Coverage for python/lsst/ip/diffim/dipoleFitTask.py: 11%

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21# 

22 

23import logging 

24import numpy as np 

25import warnings 

26 

27import lsst.afw.image as afwImage 

28import lsst.meas.base as measBase 

29import lsst.afw.table as afwTable 

30import lsst.afw.detection as afwDet 

31import lsst.geom as geom 

32import lsst.pex.exceptions as pexExcept 

33import lsst.pex.config as pexConfig 

34from lsst.pipe.base import Struct 

35from lsst.utils.timer import timeMethod 

36 

37__all__ = ("DipoleFitTask", "DipoleFitPlugin", "DipoleFitTaskConfig", "DipoleFitPluginConfig", 

38 "DipoleFitAlgorithm") 

39 

40 

41# Create a new measurement task (`DipoleFitTask`) that can handle all other SFM tasks but can 

42# pass a separate pos- and neg- exposure/image to the `DipoleFitPlugin`s `run()` method. 

43 

44 

45class DipoleFitPluginConfig(measBase.SingleFramePluginConfig): 

46 """Configuration for DipoleFitPlugin 

47 """ 

48 

49 fitAllDiaSources = pexConfig.Field( 

50 dtype=float, default=False, 

51 doc="""Attempte dipole fit of all diaSources (otherwise just the ones consisting of overlapping 

52 positive and negative footprints)""") 

53 

54 maxSeparation = pexConfig.Field( 

55 dtype=float, default=5., 

56 doc="Assume dipole is not separated by more than maxSeparation * psfSigma") 

57 

58 relWeight = pexConfig.Field( 

59 dtype=float, default=0.5, 

60 doc="""Relative weighting of pre-subtraction images (higher -> greater influence of pre-sub. 

61 images on fit)""") 

62 

63 tolerance = pexConfig.Field( 

64 dtype=float, default=1e-7, 

65 doc="Fit tolerance") 

66 

67 fitBackground = pexConfig.Field( 

68 dtype=int, default=1, 

69 doc="Set whether and how to fit for linear gradient in pre-sub. images. Possible values:" 

70 "0: do not fit background at all" 

71 "1 (default): pre-fit the background using linear least squares and then do not fit it as part" 

72 "of the dipole fitting optimization" 

73 "2: pre-fit the background using linear least squares (as in 1), and use the parameter" 

74 "estimates from that fit as starting parameters for an integrated re-fit of the background") 

75 

76 fitSeparateNegParams = pexConfig.Field( 

77 dtype=bool, default=False, 

78 doc="Include parameters to fit for negative values (flux, gradient) separately from pos.") 

79 

80 # Config params for classification of detected diaSources as dipole or not 

81 minSn = pexConfig.Field( 

82 dtype=float, default=np.sqrt(2) * 5.0, 

83 doc="Minimum quadrature sum of positive+negative lobe S/N to be considered a dipole") 

84 

85 maxFluxRatio = pexConfig.Field( 

86 dtype=float, default=0.65, 

87 doc="Maximum flux ratio in either lobe to be considered a dipole") 

88 

89 maxChi2DoF = pexConfig.Field( 

90 dtype=float, default=0.05, 

91 doc="""Maximum Chi2/DoF significance of fit to be considered a dipole. 

92 Default value means \"Choose a chi2DoF corresponding to a significance level of at most 0.05\" 

93 (note this is actually a significance, not a chi2 value).""") 

94 

95 maxFootprintArea = pexConfig.Field( 

96 dtype=int, default=1_200, 

97 doc=("Maximum area for footprints before they are ignored as large; " 

98 "non-positive means no threshold applied" 

99 "Threshold chosen for HSC and DECam data, see DM-38741 for details.")) 

100 

101 

102class DipoleFitTaskConfig(measBase.SingleFrameMeasurementConfig): 

103 """Measurement of detected diaSources as dipoles 

104 

105 Currently we keep the "old" DipoleMeasurement algorithms turned on. 

106 """ 

107 

108 def setDefaults(self): 

109 measBase.SingleFrameMeasurementConfig.setDefaults(self) 

110 

111 self.plugins.names = ["base_CircularApertureFlux", 

112 "base_PixelFlags", 

113 "base_SkyCoord", 

114 "base_PsfFlux", 

115 "base_SdssCentroid", 

116 "base_SdssShape", 

117 "base_GaussianFlux", 

118 "base_PeakLikelihoodFlux", 

119 "base_PeakCentroid", 

120 "base_NaiveCentroid", 

121 "ip_diffim_NaiveDipoleCentroid", 

122 "ip_diffim_NaiveDipoleFlux", 

123 "ip_diffim_PsfDipoleFlux", 

124 "ip_diffim_ClassificationDipole", 

125 ] 

126 

127 self.slots.calibFlux = None 

128 self.slots.modelFlux = None 

129 self.slots.gaussianFlux = None 

130 self.slots.shape = "base_SdssShape" 

131 self.slots.centroid = "ip_diffim_NaiveDipoleCentroid" 

132 self.doReplaceWithNoise = False 

133 

134 

135class DipoleFitTask(measBase.SingleFrameMeasurementTask): 

136 """A task that fits a dipole to a difference image, with an optional separate detection image. 

137 

138 Because it subclasses SingleFrameMeasurementTask, and calls 

139 SingleFrameMeasurementTask.run() from its run() method, it still 

140 can be used identically to a standard SingleFrameMeasurementTask. 

141 """ 

142 

143 ConfigClass = DipoleFitTaskConfig 

144 _DefaultName = "ip_diffim_DipoleFit" 

145 

146 def __init__(self, schema, algMetadata=None, **kwargs): 

147 

148 measBase.SingleFrameMeasurementTask.__init__(self, schema, algMetadata, **kwargs) 

149 

150 dpFitPluginConfig = self.config.plugins['ip_diffim_DipoleFit'] 

151 

152 self.dipoleFitter = DipoleFitPlugin(dpFitPluginConfig, name=self._DefaultName, 

153 schema=schema, metadata=algMetadata, 

154 logName=self.log.name) 

155 

156 @timeMethod 

157 def run(self, sources, exposure, posExp=None, negExp=None, **kwargs): 

158 """Run dipole measurement and classification 

159 

160 Parameters 

161 ---------- 

162 sources : `lsst.afw.table.SourceCatalog` 

163 ``diaSources`` that will be measured using dipole measurement 

164 exposure : `lsst.afw.image.Exposure` 

165 The difference exposure on which the ``diaSources`` of the ``sources`` parameter 

166 were detected. If neither ``posExp`` nor ``negExp`` are set, then the dipole is also 

167 fitted directly to this difference image. 

168 posExp : `lsst.afw.image.Exposure`, optional 

169 "Positive" exposure, typically a science exposure, or None if unavailable 

170 When `posExp` is `None`, will compute `posImage = exposure + negExp`. 

171 negExp : `lsst.afw.image.Exposure`, optional 

172 "Negative" exposure, typically a template exposure, or None if unavailable 

173 When `negExp` is `None`, will compute `negImage = posExp - exposure`. 

174 **kwargs 

175 Additional keyword arguments for `lsst.meas.base.sfm.SingleFrameMeasurementTask`. 

176 """ 

177 

178 measBase.SingleFrameMeasurementTask.run(self, sources, exposure, **kwargs) 

179 

180 if not sources: 

181 return 

182 

183 for source in sources: 

184 self.dipoleFitter.measure(source, exposure, posExp, negExp) 

185 

186 

187class DipoleModel: 

188 """Lightweight class containing methods for generating a dipole model for fitting 

189 to sources in diffims, used by DipoleFitAlgorithm. 

