lsst.ip.isr  17.0.1-23-ga60834d+1
isrTask.py
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21 
22 import math
23 import numpy
24 
25 import lsst.geom
26 import lsst.afw.image as afwImage
27 import lsst.afw.math as afwMath
28 import lsst.afw.table as afwTable
29 import lsst.pex.config as pexConfig
30 import lsst.pipe.base as pipeBase
31 
32 from contextlib import contextmanager
33 from lsstDebug import getDebugFrame
34 
35 from lsst.afw.cameraGeom import PIXELS, FOCAL_PLANE, NullLinearityType
36 from lsst.afw.display import getDisplay
37 from lsst.afw.geom import Polygon
38 from lsst.daf.persistence import ButlerDataRef
39 from lsst.daf.persistence.butler import NoResults
40 from lsst.meas.algorithms.detection import SourceDetectionTask
41 from lsst.meas.algorithms import Defects
42 
43 from . import isrFunctions
44 from . import isrQa
45 from . import linearize
46 
47 from .assembleCcdTask import AssembleCcdTask
48 from .crosstalk import CrosstalkTask
49 from .fringe import FringeTask
50 from .isr import maskNans
51 from .masking import MaskingTask
52 from .straylight import StrayLightTask
53 from .vignette import VignetteTask
54 
55 __all__ = ["IsrTask", "IsrTaskConfig", "RunIsrTask", "RunIsrConfig"]
56 
57 
58 class IsrTaskConfig(pexConfig.Config):
59  """Configuration parameters for IsrTask.
60 
61  Items are grouped in the order in which they are executed by the task.
62  """
63  # General ISR configuration
64 
65  # gen3 options
66  isrName = pexConfig.Field(
67  dtype=str,
68  doc="Name of ISR",
69  default="ISR",
70  )
71 
72  # input datasets
73  ccdExposure = pipeBase.InputDatasetField(
74  doc="Input exposure to process",
75  name="raw",
76  scalar=True,
77  storageClass="Exposure",
78  dimensions=["instrument", "exposure", "detector"],
79  )
80  camera = pipeBase.InputDatasetField(
81  doc="Input camera to construct complete exposures.",
82  name="camera",
83  scalar=True,
84  storageClass="TablePersistableCamera",
85  dimensions=["instrument", "calibration_label"],
86  )
87  bias = pipeBase.InputDatasetField(
88  doc="Input bias calibration.",
89  name="bias",
90  scalar=True,
91  storageClass="ImageF",
92  dimensions=["instrument", "calibration_label", "detector"],
93  )
94  dark = pipeBase.InputDatasetField(
95  doc="Input dark calibration.",
96  name="dark",
97  scalar=True,
98  storageClass="ImageF",
99  dimensions=["instrument", "calibration_label", "detector"],
100  )
101  flat = pipeBase.InputDatasetField(
102  doc="Input flat calibration.",
103  name="flat",
104  scalar=True,
105  storageClass="MaskedImageF",
106  dimensions=["instrument", "physical_filter", "calibration_label", "detector"],
107  )
108  bfKernel = pipeBase.InputDatasetField(
109  doc="Input brighter-fatter kernel.",
110  name="bfKernel",
111  scalar=True,
112  storageClass="NumpyArray",
113  dimensions=["instrument", "calibration_label"],
114  )
115  defects = pipeBase.InputDatasetField(
116  doc="Input defect tables.",
117  name="defects",
118  scalar=True,
119  storageClass="DefectsList",
120  dimensions=["instrument", "calibration_label", "detector"],
121  )
122  opticsTransmission = pipeBase.InputDatasetField(
123  doc="Transmission curve due to the optics.",
124  name="transmission_optics",
125  scalar=True,
126  storageClass="TablePersistableTransmissionCurve",
127  dimensions=["instrument", "calibration_label"],
128  )
129  filterTransmission = pipeBase.InputDatasetField(
130  doc="Transmission curve due to the filter.",
131  name="transmission_filter",
132  scalar=True,
133  storageClass="TablePersistableTransmissionCurve",
134  dimensions=["instrument", "physical_filter", "calibration_label"],
135  )
136  sensorTransmission = pipeBase.InputDatasetField(
137  doc="Transmission curve due to the sensor.",
138  name="transmission_sensor",
139  scalar=True,
140  storageClass="TablePersistableTransmissionCurve",
141  dimensions=["instrument", "calibration_label", "detector"],
142  )
143  atmosphereTransmission = pipeBase.InputDatasetField(
144  doc="Transmission curve due to the atmosphere.",
145  name="transmission_atmosphere",
146  scalar=True,
147  storageClass="TablePersistableTransmissionCurve",
148  dimensions=["instrument"],
149  )
150  illumMaskedImage = pipeBase.InputDatasetField(
151  doc="Input illumination correction.",
152  name="illum",
153  scalar=True,
154  storageClass="MaskedImageF",
155  dimensions=["instrument", "physical_filter", "calibration_label", "detector"],
156  )
157 
158  # output datasets
159  outputExposure = pipeBase.OutputDatasetField(
160  doc="Output ISR processed exposure.",
161  name="postISRCCD",
162  scalar=True,
163  storageClass="ExposureF",
164  dimensions=["instrument", "visit", "detector"],
165  )
166  outputOssThumbnail = pipeBase.OutputDatasetField(
167  doc="Output Overscan-subtracted thumbnail image.",
168  name="OssThumb",
169  scalar=True,
170  storageClass="Thumbnail",
171  dimensions=["instrument", "visit", "detector"],
172  )
173  outputFlattenedThumbnail = pipeBase.OutputDatasetField(
174  doc="Output flat-corrected thumbnail image.",
175  name="FlattenedThumb",
176  scalar=True,
177  storageClass="TextStorage",
178  dimensions=["instrument", "visit", "detector"],
179  )
180 
181  quantum = pipeBase.QuantumConfig(
182  dimensions=["visit", "detector", "instrument"],
183  )
184 
185 
186  datasetType = pexConfig.Field(
187  dtype=str,
188  doc="Dataset type for input data; users will typically leave this alone, "
189  "but camera-specific ISR tasks will override it",
190  default="raw",
191  )
192 
193  fallbackFilterName = pexConfig.Field(
194  dtype=str,
195  doc="Fallback default filter name for calibrations.",
196  optional=True
197  )
198  expectWcs = pexConfig.Field(
199  dtype=bool,
200  default=True,
201  doc="Expect input science images to have a WCS (set False for e.g. spectrographs)."
202  )
203  fwhm = pexConfig.Field(
204  dtype=float,
205  doc="FWHM of PSF in arcseconds.",
206  default=1.0,
207  )
208  qa = pexConfig.ConfigField(
209  dtype=isrQa.IsrQaConfig,
210  doc="QA related configuration options.",
211  )
212 
213  # Image conversion configuration
214  doConvertIntToFloat = pexConfig.Field(
215  dtype=bool,
216  doc="Convert integer raw images to floating point values?",
217  default=True,
218  )
219 
220  # Saturated pixel handling.
221  doSaturation = pexConfig.Field(
222  dtype=bool,
223  doc="Mask saturated pixels? NB: this is totally independent of the"
224  " interpolation option - this is ONLY setting the bits in the mask."
225  " To have them interpolated make sure doSaturationInterpolation=True",
226  default=True,
227  )
228  saturatedMaskName = pexConfig.Field(
229  dtype=str,
230  doc="Name of mask plane to use in saturation detection and interpolation",
231  default="SAT",
232  )
233  saturation = pexConfig.Field(
234  dtype=float,
235  doc="The saturation level to use if no Detector is present in the Exposure (ignored if NaN)",
236  default=float("NaN"),
237  )
238  growSaturationFootprintSize = pexConfig.Field(
239  dtype=int,
240  doc="Number of pixels by which to grow the saturation footprints",
241  default=1,
242  )
243 
244  # Suspect pixel handling.
245  doSuspect = pexConfig.Field(
246  dtype=bool,
247  doc="Mask suspect pixels?",
248  default=True,
249  )
250  suspectMaskName = pexConfig.Field(
251  dtype=str,
252  doc="Name of mask plane to use for suspect pixels",
253  default="SUSPECT",
254  )
255  numEdgeSuspect = pexConfig.Field(
256  dtype=int,
257  doc="Number of edge pixels to be flagged as untrustworthy.",
258  default=0,
259  )
260 
261  # Initial masking options.
262  doSetBadRegions = pexConfig.Field(
263  dtype=bool,
264  doc="Should we set the level of all BAD patches of the chip to the chip's average value?",
265  default=True,
266  )
267  badStatistic = pexConfig.ChoiceField(
268  dtype=str,
269  doc="How to estimate the average value for BAD regions.",
270  default='MEANCLIP',
271  allowed={
272  "MEANCLIP": "Correct using the (clipped) mean of good data",
273  "MEDIAN": "Correct using the median of the good data",
274  },
275  )
276 
277  # Overscan subtraction configuration.
278  doOverscan = pexConfig.Field(
279  dtype=bool,
280  doc="Do overscan subtraction?",
281  default=True,
282  )
283  overscanFitType = pexConfig.ChoiceField(
284  dtype=str,
285  doc="The method for fitting the overscan bias level.",
286  default='MEDIAN',
287  allowed={
288  "POLY": "Fit ordinary polynomial to the longest axis of the overscan region",
289  "CHEB": "Fit Chebyshev polynomial to the longest axis of the overscan region",
290  "LEG": "Fit Legendre polynomial to the longest axis of the overscan region",
291  "NATURAL_SPLINE": "Fit natural spline to the longest axis of the overscan region",
292  "CUBIC_SPLINE": "Fit cubic spline to the longest axis of the overscan region",
293  "AKIMA_SPLINE": "Fit Akima spline to the longest axis of the overscan region",
294  "MEAN": "Correct using the mean of the overscan region",
295  "MEANCLIP": "Correct using a clipped mean of the overscan region",
296  "MEDIAN": "Correct using the median of the overscan region",
297  },
298  )
299  overscanOrder = pexConfig.Field(
300  dtype=int,
301  doc=("Order of polynomial or to fit if overscan fit type is a polynomial, " +
302  "or number of spline knots if overscan fit type is a spline."),
303  default=1,
304  )
305  overscanNumSigmaClip = pexConfig.Field(
306  dtype=float,
307  doc="Rejection threshold (sigma) for collapsing overscan before fit",
308  default=3.0,
309  )
310  overscanIsInt = pexConfig.Field(
311  dtype=bool,
312  doc="Treat overscan as an integer image for purposes of overscan.FitType=MEDIAN",
313  default=True,
314  )
315  overscanNumLeadingColumnsToSkip = pexConfig.Field(
316  dtype=int,
317  doc="Number of columns to skip in overscan, i.e. those closest to amplifier",
318  default=0,
319  )
320  overscanNumTrailingColumnsToSkip = pexConfig.Field(
321  dtype=int,
322  doc="Number of columns to skip in overscan, i.e. those farthest from amplifier",
323  default=0,
324  )
325  overscanMaxDev = pexConfig.Field(
326  dtype=float,
327  doc="Maximum deviation from the median for overscan",
328  default=1000.0, check=lambda x: x > 0
329  )
330  overscanBiasJump = pexConfig.Field(
331  dtype=bool,
332  doc="Fit the overscan in a piecewise-fashion to correct for bias jumps?",
333  default=False,
334  )
335  overscanBiasJumpKeyword = pexConfig.Field(
336  dtype=str,
337  doc="Header keyword containing information about devices.",
338  default="NO_SUCH_KEY",
339  )
340  overscanBiasJumpDevices = pexConfig.ListField(
341  dtype=str,
342  doc="List of devices that need piecewise overscan correction.",
343  default=(),
344  )
345  overscanBiasJumpLocation = pexConfig.Field(
346  dtype=int,
347  doc="Location of bias jump along y-axis.",
348  default=0,
349  )
350 
351  # Amplifier to CCD assembly configuration
352  doAssembleCcd = pexConfig.Field(
353  dtype=bool,
354  default=True,
355  doc="Assemble amp-level exposures into a ccd-level exposure?"
356  )
357  assembleCcd = pexConfig.ConfigurableField(
358  target=AssembleCcdTask,
359  doc="CCD assembly task",
360  )
361 
362  # General calibration configuration.
363  doAssembleIsrExposures = pexConfig.Field(
364  dtype=bool,
365  default=False,
366  doc="Assemble amp-level calibration exposures into ccd-level exposure?"
367  )
368  doTrimToMatchCalib = pexConfig.Field(
369  dtype=bool,
370  default=False,
371  doc="Trim raw data to match calibration bounding boxes?"
372  )
373 
374  # Bias subtraction.
