Hide keyboard shortcuts

Hot-keys on this page

r m x p   toggle line displays

j k   next/prev highlighted chunk

0   (zero) top of page

1   (one) first highlighted chunk

1# This file is part of pipe_tasks. 

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 <https://www.gnu.org/licenses/>. 

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 .colorterms import ColortermLibrary 

37 

38__all__ = ["PhotoCalTask", "PhotoCalConfig"] 

39 

40 

41class PhotoCalConfig(pexConf.Config): 

42 """Config for PhotoCal""" 

43 match = pexConf.ConfigField("Match to reference catalog", 

44 DirectMatchConfigWithoutLoader) 

45 reserve = pexConf.ConfigurableField(target=ReserveSourcesTask, doc="Reserve sources from fitting") 

46 fluxField = pexConf.Field( 

47 dtype=str, 

48 default="slot_CalibFlux_instFlux", 

49 doc=("Name of the source instFlux field to use. The associated flag field\n" 

50 "('<name>_flags') will be implicitly included in badFlags."), 

51 ) 

52 applyColorTerms = pexConf.Field( 

53 dtype=bool, 

54 default=None, 

55 doc=("Apply photometric color terms to reference stars? One of:\n" 

56 "None: apply if colorterms and photoCatName are not None;\n" 

57 " fail if color term data is not available for the specified ref catalog and filter.\n" 

58 "True: always apply colorterms; fail if color term data is not available for the\n" 

59 " specified reference catalog and filter.\n" 

60 "False: do not apply."), 

61 optional=True, 

62 ) 

63 sigmaMax = pexConf.Field( 

64 dtype=float, 

65 default=0.25, 

66 doc="maximum sigma to use when clipping", 

67 optional=True, 

68 ) 

69 nSigma = pexConf.Field( 

70 dtype=float, 

71 default=3.0, 

72 doc="clip at nSigma", 

73 ) 

74 useMedian = pexConf.Field( 

75 dtype=bool, 

76 default=True, 

77 doc="use median instead of mean to compute zeropoint", 

78 ) 

79 nIter = pexConf.Field( 

80 dtype=int, 

81 default=20, 

82 doc="number of iterations", 

83 ) 

84 colorterms = pexConf.ConfigField( 

85 dtype=ColortermLibrary, 

86 doc="Library of photometric reference catalog name: color term dict", 

87 ) 

88 photoCatName = pexConf.Field( 

89 dtype=str, 

90 optional=True, 

91 doc=("Name of photometric reference catalog; used to select a color term dict in colorterms." 

92 " see also applyColorTerms"), 

93 ) 

94 magErrFloor = pexConf.RangeField( 

95 dtype=float, 

96 default=0.0, 

97 doc="Additional magnitude uncertainty to be added in quadrature with measurement errors.", 

98 min=0.0, 

99 ) 

100 

101 def validate(self): 

102 pexConf.Config.validate(self) 

103 if self.applyColorTerms and self.photoCatName is None: 

104 raise RuntimeError("applyColorTerms=True requires photoCatName is non-None") 

105 if self.applyColorTerms and len(self.colorterms.data) == 0: 

106 raise RuntimeError("applyColorTerms=True requires colorterms be provided") 

107 

108 def setDefaults(self): 

109 pexConf.Config.setDefaults(self) 

110 self.match.sourceSelection.doFlags = True 

111 self.match.sourceSelection.flags.bad = [ 

112 "base_PixelFlags_flag_edge", 

113 "base_PixelFlags_flag_interpolated", 

114 "base_PixelFlags_flag_saturated", 

115 ] 

116 self.match.sourceSelection.doUnresolved = True 

117 

118 

119## @addtogroup LSST_task_documentation 

120## @{ 

121## @page photoCalTask 

122## @ref PhotoCalTask_ "PhotoCalTask" 

123## Detect positive and negative sources on an exposure and return a new SourceCatalog. 

124## @} 

125 

126class PhotoCalTask(pipeBase.Task): 

127 r"""! 

128@anchor PhotoCalTask_ 

129 

130@brief Calculate the zero point of an exposure given a lsst.afw.table.ReferenceMatchVector. 