190 

191 See also: 

192 `DMTN-007: Dipole characterization for image differencing <https://dmtn-007.lsst.io>`_. 

193 """ 

194 

195 def __init__(self): 

196 import lsstDebug 

197 self.debug = lsstDebug.Info(__name__).debug 

198 self.log = logging.getLogger(__name__) 

199 

200 def makeBackgroundModel(self, in_x, pars=None): 

201 """Generate gradient model (2-d array) with up to 2nd-order polynomial 

202 

203 Parameters 

204 ---------- 

205 in_x : `numpy.array` 

206 (2, w, h)-dimensional `numpy.array`, containing the 

207 input x,y meshgrid providing the coordinates upon which to 

208 compute the gradient. This will typically be generated via 

209 `_generateXYGrid()`. `w` and `h` correspond to the width and 

210 height of the desired grid. 

211 pars : `list` of `float`, optional 

212 Up to 6 floats for up 

213 to 6 2nd-order 2-d polynomial gradient parameters, in the 

214 following order: (intercept, x, y, xy, x**2, y**2). If `pars` 

215 is emtpy or `None`, do nothing and return `None` (for speed). 

216 

217 Returns 

218 ------- 

219 result : `None` or `numpy.array` 

220 return None, or 2-d numpy.array of width/height matching 

221 input bbox, containing computed gradient values. 

222 """ 

223 

224 # Don't fit for other gradient parameters if the intercept is not included. 

225 if (pars is None) or (len(pars) <= 0) or (pars[0] is None): 

226 return 

227 

228 y, x = in_x[0, :], in_x[1, :] 

229 gradient = np.full_like(x, pars[0], dtype='float64') 

230 if len(pars) > 1 and pars[1] is not None: 

231 gradient += pars[1] * x 

232 if len(pars) > 2 and pars[2] is not None: 

233 gradient += pars[2] * y 

234 if len(pars) > 3 and pars[3] is not None: 

235 gradient += pars[3] * (x * y) 

236 if len(pars) > 4 and pars[4] is not None: 

237 gradient += pars[4] * (x * x) 

238 if len(pars) > 5 and pars[5] is not None: 

239 gradient += pars[5] * (y * y) 

240 

241 return gradient 

242 

243 def _generateXYGrid(self, bbox): 

244 """Generate a meshgrid covering the x,y coordinates bounded by bbox 

245 

246 Parameters 

247 ---------- 

248 bbox : `lsst.geom.Box2I` 

249 input Bounding Box defining the coordinate limits 

250 

251 Returns 

252 ------- 

253 in_x : `numpy.array` 

254 (2, w, h)-dimensional numpy array containing the grid indexing over x- and 

255 y- coordinates 

256 """ 

257 

258 x, y = np.mgrid[bbox.getBeginY():bbox.getEndY(), bbox.getBeginX():bbox.getEndX()] 

259 in_x = np.array([y, x]).astype(np.float64) 

260 in_x[0, :] -= np.mean(in_x[0, :]) 

261 in_x[1, :] -= np.mean(in_x[1, :]) 

262 return in_x 

263 

264 def _getHeavyFootprintSubimage(self, fp, badfill=np.nan, grow=0): 

265 """Extract the image from a ``~lsst.afw.detection.HeavyFootprint`` 

266 as an `lsst.afw.image.ImageF`. 

267 

268 Parameters 

269 ---------- 

270 fp : `lsst.afw.detection.HeavyFootprint` 

271 HeavyFootprint to use to generate the subimage 

272 badfill : `float`, optional 

273 Value to fill in pixels in extracted image that are outside the footprint 

274 grow : `int` 

275 Optionally grow the footprint by this amount before extraction 

276 

277 Returns 

278 ------- 

279 subim2 : `lsst.afw.image.ImageF` 

280 An `~lsst.afw.image.ImageF` containing the subimage. 

281 """ 

282 bbox = fp.getBBox() 

283 if grow > 0: 

284 bbox.grow(grow) 

285 

286 subim2 = afwImage.ImageF(bbox, badfill) 

287 fp.getSpans().unflatten(subim2.array, fp.getImageArray(), bbox.getCorners()[0]) 

288 return subim2 

289 

290 def fitFootprintBackground(self, source, posImage, order=1): 

291 """Fit a linear (polynomial) model of given order (max 2) to the background of a footprint. 

292 

293 Only fit the pixels OUTSIDE of the footprint, but within its bounding box. 

294 

295 Parameters 

296 ---------- 

297 source : `lsst.afw.table.SourceRecord` 

298 SourceRecord, the footprint of which is to be fit 

299 posImage : `lsst.afw.image.Exposure` 

300 The exposure from which to extract the footprint subimage 

301 order : `int` 

302 Polynomial order of background gradient to fit. 

303 

304 Returns 

305 ------- 

306 pars : `tuple` of `float` 

307 `tuple` of length (1 if order==0; 3 if order==1; 6 if order == 2), 

308 containing the resulting fit parameters 

309 """ 

310 

311 # TODO look into whether to use afwMath background methods -- see 

312 # http://lsst-web.ncsa.illinois.edu/doxygen/x_masterDoxyDoc/_background_example.html 

313 fp = source.getFootprint() 

314 bbox = fp.getBBox() 

315 bbox.grow(3) 

316 posImg = afwImage.ImageF(posImage.image, bbox, afwImage.PARENT) 

317 

318 # This code constructs the footprint image so that we can identify the pixels that are 

319 # outside the footprint (but within the bounding box). These are the pixels used for 

320 # fitting the background. 

321 posHfp = afwDet.HeavyFootprintF(fp, posImage.getMaskedImage()) 

322 posFpImg = self._getHeavyFootprintSubimage(posHfp, grow=3) 

323 

324 isBg = np.isnan(posFpImg.array).ravel() 

325 

326 data = posImg.array.ravel() 

327 data = data[isBg] 

328 B = data 

329 

330 x, y = np.mgrid[bbox.getBeginY():bbox.getEndY(), bbox.getBeginX():bbox.getEndX()] 

331 x = x.astype(np.float64).ravel() 

332 x -= np.mean(x) 

333 x = x[isBg] 

334 y = y.astype(np.float64).ravel() 

335 y -= np.mean(y) 

336 y = y[isBg] 

337 b = np.ones_like(x, dtype=np.float64) 

338 

339 M = np.vstack([b]).T # order = 0 

340 if order == 1: 

341 M = np.vstack([b, x, y]).T 

342 elif order == 2: 

343 M = np.vstack([b, x, y, x**2., y**2., x*y]).T 

344 

345 pars = np.linalg.lstsq(M, B, rcond=-1)[0] 

346 return pars 

347 

348 def makeStarModel(self, bbox, psf, xcen, ycen, flux): 

349 """Generate a 2D image model of a single PDF centered at the given coordinates. 

