Coverage for python/lsst/cp/pipe/cpCombine.py : 16%

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1# This file is part of cp_pipe.
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (https://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This program is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
13#
14# This program is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
19# You should have received a copy of the GNU General Public License
20# along with this program. If not, see <http://www.gnu.org/licenses/>.
21import numpy as np
22import time
24import lsst.pex.config as pexConfig
25import lsst.pipe.base as pipeBase
26import lsst.pipe.base.connectionTypes as cT
27import lsst.afw.math as afwMath
28import lsst.afw.image as afwImage
30from lsst.geom import Point2D
31from lsst.log import Log
32from astro_metadata_translator import merge_headers, ObservationGroup
33from astro_metadata_translator.serialize import dates_to_fits
36# CalibStatsConfig/CalibStatsTask from pipe_base/constructCalibs.py
37class CalibStatsConfig(pexConfig.Config):
38 """Parameters controlling the measurement of background statistics.
39 """
40 stat = pexConfig.Field(
41 dtype=int,
42 default=int(afwMath.MEANCLIP),
43 doc="Statistic to use to estimate background (from lsst.afw.math)",
44 )
45 clip = pexConfig.Field(
46 dtype=float,
47 default=3.0,
48 doc="Clipping threshold for background",
49 )
50 nIter = pexConfig.Field(
51 dtype=int,
52 default=3,
53 doc="Clipping iterations for background",
54 )
55 mask = pexConfig.ListField(
56 dtype=str,
57 default=["DETECTED", "BAD", "NO_DATA"],
58 doc="Mask planes to reject",
59 )
62class CalibStatsTask(pipeBase.Task):
63 """Measure statistics on the background
65 This can be useful for scaling the background, e.g., for flats and fringe frames.
66 """
67 ConfigClass = CalibStatsConfig
69 def run(self, exposureOrImage):
70 """Measure a particular statistic on an image (of some sort).
72 Parameters
73 ----------
74 exposureOrImage : `lsst.afw.image.Exposure`, `lsst.afw.image.MaskedImage`, or `lsst.afw.image.Image`
75 Exposure or image to calculate statistics on.
77 Returns
78 -------
79 results : float
80 Resulting statistic value.
81 """
82 stats = afwMath.StatisticsControl(self.config.clip, self.config.nIter,
83 afwImage.Mask.getPlaneBitMask(self.config.mask))
84 try:
85 image = exposureOrImage.getMaskedImage()
86 except Exception:
87 try:
88 image = exposureOrImage.getImage()
89 except Exception:
90 image = exposureOrImage
92 return afwMath.makeStatistics(image, self.config.stat, stats).getValue()
95class CalibCombineConnections(pipeBase.PipelineTaskConnections,
96 dimensions=("instrument", "detector")):
97 inputExps = cT.Input(
98 name="cpInputs",
99 doc="Input pre-processed exposures to combine.",
100 storageClass="Exposure",
101 dimensions=("instrument", "detector", "exposure"),
102 multiple=True,
103 )
104 inputScales = cT.Input(
105 name="cpScales",
106 doc="Input scale factors to use.",
107 storageClass="StructuredDataDict",
108 dimensions=("instrument", ),
109 multiple=False,
110 )
112 outputData = cT.Output(
113 name="cpProposal",
114 doc="Output combined proposed calibration.",
115 storageClass="ExposureF",
116 dimensions=("instrument", "detector"),
117 )
119 def __init__(self, *, config=None):
120 super().__init__(config=config)
122 if config and config.exposureScaling != 'InputList':
123 self.inputs.discard("inputScales")
125 if config and len(config.calibrationDimensions) != 0:
126 newDimensions = tuple(config.calibrationDimensions)
127 newOutputData = cT.Output(
128 name=self.outputData.name,
129 doc=self.outputData.doc,
130 storageClass=self.outputData.storageClass,
131 dimensions=self.allConnections['outputData'].dimensions + newDimensions
132 )
133 self.dimensions.update(config.calibrationDimensions)
134 self.outputData = newOutputData
136 if config.exposureScaling == 'InputList':
137 newInputScales = cT.PrerequisiteInput(
138 name=self.inputScales.name,
139 doc=self.inputScales.doc,
140 storageClass=self.inputScales.storageClass,
141 dimensions=self.allConnections['inputScales'].dimensions + newDimensions
142 )
143 self.dimensions.update(config.calibrationDimensions)
144 self.inputScales = newInputScales
147# CalibCombineConfig/CalibCombineTask from pipe_base/constructCalibs.py
148class CalibCombineConfig(pipeBase.PipelineTaskConfig,
149 pipelineConnections=CalibCombineConnections):
150 """Configuration for combining calib exposures.
