueError(
418  "useGaussianForPreConvolution=True and useScoreImageDetection=False "
419  "Gaussian PSF approximation exists only for AL subtraction w/ pre-convolution.")
420 
421 
422 class ImageDifferenceTaskRunner(pipeBase.ButlerInitializedTaskRunner):
423 
424  @staticmethod
425  def getTargetList(parsedCmd, **kwargs):
426  return pipeBase.TaskRunner.getTargetList(parsedCmd, templateIdList=parsedCmd.templateId.idList,
427  **kwargs)
428 
429 
430 class ImageDifferenceTask(pipeBase.CmdLineTask, pipeBase.PipelineTask):
431  """Subtract an image from a template and measure the result
432  """
433  ConfigClass = ImageDifferenceConfig
434  RunnerClass = ImageDifferenceTaskRunner
435  _DefaultName = "imageDifference"
436 
437  def __init__(self, butler=None, **kwargs):
438  """!Construct an ImageDifference Task
439 
440  @param[in] butler Butler object to use in constructing reference object loaders
441  """
442  super().__init__(**kwargs)
443  self.makeSubtask("getTemplate")
444 
445  self.makeSubtask("subtract")
446 
447  if self.config.subtract.name == 'al' and self.config.doDecorrelation:
448  self.makeSubtask("decorrelate")
449 
450  if self.config.doScaleTemplateVariance or self.config.doScaleDiffimVariance:
451  self.makeSubtask("scaleVariance")
452 
453  if self.config.doUseRegister:
454  self.makeSubtask("register")
455  self.schema = afwTable.SourceTable.makeMinimalSchema()
456 
457  if self.config.doSelectSources:
458  self.makeSubtask("sourceSelector")
459  if self.config.kernelSourcesFromRef:
460  self.makeSubtask('refObjLoader', butler=butler)
461  self.makeSubtask("astrometer", refObjLoader=self.refObjLoader)
462 
463  self.algMetadata = dafBase.PropertyList()
464  if self.config.doDetection:
465  self.makeSubtask("detection", schema=self.schema)
466  if self.config.doMeasurement:
467  self.makeSubtask("measurement", schema=self.schema,
468  algMetadata=self.algMetadata)
469  if self.config.doApCorr:
470  self.makeSubtask("applyApCorr", schema=self.measurement.schema)
471  if self.config.doForcedMeasurement:
472  self.schema.addField(
473  "ip_diffim_forced_PsfFlux_instFlux", "D",
474  "Forced PSF flux measured on the direct image.",
475  units="count")
476  self.schema.addField(
477  "ip_diffim_forced_PsfFlux_instFluxErr", "D",
478  "Forced PSF flux error measured on the direct image.",
479  units="count")
480  self.schema.addField(
481  "ip_diffim_forced_PsfFlux_area", "F",
482  "Forced PSF flux effective area of PSF.",
483  units="pixel")
484  self.schema.addField(
485  "ip_diffim_forced_PsfFlux_flag", "Flag",
486  "Forced PSF flux general failure flag.")
487  self.schema.addField(
488  "ip_diffim_forced_PsfFlux_flag_noGoodPixels", "Flag",
489  "Forced PSF flux not enough non-rejected pixels in data to attempt the fit.")
490  self.schema.addField(
491  "ip_diffim_forced_PsfFlux_flag_edge", "Flag",
492  "Forced PSF flux object was too close to the edge of the image to use the full PSF model.")
493  self.makeSubtask("forcedMeasurement", refSchema=self.schema)
494  if self.config.doMatchSources:
495  self.schema.addField("refMatchId", "L", "unique id of reference catalog match")
496  self.schema.addField("srcMatchId", "L", "unique id of source match")
497  if self.config.doSkySources:
498  self.makeSubtask("skySources")
499  self.skySourceKey = self.schema.addField("sky_source", type="Flag", doc="Sky objects.")
500 
501  # initialize InitOutputs
502  self.outputSchema = afwTable.SourceCatalog(self.schema)
503  self.outputSchema.getTable().setMetadata(self.algMetadata)
504 
505  @staticmethod
506  def makeIdFactory(expId, expBits):
507  """Create IdFactory instance for unique 64 bit diaSource id-s.
508 
509  Parameters
510  ----------
511  expId : `int`
512  Exposure id.
513 
514  expBits: `int`
515  Number of used bits in ``expId``.
516 
517  Note
518  ----
519  The diasource id-s consists of the ``expId`` stored fixed in the highest value
520  ``expBits`` of the 64-bit integer plus (bitwise or) a generated sequence number in the
521  low value end of the integer.