350 

351 Parameters 

352 ---------- 

353 bbox : `lsst.geom.Box` 

354 Bounding box marking pixel coordinates for generated model 

355 psf : TODO: DM-17458 

356 Psf model used to generate the 'star' 

357 xcen : `float` 

358 Desired x-centroid of the 'star' 

359 ycen : `float` 

360 Desired y-centroid of the 'star' 

361 flux : `float` 

362 Desired flux of the 'star' 

363 

364 Returns 

365 ------- 

366 p_Im : `lsst.afw.image.Image` 

367 2-d stellar image of width/height matching input ``bbox``, 

368 containing PSF with given centroid and flux 

369 """ 

370 

371 # Generate the psf image, normalize to flux 

372 psf_img = psf.computeImage(geom.Point2D(xcen, ycen)).convertF() 

373 psf_img_sum = np.nansum(psf_img.array) 

374 psf_img *= (flux/psf_img_sum) 

375 

376 # Clip the PSF image bounding box to fall within the footprint bounding box 

377 psf_box = psf_img.getBBox() 

378 psf_box.clip(bbox) 

379 psf_img = afwImage.ImageF(psf_img, psf_box, afwImage.PARENT) 

380 

381 # Then actually crop the psf image. 

382 # Usually not necessary, but if the dipole is near the edge of the image... 

383 # Would be nice if we could compare original pos_box with clipped pos_box and 

384 # see if it actually was clipped. 

385 p_Im = afwImage.ImageF(bbox) 

386 tmpSubim = afwImage.ImageF(p_Im, psf_box, afwImage.PARENT) 

387 tmpSubim += psf_img 

388 

389 return p_Im 

390 

391 def makeModel(self, x, flux, xcenPos, ycenPos, xcenNeg, ycenNeg, fluxNeg=None, 

392 b=None, x1=None, y1=None, xy=None, x2=None, y2=None, 

393 bNeg=None, x1Neg=None, y1Neg=None, xyNeg=None, x2Neg=None, y2Neg=None, 

394 **kwargs): 

395 """Generate dipole model with given parameters. 

396 

397 This is the function whose sum-of-squared difference from data 

398 is minimized by `lmfit`. 

399 

400 x : TODO: DM-17458 

401 Input independent variable. Used here as the grid on 

402 which to compute the background gradient model. 

403 flux : `float` 

404 Desired flux of the positive lobe of the dipole 

405 xcenPos, ycenPos : `float` 

406 Desired x,y-centroid of the positive lobe of the dipole 

407 xcenNeg, ycenNeg : `float` 

408 Desired x,y-centroid of the negative lobe of the dipole 

409 fluxNeg : `float`, optional 

410 Desired flux of the negative lobe of the dipole, set to 'flux' if None 

411 b, x1, y1, xy, x2, y2 : `float` 

412 Gradient parameters for positive lobe. 

413 bNeg, x1Neg, y1Neg, xyNeg, x2Neg, y2Neg : `float`, optional 

414 Gradient parameters for negative lobe. 

415 They are set to the corresponding positive values if None. 

416 

417 **kwargs : `dict` [`str`] 

418 Keyword arguments passed through ``lmfit`` and 

419 used by this function. These must include: 

420 

421 - ``psf`` Psf model used to generate the 'star' 

422 - ``rel_weight`` Used to signify least-squares weighting of posImage/negImage 

423 relative to diffim. If ``rel_weight == 0`` then posImage/negImage are ignored. 

424 - ``bbox`` Bounding box containing region to be modelled 

425 

426 Returns 

427 ------- 

428 zout : `numpy.array` 

429 Has width and height matching the input bbox, and 

430 contains the dipole model with given centroids and flux(es). If 

431 ``rel_weight`` = 0, this is a 2-d array with dimensions matching 

432 those of bbox; otherwise a stack of three such arrays, 

433 representing the dipole (diffim), positive, and negative images 

434 respectively. 

435 """ 

436 

437 psf = kwargs.get('psf') 

438 rel_weight = kwargs.get('rel_weight') # if > 0, we're including pre-sub. images 

439 fp = kwargs.get('footprint') 

440 bbox = fp.getBBox() 

441 

442 if fluxNeg is None: 

443 fluxNeg = flux 

444 

445 if self.debug: 

446 self.log.debug('%.2f %.2f %.2f %.2f %.2f %.2f', 

447 flux, fluxNeg, xcenPos, ycenPos, xcenNeg, ycenNeg) 

448 if x1 is not None: 

449 self.log.debug(' %.2f %.2f %.2f', b, x1, y1) 

450 if xy is not None: 

451 self.log.debug(' %.2f %.2f %.2f', xy, x2, y2) 

452 

453 posIm = self.makeStarModel(bbox, psf, xcenPos, ycenPos, flux) 

454 negIm = self.makeStarModel(bbox, psf, xcenNeg, ycenNeg, fluxNeg) 

455 

456 in_x = x 

457 if in_x is None: # use the footprint to generate the input grid 

458 y, x = np.mgrid[bbox.getBeginY():bbox.getEndY(), bbox.getBeginX():bbox.getEndX()] 

459 in_x = np.array([x, y]) * 1. 

460 in_x[0, :] -= in_x[0, :].mean() # center it! 

461 in_x[1, :] -= in_x[1, :].mean() 

462 

463 if b is not None: 

464 gradient = self.makeBackgroundModel(in_x, (b, x1, y1, xy, x2, y2)) 

465 

466 # If bNeg is None, then don't fit the negative background separately 

467 if bNeg is not None: 

468 gradientNeg = self.makeBackgroundModel(in_x, (bNeg, x1Neg, y1Neg, xyNeg, x2Neg, y2Neg)) 

469 else: 

470 gradientNeg = gradient 

471 

472 posIm.array[:, :] += gradient 

473 negIm.array[:, :] += gradientNeg 

474 

475 # Generate the diffIm model 

476 diffIm = afwImage.ImageF(bbox) 

477 diffIm += posIm 

478 diffIm -= negIm 

479 

480 zout = diffIm.array 

481 if rel_weight > 0.: 

482 zout = np.append([zout], [posIm.array, negIm.array], axis=0) 

483 

484 return zout 

485 

486 

487class DipoleFitAlgorithm: 

488 """Fit a dipole model using an image difference. 

489 

490 See also: 

491 `DMTN-007: Dipole characterization for image differencing <https://dmtn-007.lsst.io>`_. 

492 """ 

493 

494 # This is just a private version number to sync with the ipython notebooks that I have been 

495 # using for algorithm development. 

496 _private_version_ = '0.0.5' 

497 

498 # Below is a (somewhat incomplete) list of improvements 

499 # that would be worth investigating, given the time: 

500 

501 # todo 1. evaluate necessity for separate parameters for pos- and neg- images 

502 # todo 2. only fit background OUTSIDE footprint (DONE) and dipole params INSIDE footprint (NOT DONE)? 

503 # todo 3. correct normalization of least-squares weights based on variance planes 

504 # todo 4. account for PSFs that vary across the exposures (should be happening by default?) 

505 # todo 5. correctly account for NA/masks (i.e., ignore!) 

506 # todo 6. better exception handling in the plugin 

507 # todo 7. better classification of dipoles (e.g. by comparing chi2 fit vs. monopole?) 

508 # todo 8. (DONE) Initial fast estimate of background gradient(s) params -- perhaps using numpy.lstsq 

509 # todo 9. (NOT NEEDED - see (2)) Initial fast test whether a background gradient needs to be fit 

510 # todo 10. (DONE) better initial estimate for flux when there's a strong gradient 

511 # todo 11. (DONE) requires a new package `lmfit` -- investiate others? (astropy/scipy/iminuit?) 

