lsst.pipe.tasks 21.0.0-178-g80f1dd77+8321431476
photoCal.py
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21#
22# @package lsst.pipe.tasks.
23import math
24import sys
25
26import numpy as np
27import astropy.units as u
28
29import lsst.pex.config as pexConf
30import lsst.pipe.base as pipeBase
31from lsst.afw.image import abMagErrFromFluxErr, makePhotoCalibFromCalibZeroPoint
32import lsst.afw.table as afwTable
33from lsst.meas.astrom import DirectMatchTask, DirectMatchConfigWithoutLoader
34import lsst.afw.display as afwDisplay
35from lsst.meas.algorithms import getRefFluxField, ReserveSourcesTask
36from lsst.utils.timer import timeMethod
37from .colorterms import ColortermLibrary
38
39__all__ = ["PhotoCalTask", "PhotoCalConfig"]
40
41
42class PhotoCalConfig(pexConf.Config):
43 """Config for PhotoCal"""
44 match = pexConf.ConfigField("Match to reference catalog",
45 DirectMatchConfigWithoutLoader)
46 reserve = pexConf.ConfigurableField(target=ReserveSourcesTask, doc="Reserve sources from fitting")
47 fluxField = pexConf.Field(
48 dtype=str,
49 default="slot_CalibFlux_instFlux",
50 doc=("Name of the source instFlux field to use. The associated flag field\n"
51 "('<name>_flags') will be implicitly included in badFlags."),
52 )
53 applyColorTerms = pexConf.Field(
54 dtype=bool,
55 default=None,
56 doc=("Apply photometric color terms to reference stars? One of:\n"
57 "None: apply if colorterms and photoCatName are not None;\n"
58 " fail if color term data is not available for the specified ref catalog and filter.\n"
59 "True: always apply colorterms; fail if color term data is not available for the\n"
60 " specified reference catalog and filter.\n"
61 "False: do not apply."),
62 optional=True,
63 )
64 sigmaMax = pexConf.Field(
65 dtype=float,
66 default=0.25,
67 doc="maximum sigma to use when clipping",
68 optional=True,
69 )
70 nSigma = pexConf.Field(
71 dtype=float,
72 default=3.0,
73 doc="clip at nSigma",
74 )
75 useMedian = pexConf.Field(
76 dtype=bool,
77 default=True,
78 doc="use median instead of mean to compute zeropoint",
79 )
80 nIter = pexConf.Field(
81 dtype=int,
82 default=20,
83 doc="number of iterations",
84 )
85 colorterms = pexConf.ConfigField(
86 dtype=ColortermLibrary,
87 doc="Library of photometric reference catalog name: color term dict",
88 )
89 photoCatName = pexConf.Field(
90 dtype=str,
91 optional=True,
92 doc=("Name of photometric reference catalog; used to select a color term dict in colorterms."
93 " see also applyColorTerms"),
94 )
95 magErrFloor = pexConf.RangeField(
96 dtype=float,
97 default=0.0,
98 doc="Additional magnitude uncertainty to be added in quadrature with measurement errors.",
99 min=0.0,
100 )
101
102 def validate(self):
103 pexConf.Config.validate(self)
104 if self.applyColorTermsapplyColorTerms and self.photoCatNamephotoCatName is None:
105 raise RuntimeError("applyColorTerms=True requires photoCatName is non-None")
106 if self.applyColorTermsapplyColorTerms and len(self.colortermscolorterms.data) == 0:
107 raise RuntimeError("applyColorTerms=True requires colorterms be provided")
108
109 def setDefaults(self):
110 pexConf.Config.setDefaults(self)
111 self.matchmatch.sourceSelection.doFlags = True
112 self.matchmatch.sourceSelection.flags.bad = [
113 "base_PixelFlags_flag_edge",
114 "base_PixelFlags_flag_interpolated",
115 "base_PixelFlags_flag_saturated",
116 ]
117 self.matchmatch.sourceSelection.doUnresolved = True
118
119
120
126
127class PhotoCalTask(pipeBase.Task):
128 r"""!
129@anchor PhotoCalTask_
130
131@brief Calculate the zero point of an exposure given a lsst.afw.table.ReferenceMatchVector.