375  doBias = pexConfig.Field(
376  dtype=bool,
377  doc="Apply bias frame correction?",
378  default=True,
379  )
380  biasDataProductName = pexConfig.Field(
381  dtype=str,
382  doc="Name of the bias data product",
383  default="bias",
384  )
385 
386  # Variance construction
387  doVariance = pexConfig.Field(
388  dtype=bool,
389  doc="Calculate variance?",
390  default=True
391  )
392  gain = pexConfig.Field(
393  dtype=float,
394  doc="The gain to use if no Detector is present in the Exposure (ignored if NaN)",
395  default=float("NaN"),
396  )
397  readNoise = pexConfig.Field(
398  dtype=float,
399  doc="The read noise to use if no Detector is present in the Exposure",
400  default=0.0,
401  )
402  doEmpiricalReadNoise = pexConfig.Field(
403  dtype=bool,
404  default=False,
405  doc="Calculate empirical read noise instead of value from AmpInfo data?"
406  )
407 
408  # Linearization.
409  doLinearize = pexConfig.Field(
410  dtype=bool,
411  doc="Correct for nonlinearity of the detector's response?",
412  default=True,
413  )
414 
415  # Crosstalk.
416  doCrosstalk = pexConfig.Field(
417  dtype=bool,
418  doc="Apply intra-CCD crosstalk correction?",
419  default=False,
420  )
421  doCrosstalkBeforeAssemble = pexConfig.Field(
422  dtype=bool,
423  doc="Apply crosstalk correction before CCD assembly, and before trimming?",
424  default=True,
425  )
426  crosstalk = pexConfig.ConfigurableField(
427  target=CrosstalkTask,
428  doc="Intra-CCD crosstalk correction",
429  )
430 
431  # Masking options.
432  doDefect = pexConfig.Field(
433  dtype=bool,
434  doc="Apply correction for CCD defects, e.g. hot pixels?",
435  default=True,
436  )
437  numEdgeSuspect = pexConfig.Field(
438  dtype=int,
439  doc="Number of edge pixels to be flagged as untrustworthy.",
440  default=0,
441  )
442  doNanMasking = pexConfig.Field(
443  dtype=bool,
444  doc="Mask NAN pixels?",
445  default=True,
446  )
447  doWidenSaturationTrails = pexConfig.Field(
448  dtype=bool,
449  doc="Widen bleed trails based on their width?",
450  default=True
451  )
452 
453  # Brighter-Fatter correction.
454  doBrighterFatter = pexConfig.Field(
455  dtype=bool,
456  default=False,
457  doc="Apply the brighter fatter correction"
458  )
459  brighterFatterLevel = pexConfig.ChoiceField(
460  dtype=str,
461  default="DETECTOR",
462  doc="The level at which to correct for brighter-fatter.",
463  allowed={
464  "AMP": "Every amplifier treated separately.",
465  "DETECTOR": "One kernel per detector",
466  }
467  )
468  brighterFatterKernelFile = pexConfig.Field(
469  dtype=str,
470  default='',
471  doc="Kernel file used for the brighter fatter correction"
472  )
473  brighterFatterMaxIter = pexConfig.Field(
474  dtype=int,
475  default=10,
476  doc="Maximum number of iterations for the brighter fatter correction"
477  )
478  brighterFatterThreshold = pexConfig.Field(
479  dtype=float,
480  default=1000,
481  doc="Threshold used to stop iterating the brighter fatter correction. It is the "
482  " absolute value of the difference between the current corrected image and the one"
483  " from the previous iteration summed over all the pixels."
484  )
485  brighterFatterApplyGain = pexConfig.Field(
486  dtype=bool,
487  default=True,
488  doc="Should the gain be applied when applying the brighter fatter correction?"
489  )
490 
491  # Dark subtraction.
492  doDark = pexConfig.Field(
493  dtype=bool,
494  doc="Apply dark frame correction?",
495  default=True,
496  )
497  darkDataProductName = pexConfig.Field(
498  dtype=str,
499  doc="Name of the dark data product",
500  default="dark",
501  )
502 
503  # Camera-specific stray light removal.
504  doStrayLight = pexConfig.Field(
505  dtype=bool,
506  doc="Subtract stray light in the y-band (due to encoder LEDs)?",
507  default=False,
508  )
509  strayLight = pexConfig.ConfigurableField(
510  target=StrayLightTask,
511  doc="y-band stray light correction"
512  )
513 
514  # Flat correction.
515  doFlat = pexConfig.Field(
516  dtype=bool,
517  doc="Apply flat field correction?",
518  default=True,
519  )
520  flatDataProductName = pexConfig.Field(
521  dtype=str,
522  doc="Name of the flat data product",
523  default="flat",
524  )
525  flatScalingType = pexConfig.ChoiceField(
526  dtype=str,
527  doc="The method for scaling the flat on the fly.",
528  default='USER',
529  allowed={
530  "USER": "Scale by flatUserScale",
531  "MEAN": "Scale by the inverse of the mean",
532  "MEDIAN": "Scale by the inverse of the median",
533  },
534  )
535  flatUserScale = pexConfig.Field(
536  dtype=float,
537  doc="If flatScalingType is 'USER' then scale flat by this amount; ignored otherwise",
538  default=1.0,
539  )
540  doTweakFlat = pexConfig.Field(
541  dtype=bool,
542  doc="Tweak flats to match observed amplifier ratios?",
543  default=False
544  )
545 
546  # Amplifier normalization based on gains instead of using flats configuration.
547  doApplyGains = pexConfig.Field(
548  dtype=bool,
549  doc="Correct the amplifiers for their gains instead of applying flat correction",
550  default=False,
551  )
552  normalizeGains = pexConfig.Field(
553  dtype=bool,
554  doc="Normalize all the amplifiers in each CCD to have the same median value.",
555  default=False,
556  )
557 
558  # Fringe correction.
559  doFringe = pexConfig.Field(
560  dtype=bool,
561  doc="Apply fringe correction?",
562  default=True,
563  )
564  fringe = pexConfig.ConfigurableField(
565  target=FringeTask,
566  doc="Fringe subtraction task",
567  )
568  fringeAfterFlat = pexConfig.Field(
569  dtype=bool,
570  doc="Do fringe subtraction after flat-fielding?",
571  default=True,
572  )
573 
574  # Distortion model application.
575  doAddDistortionModel = pexConfig.Field(
576  dtype=bool,
577  doc="Apply a distortion model based on camera geometry to the WCS?",
578  default=True,
579  )
580 
581  # Initial CCD-level background statistics options.
582  doMeasureBackground = pexConfig.Field(
583  dtype=bool,
584  doc="Measure the background level on the reduced image?",
585  default=False,
586  )
587 
588  # Camera-specific masking configuration.
589  doCameraSpecificMasking = pexConfig.Field(
590  dtype=bool,
591  doc="Mask camera-specific bad regions?",
592  default=False,
593  )
594  masking = pexConfig.ConfigurableField(
595  target=MaskingTask,
596  doc="Masking task."
597  )
598 
599  # Interpolation options.
600 
601  doInterpolate = pexConfig.Field(
602  dtype=bool,
603  doc="Interpolate masked pixels?",
604  default=True,
605  )
606  doSaturationInterpolation = pexConfig.Field(
607  dtype=bool,
608  doc="Perform interpolation over pixels masked as saturated?"
609  " NB: This is independent of doSaturation; if that is False this plane"
610  " will likely be blank, resulting in a no-op here.",
611  default=True,
612  )
613  doNanInterpolation = pexConfig.Field(
614  dtype=bool,
615  doc="Perform interpolation over pixels masked as NaN?"
616  " NB: This is independent of doNanMasking; if that is False this plane"
617  " will likely be blank, resulting in a no-op here.",
618  default=True,
619  )
620  doNanInterpAfterFlat = pexConfig.Field(
621  dtype=bool,
622  doc=("If True, ensure we interpolate NaNs after flat-fielding, even if we "
623  "also have to interpolate them before flat-fielding."),
624  default=False,
625  )
626  maskListToInterpolate = pexConfig.ListField(
627  dtype=str,
628  doc="List of mask planes that should be interpolated.",
629  default=['SAT', 'BAD', 'UNMASKEDNAN'],
630  )
631  doSaveInterpPixels = pexConfig.Field(
632  dtype=bool,
633  doc="Save a copy of the pre-interpolated pixel values?",
634  default=False,
635  )
636 
637  # Default photometric calibration options.
638  fluxMag0T1 = pexConfig.DictField(
639  keytype=str,
640  itemtype=float,
641  doc="The approximate flux of a zero-magnitude object in a one-second exposure, per filter.",
642  default=dict((f, pow(10.0, 0.4*m)) for f, m in (("Unknown", 28.0),
643  ))
644  )
645  defaultFluxMag0T1 = pexConfig.Field(
646  dtype=float,
647  doc="Default value for fluxMag0T1 (for an unrecognized filter).",
648  default=pow(10.0, 0.4*28.0)
649  )
650 
651  # Vignette correction configuration.
652  doVignette = pexConfig.Field(
653  dtype=bool,
654  doc="Apply vignetting parameters?",
655  default=False,
656  )
657  vignette = pexConfig.ConfigurableField(
658  target=VignetteTask,
659  doc="Vignetting task.",
660  )
661 
662  # Transmission curve configuration.
663  doAttachTransmissionCurve = pexConfig.Field(
664  dtype=bool,
665  default=False,
666  doc="Construct and attach a wavelength-dependent throughput curve for this CCD image?"
667  )
668  doUseOpticsTransmission = pexConfig.Field(
669  dtype=bool,
670  default=True,
671  doc="Load and use transmission_optics (if doAttachTransmissionCurve is True)?"
672  )
673  doUseFilterTransmission = pexConfig.Field(
674  dtype=bool,
675  default=True,
676  doc="Load and use transmission_filter (if doAttachTransmissionCurve is True)?"
677  )
678  doUseSensorTransmission = pexConfig.Field(
679  dtype=bool,
680  default=True,
681  doc="Load and use transmission_sensor (if doAttachTransmissionCurve is True)?"
682  )
683  doUseAtmosphereTransmission = pexConfig.Field(
684  dtype=bool,
685  default=True,
686  doc="Load and use transmission_atmosphere (if doAttachTransmissionCurve is True)?"
687  )
688 
689  # Illumination correction.
690  doIlluminationCorrection = pexConfig.Field(
691  dtype=bool,
692  default=False,
693  doc="Perform illumination correction?"
694  )
695  illuminationCorrectionDataProductName = pexConfig.Field(
696  dtype=str,
697  doc="Name of the illumination correction data product.",
698  default="illumcor",
699  )
700  illumScale = pexConfig.Field(
701  dtype=float,
702  doc="Scale factor for the illumination correction.",
703  default=1.0,
704  )
705  illumFilters = pexConfig.ListField(
706  dtype=str,
707  default=[],
708  doc="Only perform illumination correction for these filters."
709  )
710 
711  # Write the outputs to disk. If ISR is run as a subtask, this may not be needed.
712  doWrite = pexConfig.Field(
713  dtype=bool,
714  doc="Persist postISRCCD?",
715  default=True,
716  )
717 
718  def validate(self):
719  super().validate()
720  if self.doFlat and self.doApplyGains:
721  raise ValueError("You may not specify both doFlat and doApplyGains")
722  if self.doSaturationInterpolation and "SAT" not in self.maskListToInterpolate:
723  self.config.maskListToInterpolate.append("SAT")
724  if self.doNanInterpolation and "UNMASKEDNAN" not in self.maskListToInterpolate:
725  self.config.maskListToInterpolate.append("UNMASKEDNAN")
726 
727 
728 class IsrTask(pipeBase.PipelineTask, pipeBase.CmdLineTask):
729  """Apply common instrument signature correction algorithms to a raw frame.
730 
731  The process for correcting imaging data is very similar from
732  camera to camera. This task provides a vanilla implementation of
733  doing these corrections, including the ability to turn certain
734  corrections off if they are not needed. The inputs to the primary
735  method, `run()`, are a raw exposure to be corrected and the
736  calibration data products. The raw input is a single chip sized
737  mosaic of all amps including overscans and other non-science
738  pixels. The method `runDataRef()` identifies and defines the
739  calibration data products, and is intended for use by a
740  `lsst.pipe.base.cmdLineTask.CmdLineTask` and takes as input only a
741  `daf.persistence.butlerSubset.ButlerDataRef`. This task may be
742  subclassed for different camera, although the most camera specific
743  methods have been split into subtasks that can be redirected
744  appropriately.
745 
746  The __init__ method sets up the subtasks for ISR processing, using
747  the defaults from `lsst.ip.isr`.
