lsst.ip.isr  18.0.0-9-g4b17231+2
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  doNanMasking = pexConfig.Field(
438  dtype=bool,
439  doc="Mask NAN pixels?",
440  default=True,
441  )
442  doWidenSaturationTrails = pexConfig.Field(
443  dtype=bool,
444  doc="Widen bleed trails based on their width?",
445  default=True
446  )
447 
448  # Brighter-Fatter correction.
449  doBrighterFatter = pexConfig.Field(
450  dtype=bool,
451  default=False,
452  doc="Apply the brighter fatter correction"
453  )
454  brighterFatterLevel = pexConfig.ChoiceField(
455  dtype=str,
456  default="DETECTOR",
457  doc="The level at which to correct for brighter-fatter.",
458  allowed={
459  "AMP": "Every amplifier treated separately.",
460  "DETECTOR": "One kernel per detector",
461  }
462  )
463  brighterFatterKernelFile = pexConfig.Field(
464  dtype=str,
465  default='',
466  doc="Kernel file used for the brighter fatter correction"
467  )
468  brighterFatterMaxIter = pexConfig.Field(
469  dtype=int,
470  default=10,
471  doc="Maximum number of iterations for the brighter fatter correction"
472  )
473  brighterFatterThreshold = pexConfig.Field(
474  dtype=float,
475  default=1000,
476  doc="Threshold used to stop iterating the brighter fatter correction. It is the "
477  " absolute value of the difference between the current corrected image and the one"
478  " from the previous iteration summed over all the pixels."
479  )
480  brighterFatterApplyGain = pexConfig.Field(
481  dtype=bool,
482  default=True,
483  doc="Should the gain be applied when applying the brighter fatter correction?"
484  )
485 
486  # Dark subtraction.
487  doDark = pexConfig.Field(
488  dtype=bool,
489  doc="Apply dark frame correction?",
490  default=True,
491  )
492  darkDataProductName = pexConfig.Field(
493  dtype=str,
494  doc="Name of the dark data product",
495  default="dark",
496  )
497 
498  # Camera-specific stray light removal.
499  doStrayLight = pexConfig.Field(
500  dtype=bool,
501  doc="Subtract stray light in the y-band (due to encoder LEDs)?",
502  default=False,
503  )
504  strayLight = pexConfig.ConfigurableField(
505  target=StrayLightTask,
506  doc="y-band stray light correction"
507  )
508 
509  # Flat correction.
510  doFlat = pexConfig.Field(
511  dtype=bool,
512  doc="Apply flat field correction?",
513  default=True,
514  )
515  flatDataProductName = pexConfig.Field(
516  dtype=str,
517  doc="Name of the flat data product",
518  default="flat",
519  )
520  flatScalingType = pexConfig.ChoiceField(
521  dtype=str,
522  doc="The method for scaling the flat on the fly.",
523  default='USER',
524  allowed={
525  "USER": "Scale by flatUserScale",
526  "MEAN": "Scale by the inverse of the mean",
527  "MEDIAN": "Scale by the inverse of the median",
528  },
529  )
530  flatUserScale = pexConfig.Field(
531  dtype=float,
532  doc="If flatScalingType is 'USER' then scale flat by this amount; ignored otherwise",
533  default=1.0,
534  )
535  doTweakFlat = pexConfig.Field(
536  dtype=bool,
537  doc="Tweak flats to match observed amplifier ratios?",
538  default=False
539  )
540 
541  # Amplifier normalization based on gains instead of using flats configuration.
542  doApplyGains = pexConfig.Field(
543  dtype=bool,
544  doc="Correct the amplifiers for their gains instead of applying flat correction",
545  default=False,
546  )
547  normalizeGains = pexConfig.Field(
548  dtype=bool,
549  doc="Normalize all the amplifiers in each CCD to have the same median value.",
550  default=False,
551  )
552 
553  # Fringe correction.
554  doFringe = pexConfig.Field(
555  dtype=bool,
556  doc="Apply fringe correction?",
557  default=True,
558  )
559  fringe = pexConfig.ConfigurableField(
560  target=FringeTask,
561  doc="Fringe subtraction task",
562  )
563  fringeAfterFlat = pexConfig.Field(
564  dtype=bool,
565  doc="Do fringe subtraction after flat-fielding?",
566  default=True,
567  )
568 
569  # Distortion model application.
570  doAddDistortionModel = pexConfig.Field(
571  dtype=bool,
572  doc="Apply a distortion model based on camera geometry to the WCS?",
573  default=True,
574  )
575 
576  # Initial CCD-level background statistics options.
577  doMeasureBackground = pexConfig.Field(
578  dtype=bool,
579  doc="Measure the background level on the reduced image?",
580  default=False,
581  )
582 
583  # Camera-specific masking configuration.
584  doCameraSpecificMasking = pexConfig.Field(
585  dtype=bool,
586  doc="Mask camera-specific bad regions?",
587  default=False,
588  )
589  masking = pexConfig.ConfigurableField(
590  target=MaskingTask,
591  doc="Masking task."
592  )
593 
594  # Interpolation options.
595 
596  doInterpolate = pexConfig.Field(
597  dtype=bool,
598  doc="Interpolate masked pixels?",
599  default=True,
600  )
601  doSaturationInterpolation = pexConfig.Field(
602  dtype=bool,
603  doc="Perform interpolation over pixels masked as saturated?"
604  " NB: This is independent of doSaturation; if that is False this plane"
605  " will likely be blank, resulting in a no-op here.",
606  default=True,
607  )
608  doNanInterpolation = pexConfig.Field(
609  dtype=bool,
610  doc="Perform interpolation over pixels masked as NaN?"
611  " NB: This is independent of doNanMasking; if that is False this plane"
612  " will likely be blank, resulting in a no-op here.",
613  default=True,
614  )
615  doNanInterpAfterFlat = pexConfig.Field(
616  dtype=bool,
617  doc=("If True, ensure we interpolate NaNs after flat-fielding, even if we "
618  "also have to interpolate them before flat-fielding."),
619  default=False,
620  )
621  maskListToInterpolate = pexConfig.ListField(
622  dtype=str,
623  doc="List of mask planes that should be interpolated.",
624  default=['SAT', 'BAD', 'UNMASKEDNAN'],
625  )
626  doSaveInterpPixels = pexConfig.Field(
627  dtype=bool,
628  doc="Save a copy of the pre-interpolated pixel values?",
629  default=False,
630  )
631 
632  # Default photometric calibration options.
633  fluxMag0T1 = pexConfig.DictField(
634  keytype=str,
635  itemtype=float,
636  doc="The approximate flux of a zero-magnitude object in a one-second exposure, per filter.",
637  default=dict((f, pow(10.0, 0.4*m)) for f, m in (("Unknown", 28.0),
638  ))
639  )
640  defaultFluxMag0T1 = pexConfig.Field(
641  dtype=float,
642  doc="Default value for fluxMag0T1 (for an unrecognized filter).",
643  default=pow(10.0, 0.4*28.0)
644  )
645 
646  # Vignette correction configuration.
647  doVignette = pexConfig.Field(
648  dtype=bool,
649  doc="Apply vignetting parameters?",
650  default=False,
651  )
652  vignette = pexConfig.ConfigurableField(
653  target=VignetteTask,
654  doc="Vignetting task.",
655  )
656 
657  # Transmission curve configuration.
658  doAttachTransmissionCurve = pexConfig.Field(
659  dtype=bool,
660  default=False,
661  doc="Construct and attach a wavelength-dependent throughput curve for this CCD image?"
662  )
663  doUseOpticsTransmission = pexConfig.Field(
664  dtype=bool,
665  default=True,
666  doc="Load and use transmission_optics (if doAttachTransmissionCurve is True)?"
667  )
668  doUseFilterTransmission = pexConfig.Field(
669  dtype=bool,
670  default=True,
671  doc="Load and use transmission_filter (if doAttachTransmissionCurve is True)?"
672  )
673  doUseSensorTransmission = pexConfig.Field(
674  dtype=bool,
675  default=True,
676  doc="Load and use transmission_sensor (if doAttachTransmissionCurve is True)?"
677  )
678  doUseAtmosphereTransmission = pexConfig.Field(
679  dtype=bool,
680  default=True,
681  doc="Load and use transmission_atmosphere (if doAttachTransmissionCurve is True)?"
682  )
683 
684  # Illumination correction.
685  doIlluminationCorrection = pexConfig.Field(
686  dtype=bool,
687  default=False,
688  doc="Perform illumination correction?"
689  )
690  illuminationCorrectionDataProductName = pexConfig.Field(
691  dtype=str,
692  doc="Name of the illumination correction data product.",
693  default="illumcor",
694  )
695  illumScale = pexConfig.Field(
696  dtype=float,
697  doc="Scale factor for the illumination correction.",
698  default=1.0,
699  )
700  illumFilters = pexConfig.ListField(
701  dtype=str,
702  default=[],
703  doc="Only perform illumination correction for these filters."
704  )
705 
706  # Write the outputs to disk. If ISR is run as a subtask, this may not be needed.
707  doWrite = pexConfig.Field(
708  dtype=bool,
709  doc="Persist postISRCCD?",
710  default=True,
711  )
712 
713  def validate(self):
714  super().validate()
715  if self.doFlat and self.doApplyGains:
716  raise ValueError("You may not specify both doFlat and doApplyGains")
717  if self.doSaturationInterpolation and "SAT" not in self.maskListToInterpolate:
718  self.config.maskListToInterpolate.append("SAT")
719  if self.doNanInterpolation and "UNMASKEDNAN" not in self.maskListToInterpolate:
720  self.config.maskListToInterpolate.append("UNMASKEDNAN")
721 
722 
723 class IsrTask(pipeBase.PipelineTask, pipeBase.CmdLineTask):
724  """Apply common instrument signature correction algorithms to a raw frame.
725 
726  The process for correcting imaging data is very similar from
727  camera to camera. This task provides a vanilla implementation of
728  doing these corrections, including the ability to turn certain
729  corrections off if they are not needed. The inputs to the primary
730  method, `run()`, are a raw exposure to be corrected and the
731  calibration data products. The raw input is a single chip sized
732  mosaic of all amps including overscans and other non-science
733  pixels. The method `runDataRef()` identifies and defines the
734  calibration data products, and is intended for use by a
735  `lsst.pipe.base.cmdLineTask.CmdLineTask` and takes as input only a
736  `daf.persistence.butlerSubset.ButlerDataRef`. This task may be
737  subclassed for different camera, although the most camera specific
738  methods have been split into subtasks that can be redirected
739  appropriately.
740 
741  The __init__ method sets up the subtasks for ISR processing, using
742  the defaults from `lsst.ip.isr`.