512 

513 def __init__(self, diffim, posImage=None, negImage=None): 

514 """Algorithm to run dipole measurement on a diaSource 

515 

516 Parameters 

517 ---------- 

518 diffim : `lsst.afw.image.Exposure` 

519 Exposure on which the diaSources were detected 

520 posImage : `lsst.afw.image.Exposure` 

521 "Positive" exposure from which the template was subtracted 

522 negImage : `lsst.afw.image.Exposure` 

523 "Negative" exposure which was subtracted from the posImage 

524 """ 

525 

526 self.diffim = diffim 

527 self.posImage = posImage 

528 self.negImage = negImage 

529 self.psfSigma = None 

530 if diffim is not None: 

531 diffimPsf = diffim.getPsf() 

532 diffimAvgPos = diffimPsf.getAveragePosition() 

533 self.psfSigma = diffimPsf.computeShape(diffimAvgPos).getDeterminantRadius() 

534 

535 self.log = logging.getLogger(__name__) 

536 

537 import lsstDebug 

538 self.debug = lsstDebug.Info(__name__).debug 

539 

540 def fitDipoleImpl(self, source, tol=1e-7, rel_weight=0.5, 

541 fitBackground=1, bgGradientOrder=1, maxSepInSigma=5., 

542 separateNegParams=True, verbose=False): 

543 """Fit a dipole model to an input difference image. 

544 

545 Actually, fits the subimage bounded by the input source's 

546 footprint) and optionally constrain the fit using the 

547 pre-subtraction images posImage and negImage. 

548 

549 Parameters 

550 ---------- 

551 source : TODO: DM-17458 

552 TODO: DM-17458 

553 tol : float, optional 

554 TODO: DM-17458 

555 rel_weight : `float`, optional 

556 TODO: DM-17458 

557 fitBackground : `int`, optional 

558 TODO: DM-17458 

559 bgGradientOrder : `int`, optional 

560 TODO: DM-17458 

561 maxSepInSigma : `float`, optional 

562 TODO: DM-17458 

563 separateNegParams : `bool`, optional 

564 TODO: DM-17458 

565 verbose : `bool`, optional 

566 TODO: DM-17458 

567 

568 Returns 

569 ------- 

570 result : `lmfit.MinimizerResult` 

571 return `lmfit.MinimizerResult` object containing the fit 

572 parameters and other information. 

573 """ 

574 

575 # Only import lmfit if someone wants to use the new DipoleFitAlgorithm. 

576 import lmfit 

577 

578 fp = source.getFootprint() 

579 bbox = fp.getBBox() 

580 subim = afwImage.MaskedImageF(self.diffim.getMaskedImage(), bbox=bbox, origin=afwImage.PARENT) 

581 

582 z = diArr = subim.image.array 

583 weights = 1. / subim.variance.array # get the weights (=1/variance) 

584 

585 if rel_weight > 0. and ((self.posImage is not None) or (self.negImage is not None)): 

586 if self.negImage is not None: 

587 negSubim = afwImage.MaskedImageF(self.negImage.getMaskedImage(), bbox, origin=afwImage.PARENT) 

588 if self.posImage is not None: 

589 posSubim = afwImage.MaskedImageF(self.posImage.getMaskedImage(), bbox, origin=afwImage.PARENT) 

590 if self.posImage is None: # no science image provided; generate it from diffim + negImage 

591 posSubim = subim.clone() 

592 posSubim += negSubim 

593 if self.negImage is None: # no template provided; generate it from the posImage - diffim 

594 negSubim = posSubim.clone() 

595 negSubim -= subim 

596 

597 z = np.append([z], [posSubim.image.array, 

598 negSubim.image.array], axis=0) 

599 # Weight the pos/neg images by rel_weight relative to the diffim 

600 weights = np.append([weights], [1. / posSubim.variance.array * rel_weight, 

601 1. / negSubim.variance.array * rel_weight], axis=0) 

602 else: 

603 rel_weight = 0. # a short-cut for "don't include the pre-subtraction data" 

604 

605 # It seems that `lmfit` requires a static functor as its optimized method, which eliminates 

606 # the ability to pass a bound method or other class method. Here we write a wrapper which 

607 # makes this possible. 

608 def dipoleModelFunctor(x, flux, xcenPos, ycenPos, xcenNeg, ycenNeg, fluxNeg=None, 

609 b=None, x1=None, y1=None, xy=None, x2=None, y2=None, 

610 bNeg=None, x1Neg=None, y1Neg=None, xyNeg=None, x2Neg=None, y2Neg=None, 

611 **kwargs): 

612 """Generate dipole model with given parameters. 

613 

614 It simply defers to `modelObj.makeModel()`, where `modelObj` comes 

615 out of `kwargs['modelObj']`. 

616 """ 

617 modelObj = kwargs.pop('modelObj') 

618 return modelObj.makeModel(x, flux, xcenPos, ycenPos, xcenNeg, ycenNeg, fluxNeg=fluxNeg, 

619 b=b, x1=x1, y1=y1, xy=xy, x2=x2, y2=y2, 

620 bNeg=bNeg, x1Neg=x1Neg, y1Neg=y1Neg, xyNeg=xyNeg, 

621 x2Neg=x2Neg, y2Neg=y2Neg, **kwargs) 

622 

623 dipoleModel = DipoleModel() 

624 

625 modelFunctor = dipoleModelFunctor # dipoleModel.makeModel does not work for now. 

626 # Create the lmfit model (lmfit uses scipy 'leastsq' option by default - Levenberg-Marquardt) 

627 # Note we can also tell it to drop missing values from the data. 

628 gmod = lmfit.Model(modelFunctor, verbose=verbose, missing='drop') 

629 

630 # Add the constraints for centroids, fluxes. 

631 # starting constraint - near centroid of footprint 

632 fpCentroid = np.array([fp.getCentroid().getX(), fp.getCentroid().getY()]) 

633 cenNeg = cenPos = fpCentroid 

634 

635 pks = fp.getPeaks() 

636 

637 if len(pks) >= 1: 

638 cenPos = pks[0].getF() # if individual (merged) peaks were detected, use those 

639 if len(pks) >= 2: # peaks are already sorted by centroid flux so take the most negative one 

640 cenNeg = pks[-1].getF() 

641 

642 # For close/faint dipoles the starting locs (min/max) might be way off, let's help them a bit. 

643 # First assume dipole is not separated by more than 5*psfSigma. 

644 maxSep = self.psfSigma * maxSepInSigma 

645 

646 # As an initial guess -- assume the dipole is close to the center of the footprint. 

647 if np.sum(np.sqrt((np.array(cenPos) - fpCentroid)**2.)) > maxSep: 

648 cenPos = fpCentroid 

649 if np.sum(np.sqrt((np.array(cenNeg) - fpCentroid)**2.)) > maxSep: 

650 cenPos = fpCentroid 

651 

652 # parameter hints/constraints: https://lmfit.github.io/lmfit-py/model.html#model-param-hints-section 

653 # might make sense to not use bounds -- see http://lmfit.github.io/lmfit-py/bounds.html 

654 # also see this discussion -- https://github.com/scipy/scipy/issues/3129 

655 gmod.set_param_hint('xcenPos', value=cenPos[0], 

656 min=cenPos[0]-maxSep, max=cenPos[0]+maxSep) 

657 gmod.set_param_hint('ycenPos', value=cenPos[1], 

658 min=cenPos[1]-maxSep, max=cenPos[1]+maxSep) 

659 gmod.set_param_hint('xcenNeg', value=cenNeg[0], 

660 min=cenNeg[0]-maxSep, max=cenNeg[0]+maxSep) 

661 gmod.set_param_hint('ycenNeg', value=cenNeg[1], 

662 min=cenNeg[1]-maxSep, max=cenNeg[1]+maxSep) 

663 

664 # Use the (flux under the dipole)*5 for an estimate. 