151 """
152 calibrationType = pexConfig.Field(
153 dtype=str,
154 default="calibration",
155 doc="Name of calibration to be generated.",
156 )
157 calibrationDimensions = pexConfig.ListField(
158 dtype=str,
159 default=[],
160 doc="List of updated dimensions to append to output."
161 )
163 exposureScaling = pexConfig.ChoiceField(
164 dtype=str,
165 allowed={
166 "None": "No scaling used.",
167 "ExposureTime": "Scale inputs by their exposure time.",
168 "DarkTime": "Scale inputs by their dark time.",
169 "MeanStats": "Scale inputs based on their mean values.",
170 "InputList": "Scale inputs based on a list of values.",
171 },
172 default=None,
173 doc="Scaling to be applied to each input exposure.",
174 )
175 scalingLevel = pexConfig.ChoiceField(
176 dtype=str,
177 allowed={
178 "DETECTOR": "Scale by detector.",
179 "AMP": "Scale by amplifier.",
180 },
181 default="DETECTOR",
182 doc="Region to scale.",
183 )
184 maxVisitsToCalcErrorFromInputVariance = pexConfig.Field(
185 dtype=int,
186 default=5,
187 doc="Maximum number of visits to estimate variance from input variance, not per-pixel spread",
188 )
190 doVignette = pexConfig.Field(
191 dtype=bool,
192 default=False,
193 doc="Copy vignette polygon to output and censor vignetted pixels?"
194 )
196 mask = pexConfig.ListField(
197 dtype=str,
198 default=["SAT", "DETECTED", "INTRP"],
199 doc="Mask planes to respect",
200 )
201 combine = pexConfig.Field(
202 dtype=int,
203 default=int(afwMath.MEANCLIP),
204 doc="Statistic to use for combination (from lsst.afw.math)",
205 )
206 clip = pexConfig.Field(
207 dtype=float,
208 default=3.0,
209 doc="Clipping threshold for combination",
210 )
211 nIter = pexConfig.Field(
212 dtype=int,
213 default=3,
214 doc="Clipping iterations for combination",
215 )
216 stats = pexConfig.ConfigurableField(
217 target=CalibStatsTask,
218 doc="Background statistics configuration",
219 )
222class CalibCombineTask(pipeBase.PipelineTask,
223 pipeBase.CmdLineTask):
224 """Task to combine calib exposures."""
225 ConfigClass = CalibCombineConfig
226 _DefaultName = 'cpCombine'
228 def __init__(self, **kwargs):
229 super().__init__(**kwargs)
230 self.makeSubtask("stats")
232 def runQuantum(self, butlerQC, inputRefs, outputRefs):
233 inputs = butlerQC.get(inputRefs)
235 dimensions = [exp.dataId.byName() for exp in inputRefs.inputExps]
236 inputs['inputDims'] = dimensions
238 outputs = self.run(**inputs)
239 butlerQC.put(outputs, outputRefs)
241 def run(self, inputExps, inputScales=None, inputDims=None):
242 """Combine calib exposures for a single detector.
244 Parameters
245 ----------
246 inputExps : `list` [`lsst.afw.image.Exposure`]
247 Input list of exposures to combine.
248 inputScales : `dict` [`dict` [`dict` [`float`]]], optional
249 Dictionary of scales, indexed by detector (`int`),
250 amplifier (`int`), and exposure (`int`). Used for
251 'inputList' scaling.