522 
523  Returns
524  -------
525  idFactory: `lsst.afw.table.IdFactory`
526  """
527  return ExposureIdInfo(expId, expBits).makeSourceIdFactory()
528 
529  @lsst.utils.inheritDoc(pipeBase.PipelineTask)
530  def runQuantum(self, butlerQC: pipeBase.ButlerQuantumContext,
531  inputRefs: pipeBase.InputQuantizedConnection,
532  outputRefs: pipeBase.OutputQuantizedConnection):
533  inputs = butlerQC.get(inputRefs)
534  self.log.info("Processing %s", butlerQC.quantum.dataId)
535  expId, expBits = butlerQC.quantum.dataId.pack("visit_detector",
536  returnMaxBits=True)
537  idFactory = self.makeIdFactory(expId=expId, expBits=expBits)
538  if self.config.coaddName == 'dcr':
539  templateExposures = inputRefs.dcrCoadds
540  else:
541  templateExposures = inputRefs.coaddExposures
542  templateStruct = self.getTemplate.runQuantum(
543  inputs['exposure'], butlerQC, inputRefs.skyMap, templateExposures
544  )
545 
546  if templateStruct.area/inputs['exposure'].getBBox().getArea() < self.config.requiredTemplateFraction:
547  message = ("Insufficient Template Coverage. (%.1f%% < %.1f%%) Not attempting subtraction. "
548  "To force subtraction, set config requiredTemplateFraction=0." % (
549  100*templateStruct.area/inputs['exposure'].getBBox().getArea(),
550  100*self.config.requiredTemplateFraction))
551  raise pipeBase.NoWorkFound(message)
552  else:
553  outputs = self.run(exposure=inputs['exposure'],
554  templateExposure=templateStruct.exposure,
555  idFactory=idFactory)
556  # Consistency with runDataref gen2 handling
557  if outputs.diaSources is None:
558  del outputs.diaSources
559  butlerQC.put(outputs, outputRefs)
560 
561  @pipeBase.timeMethod
562  def runDataRef(self, sensorRef, templateIdList=None):
563  """Subtract an image from a template coadd and measure the result.
564 
565  Data I/O wrapper around `run` using the butler in Gen2.
566 
567  Parameters
568  ----------
569  sensorRef : `lsst.daf.persistence.ButlerDataRef`
570  Sensor-level butler data reference, used for the following data products:
571 
572  Input only:
573  - calexp
574  - psf
575  - ccdExposureId
576  - ccdExposureId_bits
577  - self.config.coaddName + "Coadd_skyMap"
578  - self.config.coaddName + "Coadd"
579  Input or output, depending on config:
580  - self.config.coaddName + "Diff_subtractedExp"
581  Output, depending on config:
582  - self.config.coaddName + "Diff_matchedExp"
583  - self.config.coaddName + "Diff_src"
584 
585  Returns
586  -------
587  results : `lsst.pipe.base.Struct`
588  Returns the Struct by `run`.
589  """
590  subtractedExposureName = self.config.coaddName + "Diff_differenceExp"
591  subtractedExposure = None
592  selectSources = None
593  calexpBackgroundExposure = None
594  self.log.info("Processing %s", sensorRef.dataId)
595 
596  # We make one IdFactory that will be used by both icSrc and src datasets;
597  # I don't know if this is the way we ultimately want to do things, but at least
598  # this ensures the source IDs are fully unique.
599  idFactory = self.makeIdFactory(expId=int(sensorRef.get("ccdExposureId")),
600  expBits=sensorRef.get("ccdExposureId_bits"))
601  if self.config.doAddCalexpBackground:
602  calexpBackgroundExposure = sensorRef.get("calexpBackground")
603 
604  # Retrieve the science image we wish to analyze
605  exposure = sensorRef.get("calexp", immediate=True)
606 
607  # Retrieve the template image
608  template = self.getTemplate.runDataRef(exposure, sensorRef, templateIdList=templateIdList)
609 
610  if sensorRef.datasetExists("src"):
611  self.log.info("Source selection via src product")
612  # Sources already exist; for data release processing
613  selectSources = sensorRef.get("src")
614 
615  if not self.config.doSubtract and self.config.doDetection:
616  # If we don't do subtraction, we need the subtracted exposure from the repo
617  subtractedExposure = sensorRef.get(subtractedExposureName)
618  # Both doSubtract and doDetection cannot be False
619 
620  results = self.run(exposure=exposure,
621  selectSources=selectSources,
622  templateExposure=template.exposure,
623  templateSources=template.sources,
624  idFactory=idFactory,
625  calexpBackgroundExposure=calexpBackgroundExposure,
626  subtractedExposure=subtractedExposure)
627 
628  if self.config.doWriteSources and results.diaSources is not None:
629  sensorRef.put(results.diaSources, self.config.coaddName + "Diff_diaSrc")
630  if self.config.doWriteWarpedExp:
631  sensorRef.put(results.warpedExposure, self.config.coaddName + "Diff_warpedExp")
632  if self.config.doWriteMatchedExp:
633  sensorRef.put(results.matchedExposure, self.config.coaddName + "Diff_matchedExp")
634  if self.config.doAddMetrics and self.config.doSelectSources:
635  sensorRef.put(results.selectSources, self.config.coaddName + "Diff_kernelSrc")
636  if self.config.doWriteSubtractedExp:
637  sensorRef.put(results.subtractedExposure, subtractedExposureName)
638  if self.config.doWriteScoreExp:
639  sensorRef.put(results.scoreExposure, self.config.coaddName + "Diff_scoreExp")
640  return results
641 
642  @pipeBase.timeMethod
643  def run(self, exposure=None, selectSources=None, templateExposure=None, templateSources=None,
644  idFactory=None, calexpBackgroundExposure=None, subtractedExposure=None):
645  """PSF matches, subtract two images and perform detection on the difference image.
646 
647  Parameters
648  ----------
649  exposure : `lsst.afw.image.ExposureF`, optional
650  The science exposure, the minuend in the image subtraction.
651  Can be None only if ``config.doSubtract==False``.
652  selectSources : `lsst.afw.table.SourceCatalog`, optional
653  Identified sources on the science exposure. This catalog is used to
654  select sources in order to perform the AL PSF matching on stamp images
655  around them. The selection steps depend on config options and whether
656  ``templateSources`` and ``matchingSources`` specified.
657  templateExposure : `lsst.afw.image.ExposureF`, optional
658  The template to be subtracted from ``exposure`` in the image subtraction.
659  ``templateExposure`` is modified in place if ``config.doScaleTemplateVariance==True``.