665 # Lots of testing showed that having startingFlux be too high was better than too low. 

666 startingFlux = np.nansum(np.abs(diArr) - np.nanmedian(np.abs(diArr))) * 5. 

667 posFlux = negFlux = startingFlux 

668 

669 # TBD: set max. flux limit? 

670 gmod.set_param_hint('flux', value=posFlux, min=0.1) 

671 

672 if separateNegParams: 

673 # TBD: set max negative lobe flux limit? 

674 gmod.set_param_hint('fluxNeg', value=np.abs(negFlux), min=0.1) 

675 

676 # Fixed parameters (don't fit for them if there are no pre-sub images or no gradient fit requested): 

677 # Right now (fitBackground == 1), we fit a linear model to the background and then subtract 

678 # it from the data and then don't fit the background again (this is faster). 

679 # A slower alternative (fitBackground == 2) is to use the estimated background parameters as 

680 # starting points in the integrated model fit. That is currently not performed by default, 

681 # but might be desirable in some cases. 

682 bgParsPos = bgParsNeg = (0., 0., 0.) 

683 if ((rel_weight > 0.) and (fitBackground != 0) and (bgGradientOrder >= 0)): 

684 pbg = 0. 

685 bgFitImage = self.posImage if self.posImage is not None else self.negImage 

686 # Fit the gradient to the background (linear model) 

687 bgParsPos = bgParsNeg = dipoleModel.fitFootprintBackground(source, bgFitImage, 

688 order=bgGradientOrder) 

689 

690 # Generate the gradient and subtract it from the pre-subtraction image data 

691 if fitBackground == 1: 

692 in_x = dipoleModel._generateXYGrid(bbox) 

693 pbg = dipoleModel.makeBackgroundModel(in_x, tuple(bgParsPos)) 

694 z[1, :] -= pbg 

695 z[1, :] -= np.nanmedian(z[1, :]) 

696 posFlux = np.nansum(z[1, :]) 

697 gmod.set_param_hint('flux', value=posFlux*1.5, min=0.1) 

698 

699 if separateNegParams and self.negImage is not None: 

700 bgParsNeg = dipoleModel.fitFootprintBackground(source, self.negImage, 

701 order=bgGradientOrder) 

702 pbg = dipoleModel.makeBackgroundModel(in_x, tuple(bgParsNeg)) 

703 z[2, :] -= pbg 

704 z[2, :] -= np.nanmedian(z[2, :]) 

705 if separateNegParams: 

706 negFlux = np.nansum(z[2, :]) 

707 gmod.set_param_hint('fluxNeg', value=negFlux*1.5, min=0.1) 

708 

709 # Do not subtract the background from the images but include the background parameters in the fit 

710 if fitBackground == 2: 

711 if bgGradientOrder >= 0: 

712 gmod.set_param_hint('b', value=bgParsPos[0]) 

713 if separateNegParams: 

714 gmod.set_param_hint('bNeg', value=bgParsNeg[0]) 

715 if bgGradientOrder >= 1: 

716 gmod.set_param_hint('x1', value=bgParsPos[1]) 

717 gmod.set_param_hint('y1', value=bgParsPos[2]) 

718 if separateNegParams: 

719 gmod.set_param_hint('x1Neg', value=bgParsNeg[1]) 

720 gmod.set_param_hint('y1Neg', value=bgParsNeg[2]) 

721 if bgGradientOrder >= 2: 

722 gmod.set_param_hint('xy', value=bgParsPos[3]) 

723 gmod.set_param_hint('x2', value=bgParsPos[4]) 

724 gmod.set_param_hint('y2', value=bgParsPos[5]) 

725 if separateNegParams: 

726 gmod.set_param_hint('xyNeg', value=bgParsNeg[3]) 

727 gmod.set_param_hint('x2Neg', value=bgParsNeg[4]) 

728 gmod.set_param_hint('y2Neg', value=bgParsNeg[5]) 

729 

730 y, x = np.mgrid[bbox.getBeginY():bbox.getEndY(), bbox.getBeginX():bbox.getEndX()] 

731 in_x = np.array([x, y]).astype(np.float64) 

732 in_x[0, :] -= in_x[0, :].mean() # center it! 

733 in_x[1, :] -= in_x[1, :].mean() 

734 

735 # Instead of explicitly using a mask to ignore flagged pixels, just set the ignored pixels' 

736 # weights to 0 in the fit. TBD: need to inspect mask planes to set this mask. 

737 mask = np.ones_like(z, dtype=bool) # TBD: set mask values to False if the pixels are to be ignored 

738 

739 # I'm not sure about the variance planes in the diffim (or convolved pre-sub. images 

740 # for that matter) so for now, let's just do an un-weighted least-squares fit 

741 # (override weights computed above). 

742 weights = mask.astype(np.float64) 

743 if self.posImage is not None and rel_weight > 0.: 

744 weights = np.array([np.ones_like(diArr), np.ones_like(diArr)*rel_weight, 

745 np.ones_like(diArr)*rel_weight]) 

746 

747 # Set the weights to zero if mask is False 

748 if np.any(~mask): 

749 weights[~mask] = 0. 

750 

751 # Note that although we can, we're not required to set initial values for params here, 

752 # since we set their param_hint's above. 

753 # Can add "method" param to not use 'leastsq' (==levenberg-marquardt), e.g. "method='nelder'" 

754 with warnings.catch_warnings(): 

755 # Ignore lmfit unknown argument warnings: 

756 # "psf, rel_weight, footprint, modelObj" all become pass-through kwargs for makeModel. 

757 warnings.filterwarnings("ignore", "The keyword argument .* does not match", UserWarning) 

758 result = gmod.fit(z, weights=weights, x=in_x, max_nfev=250, 

759 method="leastsq", # TODO: try using `least_squares` here for speed/robustness 

760 verbose=verbose, 

761 # see scipy docs for the meaning of these keywords 

762 fit_kws={'ftol': tol, 'xtol': tol, 'gtol': tol, 

763 # Our model is float32 internally, so we need a larger epsfcn. 

764 'epsfcn': 1e-8}, 

765 psf=self.diffim.getPsf(), # hereon: kwargs that get passed to makeModel() 

766 rel_weight=rel_weight, 

767 footprint=fp, 

768 modelObj=dipoleModel) 

769 

770 if verbose: # the ci_report() seems to fail if neg params are constrained -- TBD why. 

771 # Never wanted in production - this takes a long time (longer than the fit!) 

772 # This is how to get confidence intervals out: 

773 # https://lmfit.github.io/lmfit-py/confidence.html and 

774 # http://cars9.uchicago.edu/software/python/lmfit/model.html 

775 print(result.fit_report(show_correl=False)) 

776 if separateNegParams: 

777 print(result.ci_report()) 

778 

779 return result 

780 

781 def fitDipole(self, source, tol=1e-7, rel_weight=0.1, 

782 fitBackground=1, maxSepInSigma=5., separateNegParams=True, 

783 bgGradientOrder=1, verbose=False, display=False): 

784 """Fit a dipole model to an input ``diaSource`` (wraps `fitDipoleImpl`). 

785 

786 Actually, fits the subimage bounded by the input source's 

787 footprint) and optionally constrain the fit using the 

788 pre-subtraction images self.posImage (science) and 

789 self.negImage (template). Wraps the output into a 

790 `pipeBase.Struct` named tuple after computing additional 

791 statistics such as orientation and SNR. 