132
133@section pipe_tasks_photocal_Contents Contents
134
135 - @ref pipe_tasks_photocal_Purpose
136 - @ref pipe_tasks_photocal_Initialize
137 - @ref pipe_tasks_photocal_IO
138 - @ref pipe_tasks_photocal_Config
139 - @ref pipe_tasks_photocal_Debug
140 - @ref pipe_tasks_photocal_Example
141
142@section pipe_tasks_photocal_Purpose Description
143
144@copybrief PhotoCalTask
145
146Calculate an Exposure's zero-point given a set of flux measurements of stars matched to an input catalogue.
147The type of flux to use is specified by PhotoCalConfig.fluxField.
148
149The algorithm clips outliers iteratively, with parameters set in the configuration.
150
151@note This task can adds fields to the schema, so any code calling this task must ensure that
152these columns are indeed present in the input match list; see @ref pipe_tasks_photocal_Example
153
154@section pipe_tasks_photocal_Initialize Task initialisation
155
156@copydoc \_\_init\_\_
157
158@section pipe_tasks_photocal_IO Inputs/Outputs to the run method
159
160@copydoc run
161
162@section pipe_tasks_photocal_Config Configuration parameters
163
164See @ref PhotoCalConfig
165
166@section pipe_tasks_photocal_Debug Debug variables
167
168The @link lsst.pipe.base.cmdLineTask.CmdLineTask command line task@endlink interface supports a
169flag @c -d to import @b debug.py from your @c PYTHONPATH; see @ref baseDebug for more about @b debug.py files.
170
171The available variables in PhotoCalTask are:
172<DL>
173 <DT> @c display
174 <DD> If True enable other debug outputs
175 <DT> @c displaySources
176 <DD> If True, display the exposure on the display's frame 1 and overlay the source catalogue.
177 <DL>
178 <DT> red o
179 <DD> Reserved objects
180 <DT> green o
181 <DD> Objects used in the photometric calibration
182 </DL>
183 <DT> @c scatterPlot
184 <DD> Make a scatter plot of flux v. reference magnitude as a function of reference magnitude.
185 - good objects in blue
186 - rejected objects in red
187 (if @c scatterPlot is 2 or more, prompt to continue after each iteration)
188</DL>
189
190@section pipe_tasks_photocal_Example A complete example of using PhotoCalTask
191
192This code is in @link examples/photoCalTask.py@endlink, and can be run as @em e.g.
193@code
194examples/photoCalTask.py
195@endcode
196@dontinclude photoCalTask.py
197
198Import the tasks (there are some other standard imports; read the file for details)
199@skipline from lsst.pipe.tasks.astrometry
200@skipline measPhotocal
201
202We need to create both our tasks before processing any data as the task constructors
203can add extra columns to the schema which we get from the input catalogue, @c scrCat:
204@skipline getSchema
205
206Astrometry first:
207@skip AstrometryTask.ConfigClass
208@until aTask
209(that @c filterMap line is because our test code doesn't use a filter that the reference catalogue recognises,
210so we tell it to use the @c r band)
211
212Then photometry:
213@skip measPhotocal
214@until pTask
215
216If the schema has indeed changed we need to add the new columns to the source table
217(yes; this should be easier!)
218@skip srcCat
219@until srcCat = cat
220
221We're now ready to process the data (we could loop over multiple exposures/catalogues using the same
222task objects):
223@skip matches
224@until result
225
226We can then unpack and use the results:
227@skip calib
228@until np.log
229
230<HR>
231To investigate the @ref pipe_tasks_photocal_Debug, put something like
232@code{.py}
233 import lsstDebug
234 def DebugInfo(name):
235 di = lsstDebug.getInfo(name) # N.b. lsstDebug.Info(name) would call us recursively
236 if name.endswith(".PhotoCal"):
237 di.display = 1
238
239 return di
240
241 lsstDebug.Info = DebugInfo
242@endcode
243into your debug.py file and run photoCalTask.py with the @c --debug flag.