660  The template exposure should cover the same sky area as the science exposure.
661  It is either a stich of patches of a coadd skymap image or a calexp
662  of the same pointing as the science exposure. Can be None only
663  if ``config.doSubtract==False`` and ``subtractedExposure`` is not None.
664  templateSources : `lsst.afw.table.SourceCatalog`, optional
665  Identified sources on the template exposure.
666  idFactory : `lsst.afw.table.IdFactory`
667  Generator object to assign ids to detected sources in the difference image.
668  calexpBackgroundExposure : `lsst.afw.image.ExposureF`, optional
669  Background exposure to be added back to the science exposure
670  if ``config.doAddCalexpBackground==True``
671  subtractedExposure : `lsst.afw.image.ExposureF`, optional
672  If ``config.doSubtract==False`` and ``config.doDetection==True``,
673  performs the post subtraction source detection only on this exposure.
674  Otherwise should be None.
675 
676  Returns
677  -------
678  results : `lsst.pipe.base.Struct`
679  ``subtractedExposure`` : `lsst.afw.image.ExposureF`
680  Difference image.
681  ``scoreExposure`` : `lsst.afw.image.ExposureF` or `None`
682  The zogy score exposure, if calculated.
683  ``matchedExposure`` : `lsst.afw.image.ExposureF`
684  The matched PSF exposure.
685  ``subtractRes`` : `lsst.pipe.base.Struct`
686  The returned result structure of the ImagePsfMatchTask subtask.
687  ``diaSources`` : `lsst.afw.table.SourceCatalog`
688  The catalog of detected sources.
689  ``selectSources`` : `lsst.afw.table.SourceCatalog`
690  The input source catalog with optionally added Qa information.
691 
692  Notes
693  -----
694  The following major steps are included:
695 
696  - warp template coadd to match WCS of image
697  - PSF match image to warped template
698  - subtract image from PSF-matched, warped template
699  - detect sources
700  - measure sources
701 
702  For details about the image subtraction configuration modes
703  see `lsst.ip.diffim`.
704  """
705  subtractRes = None
706  controlSources = None
707  subtractedExposure = None
708  scoreExposure = None
709  diaSources = None
710  kernelSources = None
711  # We'll clone exposure if modified but will still need the original
712  exposureOrig = exposure
713 
714  if self.config.doAddCalexpBackground:
715  mi = exposure.getMaskedImage()
716  mi += calexpBackgroundExposure.getImage()
717 
718  if not exposure.hasPsf():
719  raise pipeBase.TaskError("Exposure has no psf")
720  sciencePsf = exposure.getPsf()
721 
722  if self.config.doSubtract:
723  if self.config.doScaleTemplateVariance:
724  self.log.info("Rescaling template variance")
725  templateVarFactor = self.scaleVariance.run(
726  templateExposure.getMaskedImage())
727  self.log.info("Template variance scaling factor: %.2f", templateVarFactor)
728  self.metadata.add("scaleTemplateVarianceFactor", templateVarFactor)
729  self.metadata.add("psfMatchingAlgorithm", self.config.subtract.name)
730 
731  if self.config.subtract.name == 'zogy':
732  subtractRes = self.subtract.run(exposure, templateExposure, doWarping=True)
733  scoreExposure = subtractRes.scoreExp
734  subtractedExposure = subtractRes.diffExp
735  subtractRes.subtractedExposure = subtractedExposure
736  subtractRes.matchedExposure = None
737 
738  elif self.config.subtract.name == 'al':
739  # compute scienceSigmaOrig: sigma of PSF of science image before pre-convolution
740  scienceSigmaOrig = sciencePsf.computeShape().getDeterminantRadius()
741  templateSigma = templateExposure.getPsf().computeShape().getDeterminantRadius()
742 
743  # if requested, convolve the science exposure with its PSF
744  # (properly, this should be a cross-correlation, but our code does not yet support that)
745  # compute scienceSigmaPost: sigma of science exposure with pre-convolution, if done,
746  # else sigma of original science exposure
747  # TODO: DM-22762 This functional block should be moved into its own method
748  preConvPsf = None
749  if self.config.useScoreImageDetection:
750  self.log.warn("AL likelihood image: pre-convolution of PSF is not implemented.")
751  convControl = afwMath.ConvolutionControl()
752  # cannot convolve in place, so need a new image anyway
753  srcMI = exposure.maskedImage
754  exposure = exposure.clone() # New deep copy
755  srcPsf = sciencePsf
756  if self.config.useGaussianForPreConvolution:
757  self.log.infof(
758  "AL likelihood image: Using Gaussian (sigma={:.2f}) PSF estimation "
759  "for science image pre-convolution", scienceSigmaOrig)
760  # convolve with a simplified PSF model: a double Gaussian
761  kWidth, kHeight = sciencePsf.getLocalKernel().getDimensions()
762  preConvPsf = SingleGaussianPsf(kWidth, kHeight, scienceSigmaOrig)
763  else:
764  # convolve with science exposure's PSF model
765  self.log.infof(
766  "AL likelihood image: Using the science image PSF for pre-convolution.")