131 

132@section pipe_tasks_photocal_Contents Contents 

133 

134 - @ref pipe_tasks_photocal_Purpose 

135 - @ref pipe_tasks_photocal_Initialize 

136 - @ref pipe_tasks_photocal_IO 

137 - @ref pipe_tasks_photocal_Config 

138 - @ref pipe_tasks_photocal_Debug 

139 - @ref pipe_tasks_photocal_Example 

140 

141@section pipe_tasks_photocal_Purpose Description 

142 

143@copybrief PhotoCalTask 

144 

145Calculate an Exposure's zero-point given a set of flux measurements of stars matched to an input catalogue. 

146The type of flux to use is specified by PhotoCalConfig.fluxField. 

147 

148The algorithm clips outliers iteratively, with parameters set in the configuration. 

149 

150@note This task can adds fields to the schema, so any code calling this task must ensure that 

151these columns are indeed present in the input match list; see @ref pipe_tasks_photocal_Example 

152 

153@section pipe_tasks_photocal_Initialize Task initialisation 

154 

155@copydoc \_\_init\_\_ 

156 

157@section pipe_tasks_photocal_IO Inputs/Outputs to the run method 

158 

159@copydoc run 

160 

161@section pipe_tasks_photocal_Config Configuration parameters 

162 

163See @ref PhotoCalConfig 

164 

165@section pipe_tasks_photocal_Debug Debug variables 

166 

167The @link lsst.pipe.base.cmdLineTask.CmdLineTask command line task@endlink interface supports a 

168flag @c -d to import @b debug.py from your @c PYTHONPATH; see @ref baseDebug for more about @b debug.py files. 

169 

170The available variables in PhotoCalTask are: 

171<DL> 

172 <DT> @c display 

173 <DD> If True enable other debug outputs 

174 <DT> @c displaySources 

175 <DD> If True, display the exposure on the display's frame 1 and overlay the source catalogue. 

176 <DL> 

177 <DT> red o 

178 <DD> Reserved objects 

179 <DT> green o 

180 <DD> Objects used in the photometric calibration 

181 </DL> 

182 <DT> @c scatterPlot 

183 <DD> Make a scatter plot of flux v. reference magnitude as a function of reference magnitude. 

184 - good objects in blue 

185 - rejected objects in red 

186 (if @c scatterPlot is 2 or more, prompt to continue after each iteration) 

187</DL> 

188 

189@section pipe_tasks_photocal_Example A complete example of using PhotoCalTask 

190 

191This code is in @link examples/photoCalTask.py@endlink, and can be run as @em e.g. 

192@code 

193examples/photoCalTask.py 

194@endcode 

195@dontinclude photoCalTask.py 

196 

197Import the tasks (there are some other standard imports; read the file for details) 

198@skipline from lsst.pipe.tasks.astrometry 

199@skipline measPhotocal 

200 

201We need to create both our tasks before processing any data as the task constructors 

202can add extra columns to the schema which we get from the input catalogue, @c scrCat: 

203@skipline getSchema 

204 

205Astrometry first: 

206@skip AstrometryTask.ConfigClass 

207@until aTask 

208(that @c filterMap line is because our test code doesn't use a filter that the reference catalogue recognises, 

209so we tell it to use the @c r band) 

210 

211Then photometry: 

212@skip measPhotocal 

213@until pTask 

214 

215If the schema has indeed changed we need to add the new columns to the source table 

216(yes; this should be easier!) 

217@skip srcCat 

218@until srcCat = cat 

219 

220We're now ready to process the data (we could loop over multiple exposures/catalogues using the same 

221task objects): 

222@skip matches 

223@until result 

224 

225We can then unpack and use the results: 

226@skip calib 

227@until np.log 

228 

229<HR> 

230To investigate the @ref pipe_tasks_photocal_Debug, put something like 

231@code{.py} 

232 import lsstDebug 

233 def DebugInfo(name): 

234 di = lsstDebug.getInfo(name) # N.b. lsstDebug.Info(name) would call us recursively 

235 if name.endswith(".PhotoCal"): 

236 di.display = 1 

237 

238 return di 

239 

240 lsstDebug.Info = DebugInfo 

241@endcode 

242into your debug.py file and run photoCalTask.py with the @c --debug flag. 