743 
744  Parameters
745  ----------
746  args : `list`
747  Positional arguments passed to the Task constructor. None used at this time.
748  kwargs : `dict`, optional
749  Keyword arguments passed on to the Task constructor. None used at this time.
750  """
751  ConfigClass = IsrTaskConfig
752  _DefaultName = "isr"
753 
754  def __init__(self, **kwargs):
755  super().__init__(**kwargs)
756  self.makeSubtask("assembleCcd")
757  self.makeSubtask("crosstalk")
758  self.makeSubtask("strayLight")
759  self.makeSubtask("fringe")
760  self.makeSubtask("masking")
761  self.makeSubtask("vignette")
762 
763  @classmethod
764  def getInputDatasetTypes(cls, config):
765  inputTypeDict = super().getInputDatasetTypes(config)
766 
767  # Delete entries from the dictionary of InputDatasetTypes that we know we don't
768  # need because the configuration tells us we will not be bothering with the
769  # correction that uses that IDT.
770  if config.doBias is not True:
771  inputTypeDict.pop("bias", None)
772  if config.doLinearize is not True:
773  inputTypeDict.pop("linearizer", None)
774  if config.doCrosstalk is not True:
775  inputTypeDict.pop("crosstalkSources", None)
776  if config.doBrighterFatter is not True:
777  inputTypeDict.pop("bfKernel", None)
778  if config.doDefect is not True:
779  inputTypeDict.pop("defects", None)
780  if config.doDark is not True:
781  inputTypeDict.pop("dark", None)
782  if config.doFlat is not True:
783  inputTypeDict.pop("flat", None)
784  if config.doAttachTransmissionCurve is not True:
785  inputTypeDict.pop("opticsTransmission", None)
786  inputTypeDict.pop("filterTransmission", None)
787  inputTypeDict.pop("sensorTransmission", None)
788  inputTypeDict.pop("atmosphereTransmission", None)
789  if config.doUseOpticsTransmission is not True:
790  inputTypeDict.pop("opticsTransmission", None)
791  if config.doUseFilterTransmission is not True:
792  inputTypeDict.pop("filterTransmission", None)
793  if config.doUseSensorTransmission is not True:
794  inputTypeDict.pop("sensorTransmission", None)
795  if config.doUseAtmosphereTransmission is not True:
796  inputTypeDict.pop("atmosphereTransmission", None)
797  if config.doIlluminationCorrection is not True:
798  inputTypeDict.pop("illumMaskedImage", None)
799 
800  return inputTypeDict
801 
802  @classmethod
803  def getOutputDatasetTypes(cls, config):
804  outputTypeDict = super().getOutputDatasetTypes(config)
805 
806  if config.qa.doThumbnailOss is not True:
807  outputTypeDict.pop("outputOssThumbnail", None)
808  if config.qa.doThumbnailFlattened is not True:
809  outputTypeDict.pop("outputFlattenedThumbnail", None)
810  if config.doWrite is not True:
811  outputTypeDict.pop("outputExposure", None)
812 
813  return outputTypeDict
814 
815  @classmethod
816  def getPrerequisiteDatasetTypes(cls, config):
817  # Input calibration datasets should not constrain the QuantumGraph
818  # (it'd be confusing if not having flats just silently resulted in no
819  # data being processed). Our nomenclature for that is that these are
820  # "prerequisite" datasets (only "ccdExposure" == "raw" isn't).
821  names = set(cls.getInputDatasetTypes(config))
822  names.remove("ccdExposure")
823  return names
824 
825  def adaptArgsAndRun(self, inputData, inputDataIds, outputDataIds, butler):
826  try:
827  inputData['detectorNum'] = int(inputDataIds['ccdExposure']['detector'])
828  except Exception as e:
829  raise ValueError("Failure to find valid detectorNum value for Dataset %s: %s." %
830  (inputDataIds, e))
831 
832  inputData['isGen3'] = True
833 
834  if self.config.doLinearize is True:
835  if 'linearizer' not in inputData.keys():
836  detector = inputData['camera'][inputData['detectorNum']]
837  linearityName = detector.getAmpInfoCatalog()[0].getLinearityType()
838  inputData['linearizer'] = linearize.getLinearityTypeByName(linearityName)()
839 
840  if inputData['defects'] is not None:
841  # defects is loaded as a BaseCatalog with columns x0, y0, width, height.
842  # masking expects a list of defects defined by their bounding box
843  if not isinstance(inputData["defects"], Defects):
844  inputData["defects"] = Defects.fromTable(inputData["defects"])
845 
846  # Broken: DM-17169
847  # ci_hsc does not use crosstalkSources, as it's intra-CCD CT only. This needs to be
848  # fixed for non-HSC cameras in the future.
849  # inputData['crosstalkSources'] = (self.crosstalk.prepCrosstalk(inputDataIds['ccdExposure'])
850  # if self.config.doCrosstalk else None)
851 
852  # Broken: DM-17152
853  # Fringes are not tested to be handled correctly by Gen3 butler.
854  # inputData['fringes'] = (self.fringe.readFringes(inputDataIds['ccdExposure'],
855  # assembler=self.assembleCcd
856  # if self.config.doAssembleIsrExposures else None)
857  # if self.config.doFringe and
858  # self.fringe.checkFilter(inputData['ccdExposure'])
859  # else pipeBase.Struct(fringes=None))
860 
861  return super().adaptArgsAndRun(inputData, inputDataIds, outputDataIds, butler)
862 
863  def makeDatasetType(self, dsConfig):
864  return super().makeDatasetType(dsConfig)
865 
866  def readIsrData(self, dataRef, rawExposure):
867  """!Retrieve necessary frames for instrument signature removal.
868 
869  Pre-fetching all required ISR data products limits the IO
870  required by the ISR. Any conflict between the calibration data
871  available and that needed for ISR is also detected prior to
872  doing processing, allowing it to fail quickly.
873 
874  Parameters
875  ----------
876  dataRef : `daf.persistence.butlerSubset.ButlerDataRef`
877  Butler reference of the detector data to be processed
878  rawExposure : `afw.image.Exposure`
879  The raw exposure that will later be corrected with the
880  retrieved calibration data; should not be modified in this
881  method.
882 
883  Returns
884  -------
885  result : `lsst.pipe.base.Struct`
886  Result struct with components (which may be `None`):
887  - ``bias``: bias calibration frame (`afw.image.Exposure`)
888  - ``linearizer``: functor for linearization (`ip.isr.linearize.LinearizeBase`)
889  - ``crosstalkSources``: list of possible crosstalk sources (`list`)
890  - ``dark``: dark calibration frame (`afw.image.Exposure`)
891  - ``flat``: flat calibration frame (`afw.image.Exposure`)
892  - ``bfKernel``: Brighter-Fatter kernel (`numpy.ndarray`)
893  - ``defects``: list of defects (`lsst.meas.algorithms.Defects`)
894  - ``fringes``: `lsst.pipe.base.Struct` with components:
895  - ``fringes``: fringe calibration frame (`afw.image.Exposure`)
896  - ``seed``: random seed derived from the ccdExposureId for random
897  number generator (`uint32`).
898  - ``opticsTransmission``: `lsst.afw.image.TransmissionCurve`
899  A ``TransmissionCurve`` that represents the throughput of the optics,
900  to be evaluated in focal-plane coordinates.
901  - ``filterTransmission`` : `lsst.afw.image.TransmissionCurve`
902  A ``TransmissionCurve`` that represents the throughput of the filter
903  itself, to be evaluated in focal-plane coordinates.
904  - ``sensorTransmission`` : `lsst.afw.image.TransmissionCurve`
905  A ``TransmissionCurve`` that represents the throughput of the sensor
906  itself, to be evaluated in post-assembly trimmed detector coordinates.
907  - ``atmosphereTransmission`` : `lsst.afw.image.TransmissionCurve`
908  A ``TransmissionCurve`` that represents the throughput of the
909  atmosphere, assumed to be spatially constant.
910  - ``strayLightData`` : `object`
911  An opaque object containing calibration information for
912  stray-light correction. If `None`, no correction will be
913  performed.
914  - ``illumMaskedImage`` : illumination correction image (`lsst.afw.image.MaskedImage`)
915 
916  Raises
917  ------
918  NotImplementedError :
919  Raised if a per-amplifier brighter-fatter kernel is requested by the configuration.
920  """
921  ccd = rawExposure.getDetector()
922  filterName = afwImage.Filter(rawExposure.getFilter().getId()).getName() # Canonical name for filter
923  rawExposure.mask.addMaskPlane("UNMASKEDNAN") # needed to match pre DM-15862 processing.
924  biasExposure = (self.getIsrExposure(dataRef, self.config.biasDataProductName)
925  if self.config.doBias else None)
926  # immediate=True required for functors and linearizers are functors; see ticket DM-6515
927  linearizer = (dataRef.get("linearizer", immediate=True)
928  if self.doLinearize(ccd) else None)
929  crosstalkSources = (self.crosstalk.prepCrosstalk(dataRef)
930  if self.config.doCrosstalk else None)
931  darkExposure = (self.getIsrExposure(dataRef, self.config.darkDataProductName)
932  if self.config.doDark else None)
933  flatExposure = (self.getIsrExposure(dataRef, self.config.flatDataProductName)
934  if self.config.doFlat else None)
935 
936  brighterFatterKernel = None
937  if self.config.doBrighterFatter is True:
938 
939  # Use the new-style cp_pipe version of the kernel is it exists.
940  try:
941  brighterFatterKernel = dataRef.get("brighterFatterKernel")
942  except NoResults:
943  # Fall back to the old-style numpy-ndarray style kernel if necessary.
944  try:
945  brighterFatterKernel = dataRef.get("bfKernel")
946  except NoResults:
947  brighterFatterKernel = None
948  if brighterFatterKernel is not None and not isinstance(brighterFatterKernel, numpy.ndarray):
949  # If the kernel is not an ndarray, it's the cp_pipe version, so extract the kernel for
950  # this detector, or raise an error.