767  preConvPsf = srcPsf
768  afwMath.convolve(exposure.maskedImage, srcMI, preConvPsf.getLocalKernel(), convControl)
769  scienceSigmaPost = scienceSigmaOrig*math.sqrt(2)
770  else:
771  scienceSigmaPost = scienceSigmaOrig
772 
773  # If requested, find and select sources from the image
774  # else, AL subtraction will do its own source detection
775  # TODO: DM-22762 This functional block should be moved into its own method
776  if self.config.doSelectSources:
777  if selectSources is None:
778  self.log.warning("Src product does not exist; running detection, measurement,"
779  " selection")
780  # Run own detection and measurement; necessary in nightly processing
781  selectSources = self.subtract.getSelectSources(
782  exposure,
783  sigma=scienceSigmaPost,
784  doSmooth=not self.config.useScoreImageDetection,
785  idFactory=idFactory,
786  )
787 
788  if self.config.doAddMetrics:
789  # Number of basis functions
790 
791  nparam = len(makeKernelBasisList(self.subtract.config.kernel.active,
792  referenceFwhmPix=scienceSigmaPost*FwhmPerSigma,
793  targetFwhmPix=templateSigma*FwhmPerSigma))
794  # Modify the schema of all Sources
795  # DEPRECATED: This is a data dependent (nparam) output product schema
796  # outside the task constructor.
797  # NOTE: The pre-determination of nparam at this point
798  # may be incorrect as the template psf is warped later in
799  # ImagePsfMatchTask.matchExposures()
800  kcQa = KernelCandidateQa(nparam)
801  selectSources = kcQa.addToSchema(selectSources)
802  if self.config.kernelSourcesFromRef:
803  # match exposure sources to reference catalog
804  astromRet = self.astrometer.loadAndMatch(exposure=exposure, sourceCat=selectSources)
805  matches = astromRet.matches
806  elif templateSources:
807  # match exposure sources to template sources
808  mc = afwTable.MatchControl()
809  mc.findOnlyClosest = False
810  matches = afwTable.matchRaDec(templateSources, selectSources, 1.0*geom.arcseconds,
811  mc)
812  else:
813  raise RuntimeError("doSelectSources=True and kernelSourcesFromRef=False,"
814  "but template sources not available. Cannot match science "
815  "sources with template sources. Run process* on data from "
816  "which templates are built.")
817 
818  kernelSources = self.sourceSelector.run(selectSources, exposure=exposure,
819  matches=matches).sourceCat
820  random.shuffle(kernelSources, random.random)
821  controlSources = kernelSources[::self.config.controlStepSize]
822  kernelSources = [k for i, k in enumerate(kernelSources)
823  if i % self.config.controlStepSize]
824 
825  if self.config.doSelectDcrCatalog:
826  redSelector = DiaCatalogSourceSelectorTask(
827  DiaCatalogSourceSelectorConfig(grMin=self.sourceSelector.config.grMax,
828  grMax=99.999))
829  redSources = redSelector.selectStars(exposure, selectSources, matches=matches).starCat
830  controlSources.extend(redSources)
831 
832  blueSelector = DiaCatalogSourceSelectorTask(
833  DiaCatalogSourceSelectorConfig(grMin=-99.999,
834  grMax=self.sourceSelector.config.grMin))
835  blueSources = blueSelector.selectStars(exposure, selectSources,
836  matches=matches).starCat
837  controlSources.extend(blueSources)
838 
839  if self.config.doSelectVariableCatalog:
840  varSelector = DiaCatalogSourceSelectorTask(
841  DiaCatalogSourceSelectorConfig(includeVariable=True))
842  varSources = varSelector.selectStars(exposure, selectSources, matches=matches).starCat
843  controlSources.extend(varSources)
844 
845  self.log.info("Selected %d / %d sources for Psf matching (%d for control sample)",
846  len(kernelSources), len(selectSources), len(controlSources))
847 
848  allresids = {}
849  # TODO: DM-22762 This functional block should be moved into its own method
850  if self.config.doUseRegister:
851  self.log.info("Registering images")
852 
853  if templateSources is None:
854  # Run detection on the template, which is
855  # temporarily background-subtracted
856  # sigma of PSF of template image before warping
857  templateSigma = templateExposure.getPsf().computeShape().getDeterminantRadius()
858  templateSources = self.subtract.getSelectSources(
859  templateExposure,
860  sigma=templateSigma,
861  doSmooth=True,
862  idFactory=idFactory
863  )
864 
865  # Third step: we need to fit the relative astrometry.
866  #
867  wcsResults = self.fitAstrometry(templateSources, templateExposure, selectSources)
868  warpedExp = self.register.warpExposure(templateExposure, wcsResults.wcs,
869  exposure.getWcs(), exposure.getBBox())
870  templateExposure = warpedExp
871 
872  # Create debugging outputs on the astrometric
873  # residuals as a function of position. Persistence
874  # not yet implemented; expected on (I believe) #2636.