792 

793 Parameters 

794 ---------- 

795 source : `lsst.afw.table.SourceRecord` 

796 Record containing the (merged) dipole source footprint detected on the diffim 

797 tol : `float`, optional 

798 Tolerance parameter for scipy.leastsq() optimization 

799 rel_weight : `float`, optional 

800 Weighting of posImage/negImage relative to the diffim in the fit 

801 fitBackground : `int`, {0, 1, 2}, optional 

802 How to fit linear background gradient in posImage/negImage 

803 

804 - 0: do not fit background at all 

805 - 1 (default): pre-fit the background using linear least squares and then do not fit it 

806 as part of the dipole fitting optimization 

807 - 2: pre-fit the background using linear least squares (as in 1), and use the parameter 

808 estimates from that fit as starting parameters for an integrated "re-fit" of the 

809 background as part of the overall dipole fitting optimization. 

810 maxSepInSigma : `float`, optional 

811 Allowed window of centroid parameters relative to peak in input source footprint 

812 separateNegParams : `bool`, optional 

813 Fit separate parameters to the flux and background gradient in 

814 bgGradientOrder : `int`, {0, 1, 2}, optional 

815 Desired polynomial order of background gradient 

816 verbose: `bool`, optional 

817 Be verbose 

818 display 

819 Display input data, best fit model(s) and residuals in a matplotlib window. 

820 

821 Returns 

822 ------- 

823 result : `struct` 

824 `pipeBase.Struct` object containing the fit parameters and other information. 

825 

826 result : `callable` 

827 `lmfit.MinimizerResult` object for debugging and error estimation, etc. 

828 

829 Notes 

830 ----- 

831 Parameter `fitBackground` has three options, thus it is an integer: 

832 

833 """ 

834 

835 fitResult = self.fitDipoleImpl( 

836 source, tol=tol, rel_weight=rel_weight, fitBackground=fitBackground, 

837 maxSepInSigma=maxSepInSigma, separateNegParams=separateNegParams, 

838 bgGradientOrder=bgGradientOrder, verbose=verbose) 

839 

840 # Display images, model fits and residuals (currently uses matplotlib display functions) 

841 if display: 

842 fp = source.getFootprint() 

843 self.displayFitResults(fp, fitResult) 

844 

845 fitParams = fitResult.best_values 

846 if fitParams['flux'] <= 1.: # usually around 0.1 -- the minimum flux allowed -- i.e. bad fit. 

847 out = Struct(posCentroidX=np.nan, posCentroidY=np.nan, 

848 negCentroidX=np.nan, negCentroidY=np.nan, 

849 posFlux=np.nan, negFlux=np.nan, posFluxErr=np.nan, negFluxErr=np.nan, 

850 centroidX=np.nan, centroidY=np.nan, orientation=np.nan, 

851 signalToNoise=np.nan, chi2=np.nan, redChi2=np.nan) 

852 return out, fitResult 

853 

854 centroid = ((fitParams['xcenPos'] + fitParams['xcenNeg']) / 2., 

855 (fitParams['ycenPos'] + fitParams['ycenNeg']) / 2.) 

856 dx, dy = fitParams['xcenPos'] - fitParams['xcenNeg'], fitParams['ycenPos'] - fitParams['ycenNeg'] 

857 angle = np.arctan2(dy, dx) / np.pi * 180. # convert to degrees (should keep as rad?) 

858 

859 # Exctract flux value, compute signalToNoise from flux/variance_within_footprint 

860 # Also extract the stderr of flux estimate. 

861 def computeSumVariance(exposure, footprint): 

862 return np.sqrt(np.nansum(exposure[footprint.getBBox(), afwImage.PARENT].variance.array)) 

863 

864 fluxVal = fluxVar = fitParams['flux'] 

865 fluxErr = fluxErrNeg = fitResult.params['flux'].stderr 

866 if self.posImage is not None: 

867 fluxVar = computeSumVariance(self.posImage, source.getFootprint()) 

868 else: 

869 fluxVar = computeSumVariance(self.diffim, source.getFootprint()) 

870 

871 fluxValNeg, fluxVarNeg = fluxVal, fluxVar 

872 if separateNegParams: 

873 fluxValNeg = fitParams['fluxNeg'] 

874 fluxErrNeg = fitResult.params['fluxNeg'].stderr 

875 if self.negImage is not None: 

876 fluxVarNeg = computeSumVariance(self.negImage, source.getFootprint()) 

877 

878 try: 

879 signalToNoise = np.sqrt((fluxVal/fluxVar)**2 + (fluxValNeg/fluxVarNeg)**2) 

880 except ZeroDivisionError: # catch divide by zero - should never happen. 

881 signalToNoise = np.nan 

882 

883 out = Struct(posCentroidX=fitParams['xcenPos'], posCentroidY=fitParams['ycenPos'], 

884 negCentroidX=fitParams['xcenNeg'], negCentroidY=fitParams['ycenNeg'], 

885 posFlux=fluxVal, negFlux=-fluxValNeg, posFluxErr=fluxErr, negFluxErr=fluxErrNeg, 

886 centroidX=centroid[0], centroidY=centroid[1], orientation=angle, 

887 signalToNoise=signalToNoise, chi2=fitResult.chisqr, redChi2=fitResult.redchi) 

888 

889 # fitResult may be returned for debugging 

890 return out, fitResult 

891 

892 def displayFitResults(self, footprint, result): 

893 """Display data, model fits and residuals (currently uses matplotlib display functions). 

894 

895 Parameters 

896 ---------- 

897 footprint : TODO: DM-17458 

898 Footprint containing the dipole that was fit 

899 result : `lmfit.MinimizerResult` 

900 `lmfit.MinimizerResult` object returned by `lmfit` optimizer 

901 

902 Returns 

903 ------- 

904 fig : `matplotlib.pyplot.plot` 

905 """ 

906 try: 

907 import matplotlib.pyplot as plt 

908 except ImportError as err: 

909 self.log.warning('Unable to import matplotlib: %s', err) 

910 raise err 

911 

912 def display2dArray(arr, title='Data', extent=None): 

913 """Use `matplotlib.pyplot.imshow` to display a 2-D array with a given coordinate range. 

914 """ 

915 fig = plt.imshow(arr, origin='lower', interpolation='none', cmap='gray', extent=extent) 

916 plt.title(title) 

917 plt.colorbar(fig, cmap='gray') 

918 return fig 

919 

920 z = result.data 

921 fit = result.best_fit 

922 bbox = footprint.getBBox() 

923 extent = (bbox.getBeginX(), bbox.getEndX(), bbox.getBeginY(), bbox.getEndY()) 

924 if z.shape[0] == 3: 

925 fig = plt.figure(figsize=(8, 8)) 

926 for i in range(3): 

927 plt.subplot(3, 3, i*3+1) 

928 display2dArray(z[i, :], 'Data', extent=extent) 

929 plt.subplot(3, 3, i*3+2) 

930 display2dArray(fit[i, :], 'Model', extent=extent) 

931 plt.subplot(3, 3, i*3+3) 

932 display2dArray(z[i, :] - fit[i, :], 'Residual', extent=extent) 

933 return fig 

934 else: 

935 fig = plt.figure(figsize=(8, 2.5)) 

936 plt.subplot(1, 3, 1) 

937 display2dArray(z, 'Data', extent=extent) 

938 plt.subplot(1, 3, 2) 

939 display2dArray(fit, 'Model', extent=extent) 

940 plt.subplot(1, 3, 3) 

941 display2dArray(z - fit, 'Residual', extent=extent) 

942 return fig 

943 

944 plt.show() 

945 

946 

947@measBase.register("ip_diffim_DipoleFit") 

948class DipoleFitPlugin(measBase.SingleFramePlugin): 

949 """A single frame measurement plugin that fits dipoles to all merged (two-peak) ``diaSources``. 