951  if self.config.brighterFatterLevel == 'DETECTOR':
952  brighterFatterKernel = brighterFatterKernel.kernel[ccd.getId()]
953  else:
954  # TODO DM-15631 for implementing this
955  raise NotImplementedError("Per-amplifier brighter-fatter correction not implemented")
956 
957  defectList = (dataRef.get("defects")
958  if self.config.doDefect else None)
959  fringeStruct = (self.fringe.readFringes(dataRef, assembler=self.assembleCcd
960  if self.config.doAssembleIsrExposures else None)
961  if self.config.doFringe and self.fringe.checkFilter(rawExposure)
962  else pipeBase.Struct(fringes=None))
963 
964  if self.config.doAttachTransmissionCurve:
965  opticsTransmission = (dataRef.get("transmission_optics")
966  if self.config.doUseOpticsTransmission else None)
967  filterTransmission = (dataRef.get("transmission_filter")
968  if self.config.doUseFilterTransmission else None)
969  sensorTransmission = (dataRef.get("transmission_sensor")
970  if self.config.doUseSensorTransmission else None)
971  atmosphereTransmission = (dataRef.get("transmission_atmosphere")
972  if self.config.doUseAtmosphereTransmission else None)
973  else:
974  opticsTransmission = None
975  filterTransmission = None
976  sensorTransmission = None
977  atmosphereTransmission = None
978 
979  if self.config.doStrayLight:
980  strayLightData = self.strayLight.readIsrData(dataRef, rawExposure)
981  else:
982  strayLightData = None
983 
984  illumMaskedImage = (self.getIsrExposure(dataRef,
985  self.config.illuminationCorrectionDataProductName).getMaskedImage()
986  if (self.config.doIlluminationCorrection and
987  filterName in self.config.illumFilters)
988  else None)
989 
990  # Struct should include only kwargs to run()
991  return pipeBase.Struct(bias=biasExposure,
992  linearizer=linearizer,
993  crosstalkSources=crosstalkSources,
994  dark=darkExposure,
995  flat=flatExposure,
996  bfKernel=brighterFatterKernel,
997  defects=defectList,
998  fringes=fringeStruct,
999  opticsTransmission=opticsTransmission,
1000  filterTransmission=filterTransmission,
1001  sensorTransmission=sensorTransmission,
1002  atmosphereTransmission=atmosphereTransmission,
1003  strayLightData=strayLightData,
1004  illumMaskedImage=illumMaskedImage
1005  )
1006 
1007  @pipeBase.timeMethod
1008  def run(self, ccdExposure, camera=None, bias=None, linearizer=None, crosstalkSources=None,
1009  dark=None, flat=None, bfKernel=None, defects=None, fringes=pipeBase.Struct(fringes=None),
1010  opticsTransmission=None, filterTransmission=None,
1011  sensorTransmission=None, atmosphereTransmission=None,
1012  detectorNum=None, strayLightData=None, illumMaskedImage=None,
1013  isGen3=False,
1014  ):
1015  """!Perform instrument signature removal on an exposure.
1016 
1017  Steps included in the ISR processing, in order performed, are:
1018  - saturation and suspect pixel masking
1019  - overscan subtraction
1020  - CCD assembly of individual amplifiers
1021  - bias subtraction
1022  - variance image construction
1023  - linearization of non-linear response
1024  - crosstalk masking
1025  - brighter-fatter correction
1026  - dark subtraction
1027  - fringe correction
1028  - stray light subtraction
1029  - flat correction
1030  - masking of known defects and camera specific features
1031  - vignette calculation
1032  - appending transmission curve and distortion model
1033 
1034  Parameters
1035  ----------
1036  ccdExposure : `lsst.afw.image.Exposure`
1037  The raw exposure that is to be run through ISR. The
1038  exposure is modified by this method.
1039  camera : `lsst.afw.cameraGeom.Camera`, optional
1040  The camera geometry for this exposure. Used to select the
1041  distortion model appropriate for this data.
1042  bias : `lsst.afw.image.Exposure`, optional
1043  Bias calibration frame.
1044  linearizer : `lsst.ip.isr.linearize.LinearizeBase`, optional
1045  Functor for linearization.
1046  crosstalkSources : `list`, optional
1047  List of possible crosstalk sources.
1048  dark : `lsst.afw.image.Exposure`, optional
1049  Dark calibration frame.
1050  flat : `lsst.afw.image.Exposure`, optional
1051  Flat calibration frame.
1052  bfKernel : `numpy.ndarray`, optional
1053  Brighter-fatter kernel.
1054  defects : `lsst.meas.algorithms.Defects`, optional
1055  List of defects.
1056  fringes : `lsst.pipe.base.Struct`, optional
1057  Struct containing the fringe correction data, with
1058  elements:
1059  - ``fringes``: fringe calibration frame (`afw.image.Exposure`)
1060  - ``seed``: random seed derived from the ccdExposureId for random
1061  number generator (`uint32`)
1062  opticsTransmission: `lsst.afw.image.TransmissionCurve`, optional
1063  A ``TransmissionCurve`` that represents the throughput of the optics,
1064  to be evaluated in focal-plane coordinates.
1065  filterTransmission : `lsst.afw.image.TransmissionCurve`
1066  A ``TransmissionCurve`` that represents the throughput of the filter
1067  itself, to be evaluated in focal-plane coordinates.
1068  sensorTransmission : `lsst.afw.image.TransmissionCurve`
1069  A ``TransmissionCurve`` that represents the throughput of the sensor
1070  itself, to be evaluated in post-assembly trimmed detector coordinates.
1071  atmosphereTransmission : `lsst.afw.image.TransmissionCurve`
1072  A ``TransmissionCurve`` that represents the throughput of the
1073  atmosphere, assumed to be spatially constant.
1074  detectorNum : `int`, optional
1075  The integer number for the detector to process.
1076  isGen3 : bool, optional
1077  Flag this call to run() as using the Gen3 butler environment.
1078  strayLightData : `object`, optional
1079  Opaque object containing calibration information for stray-light
1080  correction. If `None`, no correction will be performed.
1081  illumMaskedImage : `lsst.afw.image.MaskedImage`, optional
1082  Illumination correction image.
1083 
1084  Returns
1085  -------
1086  result : `lsst.pipe.base.Struct`
1087  Result struct with component:
1088  - ``exposure`` : `afw.image.Exposure`
1089  The fully ISR corrected exposure.
1090  - ``outputExposure`` : `afw.image.Exposure`
1091  An alias for `exposure`
1092  - ``ossThumb`` : `numpy.ndarray`
1093  Thumbnail image of the exposure after overscan subtraction.
1094  - ``flattenedThumb`` : `numpy.ndarray`
1095  Thumbnail image of the exposure after flat-field correction.
1096 
1097  Raises
1098  ------
1099  RuntimeError
1100  Raised if a configuration option is set to True, but the
1101  required calibration data has not been specified.
1102 
1103  Notes
1104  -----
1105  The current processed exposure can be viewed by setting the
1106  appropriate lsstDebug entries in the `debug.display`
1107  dictionary. The names of these entries correspond to some of
1108  the IsrTaskConfig Boolean options, with the value denoting the
1109  frame to use. The exposure is shown inside the matching
1110  option check and after the processing of that step has
1111  finished. The steps with debug points are:
1112 
1113  doAssembleCcd
1114  doBias
1115  doCrosstalk
1116  doBrighterFatter
1117  doDark
1118  doFringe
1119  doStrayLight
1120  doFlat
1121 
1122  In addition, setting the "postISRCCD" entry displays the
1123  exposure after all ISR processing has finished.
1124 
1125  """
1126 
1127  if isGen3 is True:
1128  # Gen3 currently cannot automatically do configuration overrides.
1129  # DM-15257 looks to discuss this issue.
1130 
1131  self.config.doFringe = False
1132 
1133  # Configure input exposures;
1134  if detectorNum is None:
1135  raise RuntimeError("Must supply the detectorNum if running as Gen3.")
1136 
1137  ccdExposure = self.ensureExposure(ccdExposure, camera, detectorNum)
1138  bias = self.ensureExposure(bias, camera, detectorNum)
1139  dark = self.ensureExposure(dark, camera, detectorNum)
1140  flat = self.ensureExposure(flat, camera, detectorNum)
1141  else:
1142  if isinstance(ccdExposure, ButlerDataRef):
1143  return self.runDataRef(ccdExposure)
1144 
1145  ccd = ccdExposure.getDetector()
1146  filterName = afwImage.Filter(ccdExposure.getFilter().getId()).getName() # Canonical name for filter
1147 
1148  if not ccd:
1149  assert not self.config.doAssembleCcd, "You need a Detector to run assembleCcd."
1150  ccd = [FakeAmp(ccdExposure, self.config)]
1151 
1152  # Validate Input
1153  if self.config.doBias and bias is None:
1154  raise RuntimeError("Must supply a bias exposure if config.doBias=True.")
1155  if self.doLinearize(ccd) and linearizer is None:
1156  raise RuntimeError("Must supply a linearizer if config.doLinearize=True for this detector.")
1157  if self.config.doBrighterFatter and bfKernel is None:
1158  raise RuntimeError("Must supply a kernel if config.doBrighterFatter=True.")
1159  if self.config.doDark and dark is None:
1160  raise RuntimeError("Must supply a dark exposure if config.doDark=True.")
1161  if self.config.doFlat and flat is None:
1162  raise RuntimeError("Must supply a flat exposure if config.doFlat=True.")
1163  if self.config.doDefect and defects is None:
1164  raise RuntimeError("Must supply defects if config.doDefect=True.")
1165  if self.config.doAddDistortionModel and camera is None:
1166  raise RuntimeError("Must supply camera if config.doAddDistortionModel=True.")
1167  if (self.config.doFringe and filterName in self.fringe.config.filters and
1168  fringes.fringes is None):
1169  # The `fringes` object needs to be a pipeBase.Struct, as
1170  # we use it as a `dict` for the parameters of
1171  # `FringeTask.run()`. The `fringes.fringes` `list` may
1172  # not be `None` if `doFringe=True`. Otherwise, raise.
1173  raise RuntimeError("Must supply fringe exposure as a pipeBase.Struct.")
1174  if (self.config.doIlluminationCorrection and filterName in self.config.illumFilters and
1175  illumMaskedImage is None):
1176  raise RuntimeError("Must supply an illumcor if config.doIlluminationCorrection=True.")
1177 
1178  # Begin ISR processing.
1179  if self.config.doConvertIntToFloat:
1180  self.log.info("Converting exposure to floating point values.")
1181  ccdExposure = self.convertIntToFloat(ccdExposure)
1182 
1183  # Amplifier level processing.
1184  overscans = []
1185  for amp in ccd:
1186  # if ccdExposure is one amp, check for coverage to prevent performing ops multiple times
1187  if ccdExposure.getBBox().contains(amp.getBBox()):
1188  # Check for fully masked bad amplifiers, and generate masks for SUSPECT and SATURATED values.
1189  badAmp = self.maskAmplifier(ccdExposure, amp, defects)
1190 
1191  if self.config.doOverscan and not badAmp:
1192  # Overscan correction on amp-by-amp basis.
1193  overscanResults = self.overscanCorrection(ccdExposure, amp)
1194  self.log.debug("Corrected overscan for amplifier %s.", amp.getName())
1195  if overscanResults is not None and \
1196  self.config.qa is not None and self.config.qa.saveStats is True:
1197  if isinstance(overscanResults.overscanFit, float):
1198  qaMedian = overscanResults.overscanFit
1199  qaStdev = float("NaN")
1200  else:
1201  qaStats = afwMath.makeStatistics(overscanResults.overscanFit,
1202  afwMath.MEDIAN | afwMath.STDEVCLIP)
1203  qaMedian = qaStats.getValue(afwMath.MEDIAN)
1204  qaStdev = qaStats.getValue(afwMath.STDEVCLIP)
1205 
1206  self.metadata.set(f"ISR OSCAN {amp.getName()} MEDIAN", qaMedian)
1207  self.metadata.set(f"ISR OSCAN {amp.getName()} STDEV", qaStdev)
1208  self.log.debug(" Overscan stats for amplifer %s: %f +/- %f",
1209  amp.getName(), qaMedian, qaStdev)
1210  ccdExposure.getMetadata().set('OVERSCAN', "Overscan corrected")
1211  else:
1212  if badAmp:
1213  self.log.warn("Amplifier %s is bad.", amp.getName())
1214  overscanResults = None
1215 
1216  overscans.append(overscanResults if overscanResults is not None else None)
1217  else:
1218  self.log.info("Skipped OSCAN for %s.", amp.getName())
1219 
1220  if self.config.doCrosstalk and self.config.doCrosstalkBeforeAssemble:
1221  self.log.info("Applying crosstalk correction.")