875  if self.config.doDebugRegister:
876  # Grab matches to reference catalog
877  srcToMatch = {x.second.getId(): x.first for x in matches}
878 
879  refCoordKey = wcsResults.matches[0].first.getTable().getCoordKey()
880  inCentroidKey = wcsResults.matches[0].second.getTable().getCentroidSlot().getMeasKey()
881  sids = [m.first.getId() for m in wcsResults.matches]
882  positions = [m.first.get(refCoordKey) for m in wcsResults.matches]
883  residuals = [m.first.get(refCoordKey).getOffsetFrom(wcsResults.wcs.pixelToSky(
884  m.second.get(inCentroidKey))) for m in wcsResults.matches]
885  allresids = dict(zip(sids, zip(positions, residuals)))
886 
887  cresiduals = [m.first.get(refCoordKey).getTangentPlaneOffset(
888  wcsResults.wcs.pixelToSky(
889  m.second.get(inCentroidKey))) for m in wcsResults.matches]
890  colors = numpy.array([-2.5*numpy.log10(srcToMatch[x].get("g"))
891  + 2.5*numpy.log10(srcToMatch[x].get("r"))
892  for x in sids if x in srcToMatch.keys()])
893  dlong = numpy.array([r[0].asArcseconds() for s, r in zip(sids, cresiduals)
894  if s in srcToMatch.keys()])
895  dlat = numpy.array([r[1].asArcseconds() for s, r in zip(sids, cresiduals)
896  if s in srcToMatch.keys()])
897  idx1 = numpy.where(colors < self.sourceSelector.config.grMin)
898  idx2 = numpy.where((colors >= self.sourceSelector.config.grMin)
899  & (colors <= self.sourceSelector.config.grMax))
900  idx3 = numpy.where(colors > self.sourceSelector.config.grMax)
901  rms1Long = IqrToSigma*(
902  (numpy.percentile(dlong[idx1], 75) - numpy.percentile(dlong[idx1], 25)))
903  rms1Lat = IqrToSigma*(numpy.percentile(dlat[idx1], 75)
904  - numpy.percentile(dlat[idx1], 25))
905  rms2Long = IqrToSigma*(
906  (numpy.percentile(dlong[idx2], 75) - numpy.percentile(dlong[idx2], 25)))
907  rms2Lat = IqrToSigma*(numpy.percentile(dlat[idx2], 75)
908  - numpy.percentile(dlat[idx2], 25))
909  rms3Long = IqrToSigma*(
910  (numpy.percentile(dlong[idx3], 75) - numpy.percentile(dlong[idx3], 25)))
911  rms3Lat = IqrToSigma*(numpy.percentile(dlat[idx3], 75)
912  - numpy.percentile(dlat[idx3], 25))
913  self.log.info("Blue star offsets'': %.3f %.3f, %.3f %.3f",
914  numpy.median(dlong[idx1]), rms1Long,
915  numpy.median(dlat[idx1]), rms1Lat)
916  self.log.info("Green star offsets'': %.3f %.3f, %.3f %.3f",
917  numpy.median(dlong[idx2]), rms2Long,
918  numpy.median(dlat[idx2]), rms2Lat)
919  self.log.info("Red star offsets'': %.3f %.3f, %.3f %.3f",
920  numpy.median(dlong[idx3]), rms3Long,
921  numpy.median(dlat[idx3]), rms3Lat)
922 
923  self.metadata.add("RegisterBlueLongOffsetMedian", numpy.median(dlong[idx1]))
924  self.metadata.add("RegisterGreenLongOffsetMedian", numpy.median(dlong[idx2]))
925  self.metadata.add("RegisterRedLongOffsetMedian", numpy.median(dlong[idx3]))
926  self.metadata.add("RegisterBlueLongOffsetStd", rms1Long)
927  self.metadata.add("RegisterGreenLongOffsetStd", rms2Long)
928  self.metadata.add("RegisterRedLongOffsetStd", rms3Long)
929 
930  self.metadata.add("RegisterBlueLatOffsetMedian", numpy.median(dlat[idx1]))
931  self.metadata.add("RegisterGreenLatOffsetMedian", numpy.median(dlat[idx2]))
932  self.metadata.add("RegisterRedLatOffsetMedian", numpy.median(dlat[idx3]))
933  self.metadata.add("RegisterBlueLatOffsetStd", rms1Lat)
934  self.metadata.add("RegisterGreenLatOffsetStd", rms2Lat)
935  self.metadata.add("RegisterRedLatOffsetStd", rms3Lat)
936 
937  # warp template exposure to match exposure,
938  # PSF match template exposure to exposure,
939  # then return the difference
940 
941  # Return warped template... Construct sourceKernelCand list after subtract
942  self.log.info("Subtracting images")
943  subtractRes = self.subtract.subtractExposures(
944  templateExposure=templateExposure,
945  scienceExposure=exposure,
946  candidateList=kernelSources,
947  convolveTemplate=self.config.convolveTemplate,
948  doWarping=not self.config.doUseRegister
949  )
950  if self.config.useScoreImageDetection:
951  scoreExposure = subtractRes.subtractedExposure
952  else:
953  subtractedExposure = subtractRes.subtractedExposure
954 
955  if self.config.doDetection:
956  self.log.info("Computing diffim PSF")
957 
958  # Get Psf from the appropriate input image if it doesn't exist
959  if subtractedExposure is not None and not subtractedExposure.hasPsf():
960  if self.config.convolveTemplate:
961  subtractedExposure.setPsf(exposure.getPsf())
962  else:
963  subtractedExposure.setPsf(templateExposure.getPsf())
964 
965  # If doSubtract is False, then subtractedExposure was fetched from disk (above),
966  # thus it may have already been decorrelated. Thus, we do not decorrelate if
967  # doSubtract is False.
968 
969  # NOTE: At this point doSubtract == True
970  # useScoreImageDetection=True and doDecorrelation=True is not allowed in the config
971  if self.config.doDecorrelation and self.config.doSubtract:
972  preConvKernel = None
973  if preConvPsf is not None:
974  preConvKernel = preConvPsf.getLocalKernel()
975  decorrResult = self.decorrelate.run(exposureOrig, subtractRes.warpedExposure,
976  subtractedExposure,
977  subtractRes.psfMatchingKernel,
978  spatiallyVarying=self.config.doSpatiallyVarying,
979  preConvKernel=preConvKernel,
980  templateMatched=self.config.convolveTemplate)
981  subtractedExposure = decorrResult.correctedExposure
982 
983  # END (if subtractAlgorithm == 'AL')
984  # END (if self.config.doSubtract)
985  if self.config.doDetection:
986  self.log.info("Running diaSource detection")
987 
988  # subtractedExposure - reserved for task return value
989  # in zogy, it is always the proper difference image
990  # in AL, it may be (yet) pre-convolved and/or decorrelated
991  #
992  # detectionExposure - controls which exposure to use for detection
993  # in-place modifications will appear in task return
994  if self.config.useScoreImageDetection:
995  # zogy with score image detection enabled
996  self.log.info("Detection, diffim rescaling and measurements are "
997  "on AL likelihood or Zogy score image.")