950 

951 This measurement plugin accepts up to three input images in 

952 its `measure` method. If these are provided, it includes data 

953 from the pre-subtraction posImage (science image) and optionally 

954 negImage (template image) to constrain the fit. The meat of the 

955 fitting routines are in the class `~lsst.module.name.DipoleFitAlgorithm`. 

956 

957 Notes 

958 ----- 

959 The motivation behind this plugin and the necessity for including more than 

960 one exposure are documented in DMTN-007 (http://dmtn-007.lsst.io). 

961 

962 This class is named `ip_diffim_DipoleFit` so that it may be used alongside 

963 the existing `ip_diffim_DipoleMeasurement` classes until such a time as those 

964 are deemed to be replaceable by this. 

965 """ 

966 

967 ConfigClass = DipoleFitPluginConfig 

968 DipoleFitAlgorithmClass = DipoleFitAlgorithm # Pointer to the class that performs the fit 

969 

970 FAILURE_EDGE = 1 # too close to the edge 

971 FAILURE_FIT = 2 # failure in the fitting 

972 FAILURE_NOT_DIPOLE = 4 # input source is not a putative dipole to begin with 

973 

974 @classmethod 

975 def getExecutionOrder(cls): 

976 """Set execution order to `FLUX_ORDER`. 

977 

978 This includes algorithms that require both `getShape()` and `getCentroid()`, 

979 in addition to a Footprint and its Peaks. 

980 """ 

981 return cls.FLUX_ORDER 

982 

983 def __init__(self, config, name, schema, metadata, logName=None): 

984 if logName is None: 

985 logName = name 

986 measBase.SingleFramePlugin.__init__(self, config, name, schema, metadata, logName=logName) 

987 

988 self.log = logging.getLogger(logName) 

989 

990 self._setupSchema(config, name, schema, metadata) 

991 

992 def _setupSchema(self, config, name, schema, metadata): 

993 # Get a FunctorKey that can quickly look up the "blessed" centroid value. 

994 self.centroidKey = afwTable.Point2DKey(schema["slot_Centroid"]) 

995 

996 # Add some fields for our outputs, and save their Keys. 

997 # Use setattr() to programmatically set the pos/neg named attributes to values, e.g. 

998 # self.posCentroidKeyX = 'ip_diffim_DipoleFit_pos_centroid_x' 

999 

1000 for pos_neg in ['pos', 'neg']: 

1001 

1002 key = schema.addField( 

1003 schema.join(name, pos_neg, "instFlux"), type=float, units="count", 

1004 doc="Dipole {0} lobe flux".format(pos_neg)) 

1005 setattr(self, ''.join((pos_neg, 'FluxKey')), key) 

1006 

1007 key = schema.addField( 

1008 schema.join(name, pos_neg, "instFluxErr"), type=float, units="count", 

1009 doc="1-sigma uncertainty for {0} dipole flux".format(pos_neg)) 

1010 setattr(self, ''.join((pos_neg, 'FluxErrKey')), key) 

1011 

1012 for x_y in ['x', 'y']: 

1013 key = schema.addField( 

1014 schema.join(name, pos_neg, "centroid", x_y), type=float, units="pixel", 

1015 doc="Dipole {0} lobe centroid".format(pos_neg)) 

1016 setattr(self, ''.join((pos_neg, 'CentroidKey', x_y.upper())), key) 

1017 

1018 for x_y in ['x', 'y']: 

1019 key = schema.addField( 

1020 schema.join(name, "centroid", x_y), type=float, units="pixel", 

1021 doc="Dipole centroid") 

1022 setattr(self, ''.join(('centroidKey', x_y.upper())), key) 

1023 

1024 self.fluxKey = schema.addField( 

1025 schema.join(name, "instFlux"), type=float, units="count", 

1026 doc="Dipole overall flux") 

1027 

1028 self.orientationKey = schema.addField( 

1029 schema.join(name, "orientation"), type=float, units="deg", 

1030 doc="Dipole orientation") 

1031 

1032 self.separationKey = schema.addField( 

1033 schema.join(name, "separation"), type=float, units="pixel", 

1034 doc="Pixel separation between positive and negative lobes of dipole") 

1035 

1036 self.chi2dofKey = schema.addField( 

1037 schema.join(name, "chi2dof"), type=float, 

1038 doc="Chi2 per degree of freedom of dipole fit") 

1039 

1040 self.signalToNoiseKey = schema.addField( 

1041 schema.join(name, "signalToNoise"), type=float, 

1042 doc="Estimated signal-to-noise of dipole fit") 

1043 

1044 self.classificationFlagKey = schema.addField( 

1045 schema.join(name, "flag", "classification"), type="Flag", 

1046 doc="Flag indicating diaSource is classified as a dipole") 

1047 

1048 self.classificationAttemptedFlagKey = schema.addField( 

1049 schema.join(name, "flag", "classificationAttempted"), type="Flag", 

1050 doc="Flag indicating diaSource was attempted to be classified as a dipole") 

1051 

1052 self.flagKey = schema.addField( 

1053 schema.join(name, "flag"), type="Flag", 

1054 doc="General failure flag for dipole fit") 

1055 

1056 self.edgeFlagKey = schema.addField( 

1057 schema.join(name, "flag", "edge"), type="Flag", 

1058 doc="Flag set when dipole is too close to edge of image") 

1059 

1060 def measure(self, measRecord, exposure, posExp=None, negExp=None): 

1061 """Perform the non-linear least squares minimization on the putative dipole source. 

1062 

1063 Parameters 

1064 ---------- 

1065 measRecord : `lsst.afw.table.SourceRecord` 

1066 diaSources that will be measured using dipole measurement 

1067 exposure : `lsst.afw.image.Exposure` 

1068 Difference exposure on which the diaSources were detected; `exposure = posExp-negExp` 

1069 If both `posExp` and `negExp` are `None`, will attempt to fit the 

1070 dipole to just the `exposure` with no constraint. 

1071 posExp : `lsst.afw.image.Exposure`, optional 

1072 "Positive" exposure, typically a science exposure, or None if unavailable 

1073 When `posExp` is `None`, will compute `posImage = exposure + negExp`. 

1074 negExp : `lsst.afw.image.Exposure`, optional 

1075 "Negative" exposure, typically a template exposure, or None if unavailable 

1076 When `negExp` is `None`, will compute `negImage = posExp - exposure`. 

1077 

1078 Notes 

1079 ----- 

1080 The main functionality of this routine was placed outside of 

1081 this plugin (into `DipoleFitAlgorithm.fitDipole()`) so that 

1082 `DipoleFitAlgorithm.fitDipole()` can be called separately for 

1083 testing (@see `tests/testDipoleFitter.py`) 

1084 

1085 Returns 

1086 ------- 

1087 result : TODO: DM-17458 

1088 TODO: DM-17458 

1089 """ 

1090 

1091 result = None 

1092 pks = measRecord.getFootprint().getPeaks() 

1093 

1094 # Check if the footprint consists of a putative dipole - else don't fit it. 