1222  self.crosstalk.run(ccdExposure, crosstalkSources=crosstalkSources)
1223  self.debugView(ccdExposure, "doCrosstalk")
1224 
1225  if self.config.doAssembleCcd:
1226  self.log.info("Assembling CCD from amplifiers.")
1227  ccdExposure = self.assembleCcd.assembleCcd(ccdExposure)
1228 
1229  if self.config.expectWcs and not ccdExposure.getWcs():
1230  self.log.warn("No WCS found in input exposure.")
1231  self.debugView(ccdExposure, "doAssembleCcd")
1232 
1233  ossThumb = None
1234  if self.config.qa.doThumbnailOss:
1235  ossThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa)
1236 
1237  if self.config.doBias:
1238  self.log.info("Applying bias correction.")
1239  isrFunctions.biasCorrection(ccdExposure.getMaskedImage(), bias.getMaskedImage(),
1240  trimToFit=self.config.doTrimToMatchCalib)
1241  self.debugView(ccdExposure, "doBias")
1242 
1243  if self.config.doVariance:
1244  for amp, overscanResults in zip(ccd, overscans):
1245  if ccdExposure.getBBox().contains(amp.getBBox()):
1246  self.log.debug("Constructing variance map for amplifer %s.", amp.getName())
1247  ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox())
1248  if overscanResults is not None:
1249  self.updateVariance(ampExposure, amp,
1250  overscanImage=overscanResults.overscanImage)
1251  else:
1252  self.updateVariance(ampExposure, amp,
1253  overscanImage=None)
1254  if self.config.qa is not None and self.config.qa.saveStats is True:
1255  qaStats = afwMath.makeStatistics(ampExposure.getVariance(),
1256  afwMath.MEDIAN | afwMath.STDEVCLIP)
1257  self.metadata.set(f"ISR VARIANCE {amp.getName()} MEDIAN",
1258  qaStats.getValue(afwMath.MEDIAN))
1259  self.metadata.set(f"ISR VARIANCE {amp.getName()} STDEV",
1260  qaStats.getValue(afwMath.STDEVCLIP))
1261  self.log.debug(" Variance stats for amplifer %s: %f +/- %f.",
1262  amp.getName(), qaStats.getValue(afwMath.MEDIAN),
1263  qaStats.getValue(afwMath.STDEVCLIP))
1264 
1265  if self.doLinearize(ccd):
1266  self.log.info("Applying linearizer.")
1267  linearizer(image=ccdExposure.getMaskedImage().getImage(), detector=ccd, log=self.log)
1268 
1269  if self.config.doCrosstalk and not self.config.doCrosstalkBeforeAssemble:
1270  self.log.info("Applying crosstalk correction.")
1271  self.crosstalk.run(ccdExposure, crosstalkSources=crosstalkSources, isTrimmed=True)
1272  self.debugView(ccdExposure, "doCrosstalk")
1273 
1274  # Masking block. Optionally mask known defects, NAN pixels, widen trails, and do
1275  # anything else the camera needs. Saturated and suspect pixels have already been masked.
1276  if self.config.doDefect:
1277  self.log.info("Masking defects.")
1278  self.maskDefect(ccdExposure, defects)
1279 
1280  if self.config.doNanMasking:
1281  self.log.info("Masking NAN value pixels.")
1282  self.maskNan(ccdExposure)
1283 
1284  if self.config.doWidenSaturationTrails:
1285  self.log.info("Widening saturation trails.")
1286  isrFunctions.widenSaturationTrails(ccdExposure.getMaskedImage().getMask())
1287 
1288  if self.config.doCameraSpecificMasking:
1289  self.log.info("Masking regions for camera specific reasons.")
1290  self.masking.run(ccdExposure)
1291 
1292  if self.config.doBrighterFatter:
1293  # We need to apply flats and darks before we can interpolate, and we
1294  # need to interpolate before we do B-F, but we do B-F without the
1295  # flats and darks applied so we can work in units of electrons or holes.
1296  # This context manager applies and then removes the darks and flats.
1297  #
1298  # We also do not want to interpolate values here, so operate on temporary
1299  # images so we can apply only the BF-correction and roll back the
1300  # interpolation.
1301  interpExp = ccdExposure.clone()
1302  with self.flatContext(interpExp, flat, dark):
1303  isrFunctions.interpolateFromMask(
1304  maskedImage=interpExp.getMaskedImage(),
1305  fwhm=self.config.fwhm,
1306  growSaturatedFootprints=self.config.growSaturationFootprintSize,
1307  maskNameList=self.config.maskListToInterpolate
1308  )
1309  bfExp = interpExp.clone()
1310 
1311  self.log.info("Applying brighter fatter correction.")
1312  bfResults = isrFunctions.brighterFatterCorrection(bfExp, bfKernel,
1313  self.config.brighterFatterMaxIter,
1314  self.config.brighterFatterThreshold,
1315  self.config.brighterFatterApplyGain
1316  )
1317  if bfResults[1] == self.config.brighterFatterMaxIter:
1318  self.log.warn("Brighter fatter correction did not converge, final difference %f.",
1319  bfResults[0])
1320  else:
1321  self.log.info("Finished brighter fatter correction in %d iterations.",
1322  bfResults[1])
1323  image = ccdExposure.getMaskedImage().getImage()
1324  bfCorr = bfExp.getMaskedImage().getImage()
1325  bfCorr -= interpExp.getMaskedImage().getImage()
1326  image += bfCorr
1327 
1328  self.debugView(ccdExposure, "doBrighterFatter")
1329 
1330  if self.config.doDark:
1331  self.log.info("Applying dark correction.")
1332  self.darkCorrection(ccdExposure, dark)
1333  self.debugView(ccdExposure, "doDark")
1334 
1335  if self.config.doFringe and not self.config.fringeAfterFlat:
1336  self.log.info("Applying fringe correction before flat.")
1337  self.fringe.run(ccdExposure, **fringes.getDict())
1338  self.debugView(ccdExposure, "doFringe")
1339 
1340  if self.config.doStrayLight:
1341  if strayLightData is not None:
1342  self.log.info("Applying stray light correction.")
1343  self.strayLight.run(ccdExposure, strayLightData)
1344  self.debugView(ccdExposure, "doStrayLight")
1345  else:
1346  self.log.debug("Skipping stray light correction: no data found for this image.")
1347 
1348  if self.config.doFlat:
1349  self.log.info("Applying flat correction.")
1350  self.flatCorrection(ccdExposure, flat)
1351  self.debugView(ccdExposure, "doFlat")
1352 
1353  if self.config.doApplyGains:
1354  self.log.info("Applying gain correction instead of flat.")
1355  isrFunctions.applyGains(ccdExposure, self.config.normalizeGains)
1356 
1357  if self.config.doFringe and self.config.fringeAfterFlat:
1358  self.log.info("Applying fringe correction after flat.")
1359  self.fringe.run(ccdExposure, **fringes.getDict())
1360 
1361  if self.config.doVignette:
1362  self.log.info("Constructing Vignette polygon.")
1363  self.vignettePolygon = self.vignette.run(ccdExposure)
1364 
1365  if self.config.vignette.doWriteVignettePolygon:
1366  self.setValidPolygonIntersect(ccdExposure, self.vignettePolygon)
1367 
1368  if self.config.doAttachTransmissionCurve:
1369  self.log.info("Adding transmission curves.")
1370  isrFunctions.attachTransmissionCurve(ccdExposure, opticsTransmission=opticsTransmission,
1371  filterTransmission=filterTransmission,
1372  sensorTransmission=sensorTransmission,
1373  atmosphereTransmission=atmosphereTransmission)
1374 
1375  if self.config.doAddDistortionModel:
1376  self.log.info("Adding a distortion model to the WCS.")
1377  isrFunctions.addDistortionModel(exposure=ccdExposure, camera=camera)
1378 
1379  flattenedThumb = None
1380  if self.config.qa.doThumbnailFlattened:
1381  flattenedThumb = isrQa.makeThumbnail(ccdExposure, isrQaConfig=self.config.qa)
1382 
1383  if self.config.doIlluminationCorrection and filterName in self.config.illumFilters:
1384  self.log.info("Performing illumination correction.")
1385  isrFunctions.illuminationCorrection(ccdExposure.getMaskedImage(),
1386  illumMaskedImage, illumScale=self.config.illumScale,
1387  trimToFit=self.config.doTrimToMatchCalib)
1388 
1389  preInterpExp = None
1390  if self.config.doSaveInterpPixels:
1391  preInterpExp = ccdExposure.clone()
1392 
1393  # Reset and interpolate bad pixels.
1394  #
1395  # Large contiguous bad regions (which should have the BAD mask
1396  # bit set) should have their values set to the image median.
1397  # This group should include defects and bad amplifiers. As the
1398  # area covered by these defects are large, there's little
1399  # reason to expect that interpolation would provide a more
1400  # useful value.
1401  #
1402  # Smaller defects can be safely interpolated after the larger
1403  # regions have had their pixel values reset. This ensures
1404  # that the remaining defects adjacent to bad amplifiers (as an
1405  # example) do not attempt to interpolate extreme values.
1406  if self.config.doSetBadRegions:
1407  badPixelCount, badPixelValue = isrFunctions.setBadRegions(ccdExposure)
1408  if badPixelCount > 0:
1409  self.log.info("Set %d BAD pixels to %f.", badPixelCount, badPixelValue)
1410 
1411  if self.config.doInterpolate:
1412  self.log.info("Interpolating masked pixels.")
1413  isrFunctions.interpolateFromMask(
1414  maskedImage=ccdExposure.getMaskedImage(),
1415  fwhm=self.config.fwhm,
1416  growSaturatedFootprints=self.config.growSaturationFootprintSize,
1417  maskNameList=list(self.config.maskListToInterpolate)
1418  )
1419 
1420  self.roughZeroPoint(ccdExposure)
1421 
1422  if self.config.doMeasureBackground:
1423  self.log.info("Measuring background level.")
1424  self.measureBackground(ccdExposure, self.config.qa)
1425 
1426  if self.config.qa is not None and self.config.qa.saveStats is True:
1427  for amp in ccd:
1428  ampExposure = ccdExposure.Factory(ccdExposure, amp.getBBox())
1429  qaStats = afwMath.makeStatistics(ampExposure.getImage(),
1430  afwMath.MEDIAN | afwMath.STDEVCLIP)
1431  self.metadata.set("ISR BACKGROUND {} MEDIAN".format(amp.getName()),
1432  qaStats.getValue(afwMath.MEDIAN))
1433  self.metadata.set("ISR BACKGROUND {} STDEV".format(amp.getName()),
1434  qaStats.getValue(afwMath.STDEVCLIP))
1435  self.log.debug(" Background stats for amplifer %s: %f +/- %f",
1436  amp.getName(), qaStats.getValue(afwMath.MEDIAN),
1437  qaStats.getValue(afwMath.STDEVCLIP))
1438 
1439  self.debugView(ccdExposure, "postISRCCD")
1440 
1441  return pipeBase.Struct(
1442  exposure=ccdExposure,
1443  ossThumb=ossThumb,
1444  flattenedThumb=flattenedThumb,
1445 
1446  preInterpolatedExposure=preInterpExp,
1447  outputExposure=ccdExposure,
1448  outputOssThumbnail=ossThumb,
1449  outputFlattenedThumbnail=flattenedThumb,
1450  )
1451 
1452  @pipeBase.timeMethod
1453  def runDataRef(self, sensorRef):
1454  """Perform instrument signature removal on a ButlerDataRef of a Sensor.