243 """ 

244 ConfigClass = PhotoCalConfig 

245 _DefaultName = "photoCal" 

246 

247 def __init__(self, refObjLoader, schema=None, **kwds): 

248 """!Create the photometric calibration task. See PhotoCalTask.init for documentation 

249 """ 

250 pipeBase.Task.__init__(self, **kwds) 

251 self.scatterPlot = None 

252 self.fig = None 

253 if schema is not None: 

254 self.usedKey = schema.addField("calib_photometry_used", type="Flag", 

255 doc="set if source was used in photometric calibration") 

256 else: 

257 self.usedKey = None 

258 self.match = DirectMatchTask(config=self.config.match, refObjLoader=refObjLoader, 

259 name="match", parentTask=self) 

260 self.makeSubtask("reserve", columnName="calib_photometry", schema=schema, 

261 doc="set if source was reserved from photometric calibration") 

262 

263 def getSourceKeys(self, schema): 

264 """Return a struct containing the source catalog keys for fields used 

265 by PhotoCalTask. 

266 

267 

268 Parameters 

269 ---------- 

270 schema : `lsst.afw.table.schema` 

271 Schema of the catalog to get keys from. 

272 

273 Returns 

274 ------- 

275 result : `lsst.pipe.base.Struct` 

276 Result struct with components: 

277 

278 - ``instFlux``: Instrument flux key. 

279 - ``instFluxErr``: Instrument flux error key. 

280 """ 

281 instFlux = schema.find(self.config.fluxField).key 

282 instFluxErr = schema.find(self.config.fluxField + "Err").key 

283 return pipeBase.Struct(instFlux=instFlux, instFluxErr=instFluxErr) 

284 

285 @pipeBase.timeMethod 

286 def extractMagArrays(self, matches, filterName, sourceKeys): 

287 """!Extract magnitude and magnitude error arrays from the given matches. 

288 

289 @param[in] matches Reference/source matches, a @link lsst::afw::table::ReferenceMatchVector@endlink 

290 @param[in] filterName Name of filter being calibrated 

291 @param[in] sourceKeys Struct of source catalog keys, as returned by getSourceKeys() 

292 

293 @return Struct containing srcMag, refMag, srcMagErr, refMagErr, and magErr numpy arrays 

294 where magErr is an error in the magnitude; the error in srcMag - refMag 

295 If nonzero, config.magErrFloor will be added to magErr *only* (not srcMagErr or refMagErr), as 

296 magErr is what is later used to determine the zero point. 

297 Struct also contains refFluxFieldList: a list of field names of the reference catalog used for fluxes 

298 (1 or 2 strings) 

299 @note These magnitude arrays are the @em inputs to the photometric calibration, some may have been 

300 discarded by clipping while estimating the calibration (https://jira.lsstcorp.org/browse/DM-813) 

301 """ 

302 srcInstFluxArr = np.array([m.second.get(sourceKeys.instFlux) for m in matches]) 

303 srcInstFluxErrArr = np.array([m.second.get(sourceKeys.instFluxErr) for m in matches]) 

304 if not np.all(np.isfinite(srcInstFluxErrArr)): 

305 # this is an unpleasant hack; see DM-2308 requesting a better solution 

306 self.log.warn("Source catalog does not have flux uncertainties; using sqrt(flux).") 

307 srcInstFluxErrArr = np.sqrt(srcInstFluxArr) 

308 

309 # convert source instFlux from DN to an estimate of nJy 

310 referenceFlux = (0*u.ABmag).to_value(u.nJy) 

311 srcInstFluxArr = srcInstFluxArr * referenceFlux 

312 srcInstFluxErrArr = srcInstFluxErrArr * referenceFlux 

313 

314 if not matches: 

315 raise RuntimeError("No reference stars are available") 

316 refSchema = matches[0].first.schema 

317 

318 applyColorTerms = self.config.applyColorTerms 

319 applyCTReason = "config.applyColorTerms is %s" % (self.config.applyColorTerms,) 

320 if self.config.applyColorTerms is None: 

321 # apply color terms if color term data is available and photoCatName specified 

322 ctDataAvail = len(self.config.colorterms.data) > 0 

323 photoCatSpecified = self.config.photoCatName is not None 

324 applyCTReason += " and data %s available" % ("is" if ctDataAvail else "is not") 

325 applyCTReason += " and photoRefCat %s provided" % ("is" if photoCatSpecified else "is not") 