998  detectionExposure = scoreExposure
999  else:
1000  # AL or zogy with no score image detection
1001  detectionExposure = subtractedExposure
1002 
1003  # Rescale difference image variance plane
1004  if self.config.doScaleDiffimVariance:
1005  self.log.info("Rescaling diffim variance")
1006  diffimVarFactor = self.scaleVariance.run(detectionExposure.getMaskedImage())
1007  self.log.info("Diffim variance scaling factor: %.2f", diffimVarFactor)
1008  self.metadata.add("scaleDiffimVarianceFactor", diffimVarFactor)
1009 
1010  # Erase existing detection mask planes
1011  mask = detectionExposure.getMaskedImage().getMask()
1012  mask &= ~(mask.getPlaneBitMask("DETECTED") | mask.getPlaneBitMask("DETECTED_NEGATIVE"))
1013 
1014  table = afwTable.SourceTable.make(self.schema, idFactory)
1015  table.setMetadata(self.algMetadata)
1016  results = self.detection.run(
1017  table=table,
1018  exposure=detectionExposure,
1019  doSmooth=not self.config.useScoreImageDetection
1020  )
1021 
1022  if self.config.doMerge:
1023  fpSet = results.fpSets.positive
1024  fpSet.merge(results.fpSets.negative, self.config.growFootprint,
1025  self.config.growFootprint, False)
1026  diaSources = afwTable.SourceCatalog(table)
1027  fpSet.makeSources(diaSources)
1028  self.log.info("Merging detections into %d sources", len(diaSources))
1029  else:
1030  diaSources = results.sources
1031  # Inject skySources before measurement.
1032  if self.config.doSkySources:
1033  skySourceFootprints = self.skySources.run(
1034  mask=detectionExposure.mask,
1035  seed=detectionExposure.getInfo().getVisitInfo().getExposureId())
1036  if skySourceFootprints:
1037  for foot in skySourceFootprints:
1038  s = diaSources.addNew()
1039  s.setFootprint(foot)
1040  s.set(self.skySourceKey, True)
1041 
1042  if self.config.doMeasurement:
1043  newDipoleFitting = self.config.doDipoleFitting
1044  self.log.info("Running diaSource measurement: newDipoleFitting=%r", newDipoleFitting)
1045  if not newDipoleFitting:
1046  # Just fit dipole in diffim
1047  self.measurement.run(diaSources, detectionExposure)
1048  else:
1049  # Use (matched) template and science image (if avail.) to constrain dipole fitting
1050  if self.config.doSubtract and 'matchedExposure' in subtractRes.getDict():
1051  self.measurement.run(diaSources, detectionExposure, exposure,
1052  subtractRes.matchedExposure)
1053  else:
1054  self.measurement.run(diaSources, detectionExposure, exposure)
1055  if self.config.doApCorr:
1056  self.applyApCorr.run(
1057  catalog=diaSources,
1058  apCorrMap=detectionExposure.getInfo().getApCorrMap()
1059  )
1060 
1061  if self.config.doForcedMeasurement:
1062  # Run forced psf photometry on the PVI at the diaSource locations.
1063  # Copy the measured flux and error into the diaSource.
1064  forcedSources = self.forcedMeasurement.generateMeasCat(
1065  exposure, diaSources, detectionExposure.getWcs())
1066  self.forcedMeasurement.run(forcedSources, exposure, diaSources, detectionExposure.getWcs())
1067  mapper = afwTable.SchemaMapper(forcedSources.schema, diaSources.schema)
1068  mapper.addMapping(forcedSources.schema.find("base_PsfFlux_instFlux")[0],
1069  "ip_diffim_forced_PsfFlux_instFlux", True)
1070  mapper.addMapping(forcedSources.schema.find("base_PsfFlux_instFluxErr")[0],
1071  "ip_diffim_forced_PsfFlux_instFluxErr", True)
1072  mapper.addMapping(forcedSources.schema.find("base_PsfFlux_area")[0],
1073  "ip_diffim_forced_PsfFlux_area", True)
1074  mapper.addMapping(forcedSources.schema.find("base_PsfFlux_flag")[0],
1075  "ip_diffim_forced_PsfFlux_flag", True)
1076  mapper.addMapping(forcedSources.schema.find("base_PsfFlux_flag_noGoodPixels")[0],
1077  "ip_diffim_forced_PsfFlux_flag_noGoodPixels", True)
1078  mapper.addMapping(forcedSources.schema.find("base_PsfFlux_flag_edge")[0],
1079  "ip_diffim_forced_PsfFlux_flag_edge", True)
1080  for diaSource, forcedSource in zip(diaSources, forcedSources):
1081  diaSource.assign(forcedSource, mapper)
1082 
1083  # Match with the calexp sources if possible
1084  if self.config.doMatchSources:
1085  if selectSources is not None:
1086  # Create key,val pair where key=diaSourceId and val=sourceId
1087  matchRadAsec = self.config.diaSourceMatchRadius
1088  matchRadPixel = matchRadAsec/exposure.getWcs().getPixelScale().asArcseconds()
1089 
1090  srcMatches = afwTable.matchXy(selectSources, diaSources, matchRadPixel)
1091  srcMatchDict = dict([(srcMatch.second.getId(), srcMatch.first.getId()) for
1092  srcMatch in srcMatches])
1093  self.log.info("Matched %d / %d diaSources to sources",
1094  len(srcMatchDict), len(diaSources))
1095  else:
1096  self.log.warning("Src product does not exist; cannot match with diaSources")
1097  srcMatchDict = {}
1098 
1099  # Create key,val pair where key=diaSourceId and val=refId
1100  refAstromConfig = AstrometryConfig()
1101  refAstromConfig.matcher.maxMatchDistArcSec = matchRadAsec
1102  refAstrometer = AstrometryTask(refAstromConfig)
1103  astromRet = refAstrometer.run(exposure=exposure, sourceCat=diaSources)
1104  refMatches = astromRet.matches
1105  if refMatches is None:
1106  self.log.warning("No diaSource matches with reference catalog")
1107  refMatchDict = {}
1108  else:
1109  self.log.info("Matched %d / %d diaSources to reference catalog",
1110  len(refMatches), len(diaSources))
1111  refMatchDict = dict([(refMatch.second.getId(), refMatch.first.getId()) for
1112  refMatch in refMatches])
1113 
1114  # Assign source Ids
1115  for diaSource in diaSources:
1116  sid = diaSource.getId()
1117  if sid in srcMatchDict:
1118  diaSource.set("srcMatchId", srcMatchDict[sid])
1119  if sid in refMatchDict:
1120  diaSource.set("refMatchId", refMatchDict[sid])
1121 
1122  if self.config.doAddMetrics and self.config.doSelectSources:
1123  self.log.info("Evaluating metrics and control sample")
1124 
1125  kernelCandList = []
1126  for cell in subtractRes.kernelCellSet.getCellList():
1127  for cand in cell.begin(False): # include bad candidates
1128  kernelCandList.append(cand)
1129 
1130  # Get basis list to build control sample kernels
1131  basisList = kernelCandList[0].getKernel(KernelCandidateF.ORIG).getKernelList()
1132  nparam = len(kernelCandList[0].getKernel(KernelCandidateF.ORIG).getKernelParameters())
1133 
1134  controlCandList = (
1135  diffimTools.sourceTableToCandidateList(controlSources,
1136  subtractRes.warpedExposure, exposure,
1137  self.config.subtract.kernel.active,
1138  self.config.subtract.kernel.active.detectionConfig,
1139  self.log, doBuild=True, basisList=basisList))
1140 
1141  KernelCandidateQa.apply(kernelCandList, subtractRes.psfMatchingKernel,
1142  subtractRes.backgroundModel, dof=nparam)
1143  KernelCandidateQa.apply(controlCandList, subtractRes.psfMatchingKernel,
1144  subtractRes.backgroundModel)
1145 
1146  if self.config.doDetection:
1147  KernelCandidateQa.aggregate(selectSources, self.metadata, allresids, diaSources)
1148  else:
1149  KernelCandidateQa.aggregate(selectSources, self.metadata, allresids)
1150 
1151  self.runDebug(exposure, subtractRes, selectSources, kernelSources, diaSources)
1152  return pipeBase.Struct(
1153  subtractedExposure=subtractedExposure,
1154  scoreExposure=scoreExposure,
1155  warpedExposure=subtractRes.warpedExposure,
1156  matchedExposure=subtractRes.matchedExposure,
1157  subtractRes=subtractRes,
1158  diaSources=diaSources,
1159  selectSources=selectSources
1160  )
1161 
1162  def fitAstrometry(self, templateSources, templateExposure, selectSources):
1163  """Fit the relative astrometry between templateSources and selectSources
1164 
1165  Todo
1166  ----
1167 
1168  Remove this method. It originally fit a new WCS to the template before calling register.run
1169  because our TAN-SIP fitter behaved badly for points far from CRPIX, but that's been fixed.
1170  It remains because a subtask overrides it.
1171  """
1172  results = self.register.run(templateSources, templateExposure.getWcs(),
1173  templateExposure.getBBox(), selectSources)
1174  return results
1175 
1176  def runDebug(self, exposure, subtractRes, selectSources, kernelSources, diaSources):
1177  """Make debug plots and displays.