1455 
1456  This method contains the `CmdLineTask` interface to the ISR
1457  processing. All IO is handled here, freeing the `run()` method
1458  to manage only pixel-level calculations. The steps performed
1459  are:
1460  - Read in necessary detrending/isr/calibration data.
1461  - Process raw exposure in `run()`.
1462  - Persist the ISR-corrected exposure as "postISRCCD" if
1463  config.doWrite=True.
1464 
1465  Parameters
1466  ----------
1467  sensorRef : `daf.persistence.butlerSubset.ButlerDataRef`
1468  DataRef of the detector data to be processed
1469 
1470  Returns
1471  -------
1472  result : `lsst.pipe.base.Struct`
1473  Result struct with component:
1474  - ``exposure`` : `afw.image.Exposure`
1475  The fully ISR corrected exposure.
1476 
1477  Raises
1478  ------
1479  RuntimeError
1480  Raised if a configuration option is set to True, but the
1481  required calibration data does not exist.
1482 
1483  """
1484  self.log.info("Performing ISR on sensor %s.", sensorRef.dataId)
1485 
1486  ccdExposure = sensorRef.get(self.config.datasetType)
1487 
1488  camera = sensorRef.get("camera")
1489  if camera is None and self.config.doAddDistortionModel:
1490  raise RuntimeError("config.doAddDistortionModel is True "
1491  "but could not get a camera from the butler.")
1492  isrData = self.readIsrData(sensorRef, ccdExposure)
1493 
1494  result = self.run(ccdExposure, camera=camera, **isrData.getDict())
1495 
1496  if self.config.doWrite:
1497  sensorRef.put(result.exposure, "postISRCCD")
1498  if result.preInterpolatedExposure is not None:
1499  sensorRef.put(result.preInterpolatedExposure, "postISRCCD_uninterpolated")
1500  if result.ossThumb is not None:
1501  isrQa.writeThumbnail(sensorRef, result.ossThumb, "ossThumb")
1502  if result.flattenedThumb is not None:
1503  isrQa.writeThumbnail(sensorRef, result.flattenedThumb, "flattenedThumb")
1504 
1505  return result
1506 
1507  def getIsrExposure(self, dataRef, datasetType, immediate=True):
1508  """!Retrieve a calibration dataset for removing instrument signature.
1509 
1510  Parameters
1511  ----------
1512 
1513  dataRef : `daf.persistence.butlerSubset.ButlerDataRef`
1514  DataRef of the detector data to find calibration datasets
1515  for.
1516  datasetType : `str`
1517  Type of dataset to retrieve (e.g. 'bias', 'flat', etc).
1518  immediate : `Bool`
1519  If True, disable butler proxies to enable error handling
1520  within this routine.
1521 
1522  Returns
1523  -------
1524  exposure : `lsst.afw.image.Exposure`
1525  Requested calibration frame.
1526 
1527  Raises
1528  ------
1529  RuntimeError
1530  Raised if no matching calibration frame can be found.
1531  """
1532  try:
1533  exp = dataRef.get(datasetType, immediate=immediate)
1534  except Exception as exc1:
1535  if not self.config.fallbackFilterName:
1536  raise RuntimeError("Unable to retrieve %s for %s: %s." % (datasetType, dataRef.dataId, exc1))
1537  try:
1538  exp = dataRef.get(datasetType, filter=self.config.fallbackFilterName, immediate=immediate)
1539  except Exception as exc2:
1540  raise RuntimeError("Unable to retrieve %s for %s, even with fallback filter %s: %s AND %s." %
1541  (datasetType, dataRef.dataId, self.config.fallbackFilterName, exc1, exc2))
1542  self.log.warn("Using fallback calibration from filter %s.", self.config.fallbackFilterName)
1543 
1544  if self.config.doAssembleIsrExposures:
1545  exp = self.assembleCcd.assembleCcd(exp)
1546  return exp
1547 
1548  def ensureExposure(self, inputExp, camera, detectorNum):
1549  """Ensure that the data returned by Butler is a fully constructed exposure.
1550 
1551  ISR requires exposure-level image data for historical reasons, so if we did
1552  not recieve that from Butler, construct it from what we have, modifying the
1553  input in place.
1554 
1555  Parameters
1556  ----------
1557  inputExp : `lsst.afw.image.Exposure`, `lsst.afw.image.DecoratedImageU`, or
1558  `lsst.afw.image.ImageF`
1559  The input data structure obtained from Butler.
1560  camera : `lsst.afw.cameraGeom.camera`
1561  The camera associated with the image. Used to find the appropriate
1562  detector.
1563  detectorNum : `int`
1564  The detector this exposure should match.
1565 
1566  Returns
1567  -------
1568  inputExp : `lsst.afw.image.Exposure`
1569  The re-constructed exposure, with appropriate detector parameters.
1570 
1571  Raises
1572  ------
1573  TypeError
1574  Raised if the input data cannot be used to construct an exposure.
1575  """
1576  if isinstance(inputExp, afwImage.DecoratedImageU):
1577  inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp))
1578  elif isinstance(inputExp, afwImage.ImageF):
1579  inputExp = afwImage.makeExposure(afwImage.makeMaskedImage(inputExp))
1580  elif isinstance(inputExp, afwImage.MaskedImageF):
1581  inputExp = afwImage.makeExposure(inputExp)
1582  elif isinstance(inputExp, afwImage.Exposure):
1583  pass
1584  elif inputExp is None:
1585  # Assume this will be caught by the setup if it is a problem.
1586  return inputExp
1587  else:
1588  raise TypeError("Input Exposure is not known type in isrTask.ensureExposure: %s." %
1589  (type(inputExp), ))
1590 
1591  if inputExp.getDetector() is None:
1592  inputExp.setDetector(camera[detectorNum])
1593 
1594  return inputExp
1595 
1596  def convertIntToFloat(self, exposure):
1597  """Convert exposure image from uint16 to float.
1598 
1599  If the exposure does not need to be converted, the input is
1600  immediately returned. For exposures that are converted to use
1601  floating point pixels, the variance is set to unity and the
1602  mask to zero.
1603 
1604  Parameters
1605  ----------
1606  exposure : `lsst.afw.image.Exposure`
1607  The raw exposure to be converted.
1608 
1609  Returns
1610  -------
1611  newexposure : `lsst.afw.image.Exposure`
1612  The input ``exposure``, converted to floating point pixels.
1613 
1614  Raises
1615  ------
1616  RuntimeError
1617  Raised if the exposure type cannot be converted to float.
1618 
1619  """
1620  if isinstance(exposure, afwImage.ExposureF):
1621  # Nothing to be done
1622  self.log.debug("Exposure already of type float.")
1623  return exposure
1624  if not hasattr(exposure, "convertF"):
1625  raise RuntimeError("Unable to convert exposure (%s) to float." % type(exposure))
1626 
1627  newexposure = exposure.convertF()
1628  newexposure.variance[:] = 1
1629  newexposure.mask[:] = 0x0
1630 
1631  return newexposure
1632 
1633  def maskAmplifier(self, ccdExposure, amp, defects):
1634  """Identify bad amplifiers, saturated and suspect pixels.
1635 
1636  Parameters
1637  ----------
1638  ccdExposure : `lsst.afw.image.Exposure`
1639  Input exposure to be masked.
1640  amp : `lsst.afw.table.AmpInfoCatalog`
1641  Catalog of parameters defining the amplifier on this
1642  exposure to mask.
1643  defects : `lsst.meas.algorithms.Defects`
1644  List of defects. Used to determine if the entire
1645  amplifier is bad.
1646 
1647  Returns
1648  -------
1649  badAmp : `Bool`
1650  If this is true, the entire amplifier area is covered by
1651  defects and unusable.
1652 
1653  """
1654  maskedImage = ccdExposure.getMaskedImage()
1655 
1656  badAmp = False
1657 
1658  # Check if entire amp region is defined as a defect (need to use amp.getBBox() for correct
1659  # comparison with current defects definition.
1660  if defects is not None:
1661  badAmp = bool(sum([v.getBBox().contains(amp.getBBox()) for v in defects]))
1662 
1663  # In the case of a bad amp, we will set mask to "BAD" (here use amp.getRawBBox() for correct
1664  # association with pixels in current ccdExposure).
1665  if badAmp:
1666  dataView = afwImage.MaskedImageF(maskedImage, amp.getRawBBox(),
1667  afwImage.PARENT)
1668  maskView = dataView.getMask()
1669  maskView |= maskView.getPlaneBitMask("BAD")
1670  del maskView
1671  return badAmp
1672 
1673  # Mask remaining defects after assembleCcd() to allow for defects that cross amplifier boundaries.
1674  # Saturation and suspect pixels can be masked now, though.
1675  limits = dict()
1676  if self.config.doSaturation and not badAmp:
1677  limits.update({self.config.saturatedMaskName: amp.getSaturation()})
1678  if self.config.doSuspect and not badAmp:
1679  limits.update({self.config.suspectMaskName: amp.getSuspectLevel()})
1680  if math.isfinite(self.config.saturation):
1681  limits.update({self.config.saturatedMaskName: self.config.saturation})
1682 
1683  for maskName, maskThreshold in limits.items():
1684  if not math.isnan(maskThreshold):
1685  dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
1686  isrFunctions.makeThresholdMask(
1687  maskedImage=dataView,
1688  threshold=maskThreshold,
1689  growFootprints=0,
1690  maskName=maskName
1691  )
1692 
1693  # Determine if we've fully masked this amplifier with SUSPECT and SAT pixels.
1694  maskView = afwImage.Mask(maskedImage.getMask(), amp.getRawDataBBox(),
1695  afwImage.PARENT)
1696  maskVal = maskView.getPlaneBitMask([self.config.saturatedMaskName,
1697  self.config.suspectMaskName])
1698  if numpy.all(maskView.getArray() & maskVal > 0):
1699  badAmp = True
1700  maskView |= maskView.getPlaneBitMask("BAD")
1701 
1702  return badAmp
1703 
1704  def overscanCorrection(self, ccdExposure, amp):
1705  """Apply overscan correction in place.
1706 
1707  This method does initial pixel rejection of the overscan
1708  region. The overscan can also be optionally segmented to
1709  allow for discontinuous overscan responses to be fit
1710  separately. The actual overscan subtraction is performed by
1711  the `lsst.ip.isr.isrFunctions.overscanCorrection` function,
1712  which is called here after the amplifier is preprocessed.