244 """
245 ConfigClass = PhotoCalConfig
246 _DefaultName = "photoCal"
247
248 def __init__(self, refObjLoader, schema=None, **kwds):
249 """!Create the photometric calibration task. See PhotoCalTask.init for documentation
250 """
251 pipeBase.Task.__init__(self, **kwds)
252 self.scatterPlotscatterPlot = None
253 self.figfig = None
254 if schema is not None:
255 self.usedKeyusedKey = schema.addField("calib_photometry_used", type="Flag",
256 doc="set if source was used in photometric calibration")
257 else:
258 self.usedKeyusedKey = None
259 self.matchmatch = DirectMatchTask(config=self.config.match, refObjLoader=refObjLoader,
260 name="match", parentTask=self)
261 self.makeSubtask("reserve", columnName="calib_photometry", schema=schema,
262 doc="set if source was reserved from photometric calibration")
263
264 def getSourceKeys(self, schema):
265 """Return a struct containing the source catalog keys for fields used
266 by PhotoCalTask.
267
268
269 Parameters
270 ----------
271 schema : `lsst.afw.table.schema`
272 Schema of the catalog to get keys from.
273
274 Returns
275 -------
276 result : `lsst.pipe.base.Struct`
277 Result struct with components:
278
279 - ``instFlux``: Instrument flux key.
280 - ``instFluxErr``: Instrument flux error key.
281 """
282 instFlux = schema.find(self.config.fluxField).key
283 instFluxErr = schema.find(self.config.fluxField + "Err").key
284 return pipeBase.Struct(instFlux=instFlux, instFluxErr=instFluxErr)
285
286 @timeMethod
287 def extractMagArrays(self, matches, filterLabel, sourceKeys):
288 """!Extract magnitude and magnitude error arrays from the given matches.
289
290 @param[in] matches Reference/source matches, a @link lsst::afw::table::ReferenceMatchVector@endlink
291 @param[in] filterLabel Label of filter being calibrated
292 @param[in] sourceKeys Struct of source catalog keys, as returned by getSourceKeys()
293
294 @return Struct containing srcMag, refMag, srcMagErr, refMagErr, and magErr numpy arrays
295 where magErr is an error in the magnitude; the error in srcMag - refMag
296 If nonzero, config.magErrFloor will be added to magErr *only* (not srcMagErr or refMagErr), as
297 magErr is what is later used to determine the zero point.
298 Struct also contains refFluxFieldList: a list of field names of the reference catalog used for fluxes
299 (1 or 2 strings)
300 @note These magnitude arrays are the @em inputs to the photometric calibration, some may have been
301 discarded by clipping while estimating the calibration (https://jira.lsstcorp.org/browse/DM-813)
302 """
303 srcInstFluxArr = np.array([m.second.get(sourceKeys.instFlux) for m in matches])
304 srcInstFluxErrArr = np.array([m.second.get(sourceKeys.instFluxErr) for m in matches])
305 if not np.all(np.isfinite(srcInstFluxErrArr)):
306 # this is an unpleasant hack; see DM-2308 requesting a better solution
307 self.log.warning("Source catalog does not have flux uncertainties; using sqrt(flux).")
308 srcInstFluxErrArr = np.sqrt(srcInstFluxArr)
309
310 # convert source instFlux from DN to an estimate of nJy
311 referenceFlux = (0*u.ABmag).to_value(u.nJy)
312 srcInstFluxArr = srcInstFluxArr * referenceFlux
313 srcInstFluxErrArr = srcInstFluxErrArr * referenceFlux
314
315 if not matches:
316 raise RuntimeError("No reference stars are available")
317 refSchema = matches[0].first.schema
318
319 applyColorTerms = self.config.applyColorTerms
320 applyCTReason = "config.applyColorTerms is %s" % (self.config.applyColorTerms,)
321 if self.config.applyColorTerms is None:
322 # apply color terms if color term data is available and photoCatName specified
323 ctDataAvail = len(self.config.colorterms.data) > 0
324 photoCatSpecified = self.config.photoCatName is not None
325 applyCTReason += " and data %s available" % ("is" if ctDataAvail else "is not")