748 
749  Parameters
750  ----------
751  args : `list`
752  Positional arguments passed to the Task constructor. None used at this time.
753  kwargs : `dict`, optional
754  Keyword arguments passed on to the Task constructor. None used at this time.
755  """
756  ConfigClass = IsrTaskConfig
757  _DefaultName = "isr"
758 
759  def __init__(self, **kwargs):
760  super().__init__(**kwargs)
761  self.makeSubtask("assembleCcd")
762  self.makeSubtask("crosstalk")
763  self.makeSubtask("strayLight")
764  self.makeSubtask("fringe")
765  self.makeSubtask("masking")
766  self.makeSubtask("vignette")
767 
768  @classmethod
769  def getInputDatasetTypes(cls, config):
770  inputTypeDict = super().getInputDatasetTypes(config)
771 
772  # Delete entries from the dictionary of InputDatasetTypes that we know we don't
773  # need because the configuration tells us we will not be bothering with the
774  # correction that uses that IDT.
775  if config.doBias is not True:
776  inputTypeDict.pop("bias", None)
777  if config.doLinearize is not True:
778  inputTypeDict.pop("linearizer", None)
779  if config.doCrosstalk is not True:
780  inputTypeDict.pop("crosstalkSources", None)
781  if config.doBrighterFatter is not True:
782  inputTypeDict.pop("bfKernel", None)
783  if config.doDefect is not True:
784  inputTypeDict.pop("defects", None)
785  if config.doDark is not True:
786  inputTypeDict.pop("dark", None)
787  if config.doFlat is not True:
788  inputTypeDict.pop("flat", None)
789  if config.doAttachTransmissionCurve is not True:
790  inputTypeDict.pop("opticsTransmission", None)
791  inputTypeDict.pop("filterTransmission", None)
792  inputTypeDict.pop("sensorTransmission", None)
793  inputTypeDict.pop("atmosphereTransmission", None)
794  if config.doUseOpticsTransmission is not True:
795  inputTypeDict.pop("opticsTransmission", None)
796  if config.doUseFilterTransmission is not True:
797  inputTypeDict.pop("filterTransmission", None)
798  if config.doUseSensorTransmission is not True:
799  inputTypeDict.pop("sensorTransmission", None)
800  if config.doUseAtmosphereTransmission is not True:
801  inputTypeDict.pop("atmosphereTransmission", None)
802  if config.doIlluminationCorrection is not True:
803  inputTypeDict.pop("illumMaskedImage", None)
804 
805  return inputTypeDict
806 
807  @classmethod
808  def getOutputDatasetTypes(cls, config):
809  outputTypeDict = super().getOutputDatasetTypes(config)
810 
811  if config.qa.doThumbnailOss is not True:
812  outputTypeDict.pop("outputOssThumbnail", None)
813  if config.qa.doThumbnailFlattened is not True:
814  outputTypeDict.pop("outputFlattenedThumbnail", None)
815  if config.doWrite is not True:
816  outputTypeDict.pop("outputExposure", None)
817 
818  return outputTypeDict
819 
820  @classmethod
821  def getPrerequisiteDatasetTypes(cls, config):
822  # Input calibration datasets should not constrain the QuantumGraph
823  # (it'd be confusing if not having flats just silently resulted in no
824  # data being processed). Our nomenclature for that is that these are
825  # "prerequisite" datasets (only "ccdExposure" == "raw" isn't).
826  names = set(cls.getInputDatasetTypes(config))
827  names.remove("ccdExposure")
828  return names
829 
830  @classmethod
831  def getPerDatasetTypeDimensions(cls, config):
832  # Input calibration datasets of different types (i.e. flat, bias) need
833  # not have the same validity range. That makes calibration_label
834  # (which maps directly to a validity range) a "per-DatasetType
835  # dimension".
836  return frozenset(["calibration_label"])
837 
838  def adaptArgsAndRun(self, inputData, inputDataIds, outputDataIds, butler):
839  try:
840  inputData['detectorNum'] = int(inputDataIds['ccdExposure']['detector'])
841  except Exception as e:
842  raise ValueError(f"Failure to find valid detectorNum value for Dataset {inputDataIds}: {e}")
843 
844  inputData['isGen3'] = True
845 
846  if self.config.doLinearize is True:
847  if 'linearizer' not in inputData.keys():
848  detector = inputData['camera'][inputData['detectorNum']]
849  linearityName = detector.getAmpInfoCatalog()[0].getLinearityType()
850  inputData['linearizer'] = linearize.getLinearityTypeByName(linearityName)()
851 
852  if inputData['defects'] is not None:
853  # defects is loaded as a BaseCatalog with columns x0, y0, width, height.
854  # masking expects a list of defects defined by their bounding box
855  if not isinstance(inputData["defects"], Defects):
856  inputData["defects"] = Defects.fromTable(inputData["defects"])
857 
858  # Broken: DM-17169
859  # ci_hsc does not use crosstalkSources, as it's intra-CCD CT only. This needs to be
860  # fixed for non-HSC cameras in the future.
861  # inputData['crosstalkSources'] = (self.crosstalk.prepCrosstalk(inputDataIds['ccdExposure'])
862  # if self.config.doCrosstalk else None)
863 
864  # Broken: DM-17152
865  # Fringes are not tested to be handled correctly by Gen3 butler.
866  # inputData['fringes'] = (self.fringe.readFringes(inputDataIds['ccdExposure'],
867  # assembler=self.assembleCcd
868  # if self.config.doAssembleIsrExposures else None)
869  # if self.config.doFringe and
870  # self.fringe.checkFilter(inputData['ccdExposure'])
871  # else pipeBase.Struct(fringes=None))
872 
873  return super().adaptArgsAndRun(inputData, inputDataIds, outputDataIds, butler)
874 
875  def makeDatasetType(self, dsConfig):
876  return super().makeDatasetType(dsConfig)
877 
878  def readIsrData(self, dataRef, rawExposure):
879  """!Retrieve necessary frames for instrument signature removal.
880 
881  Pre-fetching all required ISR data products limits the IO
882  required by the ISR. Any conflict between the calibration data
883  available and that needed for ISR is also detected prior to
884  doing processing, allowing it to fail quickly.
885 
886  Parameters
887  ----------
888  dataRef : `daf.persistence.butlerSubset.ButlerDataRef`
889  Butler reference of the detector data to be processed
890  rawExposure : `afw.image.Exposure`
891  The raw exposure that will later be corrected with the
892  retrieved calibration data; should not be modified in this
893  method.
894 
895  Returns
896  -------
897  result : `lsst.pipe.base.Struct`
898  Result struct with components (which may be `None`):
899  - ``bias``: bias calibration frame (`afw.image.Exposure`)
900  - ``linearizer``: functor for linearization (`ip.isr.linearize.LinearizeBase`)
901  - ``crosstalkSources``: list of possible crosstalk sources (`list`)
902  - ``dark``: dark calibration frame (`afw.image.Exposure`)
903  - ``flat``: flat calibration frame (`afw.image.Exposure`)
904  - ``bfKernel``: Brighter-Fatter kernel (`numpy.ndarray`)
905  - ``defects``: list of defects (`lsst.meas.algorithms.Defects`)
906  - ``fringes``: `lsst.pipe.base.Struct` with components:
907  - ``fringes``: fringe calibration frame (`afw.image.Exposure`)
908  - ``seed``: random seed derived from the ccdExposureId for random
909  number generator (`uint32`).
910  - ``opticsTransmission``: `lsst.afw.image.TransmissionCurve`
911  A ``TransmissionCurve`` that represents the throughput of the optics,
912  to be evaluated in focal-plane coordinates.
913  - ``filterTransmission`` : `lsst.afw.image.TransmissionCurve`
914  A ``TransmissionCurve`` that represents the throughput of the filter
915  itself, to be evaluated in focal-plane coordinates.
916  - ``sensorTransmission`` : `lsst.afw.image.TransmissionCurve`
917  A ``TransmissionCurve`` that represents the throughput of the sensor
918  itself, to be evaluated in post-assembly trimmed detector coordinates.
919  - ``atmosphereTransmission`` : `lsst.afw.image.TransmissionCurve`
920  A ``TransmissionCurve`` that represents the throughput of the
921  atmosphere, assumed to be spatially constant.
922  - ``strayLightData`` : `object`
923  An opaque object containing calibration information for
924  stray-light correction. If `None`, no correction will be
925  performed.
926  - ``illumMaskedImage`` : illumination correction image (`lsst.afw.image.MaskedImage`)
927 
928  Raises
929  ------
930  NotImplementedError :
931  Raised if a per-amplifier brighter-fatter kernel is requested by the configuration.
932  """
933  ccd = rawExposure.getDetector()
934  filterName = afwImage.Filter(rawExposure.getFilter().getId()).getName() # Canonical name for filter
935  rawExposure.mask.addMaskPlane("UNMASKEDNAN") # needed to match pre DM-15862 processing.
936  biasExposure = (self.getIsrExposure(dataRef, self.config.biasDataProductName)
937  if self.config.doBias else None)
938  # immediate=True required for functors and linearizers are functors; see ticket DM-6515
939  linearizer = (dataRef.get("linearizer", immediate=True)
940  if self.doLinearize(ccd) else None)
941  crosstalkSources = (self.crosstalk.prepCrosstalk(dataRef)
942  if self.config.doCrosstalk else None)
943  darkExposure = (self.getIsrExposure(dataRef, self.config.darkDataProductName)
944  if self.config.doDark else None)
945  flatExposure = (self.getIsrExposure(dataRef, self.config.flatDataProductName)
946  if self.config.doFlat else None)
947 
948  brighterFatterKernel = None
949  if self.config.doBrighterFatter is True:
950 
951  # Use the new-style cp_pipe version of the kernel is it exists.
952  try:
953  brighterFatterKernel = dataRef.get("brighterFatterKernel")
954  except NoResults:
955  # Fall back to the old-style numpy-ndarray style kernel if necessary.
956  try:
957  brighterFatterKernel = dataRef.get("bfKernel")
958  except NoResults:
959  brighterFatterKernel = None
960  if brighterFatterKernel is not None and not isinstance(brighterFatterKernel, numpy.ndarray):
961  # If the kernel is not an ndarray, it's the cp_pipe version, so extract the kernel for
962  # this detector, or raise an error.
963  if self.config.brighterFatterLevel == 'DETECTOR':
964  brighterFatterKernel = brighterFatterKernel.kernel[ccd.getId()]
965  else:
966  # TODO DM-15631 for implementing this
967  raise NotImplementedError("Per-amplifier brighter-fatter correction not implemented")
968 
969  defectList = (dataRef.get("defects")
970  if self.config.doDefect else None)
971  fringeStruct = (self.fringe.readFringes(dataRef, assembler=self.assembleCcd
972  if self.config.doAssembleIsrExposures else None)
973  if self.config.doFringe and self.fringe.checkFilter(rawExposure)
974  else pipeBase.Struct(fringes=None))
975 
976  if self.config.doAttachTransmissionCurve:
977  opticsTransmission = (dataRef.get("transmission_optics")
978  if self.config.doUseOpticsTransmission else None)
979  filterTransmission = (dataRef.get("transmission_filter")
980  if self.config.doUseFilterTransmission else None)
981  sensorTransmission = (dataRef.get("transmission_sensor")
982  if self.config.doUseSensorTransmission else None)
983  atmosphereTransmission = (dataRef.get("transmission_atmosphere")
984  if self.config.doUseAtmosphereTransmission else None)
985  else:
986  opticsTransmission = None
987  filterTransmission = None
988  sensorTransmission = None
989  atmosphereTransmission = None
990 
991  if self.config.doStrayLight:
992  strayLightData = self.strayLight.readIsrData(dataRef, rawExposure)
993  else:
994  strayLightData = None
995 
996  illumMaskedImage = (self.getIsrExposure(dataRef,
997  self.config.illuminationCorrectionDataProductName).getMaskedImage()
998  if (self.config.doIlluminationCorrection and
999  filterName in self.config.illumFilters)
1000  else None)
1001 
1002  # Struct should include only kwargs to run()
1003  return pipeBase.Struct(bias=biasExposure,
1004  linearizer=linearizer,
1005  crosstalkSources=crosstalkSources,
1006  dark=darkExposure,
1007  flat=flatExposure,
1008  bfKernel=brighterFatterKernel,
1009  defects=defectList,
1010  fringes=fringeStruct,
1011  opticsTransmission=opticsTransmission,
1012  filterTransmission=filterTransmission,
1013  sensorTransmission=sensorTransmission,
1014  atmosphereTransmission=atmosphereTransmission,
1015  strayLightData=strayLightData,
1016  illumMaskedImage=illumMaskedImage
1017  )
1018 
1019  @pipeBase.timeMethod
1020  def run(self, ccdExposure, camera=None, bias=None, linearizer=None, crosstalkSources=None,
1021  dark=None, flat=None, bfKernel=None, defects=None, fringes=pipeBase.Struct(fringes=None),
1022  opticsTransmission=None, filterTransmission=None,
1023  sensorTransmission=None, atmosphereTransmission=None,
1024  detectorNum=None, strayLightData=None, illumMaskedImage=None,
1025  isGen3=False,
1026  ):
1027  """!Perform instrument signature removal on an exposure.