1713 
1714  Parameters
1715  ----------
1716  ccdExposure : `lsst.afw.image.Exposure`
1717  Exposure to have overscan correction performed.
1718  amp : `lsst.afw.table.AmpInfoCatalog`
1719  The amplifier to consider while correcting the overscan.
1720 
1721  Returns
1722  -------
1723  overscanResults : `lsst.pipe.base.Struct`
1724  Result struct with components:
1725  - ``imageFit`` : scalar or `lsst.afw.image.Image`
1726  Value or fit subtracted from the amplifier image data.
1727  - ``overscanFit`` : scalar or `lsst.afw.image.Image`
1728  Value or fit subtracted from the overscan image data.
1729  - ``overscanImage`` : `lsst.afw.image.Image`
1730  Image of the overscan region with the overscan
1731  correction applied. This quantity is used to estimate
1732  the amplifier read noise empirically.
1733 
1734  Raises
1735  ------
1736  RuntimeError
1737  Raised if the ``amp`` does not contain raw pixel information.
1738 
1739  See Also
1740  --------
1741  lsst.ip.isr.isrFunctions.overscanCorrection
1742  """
1743  if not amp.getHasRawInfo():
1744  raise RuntimeError("This method must be executed on an amp with raw information.")
1745 
1746  if amp.getRawHorizontalOverscanBBox().isEmpty():
1747  self.log.info("ISR_OSCAN: No overscan region. Not performing overscan correction.")
1748  return None
1749 
1750  statControl = afwMath.StatisticsControl()
1751  statControl.setAndMask(ccdExposure.mask.getPlaneBitMask("SAT"))
1752 
1753  # Determine the bounding boxes
1754  dataBBox = amp.getRawDataBBox()
1755  oscanBBox = amp.getRawHorizontalOverscanBBox()
1756  dx0 = 0
1757  dx1 = 0
1758 
1759  prescanBBox = amp.getRawPrescanBBox()
1760  if (oscanBBox.getBeginX() > prescanBBox.getBeginX()): # amp is at the right
1761  dx0 += self.config.overscanNumLeadingColumnsToSkip
1762  dx1 -= self.config.overscanNumTrailingColumnsToSkip
1763  else:
1764  dx0 += self.config.overscanNumTrailingColumnsToSkip
1765  dx1 -= self.config.overscanNumLeadingColumnsToSkip
1766 
1767  # Determine if we need to work on subregions of the amplifier and overscan.
1768  imageBBoxes = []
1769  overscanBBoxes = []
1770 
1771  if ((self.config.overscanBiasJump and
1772  self.config.overscanBiasJumpLocation) and
1773  (ccdExposure.getMetadata().exists(self.config.overscanBiasJumpKeyword) and
1774  ccdExposure.getMetadata().getScalar(self.config.overscanBiasJumpKeyword) in
1775  self.config.overscanBiasJumpDevices)):
1776  if amp.getReadoutCorner() in (afwTable.LL, afwTable.LR):
1777  yLower = self.config.overscanBiasJumpLocation
1778  yUpper = dataBBox.getHeight() - yLower
1779  else:
1780  yUpper = self.config.overscanBiasJumpLocation
1781  yLower = dataBBox.getHeight() - yUpper
1782 
1783  imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin(),
1784  lsst.geom.Extent2I(dataBBox.getWidth(), yLower)))
1785  overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() +
1786  lsst.geom.Extent2I(dx0, 0),
1787  lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1,
1788  yLower)))
1789 
1790  imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin() + lsst.geom.Extent2I(0, yLower),
1791  lsst.geom.Extent2I(dataBBox.getWidth(), yUpper)))
1792  overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, yLower),
1793  lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1,
1794  yUpper)))
1795  else:
1796  imageBBoxes.append(lsst.geom.Box2I(dataBBox.getBegin(),
1797  lsst.geom.Extent2I(dataBBox.getWidth(), dataBBox.getHeight())))
1798  overscanBBoxes.append(lsst.geom.Box2I(oscanBBox.getBegin() + lsst.geom.Extent2I(dx0, 0),
1799  lsst.geom.Extent2I(oscanBBox.getWidth() - dx0 + dx1,
1800  oscanBBox.getHeight())))
1801 
1802  # Perform overscan correction on subregions, ensuring saturated pixels are masked.
1803  for imageBBox, overscanBBox in zip(imageBBoxes, overscanBBoxes):
1804  ampImage = ccdExposure.maskedImage[imageBBox]
1805  overscanImage = ccdExposure.maskedImage[overscanBBox]
1806 
1807  overscanArray = overscanImage.image.array
1808  median = numpy.ma.median(numpy.ma.masked_where(overscanImage.mask.array, overscanArray))
1809  bad = numpy.where(numpy.abs(overscanArray - median) > self.config.overscanMaxDev)
1810  overscanImage.mask.array[bad] = overscanImage.mask.getPlaneBitMask("SAT")
1811 
1812  statControl = afwMath.StatisticsControl()
1813  statControl.setAndMask(ccdExposure.mask.getPlaneBitMask("SAT"))
1814 
1815  overscanResults = isrFunctions.overscanCorrection(ampMaskedImage=ampImage,
1816  overscanImage=overscanImage,
1817  fitType=self.config.overscanFitType,
1818  order=self.config.overscanOrder,
1819  collapseRej=self.config.overscanNumSigmaClip,
1820  statControl=statControl,
1821  overscanIsInt=self.config.overscanIsInt
1822  )
1823 
1824  # Measure average overscan levels and record them in the metadata.
1825  levelStat = afwMath.MEDIAN
1826  sigmaStat = afwMath.STDEVCLIP
1827 
1828  sctrl = afwMath.StatisticsControl(self.config.qa.flatness.clipSigma,
1829  self.config.qa.flatness.nIter)
1830  metadata = ccdExposure.getMetadata()
1831  ampNum = amp.getName()
1832  if self.config.overscanFitType in ("MEDIAN", "MEAN", "MEANCLIP"):
1833  metadata.set("ISR_OSCAN_LEVEL%s" % ampNum, overscanResults.overscanFit)
1834  metadata.set("ISR_OSCAN_SIGMA%s" % ampNum, 0.0)
1835  else:
1836  stats = afwMath.makeStatistics(overscanResults.overscanFit, levelStat | sigmaStat, sctrl)
1837  metadata.set("ISR_OSCAN_LEVEL%s" % ampNum, stats.getValue(levelStat))
1838  metadata.set("ISR_OSCAN_SIGMA%s" % ampNum, stats.getValue(sigmaStat))
1839 
1840  return overscanResults
1841 
1842  def updateVariance(self, ampExposure, amp, overscanImage=None):
1843  """Set the variance plane using the amplifier gain and read noise
1844 
1845  The read noise is calculated from the ``overscanImage`` if the
1846  ``doEmpiricalReadNoise`` option is set in the configuration; otherwise
1847  the value from the amplifier data is used.
1848 
1849  Parameters
1850  ----------
1851  ampExposure : `lsst.afw.image.Exposure`
1852  Exposure to process.
1853  amp : `lsst.afw.table.AmpInfoRecord` or `FakeAmp`
1854  Amplifier detector data.
1855  overscanImage : `lsst.afw.image.MaskedImage`, optional.
1856  Image of overscan, required only for empirical read noise.
1857 
1858  See also
1859  --------
1860  lsst.ip.isr.isrFunctions.updateVariance
1861  """
1862  maskPlanes = [self.config.saturatedMaskName, self.config.suspectMaskName]
1863  gain = amp.getGain()
1864 
1865  if math.isnan(gain):
1866  gain = 1.0
1867  self.log.warn("Gain set to NAN! Updating to 1.0 to generate Poisson variance.")
1868  elif gain <= 0:
1869  patchedGain = 1.0
1870  self.log.warn("Gain for amp %s == %g <= 0; setting to %f.",
1871  amp.getName(), gain, patchedGain)
1872  gain = patchedGain
1873 
1874  if self.config.doEmpiricalReadNoise and overscanImage is None:
1875  self.log.info("Overscan is none for EmpiricalReadNoise.")
1876 
1877  if self.config.doEmpiricalReadNoise and overscanImage is not None:
1878  stats = afwMath.StatisticsControl()
1879  stats.setAndMask(overscanImage.mask.getPlaneBitMask(maskPlanes))
1880  readNoise = afwMath.makeStatistics(overscanImage, afwMath.STDEVCLIP, stats).getValue()
1881  self.log.info("Calculated empirical read noise for amp %s: %f.",
1882  amp.getName(), readNoise)
1883  else:
1884  readNoise = amp.getReadNoise()
1885 
1886  isrFunctions.updateVariance(
1887  maskedImage=ampExposure.getMaskedImage(),
1888  gain=gain,
1889  readNoise=readNoise,
1890  )
1891 
1892  def darkCorrection(self, exposure, darkExposure, invert=False):
1893  """!Apply dark correction in place.
1894 
1895  Parameters
1896  ----------
1897  exposure : `lsst.afw.image.Exposure`
1898  Exposure to process.
1899  darkExposure : `lsst.afw.image.Exposure`
1900  Dark exposure of the same size as ``exposure``.
1901  invert : `Bool`, optional
1902  If True, re-add the dark to an already corrected image.
1903 
1904  Raises
1905  ------
1906  RuntimeError
1907  Raised if either ``exposure`` or ``darkExposure`` do not
1908  have their dark time defined.
1909 
1910  See Also
1911  --------
1912  lsst.ip.isr.isrFunctions.darkCorrection
1913  """
1914  expScale = exposure.getInfo().getVisitInfo().getDarkTime()
1915  if math.isnan(expScale):
1916  raise RuntimeError("Exposure darktime is NAN.")
1917  if darkExposure.getInfo().getVisitInfo() is not None:
1918  darkScale = darkExposure.getInfo().getVisitInfo().getDarkTime()
1919  else:
1920  # DM-17444: darkExposure.getInfo.getVisitInfo() is None
1921  # so getDarkTime() does not exist.
1922  self.log.warn("darkExposure.getInfo().getVisitInfo() does not exist. Using darkScale = 1.0.")
1923  darkScale = 1.0
1924 
1925  if math.isnan(darkScale):
1926  raise RuntimeError("Dark calib darktime is NAN.")
1927  isrFunctions.darkCorrection(
1928  maskedImage=exposure.getMaskedImage(),
1929  darkMaskedImage=darkExposure.getMaskedImage(),
1930  expScale=expScale,
1931  darkScale=darkScale,
1932  invert=invert,
1933  trimToFit=self.config.doTrimToMatchCalib
1934  )
1935 
1936  def doLinearize(self, detector):
1937  """!Check if linearization is needed for the detector cameraGeom.
1938 
1939  Checks config.doLinearize and the linearity type of the first
1940  amplifier.