326 applyCTReason += " and photoRefCat %s provided" % ("is" if photoCatSpecified else "is not")
327 applyColorTerms = ctDataAvail and photoCatSpecified
328
329 if applyColorTerms:
330 self.log.info("Applying color terms for filter=%r, config.photoCatName=%s because %s",
331 filterLabel.physicalLabel, self.config.photoCatName, applyCTReason)
332 colorterm = self.config.colorterms.getColorterm(filterLabel.physicalLabel,
333 self.config.photoCatName,
334 doRaise=True)
335 refCat = afwTable.SimpleCatalog(matches[0].first.schema)
336
337 # extract the matched refCat as a Catalog for the colorterm code
338 refCat.reserve(len(matches))
339 for x in matches:
340 record = refCat.addNew()
341 record.assign(x.first)
342
343 refMagArr, refMagErrArr = colorterm.getCorrectedMagnitudes(refCat)
344 fluxFieldList = [getRefFluxField(refSchema, filt) for filt in (colorterm.primary,
345 colorterm.secondary)]
346 else:
347 # no colorterms to apply
348 self.log.info("Not applying color terms because %s", applyCTReason)
349 colorterm = None
350
351 fluxFieldList = [getRefFluxField(refSchema, filterLabel.bandLabel)]
352 fluxField = getRefFluxField(refSchema, filterLabel.bandLabel)
353 fluxKey = refSchema.find(fluxField).key
354 refFluxArr = np.array([m.first.get(fluxKey) for m in matches])
355
356 try:
357 fluxErrKey = refSchema.find(fluxField + "Err").key
358 refFluxErrArr = np.array([m.first.get(fluxErrKey) for m in matches])
359 except KeyError:
360 # Reference catalogue may not have flux uncertainties; HACK DM-2308
361 self.log.warning("Reference catalog does not have flux uncertainties for %s;"
362 " using sqrt(flux).", fluxField)
363 refFluxErrArr = np.sqrt(refFluxArr)
364
365 refMagArr = u.Quantity(refFluxArr, u.nJy).to_value(u.ABmag)
366 # HACK convert to Jy until we have a replacement for this (DM-16903)
367 refMagErrArr = abMagErrFromFluxErr(refFluxErrArr*1e-9, refFluxArr*1e-9)
368
369 # compute the source catalog magnitudes and errors
370 srcMagArr = u.Quantity(srcInstFluxArr, u.nJy).to_value(u.ABmag)
371 # Fitting with error bars in both axes is hard
372 # for now ignore reference flux error, but ticket DM-2308 is a request for a better solution
373 # HACK convert to Jy until we have a replacement for this (DM-16903)
374 magErrArr = abMagErrFromFluxErr(srcInstFluxErrArr*1e-9, srcInstFluxArr*1e-9)
375 if self.config.magErrFloor != 0.0:
376 magErrArr = (magErrArr**2 + self.config.magErrFloor**2)**0.5
377
378 srcMagErrArr = abMagErrFromFluxErr(srcInstFluxErrArr*1e-9, srcInstFluxArr*1e-9)
379
380 good = np.isfinite(srcMagArr) & np.isfinite(refMagArr)
381
382 return pipeBase.Struct(
383 srcMag=srcMagArr[good],
384 refMag=refMagArr[good],
385 magErr=magErrArr[good],
386 srcMagErr=srcMagErrArr[good],
387 refMagErr=refMagErrArr[good],
388 refFluxFieldList=fluxFieldList,
389 )
390
391 @timeMethod
392 def run(self, exposure, sourceCat, expId=0):
393 """!Do photometric calibration - select matches to use and (possibly iteratively) compute
394 the zero point.
395
396 @param[in] exposure Exposure upon which the sources in the matches were detected.
397 @param[in] sourceCat A catalog of sources to use in the calibration
398 (@em i.e. a list of lsst.afw.table.Match with
399 @c first being of type lsst.afw.table.SimpleRecord and @c second type lsst.afw.table.SourceRecord ---
400 the reference object and matched object respectively).
401 (will not be modified except to set the outputField if requested.).
402
403 @return Struct of:
404 - photoCalib -- @link lsst::afw::image::PhotoCalib@endlink object containing the calibration
405 - arrays ------ Magnitude arrays returned be PhotoCalTask.extractMagArrays
406 - matches ----- Final ReferenceMatchVector, as returned by PhotoCalTask.selectMatches.