252 inputDims : `list` [`dict`]
253 List of dictionaries of input data dimensions/values.
254 Each list entry should contain:
256 ``"exposure"``
257 exposure id value (`int`)
258 ``"detector"``
259 detector id value (`int`)
261 Returns
262 -------
263 combinedExp : `lsst.afw.image.Exposure`
264 Final combined exposure generated from the inputs.
266 Raises
267 ------
268 RuntimeError
269 Raised if no input data is found. Also raised if
270 config.exposureScaling == InputList, and a necessary scale
271 was not found.
272 """
273 width, height = self.getDimensions(inputExps)
274 stats = afwMath.StatisticsControl(self.config.clip, self.config.nIter,
275 afwImage.Mask.getPlaneBitMask(self.config.mask))
276 numExps = len(inputExps)
277 if numExps < 1:
278 raise RuntimeError("No valid input data")
279 if numExps < self.config.maxVisitsToCalcErrorFromInputVariance:
280 stats.setCalcErrorFromInputVariance(True)
282 # Create output exposure for combined data.
283 combined = afwImage.MaskedImageF(width, height)
284 combinedExp = afwImage.makeExposure(combined)
286 # Apply scaling:
287 expScales = []
288 if inputDims is None:
289 inputDims = [dict() for i in inputExps]
291 for index, (exp, dims) in enumerate(zip(inputExps, inputDims)):
292 scale = 1.0
293 if exp is None:
294 self.log.warn("Input %d is None (%s); unable to scale exp.", index, dims)
295 continue
297 if self.config.exposureScaling == "ExposureTime":
298 scale = exp.getInfo().getVisitInfo().getExposureTime()
299 elif self.config.exposureScaling == "DarkTime":
300 scale = exp.getInfo().getVisitInfo().getDarkTime()
301 elif self.config.exposureScaling == "MeanStats":
302 scale = self.stats.run(exp)
303 elif self.config.exposureScaling == "InputList":
304 visitId = dims.get('exposure', None)
305 detectorId = dims.get('detector', None)
306 if visitId is None or detectorId is None:
307 raise RuntimeError(f"Could not identify scaling for input {index} ({dims})")
308 if detectorId not in inputScales['expScale']:
309 raise RuntimeError(f"Could not identify a scaling for input {index}"
310 f" detector {detectorId}")
312 if self.config.scalingLevel == "DETECTOR":
313 if visitId not in inputScales['expScale'][detectorId]:
314 raise RuntimeError(f"Could not identify a scaling for input {index}"
315 f"detector {detectorId} visit {visitId}")
316 scale = inputScales['expScale'][detectorId][visitId]
317 elif self.config.scalingLevel == 'AMP':
318 scale = [inputScales['expScale'][detectorId][amp.getName()][visitId]
319 for amp in exp.getDetector()]
320 else:
321 raise RuntimeError(f"Unknown scaling level: {self.config.scalingLevel}")
322 elif self.config.exposureScaling == 'None':
323 scale = 1.0
324 else:
325 raise RuntimeError(f"Unknown scaling type: {self.config.exposureScaling}.")
327 expScales.append(scale)
328 self.log.info("Scaling input %d by %s", index, scale)
329 self.applyScale(exp, scale)
331 self.combine(combined, inputExps, stats)
333 self.interpolateNans(combined)
335 if self.config.doVignette:
336 polygon = inputExps[0].getInfo().getValidPolygon()
337 VignetteExposure(combined, polygon=polygon, doUpdateMask=True,
338 doSetValue=True, vignetteValue=0.0)
340 # Combine headers
341 self.combineHeaders(inputExps, combinedExp,
342 calibType=self.config.calibrationType, scales=expScales)
344 # Return
345 return pipeBase.Struct(
346 outputData=combinedExp,
347 )
349 def getDimensions(self, expList):
350 """Get dimensions of the inputs.
352 Parameters
353 ----------
354 expList : `list` [`lsst.afw.image.Exposure`]
355 Exps to check the sizes of.
357 Returns
358 -------
359 width, height : `int`
360 Unique set of input dimensions.
361 """
362 dimList = [exp.getDimensions() for exp in expList if exp is not None]
363 return self.getSize(dimList)
365 def getSize(self, dimList):
366 """Determine a consistent size, given a list of image sizes.