1028 
1029  Steps included in the ISR processing, in order performed, are:
1030  - saturation and suspect pixel masking
1031  - overscan subtraction
1032  - CCD assembly of individual amplifiers
1033  - bias subtraction
1034  - variance image construction
1035  - linearization of non-linear response
1036  - crosstalk masking
1037  - brighter-fatter correction
1038  - dark subtraction
1039  - fringe correction
1040  - stray light subtraction
1041  - flat correction
1042  - masking of known defects and camera specific features
1043  - vignette calculation
1044  - appending transmission curve and distortion model
1045 
1046  Parameters
1047  ----------
1048  ccdExposure : `lsst.afw.image.Exposure`
1049  The raw exposure that is to be run through ISR. The
1050  exposure is modified by this method.
1051  camera : `lsst.afw.cameraGeom.Camera`, optional
1052  The camera geometry for this exposure. Used to select the
1053  distortion model appropriate for this data.
1054  bias : `lsst.afw.image.Exposure`, optional
1055  Bias calibration frame.
1056  linearizer : `lsst.ip.isr.linearize.LinearizeBase`, optional
1057  Functor for linearization.
1058  crosstalkSources : `list`, optional
1059  List of possible crosstalk sources.
1060  dark : `lsst.afw.image.Exposure`, optional
1061  Dark calibration frame.
1062  flat : `lsst.afw.image.Exposure`, optional
1063  Flat calibration frame.
1064  bfKernel : `numpy.ndarray`, optional
1065  Brighter-fatter kernel.
1066  defects : `lsst.meas.algorithms.Defects`, optional
1067  List of defects.
1068  fringes : `lsst.pipe.base.Struct`, optional
1069  Struct containing the fringe correction data, with
1070  elements:
1071  - ``fringes``: fringe calibration frame (`afw.image.Exposure`)
1072  - ``seed``: random seed derived from the ccdExposureId for random
1073  number generator (`uint32`)
1074  opticsTransmission: `lsst.afw.image.TransmissionCurve`, optional
1075  A ``TransmissionCurve`` that represents the throughput of the optics,
1076  to be evaluated in focal-plane coordinates.
1077  filterTransmission : `lsst.afw.image.TransmissionCurve`
1078  A ``TransmissionCurve`` that represents the throughput of the filter
1079  itself, to be evaluated in focal-plane coordinates.
1080  sensorTransmission : `lsst.afw.image.TransmissionCurve`
1081  A ``TransmissionCurve`` that represents the throughput of the sensor
1082  itself, to be evaluated in post-assembly trimmed detector coordinates.
1083  atmosphereTransmission : `lsst.afw.image.TransmissionCurve`
1084  A ``TransmissionCurve`` that represents the throughput of the
1085  atmosphere, assumed to be spatially constant.
1086  detectorNum : `int`, optional
1087  The integer number for the detector to process.
1088  isGen3 : bool, optional
1089  Flag this call to run() as using the Gen3 butler environment.
1090  strayLightData : `object`, optional
1091  Opaque object containing calibration information for stray-light
1092  correction. If `None`, no correction will be performed.
1093  illumMaskedImage : `lsst.afw.image.MaskedImage`, optional
1094  Illumination correction image.
1095 
1096  Returns
1097  -------
1098  result : `lsst.pipe.base.Struct`
1099  Result struct with component:
1100  - ``exposure`` : `afw.image.Exposure`
1101  The fully ISR corrected exposure.
1102  - ``outputExposure`` : `afw.image.Exposure`
1103  An alias for `exposure`
1104  - ``ossThumb`` : `numpy.ndarray`
1105  Thumbnail image of the exposure after overscan subtraction.
1106  - ``flattenedThumb`` : `numpy.ndarray`
1107  Thumbnail image of the exposure after flat-field correction.
1108 
1109  Raises
1110  ------
1111  RuntimeError
1112  Raised if a configuration option is set to True, but the
1113  required calibration data has not been specified.
1114 
1115  Notes
1116  -----
1117  The current processed exposure can be viewed by setting the
1118  appropriate lsstDebug entries in the `debug.display`
1119  dictionary. The names of these entries correspond to some of
1120  the IsrTaskConfig Boolean options, with the value denoting the
1121  frame to use. The exposure is shown inside the matching
1122  option check and after the processing of that step has
1123  finished. The steps with debug points are:
1124 
1125  doAssembleCcd
1126  doBias
1127  doCrosstalk
1128  doBrighterFatter
1129  doDark
1130  doFringe
1131  doStrayLight
1132  doFlat
1133 
1134  In addition, setting the "postISRCCD" entry displays the
1135  exposure after all ISR processing has finished.
1136 
1137  """
1138 
1139  if isGen3 is True:
1140  # Gen3 currently cannot automatically do configuration overrides.
1141  # DM-15257 looks to discuss this issue.
1142 
1143  self.config.doFringe = False
1144 
1145  # Configure input exposures;
1146  if detectorNum is None:
1147  raise RuntimeError("Must supply the detectorNum if running as Gen3.")
1148 
1149  ccdExposure = self.ensureExposure(ccdExposure, camera, detectorNum)
1150  bias = self.ensureExposure(bias, camera, detectorNum)
1151  dark = self.ensureExposure(dark, camera, detectorNum)
1152  flat = self.ensureExposure(flat, camera, detectorNum)
1153  else:
1154  if isinstance(ccdExposure, ButlerDataRef):
1155  return self.runDataRef(ccdExposure)
1156 
1157  ccd = ccdExposure.getDetector()
1158  filterName = afwImage.Filter(ccdExposure.getFilter().getId()).getName() # Canonical name for filter
1159 
1160  if not ccd:
1161  assert not self.config.doAssembleCcd, "You need a Detector to run assembleCcd."
1162  ccd = [FakeAmp(ccdExposure, self.config)]
1163 
1164  # Validate Input
1165  if self.config.doBias and bias is None:
1166  raise RuntimeError("Must supply a bias exposure if config.doBias=True.")
1167  if self.doLinearize(ccd) and linearizer is None:
1168  raise RuntimeError("Must supply a linearizer if config.doLinearize=True for this detector.")
1169  if self.config.doBrighterFatter and bfKernel is None:
1170  raise RuntimeError("Must supply a kernel if config.doBrighterFatter=True.")
1171  if self.config.doDark and dark is None:
1172  raise RuntimeError("Must supply a dark exposure if config.doDark=True.")
1173  if self.config.doFlat and flat is None:
1174  raise RuntimeError("Must supply a flat exposure if config.doFlat=True.")
1175  if self.config.doDefect and defects is None:
1176  raise RuntimeError("Must supply defects if config.doDefect=True.")
1177  if self.config.doAddDistortionModel and camera is None:
1178  raise RuntimeError("Must supply camera if config.doAddDistortionModel=True.")
1179  if (self.config.doFringe and filterName in self.fringe.config.filters and
1180  fringes.fringes is None):
1181  # The `fringes` object needs to be a pipeBase.Struct, as
1182  # we use it as a `dict` for the parameters of
1183  # `FringeTask.run()`. The `fringes.fringes` `list` may
1184  # not be `None` if `doFringe=True`. Otherwise, raise.
1185  raise RuntimeError("Must supply fringe exposure as a pipeBase.Struct.")
1186  if (self.config.doIlluminationCorrection and filterName in self.config.illumFilters and
1187  illumMaskedImage is None):
1188  raise RuntimeError("Must supply an illumcor if config.doIlluminationCorrection=True.")
1189 
1190  # Begin ISR processing.
1191  if self.config.doConvertIntToFloat:
1192  self.log.info("Converting exposure to floating point values.")
1193  ccdExposure = self.convertIntToFloat(ccdExposure)
1194 
1195  # Amplifier level processing.
1196  overscans = []
1197  for amp in ccd:
1198  # if ccdExposure is one amp, check for coverage to prevent performing ops multiple times
1199  if ccdExposure.getBBox().contains(amp.getBBox()):
1200  # Check for fully masked bad amplifiers, and generate masks for SUSPECT and SATURATED values.
1201  badAmp = self.maskAmplifier(ccdExposure, amp, defects)
1202 
1203  if self.config.doOverscan and not badAmp:
1204  # Overscan correction on amp-by-amp basis.
1205  overscanResults = self.overscanCorrection(ccdExposure, amp)
1206  self.log.debug(f"Corrected overscan for amplifier {amp.getName()}.")
1207  if self.config.qa is not None and self.config.qa.saveStats is True:
1208  if isinstance(overscanResults.overscanFit, float):
1209  qaMedian = overscanResults.overscanFit
1210  qaStdev = float("NaN")
1211  else:
1212  qaStats = afwMath.makeStatistics(overscanResults.overscanFit,
1213  afwMath.MEDIAN | afwMath.STDEVCLIP)
1214  qaMedian = qaStats.getValue(afwMath.MEDIAN)
1215  qaStdev = qaStats.getValue(afwMath.STDEVCLIP)
1216 
1217  self.metadata.set(f"ISR OSCAN {amp.getName()} MEDIAN", qaMedian)
1218  self.metadata.set(f"ISR OSCAN {amp.getName()} STDEV", qaStdev)
1219  self.log.debug(" Overscan stats for amplifer %s: %f +/- %f" %
1220  (amp.getName(), qaMedian, qaStdev))
1221  ccdExposure.getMetadata().set('OVERSCAN', "Overscan corrected")
1222  else:
1223  if badAmp:
1224  self.log.warn(f"Amplifier {amp.getName()} is bad.")
1225  overscanResults = None
1226 
1227  overscans.append(overscanResults if overscanResults is not None else None)
1228  else:
1229  self.log.info(f"Skipped OSCAN for {amp.getName()}.")
1230 
1231  if self.config.doCrosstalk and self.config.doCrosstalkBeforeAssemble:
1232  self.log.info("Applying crosstalk correction.")
1233  self.crosstalk.run(ccdExposure, crosstalkSources=crosstalkSources)
1234  self.debugView(ccdExposure, "doCrosstalk")
1235 
1236  if self.config.doAssembleCcd:
1237  self.log.info("Assembling CCD from amplifiers.")
1238  ccdExposure = self.assembleCcd.assembleCcd(ccdExposure)
1239 
1240  if self.config.expectWcs and not ccdExposure.getWcs():
1241  self.log.warn("No WCS found in input exposure.")
1242  self.debugView(ccdExposure, "doAssembleCcd")
1243 
1244  ossThumb = None
1245  if self.config.qa.doThumbnailOss:
1246  ossThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa)
1247 
1248  if self.config.doBias:
1249  self.log.info("Applying bias correction.")
1250  isrFunctions.biasCorrection(ccdExposure.getMaskedImage(), bias.getMaskedImage(),
1251  trimToFit=self.config.doTrimToMatchCalib)
1252  self.debugView(ccdExposure, "doBias")
1253 
1254  if self.config.doVariance:
1255  for amp, overscanResults in zip(ccd, overscans):
1256  if ccdExposure.getBBox().contains(amp.getBBox()):
1257  self.log.debug(f"Constructing variance map for amplifer {amp.getName()}.")
1258  ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox())
1259  if overscanResults is not None:
1260  self.updateVariance(ampExposure, amp,
1261  overscanImage=overscanResults.overscanImage)
1262  else:
1263  self.updateVariance(ampExposure, amp,
1264  overscanImage=None)
1265  if self.config.qa is not None and self.config.qa.saveStats is True:
1266  qaStats = afwMath.makeStatistics(ampExposure.getVariance(),
1267  afwMath.MEDIAN | afwMath.STDEVCLIP)
1268  self.metadata.set(f"ISR VARIANCE {amp.getName()} MEDIAN",
1269  qaStats.getValue(afwMath.MEDIAN))
1270  self.metadata.set(f"ISR VARIANCE {amp.getName()} STDEV",
1271  qaStats.getValue(afwMath.STDEVCLIP))
1272  self.log.debug(" Variance stats for amplifer %s: %f +/- %f." %
1273  (amp.getName(), qaStats.getValue(afwMath.MEDIAN),
1274  qaStats.getValue(afwMath.STDEVCLIP)))
1275 
1276  if self.doLinearize(ccd):
1277  self.log.info("Applying linearizer.")
1278  linearizer(image=ccdExposure.getMaskedImage().getImage(), detector=ccd, log=self.log)
1279 
1280  if self.config.doCrosstalk and not self.config.doCrosstalkBeforeAssemble:
1281  self.log.info("Applying crosstalk correction.")
1282  self.crosstalk.run(ccdExposure, crosstalkSources=crosstalkSources, isTrimmed=True)
1283  self.debugView(ccdExposure, "doCrosstalk")
1284 
1285  # Masking block. Optionally mask known defects, NAN pixels, widen trails, and do
1286  # anything else the camera needs. Saturated and suspect pixels have already been masked.
1287  if self.config.doDefect:
1288  self.log.info("Masking defects.")