1178 
1179  Todo
1180  ----
1181  Test and update for current debug display and slot names
1182  """
1183  import lsstDebug
1184  display = lsstDebug.Info(__name__).display
1185  showSubtracted = lsstDebug.Info(__name__).showSubtracted
1186  showPixelResiduals = lsstDebug.Info(__name__).showPixelResiduals
1187  showDiaSources = lsstDebug.Info(__name__).showDiaSources
1188  showDipoles = lsstDebug.Info(__name__).showDipoles
1189  maskTransparency = lsstDebug.Info(__name__).maskTransparency
1190  if display:
1191  disp = afwDisplay.getDisplay(frame=lsstDebug.frame)
1192  if not maskTransparency:
1193  maskTransparency = 0
1194  disp.setMaskTransparency(maskTransparency)
1195 
1196  if display and showSubtracted:
1197  disp.mtv(subtractRes.subtractedExposure, title="Subtracted image")
1198  mi = subtractRes.subtractedExposure.getMaskedImage()
1199  x0, y0 = mi.getX0(), mi.getY0()
1200  with disp.Buffering():
1201  for s in diaSources:
1202  x, y = s.getX() - x0, s.getY() - y0
1203  ctype = "red" if s.get("flags_negative") else "yellow"
1204  if (s.get("base_PixelFlags_flag_interpolatedCenter")
1205  or s.get("base_PixelFlags_flag_saturatedCenter")
1206  or s.get("base_PixelFlags_flag_crCenter")):
1207  ptype = "x"
1208  elif (s.get("base_PixelFlags_flag_interpolated")
1209  or s.get("base_PixelFlags_flag_saturated")
1210  or s.get("base_PixelFlags_flag_cr")):
1211  ptype = "+"
1212  else:
1213  ptype = "o"
1214  disp.dot(ptype, x, y, size=4, ctype=ctype)
1215  lsstDebug.frame += 1
1216 
1217  if display and showPixelResiduals and selectSources:
1218  nonKernelSources = []
1219  for source in selectSources:
1220  if source not in kernelSources:
1221  nonKernelSources.append(source)
1222 
1223  diUtils.plotPixelResiduals(exposure,
1224  subtractRes.warpedExposure,
1225  subtractRes.subtractedExposure,
1226  subtractRes.kernelCellSet,
1227  subtractRes.psfMatchingKernel,
1228  subtractRes.backgroundModel,
1229  nonKernelSources,
1230  self.subtract.config.kernel.active.detectionConfig,
1231  origVariance=False)
1232  diUtils.plotPixelResiduals(exposure,
1233  subtractRes.warpedExposure,
1234  subtractRes.subtractedExposure,
1235  subtractRes.kernelCellSet,
1236  subtractRes.psfMatchingKernel,
1237  subtractRes.backgroundModel,
1238  nonKernelSources,
1239  self.subtract.config.kernel.active.detectionConfig,
1240  origVariance=True)
1241  if display and showDiaSources:
1242  flagChecker = SourceFlagChecker(diaSources)
1243  isFlagged = [flagChecker(x) for x in diaSources]
1244  isDipole = [x.get("ip_diffim_ClassificationDipole_value") for x in diaSources]
1245  diUtils.showDiaSources(diaSources, subtractRes.subtractedExposure, isFlagged, isDipole,
1246  frame=lsstDebug.frame)
1247  lsstDebug.frame += 1
1248 
1249  if display and showDipoles:
1250  DipoleAnalysis().displayDipoles(subtractRes.subtractedExposure, diaSources,
1251  frame=lsstDebug.frame)
1252  lsstDebug.frame += 1
1253 
1254  def _getConfigName(self):
1255  """Return the name of the config dataset
1256  """
1257  return "%sDiff_config" % (self.config.coaddName,)
1258 
1259  def _getMetadataName(self):
1260  """Return the name of the metadata dataset
1261  """
1262  return "%sDiff_metadata" % (self.config.coaddName,)
1263 
1264  def getSchemaCatalogs(self):
1265  """Return a dict of empty catalogs for each catalog dataset produced by this task."""
1266  return {self.config.coaddName + "Diff_diaSrc": self.outputSchema}
1267 
1268  @classmethod
1269  def _makeArgumentParser(cls):
1270  """Create an argument parser
1271  """
1272  parser = pipeBase.ArgumentParser(name=cls._DefaultName)
1273  parser.add_id_argument("--id", "calexp", help="data ID, e.g. --id visit=12345 ccd=1,2")
1274  parser.add_id_argument("--templateId", "calexp", doMakeDataRefList=True,
1275  help="Template data ID in case of calexp template,"
1276  " e.g. --templateId visit=6789")
1277  return parser
1278 
1279 
1280 class Winter2013ImageDifferenceConfig(ImageDifferenceConfig):
1281  winter2013WcsShift = pexConfig.Field(dtype=float, default=0.0,
1282  doc="Shift stars going into RegisterTask by this amount")
1283  winter2013WcsRms = pexConfig.Field(dtype=float, default=0.0,
1284  doc="Perturb stars going into RegisterTask by this amount")
1285 
1286  def setDefaults(self):
1287  ImageDifferenceConfig.setDefaults(self)
1288  self.getTemplate.retarget(GetCalexpAsTemplateTask)
1289 
1290 
1291 class Winter2013ImageDifferenceTask(ImageDifferenceTask):
1292  """!Image difference Task used in the Winter 2013 data challege.
1293  Enables testing the effects of registration shifts and scatter.
1294 
1295  For use with winter 2013 simulated images:
1296  Use --templateId visit=88868666 for sparse data
1297  --templateId visit=22222200 for dense data (g)
1298  --templateId visit=11111100 for dense data (i)
1299  """
1300  ConfigClass = Winter2013ImageDifferenceConfig
1301  _DefaultName = "winter2013ImageDifference"
1302 
1303  def __init__(self, **kwargs):
1304  ImageDifferenceTask.__init__(self, **kwargs)
1305 
1306  def fitAstrometry(self, templateSources, templateExposure, selectSources):
1307  """Fit the relative astrometry between templateSources and selectSources"""
1308  if self.config.winter2013WcsShift > 0.0:
1309  offset = geom.Extent2D(self.config.winter2013WcsShift,
1310  self.config.winter2013WcsShift)
1311  cKey = templateSources[0].getTable().getCentroidSlot().getMeasKey()
1312  for source in templateSources:
1313  centroid = source.get(cKey)
1314  source.set(cKey, centroid + offset)
1315  elif self.config.winter2013WcsRms > 0.0:
1316  cKey = templateSources[0].getTable().getCentroidSlot().getMeasKey()
1317  for source in templateSources:
1318  offset = geom.Extent2D(self.config.winter2013WcsRms*numpy.random.normal(),
1319  self.config.winter2013WcsRms*numpy.random.normal())
1320  centroid = source.get(cKey)
1321  source.set(cKey, centroid + offset)
1322 
1323  results = self.register.run(templateSources, templateExposure.getWcs(),
1324  templateExposure.getBBox(), selectSources)
1325  return results
def run(self, skyInfo, tempExpRefList, imageScalerList, weightList, altMaskList=None, mask=None, supplementaryData=None)