368 Parameters
369 -----------
370 dimList : iterable of `tuple` (`int`, `int`)
371 List of dimensions.
373 Raises
374 ------
375 RuntimeError
376 If input dimensions are inconsistent.
378 Returns
379 --------
380 width, height : `int`
381 Common dimensions.
382 """
383 dim = set((w, h) for w, h in dimList)
384 if len(dim) != 1:
385 raise RuntimeError("Inconsistent dimensions: %s" % dim)
386 return dim.pop()
388 def applyScale(self, exposure, scale=None):
389 """Apply scale to input exposure.
391 This implementation applies a flux scaling: the input exposure is
392 divided by the provided scale.
394 Parameters
395 ----------
396 exposure : `lsst.afw.image.Exposure`
397 Exposure to scale.
398 scale : `float` or `list` [`float`], optional
399 Constant scale to divide the exposure by.
400 """
401 if scale is not None:
402 mi = exposure.getMaskedImage()
403 if isinstance(scale, list):
404 for amp, ampScale in zip(exposure.getDetector(), scale):
405 ampIm = mi[amp.getBBox()]
406 ampIm /= ampScale
407 else:
408 mi /= scale
410 def combine(self, target, expList, stats):
411 """Combine multiple images.
413 Parameters
414 ----------
415 target : `lsst.afw.image.Exposure`
416 Output exposure to construct.
417 expList : `list` [`lsst.afw.image.Exposure`]
418 Input exposures to combine.
419 stats : `lsst.afw.math.StatisticsControl`
420 Control explaining how to combine the input images.
421 """
422 images = [img.getMaskedImage() for img in expList if img is not None]
423 afwMath.statisticsStack(target, images, afwMath.Property(self.config.combine), stats)
425 def combineHeaders(self, expList, calib, calibType="CALIB", scales=None):
426 """Combine input headers to determine the set of common headers,
427 supplemented by calibration inputs.
429 Parameters
430 ----------
431 expList : `list` of `lsst.afw.image.Exposure`
432 Input list of exposures to combine.
433 calib : `lsst.afw.image.Exposure`
434 Output calibration to construct headers for.
435 calibType: `str`, optional
436 OBSTYPE the output should claim.
437 scales: `list` of `float`, optional
438 Scale values applied to each input to record.
440 Returns
441 -------
442 header : `lsst.daf.base.PropertyList`
443 Constructed header.
444 """
445 # Header
446 header = calib.getMetadata()
447 header.set("OBSTYPE", calibType)
449 # Keywords we care about
450 comments = {"TIMESYS": "Time scale for all dates",
451 "DATE-OBS": "Start date of earliest input observation",
452 "MJD-OBS": "[d] Start MJD of earliest input observation",
453 "DATE-END": "End date of oldest input observation",
454 "MJD-END": "[d] End MJD of oldest input observation",
455 "MJD-AVG": "[d] MJD midpoint of all input observations",
456 "DATE-AVG": "Midpoint date of all input observations"}
458 # Creation date
459 now = time.localtime()
460 calibDate = time.strftime("%Y-%m-%d", now)
461 calibTime = time.strftime("%X %Z", now)
462 header.set("CALIB_CREATE_DATE", calibDate)
463 header.set("CALIB_CREATE_TIME", calibTime)
465 # Merge input headers
466 inputHeaders = [exp.getMetadata() for exp in expList if exp is not None]
467 merged = merge_headers(inputHeaders, mode='drop')
468 for k, v in merged.items():
469 if k not in header:
470 md = expList[0].getMetadata()
471 comment = md.getComment(k) if k in md else None
472 header.set(k, v, comment=comment)
474 # Construct list of visits
475 visitInfoList = [exp.getInfo().getVisitInfo() for exp in expList if exp is not None]
476 for i, visit in enumerate(visitInfoList):
477 if visit is None:
478 continue
479 header.set("CPP_INPUT_%d" % (i,), visit.getExposureId())
480 header.set("CPP_INPUT_DATE_%d" % (i,), str(visit.getDate()))
481 header.set("CPP_INPUT_EXPT_%d" % (i,), visit.getExposureTime())
482 if scales is not None:
483 header.set("CPP_INPUT_SCALE_%d" % (i,), scales[i])
485 # Not yet working: DM-22302
486 # Create an observation group so we can add some standard headers
487 # independent of the form in the input files.