326 applyColorTerms = ctDataAvail and photoCatSpecified 

327 

328 if applyColorTerms: 

329 self.log.info("Applying color terms for filterName=%r, config.photoCatName=%s because %s", 

330 filterName, self.config.photoCatName, applyCTReason) 

331 colorterm = self.config.colorterms.getColorterm( 

332 filterName=filterName, photoCatName=self.config.photoCatName, doRaise=True) 

333 refCat = afwTable.SimpleCatalog(matches[0].first.schema) 

334 

335 # extract the matched refCat as a Catalog for the colorterm code 

336 refCat.reserve(len(matches)) 

337 for x in matches: 

338 record = refCat.addNew() 

339 record.assign(x.first) 

340 

341 refMagArr, refMagErrArr = colorterm.getCorrectedMagnitudes(refCat, filterName) 

342 fluxFieldList = [getRefFluxField(refSchema, filt) for filt in (colorterm.primary, 

343 colorterm.secondary)] 

344 else: 

345 # no colorterms to apply 

346 self.log.info("Not applying color terms because %s", applyCTReason) 

347 colorterm = None 

348 

349 fluxFieldList = [getRefFluxField(refSchema, filterName)] 

350 fluxField = getRefFluxField(refSchema, filterName) 

351 fluxKey = refSchema.find(fluxField).key 

352 refFluxArr = np.array([m.first.get(fluxKey) for m in matches]) 

353 

354 try: 

355 fluxErrKey = refSchema.find(fluxField + "Err").key 

356 refFluxErrArr = np.array([m.first.get(fluxErrKey) for m in matches]) 

357 except KeyError: 

358 # Reference catalogue may not have flux uncertainties; HACK DM-2308 

359 self.log.warn("Reference catalog does not have flux uncertainties for %s; using sqrt(flux).", 

360 fluxField) 

361 refFluxErrArr = np.sqrt(refFluxArr) 

362 

363 refMagArr = u.Quantity(refFluxArr, u.nJy).to_value(u.ABmag) 

364 # HACK convert to Jy until we have a replacement for this (DM-16903) 

365 refMagErrArr = abMagErrFromFluxErr(refFluxErrArr*1e-9, refFluxArr*1e-9) 

366 

367 # compute the source catalog magnitudes and errors 

368 srcMagArr = u.Quantity(srcInstFluxArr, u.nJy).to_value(u.ABmag) 

369 # Fitting with error bars in both axes is hard 

370 # for now ignore reference flux error, but ticket DM-2308 is a request for a better solution 

371 # HACK convert to Jy until we have a replacement for this (DM-16903) 

372 magErrArr = abMagErrFromFluxErr(srcInstFluxErrArr*1e-9, srcInstFluxArr*1e-9) 

373 if self.config.magErrFloor != 0.0: 

374 magErrArr = (magErrArr**2 + self.config.magErrFloor**2)**0.5 

375 

376 srcMagErrArr = abMagErrFromFluxErr(srcInstFluxErrArr*1e-9, srcInstFluxArr*1e-9) 

377 

378 good = np.isfinite(srcMagArr) & np.isfinite(refMagArr) 

379 

380 return pipeBase.Struct( 

381 srcMag=srcMagArr[good], 

382 refMag=refMagArr[good], 

383 magErr=magErrArr[good], 

384 srcMagErr=srcMagErrArr[good], 

385 refMagErr=refMagErrArr[good], 

386 refFluxFieldList=fluxFieldList, 

387 ) 

388 

389 @pipeBase.timeMethod 

390 def run(self, exposure, sourceCat, expId=0): 

391 """!Do photometric calibration - select matches to use and (possibly iteratively) compute 

392 the zero point. 

393 

394 @param[in] exposure Exposure upon which the sources in the matches were detected. 

395 @param[in] sourceCat A catalog of sources to use in the calibration 

396 (@em i.e. a list of lsst.afw.table.Match with 

397 @c first being of type lsst.afw.table.SimpleRecord and @c second type lsst.afw.table.SourceRecord --- 

398 the reference object and matched object respectively). 

399 (will not be modified except to set the outputField if requested.). 