407 - zp ---------- Photometric zero point (mag)
408 - sigma ------- Standard deviation of fit of photometric zero point (mag)
409 - ngood ------- Number of sources used to fit photometric zero point
410
411 The exposure is only used to provide the name of the filter being calibrated (it may also be
412 used to generate debugging plots).
413
414 The reference objects:
415 - Must include a field @c photometric; True for objects which should be considered as
416 photometric standards
417 - Must include a field @c flux; the flux used to impose a magnitude limit and also to calibrate
418 the data to (unless a color term is specified, in which case ColorTerm.primary is used;
419 See https://jira.lsstcorp.org/browse/DM-933)
420 - May include a field @c stargal; if present, True means that the object is a star
421 - May include a field @c var; if present, True means that the object is variable
422
423 The measured sources:
424 - Must include PhotoCalConfig.fluxField; the flux measurement to be used for calibration
425
426 @throws RuntimeError with the following strings:
427
428 <DL>
429 <DT> No matches to use for photocal
430 <DD> No matches are available (perhaps no sources/references were selected by the matcher).
431 <DT> No reference stars are available
432 <DD> No matches are available from which to extract magnitudes.
433 </DL>
434 """
435 import lsstDebug
436
437 display = lsstDebug.Info(__name__).display
438 displaySources = display and lsstDebug.Info(__name__).displaySources
439 self.scatterPlotscatterPlot = display and lsstDebug.Info(__name__).scatterPlot
440
441 if self.scatterPlotscatterPlot:
442 from matplotlib import pyplot
443 try:
444 self.figfig.clf()
445 except Exception:
446 self.figfig = pyplot.figure()
447
448 filterLabel = exposure.getFilterLabel()
449
450 # Match sources
451 matchResults = self.matchmatch.run(sourceCat, filterLabel.bandLabel)
452 matches = matchResults.matches
453
454 reserveResults = self.reserve.run([mm.second for mm in matches], expId=expId)
455 if displaySources:
456 self.displaySourcesdisplaySources(exposure, matches, reserveResults.reserved)
457 if reserveResults.reserved.sum() > 0:
458 matches = [mm for mm, use in zip(matches, reserveResults.use) if use]
459 if len(matches) == 0:
460 raise RuntimeError("No matches to use for photocal")
461 if self.usedKeyusedKey is not None:
462 for mm in matches:
463 mm.second.set(self.usedKeyusedKey, True)
464
465 # Prepare for fitting
466 sourceKeys = self.getSourceKeysgetSourceKeys(matches[0].second.schema)
467 arrays = self.extractMagArraysextractMagArrays(matches, filterLabel, sourceKeys)
468
469 # Fit for zeropoint
470 r = self.getZeroPointgetZeroPoint(arrays.srcMag, arrays.refMag, arrays.magErr)
471 self.log.info("Magnitude zero point: %f +/- %f from %d stars", r.zp, r.sigma, r.ngood)
472
473 # Prepare the results
474 flux0 = 10**(0.4*r.zp) # Flux of mag=0 star
475 flux0err = 0.4*math.log(10)*flux0*r.sigma # Error in flux0
476 photoCalib = makePhotoCalibFromCalibZeroPoint(flux0, flux0err)
477
478 return pipeBase.Struct(
479 photoCalib=photoCalib,
480 arrays=arrays,
481 matches=matches,
482 zp=r.zp,
483 sigma=r.sigma,
484 ngood=r.ngood,
485 )
486
487 def displaySources(self, exposure, matches, reserved, frame=1):
488 """Display sources we'll use for photocal
489
490 Sources that will be actually used will be green.
491 Sources reserved from the fit will be red.
492
493 Parameters
494 ----------
495 exposure : `lsst.afw.image.ExposureF`
496 Exposure to display.
497 matches : `list` of `lsst.afw.table.RefMatch`
498 Matches used for photocal.
499 reserved : `numpy.ndarray` of type `bool`
500 Boolean array indicating sources that are reserved.
501 frame : `int`
502 Frame number for display.
503 """
504 disp = afwDisplay.getDisplay(frame=frame)
505 disp.mtv(exposure, title="photocal")
506 with disp.Buffering():
507 for mm, rr in zip(matches, reserved):
508 x, y = mm.second.getCentroid()
509 ctype = afwDisplay.RED if rr else afwDisplay.GREEN
510 disp.dot("o", x, y, size=4, ctype=ctype)
511
512 def getZeroPoint(self, src, ref, srcErr=None, zp0=None):
513 """!Flux calibration code, returning (ZeroPoint, Distribution Width, Number of stars)
514
515 We perform nIter iterations of a simple sigma-clipping algorithm with a couple of twists:
516 1. We use the median/interquartile range to estimate the position to clip around, and the
517 "sigma" to use.