1095 if ( 

1096 # One peak in the footprint (not a dipole) 

1097 (len(pks) <= 1) 

1098 # Peaks are the same sign (not a dipole) 

1099 or (len(pks) > 1 and (np.sign(pks[0].getPeakValue()) 

1100 == np.sign(pks[-1].getPeakValue()))) 

1101 # Footprint is too large (not a dipole) 

1102 or (measRecord.getFootprint().getArea() > self.config.maxFootprintArea) 

1103 ): 

1104 measRecord.set(self.classificationFlagKey, False) 

1105 measRecord.set(self.classificationAttemptedFlagKey, False) 

1106 self.fail(measRecord, measBase.MeasurementError('not a dipole', self.FAILURE_NOT_DIPOLE)) 

1107 if not self.config.fitAllDiaSources: 

1108 return result 

1109 

1110 try: 

1111 alg = self.DipoleFitAlgorithmClass(exposure, posImage=posExp, negImage=negExp) 

1112 result, _ = alg.fitDipole( 

1113 measRecord, rel_weight=self.config.relWeight, 

1114 tol=self.config.tolerance, 

1115 maxSepInSigma=self.config.maxSeparation, 

1116 fitBackground=self.config.fitBackground, 

1117 separateNegParams=self.config.fitSeparateNegParams, 

1118 verbose=False, display=False) 

1119 except pexExcept.LengthError: 

1120 self.fail(measRecord, measBase.MeasurementError('edge failure', self.FAILURE_EDGE)) 

1121 except Exception as e: 

1122 self.fail(measRecord, measBase.MeasurementError('Exception in dipole fit', self.FAILURE_FIT)) 

1123 self.log.error("Exception in dipole fit. %s: %s", e.__class__.__name__, e) 

1124 

1125 if result is None: 

1126 measRecord.set(self.classificationFlagKey, False) 

1127 measRecord.set(self.classificationAttemptedFlagKey, False) 

1128 return result 

1129 

1130 self.log.debug("Dipole fit result: %d %s", measRecord.getId(), str(result)) 

1131 

1132 if result.posFlux <= 1.: # usually around 0.1 -- the minimum flux allowed -- i.e. bad fit. 

1133 self.fail(measRecord, measBase.MeasurementError('dipole fit failure', self.FAILURE_FIT)) 

1134 

1135 # add chi2, coord/flux uncertainties (TBD), dipole classification 

1136 # Add the relevant values to the measRecord 

1137 measRecord[self.posFluxKey] = result.posFlux 

1138 measRecord[self.posFluxErrKey] = result.signalToNoise # to be changed to actual sigma! 

1139 measRecord[self.posCentroidKeyX] = result.posCentroidX 

1140 measRecord[self.posCentroidKeyY] = result.posCentroidY 

1141 

1142 measRecord[self.negFluxKey] = result.negFlux 

1143 measRecord[self.negFluxErrKey] = result.signalToNoise # to be changed to actual sigma! 

1144 measRecord[self.negCentroidKeyX] = result.negCentroidX 

1145 measRecord[self.negCentroidKeyY] = result.negCentroidY 

1146 

1147 # Dia source flux: average of pos+neg 

1148 measRecord[self.fluxKey] = (abs(result.posFlux) + abs(result.negFlux))/2. 

1149 measRecord[self.orientationKey] = result.orientation 

1150 measRecord[self.separationKey] = np.sqrt((result.posCentroidX - result.negCentroidX)**2. 

1151 + (result.posCentroidY - result.negCentroidY)**2.) 

1152 measRecord[self.centroidKeyX] = result.centroidX 

1153 measRecord[self.centroidKeyY] = result.centroidY 

1154 

1155 measRecord[self.signalToNoiseKey] = result.signalToNoise 

1156 measRecord[self.chi2dofKey] = result.redChi2 

1157 

1158 self.doClassify(measRecord, result.chi2) 

1159 

1160 def doClassify(self, measRecord, chi2val): 

1161 """Classify a source as a dipole. 

1162 

1163 Parameters 

1164 ---------- 

1165 measRecord : TODO: DM-17458 

1166 TODO: DM-17458 

1167 chi2val : TODO: DM-17458 

1168 TODO: DM-17458 

1169 

1170 Notes 

1171 ----- 

1172 Sources are classified as dipoles, or not, according to three criteria: 

1173 

1174 1. Does the total signal-to-noise surpass the ``minSn``? 

1175 2. Are the pos/neg fluxes greater than 1.0 and no more than 0.65 (``maxFluxRatio``) 

1176 of the total flux? By default this will never happen since ``posFlux == negFlux``. 

1177 3. Is it a good fit (``chi2dof`` < 1)? (Currently not used.) 

1178 """ 

1179 

1180 # First, does the total signal-to-noise surpass the minSn? 

1181 passesSn = measRecord[self.signalToNoiseKey] > self.config.minSn 

1182 

1183 # Second, are the pos/neg fluxes greater than 1.0 and no more than 0.65 (param maxFluxRatio) 

1184 # of the total flux? By default this will never happen since posFlux = negFlux. 

1185 passesFluxPos = (abs(measRecord[self.posFluxKey]) 

1186 / (measRecord[self.fluxKey]*2.)) < self.config.maxFluxRatio 

1187 passesFluxPos &= (abs(measRecord[self.posFluxKey]) >= 1.0) 

1188 passesFluxNeg = (abs(measRecord[self.negFluxKey]) 

1189 / (measRecord[self.fluxKey]*2.)) < self.config.maxFluxRatio 

1190 passesFluxNeg &= (abs(measRecord[self.negFluxKey]) >= 1.0) 

1191 allPass = (passesSn and passesFluxPos and passesFluxNeg) # and passesChi2) 

1192 

1193 # Third, is it a good fit (chi2dof < 1)? 

1194 # Use scipy's chi2 cumulative distrib to estimate significance 

1195 # This doesn't really work since I don't trust the values in the variance plane (which 

1196 # affects the least-sq weights, which affects the resulting chi2). 

1197 # But I'm going to keep this here for future use. 

1198 if False: 

1199 from scipy.stats import chi2 

1200 ndof = chi2val / measRecord[self.chi2dofKey] 

1201 significance = chi2.cdf(chi2val, ndof) 

1202 passesChi2 = significance < self.config.maxChi2DoF 

1203 allPass = allPass and passesChi2 

1204 

1205 measRecord.set(self.classificationAttemptedFlagKey, True) 

1206 

1207 if allPass: # Note cannot pass `allPass` into the `measRecord.set()` call below...? 

1208 measRecord.set(self.classificationFlagKey, True) 

1209 else: 

1210 measRecord.set(self.classificationFlagKey, False) 

1211 

1212 def fail(self, measRecord, error=None): 

1213 """Catch failures and set the correct flags. 

1214 """ 

1215 

1216 measRecord.set(self.flagKey, True) 

1217 if error is not None: 

1218 if error.getFlagBit() == self.FAILURE_EDGE: 

1219 self.log.warning('DipoleFitPlugin not run on record %d: %s', measRecord.getId(), str(error)) 

1220 measRecord.set(self.edgeFlagKey, True) 

1221 if error.getFlagBit() == self.FAILURE_FIT: 

1222 self.log.warning('DipoleFitPlugin failed on record %d: %s', measRecord.getId(), str(error)) 

1223 measRecord.set(self.flagKey, True) 

1224 if error.getFlagBit() == self.FAILURE_NOT_DIPOLE: 

1225 self.log.debug('DipoleFitPlugin not run on record %d: %s', 

1226 measRecord.getId(), str(error)) 

1227 measRecord.set(self.classificationAttemptedFlagKey, False) 

1228 measRecord.set(self.flagKey, True) 

1229 else: 

1230 self.log.warning('DipoleFitPlugin failed on record %d', measRecord.getId())