400 

401 @return Struct of: 

402 - photoCalib -- @link lsst::afw::image::PhotoCalib@endlink object containing the calibration 

403 - arrays ------ Magnitude arrays returned be PhotoCalTask.extractMagArrays 

404 - matches ----- Final ReferenceMatchVector, as returned by PhotoCalTask.selectMatches. 

405 - zp ---------- Photometric zero point (mag) 

406 - sigma ------- Standard deviation of fit of photometric zero point (mag) 

407 - ngood ------- Number of sources used to fit photometric zero point 

408 

409 The exposure is only used to provide the name of the filter being calibrated (it may also be 

410 used to generate debugging plots). 

411 

412 The reference objects: 

413 - Must include a field @c photometric; True for objects which should be considered as 

414 photometric standards 

415 - Must include a field @c flux; the flux used to impose a magnitude limit and also to calibrate 

416 the data to (unless a color term is specified, in which case ColorTerm.primary is used; 

417 See https://jira.lsstcorp.org/browse/DM-933) 

418 - May include a field @c stargal; if present, True means that the object is a star 

419 - May include a field @c var; if present, True means that the object is variable 

420 

421 The measured sources: 

422 - Must include PhotoCalConfig.fluxField; the flux measurement to be used for calibration 

423 

424 @throws RuntimeError with the following strings: 

425 

426 <DL> 

427 <DT> No matches to use for photocal 

428 <DD> No matches are available (perhaps no sources/references were selected by the matcher). 

429 <DT> No reference stars are available 

430 <DD> No matches are available from which to extract magnitudes. 

431 </DL> 

432 """ 

433 import lsstDebug 

434 

435 display = lsstDebug.Info(__name__).display 

436 displaySources = display and lsstDebug.Info(__name__).displaySources 

437 self.scatterPlot = display and lsstDebug.Info(__name__).scatterPlot 

438 

439 if self.scatterPlot: 

440 from matplotlib import pyplot 

441 try: 

442 self.fig.clf() 

443 except Exception: 

444 self.fig = pyplot.figure() 

445 

446 filterName = exposure.getFilter().getName() 

447 

448 # Match sources 

449 matchResults = self.match.run(sourceCat, filterName) 

450 matches = matchResults.matches 

451 

452 reserveResults = self.reserve.run([mm.second for mm in matches], expId=expId) 

453 if displaySources: 

454 self.displaySources(exposure, matches, reserveResults.reserved) 

455 if reserveResults.reserved.sum() > 0: 

456 matches = [mm for mm, use in zip(matches, reserveResults.use) if use] 

457 if len(matches) == 0: 

458 raise RuntimeError("No matches to use for photocal") 

459 if self.usedKey is not None: 

460 for mm in matches: 

461 mm.second.set(self.usedKey, True) 

462 

463 # Prepare for fitting 

464 sourceKeys = self.getSourceKeys(matches[0].second.schema) 

465 arrays = self.extractMagArrays(matches=matches, filterName=filterName, sourceKeys=sourceKeys) 

466 

467 # Fit for zeropoint 

468 r = self.getZeroPoint(arrays.srcMag, arrays.refMag, arrays.magErr) 

469 self.log.info("Magnitude zero point: %f +/- %f from %d stars", r.zp, r.sigma, r.ngood) 

470 

471 # Prepare the results 

472 flux0 = 10**(0.4*r.zp) # Flux of mag=0 star 

473 flux0err = 0.4*math.log(10)*flux0*r.sigma # Error in flux0 

474 photoCalib = makePhotoCalibFromCalibZeroPoint(flux0, flux0err) 

475 

476 return pipeBase.Struct( 

477 photoCalib=photoCalib, 

478 arrays=arrays, 

479 matches=matches, 

480 zp=r.zp, 

481 sigma=r.sigma, 

482 ngood=r.ngood, 

483 ) 

484 

485 def displaySources(self, exposure, matches, reserved, frame=1): 

486 """Display sources we'll use for photocal 

487 

488 Sources that will be actually used will be green. 

489 Sources reserved from the fit will be red. 

490 

491 Parameters 

492 ---------- 

493 exposure : `lsst.afw.image.ExposureF` 

494 Exposure to display. 

495 matches : `list` of `lsst.afw.table.RefMatch` 

496 Matches used for photocal. 

497 reserved : `numpy.ndarray` of type `bool` 

498 Boolean array indicating sources that are reserved. 

499 frame : `int` 

500 Frame number for display. 

501 """ 