518 2. We never allow sigma to go _above_ a critical value sigmaMax --- if we do, a sufficiently
519 large estimate will prevent the clipping from ever taking effect.
520 3. Rather than start with the median we start with a crude mode. This means that a set of magnitude
521 residuals with a tight core and asymmetrical outliers will start in the core. We use the width of
522 this core to set our maximum sigma (see 2.)
523
524 @return Struct of:
525 - zp ---------- Photometric zero point (mag)
526 - sigma ------- Standard deviation of fit of zero point (mag)
527 - ngood ------- Number of sources used to fit zero point
528 """
529 sigmaMax = self.config.sigmaMax
530
531 dmag = ref - src
532
533 indArr = np.argsort(dmag)
534 dmag = dmag[indArr]
535
536 if srcErr is not None:
537 dmagErr = srcErr[indArr]
538 else:
539 dmagErr = np.ones(len(dmag))
540
541 # need to remove nan elements to avoid errors in stats calculation with numpy
542 ind_noNan = np.array([i for i in range(len(dmag))
543 if (not np.isnan(dmag[i]) and not np.isnan(dmagErr[i]))])
544 dmag = dmag[ind_noNan]
545 dmagErr = dmagErr[ind_noNan]
546
547 IQ_TO_STDEV = 0.741301109252802 # 1 sigma in units of interquartile (assume Gaussian)
548
549 npt = len(dmag)
550 ngood = npt
551 good = None # set at end of first iteration
552 for i in range(self.config.nIter):
553 if i > 0:
554 npt = sum(good)
555
556 center = None
557 if i == 0:
558 #
559 # Start by finding the mode
560 #
561 nhist = 20
562 try:
563 hist, edges = np.histogram(dmag, nhist, new=True)
564 except TypeError:
565 hist, edges = np.histogram(dmag, nhist) # they removed new=True around numpy 1.5
566 imode = np.arange(nhist)[np.where(hist == hist.max())]
567
568 if imode[-1] - imode[0] + 1 == len(imode): # Multiple modes, but all contiguous
569 if zp0:
570 center = zp0
571 else:
572 center = 0.5*(edges[imode[0]] + edges[imode[-1] + 1])
573
574 peak = sum(hist[imode])/len(imode) # peak height
575
576 # Estimate FWHM of mode
577 j = imode[0]
578 while j >= 0 and hist[j] > 0.5*peak:
579 j -= 1
580 j = max(j, 0)
581 q1 = dmag[sum(hist[range(j)])]
582
583 j = imode[-1]
584 while j < nhist and hist[j] > 0.5*peak:
585 j += 1
586 j = min(j, nhist - 1)
587 j = min(sum(hist[range(j)]), npt - 1)
588 q3 = dmag[j]
589
590 if q1 == q3:
591 q1 = dmag[int(0.25*npt)]
592 q3 = dmag[int(0.75*npt)]
593
594 sig = (q3 - q1)/2.3 # estimate of standard deviation (based on FWHM; 2.358 for Gaussian)
595
596 if sigmaMax is None:
597 sigmaMax = 2*sig # upper bound on st. dev. for clipping. multiplier is a heuristic
598
599 self.log.debug("Photo calibration histogram: center = %.2f, sig = %.2f", center, sig)
600
601 else:
602 if sigmaMax is None:
603 sigmaMax = dmag[-1] - dmag[0]
604
605 center = np.median(dmag)
606 q1 = dmag[int(0.25*npt)]
607 q3 = dmag[int(0.75*npt)]
608 sig = (q3 - q1)/2.3 # estimate of standard deviation (based on FWHM; 2.358 for Gaussian)
609
610 if center is None: # usually equivalent to (i > 0)
611 gdmag = dmag[good]
612 if self.config.useMedian:
613 center = np.median(gdmag)
614 else:
615 gdmagErr = dmagErr[good]
616 center = np.average(gdmag, weights=gdmagErr)
617
618 q3 = gdmag[min(int(0.75*npt + 0.5), npt - 1)]
619 q1 = gdmag[min(int(0.25*npt + 0.5), npt - 1)]