1289  self.maskDefect(ccdExposure, defects)
1290 
1291  if self.config.doNanMasking:
1292  self.log.info("Masking NAN value pixels.")
1293  self.maskNan(ccdExposure)
1294 
1295  if self.config.doWidenSaturationTrails:
1296  self.log.info("Widening saturation trails.")
1297  isrFunctions.widenSaturationTrails(ccdExposure.getMaskedImage().getMask())
1298 
1299  if self.config.doCameraSpecificMasking:
1300  self.log.info("Masking regions for camera specific reasons.")
1301  self.masking.run(ccdExposure)
1302 
1303  if self.config.doBrighterFatter:
1304  # We need to apply flats and darks before we can interpolate, and we
1305  # need to interpolate before we do B-F, but we do B-F without the
1306  # flats and darks applied so we can work in units of electrons or holes.
1307  # This context manager applies and then removes the darks and flats.
1308  #
1309  # We also do not want to interpolate values here, so operate on temporary
1310  # images so we can apply only the BF-correction and roll back the
1311  # interpolation.
1312  interpExp = ccdExposure.clone()
1313  with self.flatContext(interpExp, flat, dark):
1314  isrFunctions.interpolateFromMask(
1315  maskedImage=ccdExposure.getMaskedImage(),
1316  fwhm=self.config.fwhm,
1317  growSaturatedFootprints=self.config.growSaturationFootprintSize,
1318  maskNameList=self.config.maskListToInterpolate
1319  )
1320  bfExp = interpExp.clone()
1321 
1322  self.log.info("Applying brighter fatter correction.")
1323  bfResults = isrFunctions.brighterFatterCorrection(bfExp, bfKernel,
1324  self.config.brighterFatterMaxIter,
1325  self.config.brighterFatterThreshold,
1326  self.config.brighterFatterApplyGain
1327  )
1328  if bfResults[1] == self.config.brighterFatterMaxIter:
1329  self.log.warn("Brighter fatter correction did not converge, final difference {bfResults[0]}.")
1330  else:
1331  self.log.info("Finished brighter fatter correction in {bfResults[1]} iterations.")
1332  image = ccdExposure.getMaskedImage().getImage()
1333  bfCorr = bfExp.getMaskedImage().getImage()
1334  bfCorr -= interpExp.getMaskedImage().getImage()
1335  image += bfCorr
1336 
1337  self.debugView(ccdExposure, "doBrighterFatter")
1338 
1339  if self.config.doDark:
1340  self.log.info("Applying dark correction.")
1341  self.darkCorrection(ccdExposure, dark)
1342  self.debugView(ccdExposure, "doDark")
1343 
1344  if self.config.doFringe and not self.config.fringeAfterFlat:
1345  self.log.info("Applying fringe correction before flat.")
1346  self.fringe.run(ccdExposure, **fringes.getDict())
1347  self.debugView(ccdExposure, "doFringe")
1348 
1349  if self.config.doStrayLight:
1350  if strayLightData is not None:
1351  self.log.info("Applying stray light correction.")
1352  self.strayLight.run(ccdExposure, strayLightData)
1353  self.debugView(ccdExposure, "doStrayLight")
1354  else:
1355  self.log.debug("Skipping stray light correction: no data found for this image.")
1356 
1357  if self.config.doFlat:
1358  self.log.info("Applying flat correction.")
1359  self.flatCorrection(ccdExposure, flat)
1360  self.debugView(ccdExposure, "doFlat")
1361 
1362  if self.config.doApplyGains:
1363  self.log.info("Applying gain correction instead of flat.")
1364  isrFunctions.applyGains(ccdExposure, self.config.normalizeGains)
1365 
1366  if self.config.doFringe and self.config.fringeAfterFlat:
1367  self.log.info("Applying fringe correction after flat.")
1368  self.fringe.run(ccdExposure, **fringes.getDict())
1369 
1370  if self.config.doVignette:
1371  self.log.info("Constructing Vignette polygon.")
1372  self.vignettePolygon = self.vignette.run(ccdExposure)
1373 
1374  if self.config.vignette.doWriteVignettePolygon:
1375  self.setValidPolygonIntersect(ccdExposure, self.vignettePolygon)
1376 
1377  if self.config.doAttachTransmissionCurve:
1378  self.log.info("Adding transmission curves.")
1379  isrFunctions.attachTransmissionCurve(ccdExposure, opticsTransmission=opticsTransmission,
1380  filterTransmission=filterTransmission,
1381  sensorTransmission=sensorTransmission,
1382  atmosphereTransmission=atmosphereTransmission)
1383 
1384  if self.config.doAddDistortionModel:
1385  self.log.info("Adding a distortion model to the WCS.")
1386  isrFunctions.addDistortionModel(exposure=ccdExposure, camera=camera)
1387 
1388  flattenedThumb = None
1389  if self.config.qa.doThumbnailFlattened:
1390  flattenedThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa)
1391 
1392  if self.config.doIlluminationCorrection and filterName in self.config.illumFilters:
1393  self.log.info("Performing illumination correction.")
1394  isrFunctions.illuminationCorrection(ccdExposure.getMaskedImage(),
1395  illumMaskedImage, illumScale=self.config.illumScale,
1396  trimToFit=self.config.doTrimToMatchCalib)
1397 
1398  preInterpExp = None
1399  if self.config.doSaveInterpPixels:
1400  preInterpExp = ccdExposure.clone()
1401 
1402  if self.config.doInterpolate:
1403  self.log.info("Interpolating masked pixels.")
1404  isrFunctions.interpolateFromMask(
1405  maskedImage=ccdExposure.getMaskedImage(),
1406  fwhm=self.config.fwhm,
1407  growSaturatedFootprints=self.config.growSaturationFootprintSize,
1408  maskNameList=list(self.config.maskListToInterpolate)
1409  )
1410 
1411  if self.config.doSetBadRegions:
1412  # This is like an interpolation.
1413  badPixelCount, badPixelValue = isrFunctions.setBadRegions(ccdExposure)
1414  if badPixelCount > 0:
1415  self.log.info("Set %d BAD pixels to %f." % (badPixelCount, badPixelValue))
1416 
1417  self.roughZeroPoint(ccdExposure)
1418 
1419  if self.config.doMeasureBackground:
1420  self.log.info("Measuring background level.")
1421  self.measureBackground(ccdExposure, self.config.qa)
1422 
1423  if self.config.qa is not None and self.config.qa.saveStats is True:
1424  for amp in ccd:
1425  ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox())
1426  qaStats = afwMath.makeStatistics(ampExposure.getImage(),
1427  afwMath.MEDIAN | afwMath.STDEVCLIP)
1428  self.metadata.set("ISR BACKGROUND {} MEDIAN".format(amp.getName()),
1429  qaStats.getValue(afwMath.MEDIAN))
1430  self.metadata.set("ISR BACKGROUND {} STDEV".format(amp.getName()),
1431  qaStats.getValue(afwMath.STDEVCLIP))
1432  self.log.debug(" Background stats for amplifer %s: %f +/- %f" %
1433  (amp.getName(), qaStats.getValue(afwMath.MEDIAN),
1434  qaStats.getValue(afwMath.STDEVCLIP)))
1435 
1436  self.debugView(ccdExposure, "postISRCCD")
1437 
1438  return pipeBase.Struct(
1439  exposure=ccdExposure,
1440  ossThumb=ossThumb,
1441  flattenedThumb=flattenedThumb,
1442 
1443  preInterpolatedExposure=preInterpExp,
1444  outputExposure=ccdExposure,
1445  outputOssThumbnail=ossThumb,
1446  outputFlattenedThumbnail=flattenedThumb,
1447  )
1448 
1449  @pipeBase.timeMethod
1450  def runDataRef(self, sensorRef):
1451  """Perform instrument signature removal on a ButlerDataRef of a Sensor.
1452 
1453  This method contains the `CmdLineTask` interface to the ISR
1454  processing. All IO is handled here, freeing the `run()` method
1455  to manage only pixel-level calculations. The steps performed
1456  are:
1457  - Read in necessary detrending/isr/calibration data.
1458  - Process raw exposure in `run()`.
1459  - Persist the ISR-corrected exposure as "postISRCCD" if
1460  config.doWrite=True.
1461 
1462  Parameters
1463  ----------
1464  sensorRef : `daf.persistence.butlerSubset.ButlerDataRef`
1465  DataRef of the detector data to be processed
1466 
1467  Returns
1468  -------
1469  result : `lsst.pipe.base.Struct`
1470  Result struct with component:
1471  - ``exposure`` : `afw.image.Exposure`
1472  The fully ISR corrected exposure.
1473 
1474  Raises
1475  ------
1476  RuntimeError
1477  Raised if a configuration option is set to True, but the
1478  required calibration data does not exist.
1479 
1480  """
1481  self.log.info("Performing ISR on sensor %s." % (sensorRef.dataId))
1482 
1483  ccdExposure = sensorRef.get(self.config.datasetType)
1484 
1485  camera = sensorRef.get("camera")
1486  if camera is None and self.config.doAddDistortionModel:
1487  raise RuntimeError("config.doAddDistortionModel is True "
1488  "but could not get a camera from the butler.")
1489  isrData = self.readIsrData(sensorRef, ccdExposure)
1490 
1491  result = self.run(ccdExposure, camera=camera, **isrData.getDict())
1492 
1493  if self.config.doWrite:
1494  sensorRef.put(result.exposure, "postISRCCD")
1495  if result.preInterpolatedExposure is not None:
1496  sensorRef.put(result.preInterpolatedExposure, "postISRCCD_uninterpolated")
1497  if result.ossThumb is not None:
1498  isrQa.writeThumbnail(sensorRef, result.ossThumb, "ossThumb")
1499  if result.flattenedThumb is not None:
1500  isrQa.writeThumbnail(sensorRef, result.flattenedThumb, "flattenedThumb")
1501 
1502  return result
1503 
1504  def getIsrExposure(self, dataRef, datasetType, immediate=True):
1505  """!Retrieve a calibration dataset for removing instrument signature.
1506 
1507  Parameters
1508  ----------
1509 
1510  dataRef : `daf.persistence.butlerSubset.ButlerDataRef`
1511  DataRef of the detector data to find calibration datasets
1512  for.
1513  datasetType : `str`
1514  Type of dataset to retrieve (e.g. 'bias', 'flat', etc).
1515  immediate : `Bool`
1516  If True, disable butler proxies to enable error handling
1517  within this routine.
1518 
1519  Returns
1520  -------
1521  exposure : `lsst.afw.image.Exposure`
1522  Requested calibration frame.
1523 
1524  Raises
1525  ------
1526  RuntimeError
1527  Raised if no matching calibration frame can be found.
1528  """
1529  try:
1530  exp = dataRef.get(datasetType, immediate=immediate)
1531  except Exception as exc1:
1532  if not self.config.fallbackFilterName:
1533  raise RuntimeError("Unable to retrieve %s for %s: %s." % (datasetType, dataRef.dataId, exc1))
1534  try:
1535  exp = dataRef.get(datasetType, filter=self.config.fallbackFilterName, immediate=immediate)
1536  except Exception as exc2:
1537  raise RuntimeError("Unable to retrieve %s for %s, even with fallback filter %s: %s AND %s." %
1538  (datasetType, dataRef.dataId, self.config.fallbackFilterName, exc1, exc2))
1539  self.log.warn("Using fallback calibration from filter %s." % self.config.fallbackFilterName)
1540 
1541  if self.config.doAssembleIsrExposures:
1542  exp = self.assembleCcd.assembleCcd(exp)
1543  return exp
1544 
1545  def ensureExposure(self, inputExp, camera, detectorNum):
1546  """Ensure that the data returned by Butler is a fully constructed exposure.
1547 
1548  ISR requires exposure-level image data for historical reasons, so if we did
1549  not recieve that from Butler, construct it from what we have, modifying the
1550  input in place.
1551 
1552  Parameters
1553  ----------
1554  inputExp : `lsst.afw.image.Exposure`, `lsst.afw.image.DecoratedImageU`, or
1555  `lsst.afw.image.ImageF`
1556  The input data structure obtained from Butler.
1557  camera : `lsst.afw.cameraGeom.camera`
1558  The camera associated with the image. Used to find the appropriate
1559  detector.
1560  detectorNum : `int`
1561  The detector this exposure should match.
1562 
1563  Returns
1564  -------
1565  inputExp : `lsst.afw.image.Exposure`
1566  The re-constructed exposure, with appropriate detector parameters.
1567 
1568  Raises
1569  ------
1570  TypeError
1571  Raised if the input data cannot be used to construct an exposure.
1572  """
1573  if isinstance(inputExp, afwImage.DecoratedImageU):
1574  inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp))
1575  elif isinstance(inputExp, afwImage.ImageF):
1576  inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp))
1577  elif isinstance(inputExp, afwImage.MaskedImageF):
1578  inputExp = afwImage.makeExposure(inputExp)
1579  elif isinstance(inputExp, afwImage.Exposure):
1580  pass
1581  elif inputExp is None:
1582  # Assume this will be caught by the setup if it is a problem.