1941 
1942  Parameters
1943  ----------
1944  detector : `lsst.afw.cameraGeom.Detector`
1945  Detector to get linearity type from.
1946 
1947  Returns
1948  -------
1949  doLinearize : `Bool`
1950  If True, linearization should be performed.
1951  """
1952  return self.config.doLinearize and \
1953  detector.getAmpInfoCatalog()[0].getLinearityType() != NullLinearityType
1954 
1955  def flatCorrection(self, exposure, flatExposure, invert=False):
1956  """!Apply flat correction in place.
1957 
1958  Parameters
1959  ----------
1960  exposure : `lsst.afw.image.Exposure`
1961  Exposure to process.
1962  flatExposure : `lsst.afw.image.Exposure`
1963  Flat exposure of the same size as ``exposure``.
1964  invert : `Bool`, optional
1965  If True, unflatten an already flattened image.
1966 
1967  See Also
1968  --------
1969  lsst.ip.isr.isrFunctions.flatCorrection
1970  """
1971  isrFunctions.flatCorrection(
1972  maskedImage=exposure.getMaskedImage(),
1973  flatMaskedImage=flatExposure.getMaskedImage(),
1974  scalingType=self.config.flatScalingType,
1975  userScale=self.config.flatUserScale,
1976  invert=invert,
1977  trimToFit=self.config.doTrimToMatchCalib
1978  )
1979 
1980  def saturationDetection(self, exposure, amp):
1981  """!Detect saturated pixels and mask them using mask plane config.saturatedMaskName, in place.
1982 
1983  Parameters
1984  ----------
1985  exposure : `lsst.afw.image.Exposure`
1986  Exposure to process. Only the amplifier DataSec is processed.
1987  amp : `lsst.afw.table.AmpInfoCatalog`
1988  Amplifier detector data.
1989 
1990  See Also
1991  --------
1992  lsst.ip.isr.isrFunctions.makeThresholdMask
1993  """
1994  if not math.isnan(amp.getSaturation()):
1995  maskedImage = exposure.getMaskedImage()
1996  dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
1997  isrFunctions.makeThresholdMask(
1998  maskedImage=dataView,
1999  threshold=amp.getSaturation(),
2000  growFootprints=0,
2001  maskName=self.config.saturatedMaskName,
2002  )
2003 
2004  def saturationInterpolation(self, exposure):
2005  """!Interpolate over saturated pixels, in place.
2006 
2007  This method should be called after `saturationDetection`, to
2008  ensure that the saturated pixels have been identified in the
2009  SAT mask. It should also be called after `assembleCcd`, since
2010  saturated regions may cross amplifier boundaries.
2011 
2012  Parameters
2013  ----------
2014  exposure : `lsst.afw.image.Exposure`
2015  Exposure to process.
2016 
2017  See Also
2018  --------
2019  lsst.ip.isr.isrTask.saturationDetection
2020  lsst.ip.isr.isrFunctions.interpolateFromMask
2021  """
2022  isrFunctions.interpolateFromMask(
2023  maskedImage=exposure.getMaskedImage(),
2024  fwhm=self.config.fwhm,
2025  growSaturatedFootprints=self.config.growSaturationFootprintSize,
2026  maskNameList=list(self.config.saturatedMaskName),
2027  )
2028 
2029  def suspectDetection(self, exposure, amp):
2030  """!Detect suspect pixels and mask them using mask plane config.suspectMaskName, in place.
2031 
2032  Parameters
2033  ----------
2034  exposure : `lsst.afw.image.Exposure`
2035  Exposure to process. Only the amplifier DataSec is processed.
2036  amp : `lsst.afw.table.AmpInfoCatalog`
2037  Amplifier detector data.
2038 
2039  See Also
2040  --------
2041  lsst.ip.isr.isrFunctions.makeThresholdMask
2042 
2043  Notes
2044  -----
2045  Suspect pixels are pixels whose value is greater than amp.getSuspectLevel().
2046  This is intended to indicate pixels that may be affected by unknown systematics;
2047  for example if non-linearity corrections above a certain level are unstable
2048  then that would be a useful value for suspectLevel. A value of `nan` indicates
2049  that no such level exists and no pixels are to be masked as suspicious.
2050  """
2051  suspectLevel = amp.getSuspectLevel()
2052  if math.isnan(suspectLevel):
2053  return
2054 
2055  maskedImage = exposure.getMaskedImage()
2056  dataView = maskedImage.Factory(maskedImage, amp.getRawBBox())
2057  isrFunctions.makeThresholdMask(
2058  maskedImage=dataView,
2059  threshold=suspectLevel,
2060  growFootprints=0,
2061  maskName=self.config.suspectMaskName,
2062  )
2063 
2064  def maskDefect(self, exposure, defectBaseList):
2065  """!Mask defects using mask plane "BAD", in place.
2066 
2067  Parameters
2068  ----------
2069  exposure : `lsst.afw.image.Exposure`
2070  Exposure to process.
2071  defectBaseList : `lsst.meas.algorithms.Defects` or `list` of
2072  `lsst.afw.image.DefectBase`.
2073  List of defects to mask and interpolate.
2074 
2075  Notes
2076  -----
2077  Call this after CCD assembly, since defects may cross amplifier boundaries.
2078  """
2079  maskedImage = exposure.getMaskedImage()
2080  if not isinstance(defectBaseList, Defects):
2081  # Promotes DefectBase to Defect
2082  defectList = Defects(defectBaseList)
2083  else:
2084  defectList = defectBaseList
2085  defectList.maskPixels(maskedImage, maskName="BAD")
2086 
2087  if self.config.numEdgeSuspect > 0:
2088  goodBBox = maskedImage.getBBox()
2089  # This makes a bbox numEdgeSuspect pixels smaller than the image on each side
2090  goodBBox.grow(-self.config.numEdgeSuspect)
2091  # Mask pixels outside goodBBox as SUSPECT
2092  SourceDetectionTask.setEdgeBits(
2093  maskedImage,
2094  goodBBox,
2095  maskedImage.getMask().getPlaneBitMask("SUSPECT")
2096  )
2097 
2098  def maskAndInterpolateDefects(self, exposure, defectBaseList):
2099  """Mask and interpolate defects using mask plane "BAD", in place.
2100 
2101  Parameters
2102  ----------
2103  exposure : `lsst.afw.image.Exposure`
2104  Exposure to process.
2105  defectBaseList : `List` of `Defects`
2106 
2107  """
2108  self.maskDefects(exposure, defectBaseList)
2109  isrFunctions.interpolateFromMask(
2110  maskedImage=exposure.getMaskedImage(),
2111  fwhm=self.config.fwhm,
2112  growSaturatedFootprints=0,
2113  maskNameList=["BAD"],
2114  )
2115 
2116  def maskNan(self, exposure):
2117  """Mask NaNs using mask plane "UNMASKEDNAN", in place.
2118 
2119  Parameters
2120  ----------
2121  exposure : `lsst.afw.image.Exposure`
2122  Exposure to process.
2123 
2124  Notes
2125  -----
2126  We mask over all NaNs, including those that are masked with
2127  other bits (because those may or may not be interpolated over
2128  later, and we want to remove all NaNs). Despite this
2129  behaviour, the "UNMASKEDNAN" mask plane is used to preserve
2130  the historical name.
2131  """
2132  maskedImage = exposure.getMaskedImage()
2133 
2134  # Find and mask NaNs
2135  maskedImage.getMask().addMaskPlane("UNMASKEDNAN")
2136  maskVal = maskedImage.getMask().getPlaneBitMask("UNMASKEDNAN")
2137  numNans = maskNans(maskedImage, maskVal)
2138  self.metadata.set("NUMNANS", numNans)
2139  if numNans > 0:
2140  self.log.warn("There were %d unmasked NaNs.", numNans)
2141 
2142  def maskAndInterpolateNan(self, exposure):
2143  """"Mask and interpolate NaNs using mask plane "UNMASKEDNAN", in place.
2144 
2145  Parameters
2146  ----------
2147  exposure : `lsst.afw.image.Exposure`
2148  Exposure to process.
2149 
2150  See Also
2151  --------
2152  lsst.ip.isr.isrTask.maskNan()
2153  """
2154  self.maskNan(exposure)
2155  isrFunctions.interpolateFromMask(
2156  maskedImage=exposure.getMaskedImage(),
2157  fwhm=self.config.fwhm,
2158  growSaturatedFootprints=0,
2159  maskNameList=["UNMASKEDNAN"],
2160  )
2161 
2162  def measureBackground(self, exposure, IsrQaConfig=None):
2163  """Measure the image background in subgrids, for quality control purposes.
2164 
2165  Parameters
2166  ----------
2167  exposure : `lsst.afw.image.Exposure`
2168  Exposure to process.
2169  IsrQaConfig : `lsst.ip.isr.isrQa.IsrQaConfig`
2170  Configuration object containing parameters on which background
2171  statistics and subgrids to use.
2172  """
2173  if IsrQaConfig is not None:
2174  statsControl = afwMath.StatisticsControl(IsrQaConfig.flatness.clipSigma,
2175  IsrQaConfig.flatness.nIter)
2176  maskVal = exposure.getMaskedImage().getMask().getPlaneBitMask(["BAD", "SAT", "DETECTED"])
2177  statsControl.setAndMask(maskVal)
2178  maskedImage = exposure.getMaskedImage()
2179  stats = afwMath.makeStatistics(maskedImage, afwMath.MEDIAN | afwMath.STDEVCLIP, statsControl)
2180  skyLevel = stats.getValue(afwMath.MEDIAN)
2181  skySigma = stats.getValue(afwMath.STDEVCLIP)
2182  self.log.info("Flattened sky level: %f +/- %f.", skyLevel, skySigma)
2183  metadata = exposure.getMetadata()
2184  metadata.set('SKYLEVEL', skyLevel)
2185  metadata.set('SKYSIGMA', skySigma)
2186 
2187  # calcluating flatlevel over the subgrids
2188  stat = afwMath.MEANCLIP if IsrQaConfig.flatness.doClip else afwMath.MEAN
2189  meshXHalf = int(IsrQaConfig.flatness.meshX/2.)
2190  meshYHalf = int(IsrQaConfig.flatness.meshY/2.)