620
621 sig = IQ_TO_STDEV*(q3 - q1) # estimate of standard deviation
622
623 good = abs(dmag - center) < self.config.nSigma*min(sig, sigmaMax) # don't clip too softly
624
625 # =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
626 if self.scatterPlotscatterPlot:
627 try:
628 self.figfig.clf()
629
630 axes = self.figfig.add_axes((0.1, 0.1, 0.85, 0.80))
631
632 axes.plot(ref[good], dmag[good] - center, "b+")
633 axes.errorbar(ref[good], dmag[good] - center, yerr=dmagErr[good],
634 linestyle='', color='b')
635
636 bad = np.logical_not(good)
637 if len(ref[bad]) > 0:
638 axes.plot(ref[bad], dmag[bad] - center, "r+")
639 axes.errorbar(ref[bad], dmag[bad] - center, yerr=dmagErr[bad],
640 linestyle='', color='r')
641
642 axes.plot((-100, 100), (0, 0), "g-")
643 for x in (-1, 1):
644 axes.plot((-100, 100), x*0.05*np.ones(2), "g--")
645
646 axes.set_ylim(-1.1, 1.1)
647 axes.set_xlim(24, 13)
648 axes.set_xlabel("Reference")
649 axes.set_ylabel("Reference - Instrumental")
650
651 self.figfig.show()
652
653 if self.scatterPlotscatterPlot > 1:
654 reply = None
655 while i == 0 or reply != "c":
656 try:
657 reply = input("Next iteration? [ynhpc] ")
658 except EOFError:
659 reply = "n"
660
661 if reply == "h":
662 print("Options: c[ontinue] h[elp] n[o] p[db] y[es]", file=sys.stderr)
663 continue
664
665 if reply in ("", "c", "n", "p", "y"):
666 break
667 else:
668 print("Unrecognised response: %s" % reply, file=sys.stderr)
669
670 if reply == "n":
671 break
672 elif reply == "p":
673 import pdb
674 pdb.set_trace()
675 except Exception as e:
676 print("Error plotting in PhotoCal.getZeroPoint: %s" % e, file=sys.stderr)
677
678 # =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
679
680 old_ngood = ngood
681 ngood = sum(good)
682 if ngood == 0:
683 msg = "PhotoCal.getZeroPoint: no good stars remain"
684
685 if i == 0: # failed the first time round -- probably all fell in one bin
686 center = np.average(dmag, weights=dmagErr)
687 msg += " on first iteration; using average of all calibration stars"
688
689 self.log.warning(msg)
690
691 return pipeBase.Struct(
692 zp=center,
693 sigma=sig,
694 ngood=len(dmag))
695 elif ngood == old_ngood:
696 break
697
698 if False:
699 ref = ref[good]
700 dmag = dmag[good]
701 dmagErr = dmagErr[good]
702
703 dmag = dmag[good]
704 dmagErr = dmagErr[good]
705 zp, weightSum = np.average(dmag, weights=1/dmagErr**2, returned=True)
706 sigma = np.sqrt(1.0/weightSum)
707 return pipeBase.Struct(
708 zp=zp,
709 sigma=sigma,
710 ngood=len(dmag),
711 )
def getZeroPoint(self, src, ref, srcErr=None, zp0=None)
Flux calibration code, returning (ZeroPoint, Distribution Width, Number of stars)
Definition: photoCal.py:512
def extractMagArrays(self, matches, filterLabel, sourceKeys)
Extract magnitude and magnitude error arrays from the given matches.
Definition: photoCal.py:287
def __init__(self, refObjLoader, schema=None, **kwds)
Create the photometric calibration task.
Definition: photoCal.py:248
def displaySources(self, exposure, matches, reserved, frame=1)
Definition: photoCal.py:487
def run(self, exposure, sourceCat, expId=0)
Do photometric calibration - select matches to use and (possibly iteratively) compute the zero point.
Definition: photoCal.py:392