1583  return inputExp
1584  else:
1585  raise TypeError(f"Input Exposure is not known type in isrTask.ensureExposure: {type(inputExp)}.")
1586 
1587  if inputExp.getDetector() is None:
1588  inputExp.setDetector(camera[detectorNum])
1589 
1590  return inputExp
1591 
1592  def convertIntToFloat(self, exposure):
1593  """Convert exposure image from uint16 to float.
1594 
1595  If the exposure does not need to be converted, the input is
1596  immediately returned. For exposures that are converted to use
1597  floating point pixels, the variance is set to unity and the
1598  mask to zero.
1599 
1600  Parameters
1601  ----------
1602  exposure : `lsst.afw.image.Exposure`
1603  The raw exposure to be converted.
1604 
1605  Returns
1606  -------
1607  newexposure : `lsst.afw.image.Exposure`
1608  The input ``exposure``, converted to floating point pixels.
1609 
1610  Raises
1611  ------
1612  RuntimeError
1613  Raised if the exposure type cannot be converted to float.
1614 
1615  """
1616  if isinstance(exposure, afwImage.ExposureF):
1617  # Nothing to be done
1618  self.log.debug("Exposure already of type float.")
1619  return exposure
1620  if not hasattr(exposure, "convertF"):
1621  raise RuntimeError("Unable to convert exposure (%s) to float." % type(exposure))
1622 
1623  newexposure = exposure.convertF()
1624  newexposure.variance[:] = 1
1625  newexposure.mask[:] = 0x0
1626 
1627  return newexposure
1628 
1629  def maskAmplifier(self, ccdExposure, amp, defects):
1630  """Identify bad amplifiers, saturated and suspect pixels.
1631 
1632  Parameters
1633  ----------
1634  ccdExposure : `lsst.afw.image.Exposure`
1635  Input exposure to be masked.
1636  amp : `lsst.afw.table.AmpInfoCatalog`
1637  Catalog of parameters defining the amplifier on this
1638  exposure to mask.
1639  defects : `lsst.meas.algorithms.Defects`
1640  List of defects. Used to determine if the entire
1641  amplifier is bad.
1642 
1643  Returns
1644  -------
1645  badAmp : `Bool`
1646  If this is true, the entire amplifier area is covered by
1647  defects and unusable.
1648 
1649  """
1650  maskedImage = ccdExposure.getMaskedImage()
1651 
1652  badAmp = False
1653 
1654  # Check if entire amp region is defined as a defect (need to use amp.getBBox() for correct
1655  # comparison with current defects definition.
1656  if defects is not None:
1657  badAmp = bool(sum([v.getBBox().contains(amp.getBBox()) for v in defects]))
1658 
1659  # In the case of a bad amp, we will set mask to "BAD" (here use amp.getRawBBox() for correct
1660  # association with pixels in current ccdExposure).
1661  if badAmp:
1662  dataView = afwImage.MaskedImageF(maskedImage, amp.getRawBBox(),
1663  afwImage.PARENT)
1664  maskView = dataView.getMask()
1665  maskView |= maskView.getPlaneBitMask("BAD")
1666  del maskView
1667  return badAmp
1668 
1669  # Mask remaining defects after assembleCcd() to allow for defects that cross amplifier boundaries.
1670  # Saturation and suspect pixels can be masked now, though.
1671  limits = dict()
1672  if self.config.doSaturation and not badAmp:
1673  limits.update({self.config.saturatedMaskName: amp.getSaturation()})
1674  if self.config.doSuspect and not badAmp:
1675  limits.update({self.config.suspectMaskName: amp.getSuspectLevel()})
1676  if math.isfinite(self.config.saturation):
1677  limits.update({self.config.saturatedMaskName: self.config.saturation})
1678 
1679  for maskName, maskThreshold in limits.items():
1680  if not math.isnan(maskThreshold):
1681  dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
1682  isrFunctions.makeThresholdMask(
1683  maskedImage=dataView,
1684  threshold=maskThreshold,
1685  growFootprints=0,
1686  maskName=maskName
1687  )
1688 
1689  # Determine if we've fully masked this amplifier with SUSPECT and SAT pixels.
1690  maskView = afwImage.Mask(maskedImage.getMask(), amp.getRawDataBBox(),
1691  afwImage.PARENT)
1692  maskVal = maskView.getPlaneBitMask([self.config.saturatedMaskName,
1693  self.config.suspectMaskName])
1694  if numpy.all(maskView.getArray() & maskVal > 0):
1695  badAmp = True
1696 
1697  return badAmp
1698 
1699  def overscanCorrection(self, ccdExposure, amp):
1700  """Apply overscan correction in place.
1701 
1702  This method does initial pixel rejection of the overscan
1703  region. The overscan can also be optionally segmented to
1704  allow for discontinuous overscan responses to be fit
1705  separately. The actual overscan subtraction is performed by
1706  the `lsst.ip.isr.isrFunctions.overscanCorrection` function,
1707  which is called here after the amplifier is preprocessed.
1708 
1709  Parameters
1710  ----------
1711  ccdExposure : `lsst.afw.image.Exposure`
1712  Exposure to have overscan correction performed.
1713  amp : `lsst.afw.table.AmpInfoCatalog`
1714  The amplifier to consider while correcting the overscan.
1715 
1716  Returns
1717  -------
1718  overscanResults : `lsst.pipe.base.Struct`
1719  Result struct with components:
1720  - ``imageFit`` : scalar or `lsst.afw.image.Image`
1721  Value or fit subtracted from the amplifier image data.
1722  - ``overscanFit`` : scalar or `lsst.afw.image.Image`
1723  Value or fit subtracted from the overscan image data.
1724  - ``overscanImage`` : `lsst.afw.image.Image`
1725  Image of the overscan region with the overscan
1726  correction applied. This quantity is used to estimate
1727  the amplifier read noise empirically.
1728 
1729  Raises
1730  ------
1731  RuntimeError
1732  Raised if the ``amp`` does not contain raw pixel information.
1733 
1734  See Also
1735  --------
1736  lsst.ip.isr.isrFunctions.overscanCorrection
1737  """
1738  if not amp.getHasRawInfo():
1739  raise RuntimeError("This method must be executed on an amp with raw information.")
1740 
1741  if amp.getRawHorizontalOverscanBBox().isEmpty():
1742  self.log.info("ISR_OSCAN: No overscan region. Not performing overscan correction.")
1743  return None
1744 
1745  statControl = afwMath.StatisticsControl()
1746  statControl.setAndMask(ccdExposure.mask.getPlaneBitMask("SAT"))
1747 
1748  # Determine the bounding boxes
1749  dataBBox = amp.getRawDataBBox()
1750  oscanBBox = amp.getRawHorizontalOverscanBBox()
1751  dx0 = 0
1752  dx1 = 0
1753 
1754  prescanBBox = amp.getRawPrescanBBox()
1755  if (oscanBBox.getBeginX() > prescanBBox.getBeginX()): # amp is at the right
1756  dx0 += self.config.overscanNumLeadingColumnsToSkip
1757  dx1 -= self.config.overscanNumTrailingColumnsToSkip
1758  else:
1759  dx0 += self.config.overscanNumTrailingColumnsToSkip
1760  dx1 -= self.config.overscanNumLeadingColumnsToSkip
1761 
1762  # Determine if we need to work on subregions of the amplifier and overscan.
1763  imageBBoxes = []
1764  overscanBBoxes = []
1765 
1766  if ((self.config.overscanBiasJump and
1767  self.config.overscanBiasJumpLocation) and
1768  (ccdExposure.getMetadata().exists(self.config.overscanBiasJumpKeyword) and
1769  ccdExposure.getMetadata().getScalar(self.config.overscanBiasJumpKeyword) in
1770  self.config.overscanBiasJumpDevices)):
1771  if amp.getReadoutCorner() in (afwTable.LL, afwTable.LR):
1772  yLower = self.config.overscanBiasJumpLocation
1773  yUpper = dataBBox.getHeight() - yLower
1774  else:
1775  yUpper = self.config.overscanBiasJumpLocation
1776  yLower = dataBBox.getHeight() - yUpper
1777 
1778  imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin(),
1779  lsst.geom.Extent2I(dataBBox.getWidth(), yLower)))
1780  overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() +
1781  lsst.geom.Extent2I(dx0, 0),
1782  lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1,
1783  yLower)))
1784 
1785  imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin() + lsst.geom.Extent2I(0, yLower),
1786  lsst.geom.Extent2I(dataBBox.getWidth(), yUpper)))
1787  overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, yLower),
1788  lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1,
1789  yUpper)))
1790  else:
1791  imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin(),
1792  lsst.geom.Extent2I(dataBBox.getWidth(), dataBBox.getHeight())))
1793  overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, 0),
1794  lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1,
1795  oscanBBox.getHeight())))
1796 
1797  # Perform overscan correction on subregions, ensuring saturated pixels are masked.
1798  for imageBBox, overscanBBox in zip(imageBBoxes, overscanBBoxes):
1799  ampImage = ccdExposure.maskedImage[imageBBox]
1800  overscanImage = ccdExposure.maskedImage[overscanBBox]
1801 
1802  overscanArray = overscanImage.image.array
1803  median = numpy.ma.median(numpy.ma.masked_where(overscanImage.mask.array, overscanArray))
1804  bad = numpy.where(numpy.abs(overscanArray - median) > self.config.overscanMaxDev)
1805  overscanImage.mask.array[bad] = overscanImage.mask.getPlaneBitMask("SAT")
1806 
1807  statControl = afwMath.StatisticsControl()
1808  statControl.setAndMask(ccdExposure.mask.getPlaneBitMask("SAT"))
1809 
1810  overscanResults = isrFunctions.overscanCorrection(ampMaskedImage=ampImage,
1811  overscanImage=overscanImage,
1812  fitType=self.config.overscanFitType,
1813  order=self.config.overscanOrder,
1814  collapseRej=self.config.overscanNumSigmaClip,
1815  statControl=statControl,
1816  overscanIsInt=self.config.overscanIsInt
1817  )
1818 
1819  # Measure average overscan levels and record them in the metadata.
1820  levelStat = afwMath.MEDIAN
1821  sigmaStat = afwMath.STDEVCLIP
1822 
1823  sctrl = afwMath.StatisticsControl(self.config.qa.flatness.clipSigma,
1824  self.config.qa.flatness.nIter)
1825  metadata = ccdExposure.getMetadata()
1826  ampNum = amp.getName()
1827  if self.config.overscanFitType in ("MEDIAN", "MEAN", "MEANCLIP"):
1828  metadata.set("ISR_OSCAN_LEVEL%s" % ampNum, overscanResults.overscanFit)
1829  metadata.set("ISR_OSCAN_SIGMA%s" % ampNum, 0.0)
1830  else:
1831  stats = afwMath.makeStatistics(overscanResults.overscanFit, levelStat | sigmaStat, sctrl)
1832  metadata.set("ISR_OSCAN_LEVEL%s" % ampNum, stats.getValue(levelStat))
1833  metadata.set("ISR_OSCAN_SIGMA%s" % ampNum, stats.getValue(sigmaStat))
1834 
1835  return overscanResults
1836 
1837  def updateVariance(self, ampExposure, amp, overscanImage=None):
1838  """Set the variance plane using the amplifier gain and read noise
1839 
1840  The read noise is calculated from the ``overscanImage`` if the
1841  ``doEmpiricalReadNoise`` option is set in the configuration; otherwise
1842  the value from the amplifier data is used.
1843 
1844  Parameters
1845  ----------
1846  ampExposure : `lsst.afw.image.Exposure`
1847  Exposure to process.
1848  amp : `lsst.afw.table.AmpInfoRecord` or `FakeAmp`
1849  Amplifier detector data.
1850  overscanImage : `lsst.afw.image.MaskedImage`, optional.
1851  Image of overscan, required only for empirical read noise.
1852 
1853  See also
1854  --------
1855  lsst.ip.isr.isrFunctions.updateVariance
1856  """
1857  maskPlanes = [self.config.saturatedMaskName, self.config.suspectMaskName]
1858  gain = amp.getGain()
1859 
1860  if math.isnan(gain):
1861  gain = 1.0
1862  self.log.warn("Gain set to NAN! Updating to 1.0 to generate Poisson variance.")
1863  elif gain <= 0:
1864  patchedGain = 1.0
1865  self.log.warn("Gain for amp %s == %g <= 0; setting to %f." %
1866  (amp.getName(), gain, patchedGain))
1867  gain = patchedGain
1868 
1869  if self.config.doEmpiricalReadNoise and overscanImage is None:
1870  self.log.info("Overscan is none for EmpiricalReadNoise.")