2191  nX = int((exposure.getWidth() + meshXHalf) / IsrQaConfig.flatness.meshX)
2192  nY = int((exposure.getHeight() + meshYHalf) / IsrQaConfig.flatness.meshY)
2193  skyLevels = numpy.zeros((nX, nY))
2194 
2195  for j in range(nY):
2196  yc = meshYHalf + j * IsrQaConfig.flatness.meshY
2197  for i in range(nX):
2198  xc = meshXHalf + i * IsrQaConfig.flatness.meshX
2199 
2200  xLLC = xc - meshXHalf
2201  yLLC = yc - meshYHalf
2202  xURC = xc + meshXHalf - 1
2203  yURC = yc + meshYHalf - 1
2204 
2205  bbox = lsst.geom.Box2I(lsst.geom.Point2I(xLLC, yLLC), lsst.geom.Point2I(xURC, yURC))
2206  miMesh = maskedImage.Factory(exposure.getMaskedImage(), bbox, afwImage.LOCAL)
2207 
2208  skyLevels[i, j] = afwMath.makeStatistics(miMesh, stat, statsControl).getValue()
2209 
2210  good = numpy.where(numpy.isfinite(skyLevels))
2211  skyMedian = numpy.median(skyLevels[good])
2212  flatness = (skyLevels[good] - skyMedian) / skyMedian
2213  flatness_rms = numpy.std(flatness)
2214  flatness_pp = flatness.max() - flatness.min() if len(flatness) > 0 else numpy.nan
2215 
2216  self.log.info("Measuring sky levels in %dx%d grids: %f.", nX, nY, skyMedian)
2217  self.log.info("Sky flatness in %dx%d grids - pp: %f rms: %f.",
2218  nX, nY, flatness_pp, flatness_rms)
2219 
2220  metadata.set('FLATNESS_PP', float(flatness_pp))
2221  metadata.set('FLATNESS_RMS', float(flatness_rms))
2222  metadata.set('FLATNESS_NGRIDS', '%dx%d' % (nX, nY))
2223  metadata.set('FLATNESS_MESHX', IsrQaConfig.flatness.meshX)
2224  metadata.set('FLATNESS_MESHY', IsrQaConfig.flatness.meshY)
2225 
2226  def roughZeroPoint(self, exposure):
2227  """Set an approximate magnitude zero point for the exposure.
2228 
2229  Parameters
2230  ----------
2231  exposure : `lsst.afw.image.Exposure`
2232  Exposure to process.
2233  """
2234  filterName = afwImage.Filter(exposure.getFilter().getId()).getName() # Canonical name for filter
2235  if filterName in self.config.fluxMag0T1:
2236  fluxMag0 = self.config.fluxMag0T1[filterName]
2237  else:
2238  self.log.warn("No rough magnitude zero point set for filter %s.", filterName)
2239  fluxMag0 = self.config.defaultFluxMag0T1
2240 
2241  expTime = exposure.getInfo().getVisitInfo().getExposureTime()
2242  if not expTime > 0: # handle NaN as well as <= 0
2243  self.log.warn("Non-positive exposure time; skipping rough zero point.")
2244  return
2245 
2246  self.log.info("Setting rough magnitude zero point: %f", 2.5*math.log10(fluxMag0*expTime))
2247  exposure.setPhotoCalib(afwImage.makePhotoCalibFromCalibZeroPoint(fluxMag0*expTime, 0.0))
2248 
2249  def setValidPolygonIntersect(self, ccdExposure, fpPolygon):
2250  """!Set the valid polygon as the intersection of fpPolygon and the ccd corners.
2251 
2252  Parameters
2253  ----------
2254  ccdExposure : `lsst.afw.image.Exposure`
2255  Exposure to process.
2256  fpPolygon : `lsst.afw.geom.Polygon`
2257  Polygon in focal plane coordinates.
2258  """
2259  # Get ccd corners in focal plane coordinates
2260  ccd = ccdExposure.getDetector()
2261  fpCorners = ccd.getCorners(FOCAL_PLANE)
2262  ccdPolygon = Polygon(fpCorners)
2263 
2264  # Get intersection of ccd corners with fpPolygon
2265  intersect = ccdPolygon.intersectionSingle(fpPolygon)
2266 
2267  # Transform back to pixel positions and build new polygon
2268  ccdPoints = ccd.transform(intersect, FOCAL_PLANE, PIXELS)
2269  validPolygon = Polygon(ccdPoints)
2270  ccdExposure.getInfo().setValidPolygon(validPolygon)
2271 
2272  @contextmanager
2273  def flatContext(self, exp, flat, dark=None):
2274  """Context manager that applies and removes flats and darks,
2275  if the task is configured to apply them.
2276 
2277  Parameters
2278  ----------
2279  exp : `lsst.afw.image.Exposure`
2280  Exposure to process.
2281  flat : `lsst.afw.image.Exposure`
2282  Flat exposure the same size as ``exp``.
2283  dark : `lsst.afw.image.Exposure`, optional
2284  Dark exposure the same size as ``exp``.
2285 
2286  Yields
2287  ------
2288  exp : `lsst.afw.image.Exposure`
2289  The flat and dark corrected exposure.
2290  """
2291  if self.config.doDark and dark is not None:
2292  self.darkCorrection(exp, dark)
2293  if self.config.doFlat:
2294  self.flatCorrection(exp, flat)
2295  try:
2296  yield exp
2297  finally:
2298  if self.config.doFlat:
2299  self.flatCorrection(exp, flat, invert=True)
2300  if self.config.doDark and dark is not None:
2301  self.darkCorrection(exp, dark, invert=True)
2302 
2303  def debugView(self, exposure, stepname):
2304  """Utility function to examine ISR exposure at different stages.
2305 
2306  Parameters
2307  ----------
2308  exposure : `lsst.afw.image.Exposure`
2309  Exposure to view.
2310  stepname : `str`
2311  State of processing to view.
2312  """
2313  frame = getDebugFrame(self._display, stepname)
2314  if frame:
2315  display = getDisplay(frame)
2316  display.scale('asinh', 'zscale')
2317  display.mtv(exposure)
2318  prompt = "Press Enter to continue [c]... "
2319  while True:
2320  ans = input(prompt).lower()
2321  if ans in ("", "c",):
2322  break
2323 
2324 
2325 class FakeAmp(object):
2326  """A Detector-like object that supports returning gain and saturation level
2327 
2328  This is used when the input exposure does not have a detector.
2329 
2330  Parameters
2331  ----------
2332  exposure : `lsst.afw.image.Exposure`
2333  Exposure to generate a fake amplifier for.
2334  config : `lsst.ip.isr.isrTaskConfig`
2335  Configuration to apply to the fake amplifier.
2336  """
2337 
2338  def __init__(self, exposure, config):
2339  self._bbox = exposure.getBBox(afwImage.LOCAL)
2341  self._gain = config.gain
2342  self._readNoise = config.readNoise
2343  self._saturation = config.saturation
2344 
2345  def getBBox(self):
2346  return self._bbox
2347 
2348  def getRawBBox(self):
2349  return self._bbox
2350 
2351  def getHasRawInfo(self):
2352  return True # but see getRawHorizontalOverscanBBox()
2353 
2355  return self._RawHorizontalOverscanBBox
2356 
2357  def getGain(self):
2358  return self._gain
2359 
2360  def getReadNoise(self):
2361  return self._readNoise
2362 
2363  def getSaturation(self):
2364  return self._saturation
2365 
2366  def getSuspectLevel(self):
2367  return float("NaN")
2368 
2369 
2370 class RunIsrConfig(pexConfig.Config):
2371  isr = pexConfig.ConfigurableField(target=IsrTask, doc="Instrument signature removal")
2372 
2373 
2374 class RunIsrTask(pipeBase.CmdLineTask):
2375  """Task to wrap the default IsrTask to allow it to be retargeted.
2376 
2377  The standard IsrTask can be called directly from a command line
2378  program, but doing so removes the ability of the task to be
2379  retargeted. As most cameras override some set of the IsrTask
2380  methods, this would remove those data-specific methods in the
2381  output post-ISR images. This wrapping class fixes the issue,
2382  allowing identical post-ISR images to be generated by both the
2383  processCcd and isrTask code.
2384  """
2385  ConfigClass = RunIsrConfig
2386  _DefaultName = "runIsr"
2387 
2388  def __init__(self, *args, **kwargs):
2389  super().__init__(*args, **kwargs)
2390  self.makeSubtask("isr")
2391 
2392  def runDataRef(self, dataRef):
2393  """
2394  Parameters
2395  ----------
2396  dataRef : `lsst.daf.persistence.ButlerDataRef`
2397  data reference of the detector data to be processed
2398 
2399  Returns
2400  -------
2401  result : `pipeBase.Struct`
2402  Result struct with component:
2403 
2404  - exposure : `lsst.afw.image.Exposure`
2405  Post-ISR processed exposure.
2406  """
2407  return self.isr.runDataRef(dataRef)
def getInputDatasetTypes(cls, config)
Definition: isrTask.py:764
def runDataRef(self, sensorRef)
Definition: isrTask.py:1453
def measureBackground(self, exposure, IsrQaConfig=None)
Definition: isrTask.py:2162
def debugView(self, exposure, stepname)
Definition: isrTask.py:2303
def __init__(self, kwargs)
Definition: isrTask.py:754
def ensureExposure(self, inputExp, camera, detectorNum)
Definition: isrTask.py:1548
def readIsrData(self, dataRef, rawExposure)
Retrieve necessary frames for instrument signature removal.
Definition: isrTask.py:866
def adaptArgsAndRun(self, inputData, inputDataIds, outputDataIds, butler)
Definition: isrTask.py:825
def runDataRef(self, dataRef)
Definition: isrTask.py:2392
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:1014
def __init__(self, args, kwargs)
Definition: isrTask.py:2388
def getPrerequisiteDatasetTypes(cls, config)
Definition: isrTask.py:816
def roughZeroPoint(self, exposure)
Definition: isrTask.py:2226
def maskAndInterpolateDefects(self, exposure, defectBaseList)
Definition: isrTask.py:2098
def getRawHorizontalOverscanBBox(self)
Definition: isrTask.py:2354
def maskNan(self, exposure)
Definition: isrTask.py:2116
def getOutputDatasetTypes(cls, config)
Definition: isrTask.py:803
def maskDefect(self, exposure, defectBaseList)
Mask defects using mask plane "BAD", in place.
Definition: isrTask.py:2064
def overscanCorrection(self, ccdExposure, amp)
Definition: isrTask.py:1704
def convertIntToFloat(self, exposure)
Definition: isrTask.py:1596
def flatCorrection(self, exposure, flatExposure, invert=False)
Apply flat correction in place.
Definition: isrTask.py:1955
def makeDatasetType(self, dsConfig)
Definition: isrTask.py:863
def getIsrExposure(self, dataRef, datasetType, immediate=True)
Retrieve a calibration dataset for removing instrument signature.
Definition: isrTask.py:1507
def darkCorrection(self, exposure, darkExposure, invert=False)
Apply dark correction in place.
Definition: isrTask.py:1892
def doLinearize(self, detector)
Check if linearization is needed for the detector cameraGeom.
Definition: isrTask.py:1936
def setValidPolygonIntersect(self, ccdExposure, fpPolygon)
Set the valid polygon as the intersection of fpPolygon and the ccd corners.
Definition: isrTask.py:2249
def maskAmplifier(self, ccdExposure, amp, defects)
Definition: isrTask.py:1633
def flatContext(self, exp, flat, dark=None)
Definition: isrTask.py:2273
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:1842
def maskAndInterpolateNan(self, exposure)
Definition: isrTask.py:2142
def suspectDetection(self, exposure, amp)
Detect suspect pixels and mask them using mask plane config.suspectMaskName, in place.
Definition: isrTask.py:2029
def saturationInterpolation(self, exposure)
Interpolate over saturated pixels, in place.
Definition: isrTask.py:2004
def saturationDetection(self, exposure, amp)
Detect saturated pixels and mask them using mask plane config.saturatedMaskName, in place...
Definition: isrTask.py:1980
def __init__(self, exposure, config)
Definition: isrTask.py:2338