502 disp = afwDisplay.getDisplay(frame=frame) 

503 disp.mtv(exposure, title="photocal") 

504 with disp.Buffering(): 

505 for mm, rr in zip(matches, reserved): 

506 x, y = mm.second.getCentroid() 

507 ctype = afwDisplay.RED if rr else afwDisplay.GREEN 

508 disp.dot("o", x, y, size=4, ctype=ctype) 

509 

510 def getZeroPoint(self, src, ref, srcErr=None, zp0=None): 

511 """!Flux calibration code, returning (ZeroPoint, Distribution Width, Number of stars) 

512 

513 We perform nIter iterations of a simple sigma-clipping algorithm with a couple of twists: 

514 1. We use the median/interquartile range to estimate the position to clip around, and the 

515 "sigma" to use. 

516 2. We never allow sigma to go _above_ a critical value sigmaMax --- if we do, a sufficiently 

517 large estimate will prevent the clipping from ever taking effect. 

518 3. Rather than start with the median we start with a crude mode. This means that a set of magnitude 

519 residuals with a tight core and asymmetrical outliers will start in the core. We use the width of 

520 this core to set our maximum sigma (see 2.) 

521 

522 @return Struct of: 

523 - zp ---------- Photometric zero point (mag) 

524 - sigma ------- Standard deviation of fit of zero point (mag) 

525 - ngood ------- Number of sources used to fit zero point 

526 """ 

527 sigmaMax = self.config.sigmaMax 

528 

529 dmag = ref - src 

530 

531 indArr = np.argsort(dmag) 

532 dmag = dmag[indArr] 

533 

534 if srcErr is not None: 

535 dmagErr = srcErr[indArr] 

536 else: 

537 dmagErr = np.ones(len(dmag)) 

538 

539 # need to remove nan elements to avoid errors in stats calculation with numpy 

540 ind_noNan = np.array([i for i in range(len(dmag)) 

541 if (not np.isnan(dmag[i]) and not np.isnan(dmagErr[i]))]) 

542 dmag = dmag[ind_noNan] 

543 dmagErr = dmagErr[ind_noNan] 

544 

545 IQ_TO_STDEV = 0.741301109252802 # 1 sigma in units of interquartile (assume Gaussian) 

546 

547 npt = len(dmag) 

548 ngood = npt 

549 good = None # set at end of first iteration 

550 for i in range(self.config.nIter): 

551 if i > 0: 

552 npt = sum(good) 

553 

554 center = None 

555 if i == 0: 

556 # 

557 # Start by finding the mode 

558 # 

559 nhist = 20 

560 try: 

561 hist, edges = np.histogram(dmag, nhist, new=True) 

562 except TypeError: 

563 hist, edges = np.histogram(dmag, nhist) # they removed new=True around numpy 1.5 

564 imode = np.arange(nhist)[np.where(hist == hist.max())] 

565 

566 if imode[-1] - imode[0] + 1 == len(imode): # Multiple modes, but all contiguous 

567 if zp0: 

568 center = zp0 

569 else: 

570 center = 0.5*(edges[imode[0]] + edges[imode[-1] + 1]) 

571 

572 peak = sum(hist[imode])/len(imode) # peak height 

573 

574 # Estimate FWHM of mode 

575 j = imode[0] 

576 while j >= 0 and hist[j] > 0.5*peak: 

577 j -= 1 

578 j = max(j, 0) 

579 q1 = dmag[sum(hist[range(j)])] 

580 

581 j = imode[-1] 

582 while j < nhist and hist[j] > 0.5*peak: 

583 j += 1 

584 j = min(j, nhist - 1) 

585 j = min(sum(hist[range(j)]), npt - 1) 

586 q3 = dmag[j] 

587 

588 if q1 == q3: 

589 q1 = dmag[int(0.25*npt)] 

590 q3 = dmag[int(0.75*npt)] 

591 

592 sig = (q3 - q1)/2.3 # estimate of standard deviation (based on FWHM; 2.358 for Gaussian) 

593 

594 if sigmaMax is None: 

595 sigmaMax = 2*sig # upper bound on st. dev. for clipping. multiplier is a heuristic 

596 

597 self.log.debug("Photo calibration histogram: center = %.2f, sig = %.2f", center, sig) 