488 # Use try block in case we are dealing with unexpected data headers
489 try:
490 group = ObservationGroup(visitInfoList, pedantic=False)
491 except Exception:
492 self.log.warn("Exception making an obs group for headers. Continuing.")
493 # Fall back to setting a DATE-OBS from the calibDate
494 dateCards = {"DATE-OBS": "{}T00:00:00.00".format(calibDate)}
495 comments["DATE-OBS"] = "Date of start of day of calibration midpoint"
496 else:
497 oldest, newest = group.extremes()
498 dateCards = dates_to_fits(oldest.datetime_begin, newest.datetime_end)
500 for k, v in dateCards.items():
501 header.set(k, v, comment=comments.get(k, None))
503 return header
505 def interpolateNans(self, exp):
506 """Interpolate over NANs in the combined image.
508 NANs can result from masked areas on the CCD. We don't want them getting
509 into our science images, so we replace them with the median of the image.
511 Parameters
512 ----------
513 exp : `lsst.afw.image.Exposure`
514 Exp to check for NaNs.
515 """
516 array = exp.getImage().getArray()
517 bad = np.isnan(array)
519 median = np.median(array[np.logical_not(bad)])
520 count = np.sum(np.logical_not(bad))
521 array[bad] = median
522 if count > 0:
523 self.log.warn("Found %s NAN pixels", count)
526def VignetteExposure(exposure, polygon=None,
527 doUpdateMask=True, maskPlane='BAD',
528 doSetValue=False, vignetteValue=0.0,
529 log=None):
530 """Apply vignetted polygon to image pixels.
532 Parameters
533 ----------
534 exposure : `lsst.afw.image.Exposure`
535 Image to be updated.
536 doUpdateMask : `bool`, optional
537 Update the exposure mask for vignetted area?
538 maskPlane : `str`, optional,
539 Mask plane to assign.
540 doSetValue : `bool`, optional
541 Set image value for vignetted area?
542 vignetteValue : `float`, optional
543 Value to assign.
544 log : `lsst.log.Log`, optional
545 Log to write to.
547 Raises
548 ------
549 RuntimeError
550 Raised if no valid polygon exists.
551 """
552 polygon = polygon if polygon else exposure.getInfo().getValidPolygon()
553 if not polygon:
554 raise RuntimeError("Could not find valid polygon!")
555 log = log if log else Log.getLogger(__name__.partition(".")[2])
557 fullyIlluminated = True
558 for corner in exposure.getBBox().getCorners():
559 if not polygon.contains(Point2D(corner)):
560 fullyIlluminated = False
562 log.info("Exposure is fully illuminated? %s", fullyIlluminated)
564 if not fullyIlluminated:
565 # Scan pixels.
566 mask = exposure.getMask()
567 numPixels = mask.getBBox().getArea()
569 xx, yy = np.meshgrid(np.arange(0, mask.getWidth(), dtype=int),
570 np.arange(0, mask.getHeight(), dtype=int))
572 vignMask = np.array([not polygon.contains(Point2D(x, y)) for x, y in
573 zip(xx.reshape(numPixels), yy.reshape(numPixels))])
574 vignMask = vignMask.reshape(mask.getHeight(), mask.getWidth())
576 if doUpdateMask:
577 bitMask = mask.getPlaneBitMask(maskPlane)
578 maskArray = mask.getArray()
579 maskArray[vignMask] |= bitMask
580 if doSetValue:
581 imageArray = exposure.getImage().getArray()
582 imageArray[vignMask] = vignetteValue
583 log.info("Exposure contains %d vignetted pixels.",
584 np.count_nonzero(vignMask))