1871 
1872  if self.config.doEmpiricalReadNoise and overscanImage is not None:
1873  stats = afwMath.StatisticsControl()
1874  stats.setAndMask(overscanImage.mask.getPlaneBitMask(maskPlanes))
1875  readNoise = afwMath.makeStatistics(overscanImage, afwMath.STDEVCLIP, stats).getValue()
1876  self.log.info("Calculated empirical read noise for amp %s: %f.", amp.getName(), readNoise)
1877  else:
1878  readNoise = amp.getReadNoise()
1879 
1880  isrFunctions.updateVariance(
1881  maskedImage=ampExposure.getMaskedImage(),
1882  gain=gain,
1883  readNoise=readNoise,
1884  )
1885 
1886  def darkCorrection(self, exposure, darkExposure, invert=False):
1887  """!Apply dark correction in place.
1888 
1889  Parameters
1890  ----------
1891  exposure : `lsst.afw.image.Exposure`
1892  Exposure to process.
1893  darkExposure : `lsst.afw.image.Exposure`
1894  Dark exposure of the same size as ``exposure``.
1895  invert : `Bool`, optional
1896  If True, re-add the dark to an already corrected image.
1897 
1898  Raises
1899  ------
1900  RuntimeError
1901  Raised if either ``exposure`` or ``darkExposure`` do not
1902  have their dark time defined.
1903 
1904  See Also
1905  --------
1906  lsst.ip.isr.isrFunctions.darkCorrection
1907  """
1908  expScale = exposure.getInfo().getVisitInfo().getDarkTime()
1909  if math.isnan(expScale):
1910  raise RuntimeError("Exposure darktime is NAN.")
1911  if darkExposure.getInfo().getVisitInfo() is not None:
1912  darkScale = darkExposure.getInfo().getVisitInfo().getDarkTime()
1913  else:
1914  # DM-17444: darkExposure.getInfo.getVisitInfo() is None
1915  # so getDarkTime() does not exist.
1916  self.log.warn("darkExposure.getInfo().getVisitInfo() does not exist. Using darkScale = 1.0.")
1917  darkScale = 1.0
1918 
1919  if math.isnan(darkScale):
1920  raise RuntimeError("Dark calib darktime is NAN.")
1921  isrFunctions.darkCorrection(
1922  maskedImage=exposure.getMaskedImage(),
1923  darkMaskedImage=darkExposure.getMaskedImage(),
1924  expScale=expScale,
1925  darkScale=darkScale,
1926  invert=invert,
1927  trimToFit=self.config.doTrimToMatchCalib
1928  )
1929 
1930  def doLinearize(self, detector):
1931  """!Check if linearization is needed for the detector cameraGeom.
1932 
1933  Checks config.doLinearize and the linearity type of the first
1934  amplifier.
1935 
1936  Parameters
1937  ----------
1938  detector : `lsst.afw.cameraGeom.Detector`
1939  Detector to get linearity type from.
1940 
1941  Returns
1942  -------
1943  doLinearize : `Bool`
1944  If True, linearization should be performed.
1945  """
1946  return self.config.doLinearize and \
1947  detector.getAmpInfoCatalog()[0].getLinearityType() != NullLinearityType
1948 
1949  def flatCorrection(self, exposure, flatExposure, invert=False):
1950  """!Apply flat correction in place.
1951 
1952  Parameters
1953  ----------
1954  exposure : `lsst.afw.image.Exposure`
1955  Exposure to process.
1956  flatExposure : `lsst.afw.image.Exposure`
1957  Flat exposure of the same size as ``exposure``.
1958  invert : `Bool`, optional
1959  If True, unflatten an already flattened image.
1960 
1961  See Also
1962  --------
1963  lsst.ip.isr.isrFunctions.flatCorrection
1964  """
1965  isrFunctions.flatCorrection(
1966  maskedImage=exposure.getMaskedImage(),
1967  flatMaskedImage=flatExposure.getMaskedImage(),
1968  scalingType=self.config.flatScalingType,
1969  userScale=self.config.flatUserScale,
1970  invert=invert,
1971  trimToFit=self.config.doTrimToMatchCalib
1972  )
1973 
1974  def saturationDetection(self, exposure, amp):
1975  """!Detect saturated pixels and mask them using mask plane config.saturatedMaskName, in place.
1976 
1977  Parameters
1978  ----------
1979  exposure : `lsst.afw.image.Exposure`
1980  Exposure to process. Only the amplifier DataSec is processed.
1981  amp : `lsst.afw.table.AmpInfoCatalog`
1982  Amplifier detector data.
1983 
1984  See Also
1985  --------
1986  lsst.ip.isr.isrFunctions.makeThresholdMask
1987  """
1988  if not math.isnan(amp.getSaturation()):
1989  maskedImage = exposure.getMaskedImage()
1990  dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
1991  isrFunctions.makeThresholdMask(
1992  maskedImage=dataView,
1993  threshold=amp.getSaturation(),
1994  growFootprints=0,
1995  maskName=self.config.saturatedMaskName,
1996  )
1997 
1998  def saturationInterpolation(self, exposure):
1999  """!Interpolate over saturated pixels, in place.
2000 
2001  This method should be called after `saturationDetection`, to
2002  ensure that the saturated pixels have been identified in the
2003  SAT mask. It should also be called after `assembleCcd`, since
2004  saturated regions may cross amplifier boundaries.
2005 
2006  Parameters
2007  ----------
2008  exposure : `lsst.afw.image.Exposure`
2009  Exposure to process.
2010 
2011  See Also
2012  --------
2013  lsst.ip.isr.isrTask.saturationDetection
2014  lsst.ip.isr.isrFunctions.interpolateFromMask
2015  """
2016  isrFunctions.interpolateFromMask(
2017  maskedImage=exposure.getMaskedImage(),
2018  fwhm=self.config.fwhm,
2019  growSaturatedFootprints=self.config.growSaturationFootprintSize,
2020  maskNameList=list(self.config.saturatedMaskName),
2021  )
2022 
2023  def suspectDetection(self, exposure, amp):
2024  """!Detect suspect pixels and mask them using mask plane config.suspectMaskName, in place.
2025 
2026  Parameters
2027  ----------
2028  exposure : `lsst.afw.image.Exposure`
2029  Exposure to process. Only the amplifier DataSec is processed.
2030  amp : `lsst.afw.table.AmpInfoCatalog`
2031  Amplifier detector data.
2032 
2033  See Also
2034  --------
2035  lsst.ip.isr.isrFunctions.makeThresholdMask
2036 
2037  Notes
2038  -----
2039  Suspect pixels are pixels whose value is greater than amp.getSuspectLevel().
2040  This is intended to indicate pixels that may be affected by unknown systematics;
2041  for example if non-linearity corrections above a certain level are unstable
2042  then that would be a useful value for suspectLevel. A value of `nan` indicates
2043  that no such level exists and no pixels are to be masked as suspicious.
2044  """
2045  suspectLevel = amp.getSuspectLevel()
2046  if math.isnan(suspectLevel):
2047  return
2048 
2049  maskedImage = exposure.getMaskedImage()
2050  dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
2051  isrFunctions.makeThresholdMask(
2052  maskedImage=dataView,
2053  threshold=suspectLevel,
2054  growFootprints=0,
2055  maskName=self.config.suspectMaskName,
2056  )
2057 
2058  def maskDefect(self, exposure, defectBaseList):
2059  """!Mask defects using mask plane "BAD", in place.
2060 
2061  Parameters
2062  ----------
2063  exposure : `lsst.afw.image.Exposure`
2064  Exposure to process.
2065  defectBaseList : `lsst.meas.algorithms.Defects` or `list` of
2066  `lsst.afw.image.DefectBase`.
2067  List of defects to mask and interpolate.
2068 
2069  Notes
2070  -----
2071  Call this after CCD assembly, since defects may cross amplifier boundaries.
2072  """
2073  maskedImage = exposure.getMaskedImage()
2074  if not isinstance(defectBaseList, Defects):
2075  # Promotes DefectBase to Defect
2076  defectList = Defects(defectBaseList)
2077  else:
2078  defectList = defectBaseList
2079  defectList.maskPixels(maskedImage, maskName="BAD")
2080 
2081  if self.config.numEdgeSuspect > 0:
2082  goodBBox = maskedImage.getBBox()
2083  # This makes a bbox numEdgeSuspect pixels smaller than the image on each side
2084  goodBBox.grow(-self.config.numEdgeSuspect)
2085  # Mask pixels outside goodBBox as SUSPECT
2086  SourceDetectionTask.setEdgeBits(
2087  maskedImage,
2088  goodBBox,
2089  maskedImage.getMask().getPlaneBitMask("SUSPECT")
2090  )
2091 
2092  def maskAndInterpolateDefects(self, exposure, defectBaseList):
2093  """Mask and interpolate defects using mask plane "BAD", in place.
2094 
2095  Parameters
2096  ----------
2097  exposure : `lsst.afw.image.Exposure`
2098  Exposure to process.
2099  defectBaseList : `List` of `Defects`
2100 
2101  """
2102  self.maskDefects(exposure, defectBaseList)
2103  isrFunctions.interpolateFromMask(
2104  maskedImage=exposure.getMaskedImage(),
2105  fwhm=self.config.fwhm,
2106  growSaturatedFootprints=0,
2107  maskNameList=["BAD"],
2108  )
2109 
2110  def maskNan(self, exposure):
2111  """Mask NaNs using mask plane "UNMASKEDNAN", in place.
2112 
2113  Parameters
2114  ----------
2115  exposure : `lsst.afw.image.Exposure`
2116  Exposure to process.
2117 
2118  Notes
2119  -----
2120  We mask over all NaNs, including those that are masked with
2121  other bits (because those may or may not be interpolated over
2122  later, and we want to remove all NaNs). Despite this
2123  behaviour, the "UNMASKEDNAN" mask plane is used to preserve
2124  the historical name.
2125  """
2126  maskedImage = exposure.getMaskedImage()
2127 
2128  # Find and mask NaNs
2129  maskedImage.getMask().addMaskPlane("UNMASKEDNAN")
2130  maskVal = maskedImage.getMask().getPlaneBitMask("UNMASKEDNAN")
2131  numNans = maskNans(maskedImage, maskVal)
2132  self.metadata.set("NUMNANS", numNans)
2133  if numNans > 0:
2134  self.log.warn(f"There were {numNans} unmasked NaNs.")
2135 
2136  def maskAndInterpolateNan(self, exposure):
2137  """"Mask and interpolate NaNs using mask plane "UNMASKEDNAN", in place.
2138 
2139  Parameters
2140  ----------
2141  exposure : `lsst.afw.image.Exposure`
2142  Exposure to process.
2143 
2144  See Also
2145  --------
2146  lsst.ip.isr.isrTask.maskNan()
2147  """
2148  self.maskNan(exposure)
2149  isrFunctions.interpolateFromMask(
2150  maskedImage=exposure.getMaskedImage(),
2151  fwhm=self.config.fwhm,
2152  growSaturatedFootprints=0,
2153  maskNameList=["UNMASKEDNAN"],
2154  )
2155 
2156  def measureBackground(self, exposure, IsrQaConfig=None):
2157  """Measure the image background in subgrids, for quality control purposes.
2158 
2159  Parameters
2160  ----------
2161  exposure : `lsst.afw.image.Exposure`
2162  Exposure to process.
2163  IsrQaConfig : `lsst.ip.isr.isrQa.IsrQaConfig`
2164  Configuration object containing parameters on which background
2165  statistics and subgrids to use.
2166  """
2167  if IsrQaConfig is not None:
2168  statsControl = afwMath.StatisticsControl(IsrQaConfig.flatness.clipSigma,
2169  IsrQaConfig.flatness.nIter)
2170  maskVal = exposure.getMaskedImage().getMask().getPlaneBitMask(["BAD", "SAT", "DETECTED"])
2171  statsControl.setAndMask(maskVal)
2172  maskedImage = exposure.getMaskedImage()
2173  stats = afwMath.makeStatistics(maskedImage, afwMath.MEDIAN | afwMath.STDEVCLIP, statsControl)
2174  skyLevel = stats.getValue(afwMath.MEDIAN)
2175  skySigma = stats.getValue(afwMath.STDEVCLIP)
2176  self.log.info("Flattened sky level: %f +/- %f." % (skyLevel, skySigma))
2177  metadata = exposure.getMetadata()
2178  metadata.set('SKYLEVEL', skyLevel)
2179  metadata.set('SKYSIGMA', skySigma)
2180 
2181  # calcluating flatlevel over the subgrids
2182  stat = afwMath.MEANCLIP if IsrQaConfig.flatness.doClip else afwMath.MEAN
2183  meshXHalf = int(IsrQaConfig.flatness.meshX/2.)
2184  meshYHalf = int(IsrQaConfig.flatness.meshY/2.)