598 

599 else: 

600 if sigmaMax is None: 

601 sigmaMax = dmag[-1] - dmag[0] 

602 

603 center = np.median(dmag) 

604 q1 = dmag[int(0.25*npt)] 

605 q3 = dmag[int(0.75*npt)] 

606 sig = (q3 - q1)/2.3 # estimate of standard deviation (based on FWHM; 2.358 for Gaussian) 

607 

608 if center is None: # usually equivalent to (i > 0) 

609 gdmag = dmag[good] 

610 if self.config.useMedian: 

611 center = np.median(gdmag) 

612 else: 

613 gdmagErr = dmagErr[good] 

614 center = np.average(gdmag, weights=gdmagErr) 

615 

616 q3 = gdmag[min(int(0.75*npt + 0.5), npt - 1)] 

617 q1 = gdmag[min(int(0.25*npt + 0.5), npt - 1)] 

618 

619 sig = IQ_TO_STDEV*(q3 - q1) # estimate of standard deviation 

620 

621 good = abs(dmag - center) < self.config.nSigma*min(sig, sigmaMax) # don't clip too softly 

622 

623 # =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- 

624 if self.scatterPlot: 

625 try: 

626 self.fig.clf() 

627 

628 axes = self.fig.add_axes((0.1, 0.1, 0.85, 0.80)) 

629 

630 axes.plot(ref[good], dmag[good] - center, "b+") 

631 axes.errorbar(ref[good], dmag[good] - center, yerr=dmagErr[good], 

632 linestyle='', color='b') 

633 

634 bad = np.logical_not(good) 

635 if len(ref[bad]) > 0: 

636 axes.plot(ref[bad], dmag[bad] - center, "r+") 

637 axes.errorbar(ref[bad], dmag[bad] - center, yerr=dmagErr[bad], 

638 linestyle='', color='r') 

639 

640 axes.plot((-100, 100), (0, 0), "g-") 

641 for x in (-1, 1): 

642 axes.plot((-100, 100), x*0.05*np.ones(2), "g--") 

643 

644 axes.set_ylim(-1.1, 1.1) 

645 axes.set_xlim(24, 13) 

646 axes.set_xlabel("Reference") 

647 axes.set_ylabel("Reference - Instrumental") 

648 

649 self.fig.show() 

650 

651 if self.scatterPlot > 1: 

652 reply = None 

653 while i == 0 or reply != "c": 

654 try: 

655 reply = input("Next iteration? [ynhpc] ") 

656 except EOFError: 

657 reply = "n" 

658 

659 if reply == "h": 

660 print("Options: c[ontinue] h[elp] n[o] p[db] y[es]", file=sys.stderr) 

661 continue 

662 

663 if reply in ("", "c", "n", "p", "y"): 

664 break 

665 else: 

666 print("Unrecognised response: %s" % reply, file=sys.stderr) 

667 

668 if reply == "n": 

669 break 

670 elif reply == "p": 

671 import pdb 

672 pdb.set_trace() 

673 except Exception as e: 

674 print("Error plotting in PhotoCal.getZeroPoint: %s" % e, file=sys.stderr) 

675 

676 # =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- 

677 

678 old_ngood = ngood 

679 ngood = sum(good) 

680 if ngood == 0: 

681 msg = "PhotoCal.getZeroPoint: no good stars remain" 

682 

683 if i == 0: # failed the first time round -- probably all fell in one bin 

684 center = np.average(dmag, weights=dmagErr) 

685 msg += " on first iteration; using average of all calibration stars" 

686 

687 self.log.warn(msg) 

688 

689 return pipeBase.Struct( 

690 zp=center, 

691 sigma=sig, 

692 ngood=len(dmag)) 

693 elif ngood == old_ngood: 

694 break 

695 

696 if False: 

697 ref = ref[good] 

698 dmag = dmag[good] 

699 dmagErr = dmagErr[good] 

700 

701 dmag = dmag[good] 

702 dmagErr = dmagErr[good] 

703 zp, weightSum = np.average(dmag, weights=1/dmagErr**2, returned=True) 

704 sigma = np.sqrt(1.0/weightSum) 

705 return pipeBase.Struct( 

706 zp=zp, 

707 sigma=sigma, 

708 ngood=len(dmag), 

709 )