2185  nX = int((exposure.getWidth() + meshXHalf) / IsrQaConfig.flatness.meshX)
2186  nY = int((exposure.getHeight() + meshYHalf) / IsrQaConfig.flatness.meshY)
2187  skyLevels = numpy.zeros((nX, nY))
2188 
2189  for j in range(nY):
2190  yc = meshYHalf + j * IsrQaConfig.flatness.meshY
2191  for i in range(nX):
2192  xc = meshXHalf + i * IsrQaConfig.flatness.meshX
2193 
2194  xLLC = xc - meshXHalf
2195  yLLC = yc - meshYHalf
2196  xURC = xc + meshXHalf - 1
2197  yURC = yc + meshYHalf - 1
2198 
2199  bbox = lsst.geom.Box2I(lsst.geom.Point2I(xLLC, yLLC), lsst.geom.Point2I(xURC, yURC))
2200  miMesh = maskedImage.Factory(exposure.getMaskedImage(), bbox, afwImage.LOCAL)
2201 
2202  skyLevels[i, j] = afwMath.makeStatistics(miMesh, stat, statsControl).getValue()
2203 
2204  good = numpy.where(numpy.isfinite(skyLevels))
2205  skyMedian = numpy.median(skyLevels[good])
2206  flatness = (skyLevels[good] - skyMedian) / skyMedian
2207  flatness_rms = numpy.std(flatness)
2208  flatness_pp = flatness.max() - flatness.min() if len(flatness) > 0 else numpy.nan
2209 
2210  self.log.info("Measuring sky levels in %dx%d grids: %f." % (nX, nY, skyMedian))
2211  self.log.info("Sky flatness in %dx%d grids - pp: %f rms: %f." %
2212  (nX, nY, flatness_pp, flatness_rms))
2213 
2214  metadata.set('FLATNESS_PP', float(flatness_pp))
2215  metadata.set('FLATNESS_RMS', float(flatness_rms))
2216  metadata.set('FLATNESS_NGRIDS', '%dx%d' % (nX, nY))
2217  metadata.set('FLATNESS_MESHX', IsrQaConfig.flatness.meshX)
2218  metadata.set('FLATNESS_MESHY', IsrQaConfig.flatness.meshY)
2219 
2220  def roughZeroPoint(self, exposure):
2221  """Set an approximate magnitude zero point for the exposure.
2222 
2223  Parameters
2224  ----------
2225  exposure : `lsst.afw.image.Exposure`
2226  Exposure to process.
2227  """
2228  filterName = afwImage.Filter(exposure.getFilter().getId()).getName() # Canonical name for filter
2229  if filterName in self.config.fluxMag0T1:
2230  fluxMag0 = self.config.fluxMag0T1[filterName]
2231  else:
2232  self.log.warn("No rough magnitude zero point set for filter %s." % filterName)
2233  fluxMag0 = self.config.defaultFluxMag0T1
2234 
2235  expTime = exposure.getInfo().getVisitInfo().getExposureTime()
2236  if not expTime > 0: # handle NaN as well as <= 0
2237  self.log.warn("Non-positive exposure time; skipping rough zero point.")
2238  return
2239 
2240  self.log.info("Setting rough magnitude zero point: %f" % (2.5*math.log10(fluxMag0*expTime),))
2241  exposure.setPhotoCalib(afwImage.makePhotoCalibFromCalibZeroPoint(fluxMag0*expTime, 0.0))
2242 
2243  def setValidPolygonIntersect(self, ccdExposure, fpPolygon):
2244  """!Set the valid polygon as the intersection of fpPolygon and the ccd corners.
2245 
2246  Parameters
2247  ----------
2248  ccdExposure : `lsst.afw.image.Exposure`
2249  Exposure to process.
2250  fpPolygon : `lsst.afw.geom.Polygon`
2251  Polygon in focal plane coordinates.
2252  """
2253  # Get ccd corners in focal plane coordinates
2254  ccd = ccdExposure.getDetector()
2255  fpCorners = ccd.getCorners(FOCAL_PLANE)
2256  ccdPolygon = Polygon(fpCorners)
2257 
2258  # Get intersection of ccd corners with fpPolygon
2259  intersect = ccdPolygon.intersectionSingle(fpPolygon)
2260 
2261  # Transform back to pixel positions and build new polygon
2262  ccdPoints = ccd.transform(intersect, FOCAL_PLANE, PIXELS)
2263  validPolygon = Polygon(ccdPoints)
2264  ccdExposure.getInfo().setValidPolygon(validPolygon)
2265 
2266  @contextmanager
2267  def flatContext(self, exp, flat, dark=None):
2268  """Context manager that applies and removes flats and darks,
2269  if the task is configured to apply them.
2270 
2271  Parameters
2272  ----------
2273  exp : `lsst.afw.image.Exposure`
2274  Exposure to process.
2275  flat : `lsst.afw.image.Exposure`
2276  Flat exposure the same size as ``exp``.
2277  dark : `lsst.afw.image.Exposure`, optional
2278  Dark exposure the same size as ``exp``.
2279 
2280  Yields
2281  ------
2282  exp : `lsst.afw.image.Exposure`
2283  The flat and dark corrected exposure.
2284  """
2285  if self.config.doDark and dark is not None:
2286  self.darkCorrection(exp, dark)
2287  if self.config.doFlat:
2288  self.flatCorrection(exp, flat)
2289  try:
2290  yield exp
2291  finally:
2292  if self.config.doFlat:
2293  self.flatCorrection(exp, flat, invert=True)
2294  if self.config.doDark and dark is not None:
2295  self.darkCorrection(exp, dark, invert=True)
2296 
2297  def debugView(self, exposure, stepname):
2298  """Utility function to examine ISR exposure at different stages.
2299 
2300  Parameters
2301  ----------
2302  exposure : `lsst.afw.image.Exposure`
2303  Exposure to view.
2304  stepname : `str`
2305  State of processing to view.
2306  """
2307  frame = getDebugFrame(self._display, stepname)
2308  if frame:
2309  display = getDisplay(frame)
2310  display.scale('asinh', 'zscale')
2311  display.mtv(exposure)
2312 
2313 
2314 class FakeAmp(object):
2315  """A Detector-like object that supports returning gain and saturation level
2316 
2317  This is used when the input exposure does not have a detector.
2318 
2319  Parameters
2320  ----------
2321  exposure : `lsst.afw.image.Exposure`
2322  Exposure to generate a fake amplifier for.
2323  config : `lsst.ip.isr.isrTaskConfig`
2324  Configuration to apply to the fake amplifier.
2325  """
2326 
2327  def __init__(self, exposure, config):
2328  self._bbox = exposure.getBBox(afwImage.LOCAL)
2330  self._gain = config.gain
2331  self._readNoise = config.readNoise
2332  self._saturation = config.saturation
2333 
2334  def getBBox(self):
2335  return self._bbox
2336 
2337  def getRawBBox(self):
2338  return self._bbox
2339 
2340  def getHasRawInfo(self):
2341  return True # but see getRawHorizontalOverscanBBox()
2342 
2344  return self._RawHorizontalOverscanBBox
2345 
2346  def getGain(self):
2347  return self._gain
2348 
2349  def getReadNoise(self):
2350  return self._readNoise
2351 
2352  def getSaturation(self):
2353  return self._saturation
2354 
2355  def getSuspectLevel(self):
2356  return float("NaN")
2357 
2358 
2359 class RunIsrConfig(pexConfig.Config):
2360  isr = pexConfig.ConfigurableField(target=IsrTask, doc="Instrument signature removal")
2361 
2362 
2363 class RunIsrTask(pipeBase.CmdLineTask):
2364  """Task to wrap the default IsrTask to allow it to be retargeted.
2365 
2366  The standard IsrTask can be called directly from a command line
2367  program, but doing so removes the ability of the task to be
2368  retargeted. As most cameras override some set of the IsrTask
2369  methods, this would remove those data-specific methods in the
2370  output post-ISR images. This wrapping class fixes the issue,
2371  allowing identical post-ISR images to be generated by both the
2372  processCcd and isrTask code.
2373  """
2374  ConfigClass = RunIsrConfig
2375  _DefaultName = "runIsr"
2376 
2377  def __init__(self, *args, **kwargs):
2378  super().__init__(*args, **kwargs)
2379  self.makeSubtask("isr")
2380 
2381  def runDataRef(self, dataRef):
2382  """
2383  Parameters
2384  ----------
2385  dataRef : `lsst.daf.persistence.ButlerDataRef`
2386  data reference of the detector data to be processed
2387 
2388  Returns
2389  -------
2390  result : `pipeBase.Struct`
2391  Result struct with component:
2392 
2393  - exposure : `lsst.afw.image.Exposure`
2394  Post-ISR processed exposure.
2395  """
2396  return self.isr.runDataRef(dataRef)
def getInputDatasetTypes(cls, config)
Definition: isrTask.py:769
def runDataRef(self, sensorRef)
Definition: isrTask.py:1450
def measureBackground(self, exposure, IsrQaConfig=None)
Definition: isrTask.py:2156
def debugView(self, exposure, stepname)
Definition: isrTask.py:2297
def __init__(self, kwargs)
Definition: isrTask.py:759
def ensureExposure(self, inputExp, camera, detectorNum)
Definition: isrTask.py:1545
def readIsrData(self, dataRef, rawExposure)
Retrieve necessary frames for instrument signature removal.
Definition: isrTask.py:878
def adaptArgsAndRun(self, inputData, inputDataIds, outputDataIds, butler)
Definition: isrTask.py:838
def runDataRef(self, dataRef)
Definition: isrTask.py:2381
def run(self, ccdExposure, camera=None, bias=None, linearizer=None, crosstalkSources=None, dark=None, flat=None, bfKernel=None, defects=None, fringes=pipeBase.Struct(fringes=None), opticsTransmission=None, filterTransmission=None, sensorTransmission=None, atmosphereTransmission=None, detectorNum=None, strayLightData=None, illumMaskedImage=None, isGen3=False)
Perform instrument signature removal on an exposure.
Definition: isrTask.py:1026
def __init__(self, args, kwargs)
Definition: isrTask.py:2377
def getPrerequisiteDatasetTypes(cls, config)
Definition: isrTask.py:821
def roughZeroPoint(self, exposure)
Definition: isrTask.py:2220
def maskAndInterpolateDefects(self, exposure, defectBaseList)
Definition: isrTask.py:2092
def getRawHorizontalOverscanBBox(self)
Definition: isrTask.py:2343
def maskNan(self, exposure)
Definition: isrTask.py:2110
def getOutputDatasetTypes(cls, config)
Definition: isrTask.py:808
def maskDefect(self, exposure, defectBaseList)
Mask defects using mask plane "BAD", in place.
Definition: isrTask.py:2058
def overscanCorrection(self, ccdExposure, amp)
Definition: isrTask.py:1699
def convertIntToFloat(self, exposure)
Definition: isrTask.py:1592
def flatCorrection(self, exposure, flatExposure, invert=False)
Apply flat correction in place.
Definition: isrTask.py:1949
def makeDatasetType(self, dsConfig)
Definition: isrTask.py:875
def getIsrExposure(self, dataRef, datasetType, immediate=True)
Retrieve a calibration dataset for removing instrument signature.
Definition: isrTask.py:1504
def darkCorrection(self, exposure, darkExposure, invert=False)
Apply dark correction in place.
Definition: isrTask.py:1886
def doLinearize(self, detector)
Check if linearization is needed for the detector cameraGeom.
Definition: isrTask.py:1930
def setValidPolygonIntersect(self, ccdExposure, fpPolygon)
Set the valid polygon as the intersection of fpPolygon and the ccd corners.
Definition: isrTask.py:2243
def maskAmplifier(self, ccdExposure, amp, defects)
Definition: isrTask.py:1629
def getPerDatasetTypeDimensions(cls, config)
Definition: isrTask.py:831
def flatContext(self, exp, flat, dark=None)
Definition: isrTask.py:2267
size_t maskNans(afw::image::MaskedImage< PixelT > const &mi, afw::image::MaskPixel maskVal, afw::image::MaskPixel allow=0)
Mask NANs in an image.
Definition: Isr.cc:35
def updateVariance(self, ampExposure, amp, overscanImage=None)
Definition: isrTask.py:1837
def maskAndInterpolateNan(self, exposure)
Definition: isrTask.py:2136
def suspectDetection(self, exposure, amp)
Detect suspect pixels and mask them using mask plane config.suspectMaskName, in place.
Definition: isrTask.py:2023
def saturationInterpolation(self, exposure)
Interpolate over saturated pixels, in place.
Definition: isrTask.py:1998
def saturationDetection(self, exposure, amp)
Detect saturated pixels and mask them using mask plane config.saturatedMaskName, in place...
Definition: isrTask.py:1974
def __init__(self, exposure, config)
Definition: isrTask.py:2327