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

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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.detection as afwDet 

30import lsst.geom as geom 

31import lsst.pex.exceptions as pexExcept 

32import lsst.pex.config as pexConfig 

33from lsst.pipe.base import Struct 

34from lsst.utils.timer import timeMethod 

35 

36__all__ = ("DipoleFitTask", "DipoleFitPlugin", "DipoleFitTaskConfig", "DipoleFitPluginConfig", 

37 "DipoleFitAlgorithm") 

38 

39 

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

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

42 

43 

44class DipoleFitPluginConfig(measBase.SingleFramePluginConfig): 

45 """Configuration for DipoleFitPlugin 

46 """ 

47 

48 fitAllDiaSources = pexConfig.Field( 

49 dtype=bool, default=False, 

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

51 positive and negative footprints)""") 

52 

53 maxSeparation = pexConfig.Field( 

54 dtype=float, default=5., 

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

56 

57 relWeight = pexConfig.Field( 

58 dtype=float, default=0.5, 

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

60 images on fit)""") 

61 

62 tolerance = pexConfig.Field( 

63 dtype=float, default=1e-7, 

64 doc="Fit tolerance") 

65 

66 fitBackground = pexConfig.Field( 

67 dtype=int, default=1, 

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

69 "0: do not fit background at all" 

70 "1 (default): pre-fit the background using linear least squares and then do not fit it as part" 

71 "of the dipole fitting optimization" 

72 "2: pre-fit the background using linear least squares (as in 1), and use the parameter" 

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

74 

75 fitSeparateNegParams = pexConfig.Field( 

76 dtype=bool, default=False, 

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

78 

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

80 minSn = pexConfig.Field( 

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

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

83 

84 maxFluxRatio = pexConfig.Field( 

85 dtype=float, default=0.65, 

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

87 

88 maxChi2DoF = pexConfig.Field( 

89 dtype=float, default=0.05, 

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

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

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

93 

94 maxFootprintArea = pexConfig.Field( 

95 dtype=int, default=1_200, 

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

97 "non-positive means no threshold applied" 

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

99 

100 

101class DipoleFitTaskConfig(measBase.SingleFrameMeasurementConfig): 

102 

103 def setDefaults(self): 

104 measBase.SingleFrameMeasurementConfig.setDefaults(self) 

105 

106 self.plugins.names = ["base_SdssCentroid", 

107 "ip_diffim_DipoleFit", 

108 "base_CircularApertureFlux", 

109 "base_PixelFlags", 

110 "base_SkyCoord", 

111 "base_PsfFlux", 

112 "base_SdssShape", 

113 "base_GaussianFlux", 

114 ] 

115 # Only measure the apertures we need to report in the alert stream. 

116 self.plugins["base_CircularApertureFlux"].radii = [12.0] 

117 

118 self.slots.calibFlux = None 

119 self.slots.modelFlux = None 

120 self.slots.gaussianFlux = None 

121 self.slots.shape = "base_SdssShape" 

122 # This will be switched to "ip_diffim_DipoleFit" as this task runs. 

123 self.slots.centroid = "base_SdssCentroid" 

124 self.doReplaceWithNoise = False 

125 

126 

127class DipoleFitTask(measBase.SingleFrameMeasurementTask): 

128 """A task that fits a dipole to a difference image, with an optional 

129 separate detection image. 

130 

131 Because it subclasses SingleFrameMeasurementTask, and calls 

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

133 can be used identically to a standard SingleFrameMeasurementTask. 

134 """ 

135 

136 ConfigClass = DipoleFitTaskConfig 

137 _DefaultName = "dipoleFit" 

138 

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

140 super().__init__(schema, algMetadata, **kwargs) 

141 

142 # Enforce a specific plugin order, so that DipoleFit can fall back on 

143 # SdssCentroid for non-dipoles 

144 self.plugins_pre = self.plugins.copy() 

145 self.plugins_post = self.plugins.copy() 

146 self.plugins_pre.clear() 

147 self.plugins_pre["base_SdssCentroid"] = self.plugins["base_SdssCentroid"] 

148 self.plugins_post.pop("base_SdssCentroid") 

149 self.dipoleFit = self.plugins_post.pop("ip_diffim_DipoleFit") 

150 del self.plugins 

151 

152 @timeMethod 

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

154 """Run dipole measurement and classification. 

155 

156 Run SdssCentroid first, then switch the centroid slot, then DipoleFit 

157 then the rest; DipoleFit will fall back on SdssCentroid for sources 

158 not containing positive+negative peaks. 

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 ``sources`` were detected. 

166 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 # Run plugins in a very specific order, so DipoleFitPlugin has a 

178 # centroid to fall back on. 

179 self.plugins = self.plugins_pre 

180 super().run(sources, exposure, **kwargs) 

181 

182 for source in sources: 

183 self.dipoleFit.measureDipoles(source, exposure, posExp, negExp) 

184 # Use the new DipoleFit outputs for subsequent measurements, now that 

185 # non-dipoles have been filled in with the earlier centroid values. 

186 sources.schema.getAliasMap().set("slot_Centroid", "ip_diffim_DipoleFit") 

187 

188 self.plugins = self.plugins_post 

189 super().run(sources, exposure, **kwargs) 

190 

191 

192class DipoleModel: 

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

194 to sources in diffims, used by DipoleFitAlgorithm. 

195 

196 See also: 

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

198 """ 

199 

200 def __init__(self): 

201 import lsstDebug 

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

203 self.log = logging.getLogger(__name__) 

204 

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

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

207 

208 Parameters 

209 ---------- 

210 in_x : `numpy.array` 

211 (2, w, h)-dimensional `numpy.array`, containing the 

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

213 compute the gradient. This will typically be generated via 

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

215 height of the desired grid. 

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

217 Up to 6 floats for up 

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

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

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

221 

222 Returns 

223 ------- 

224 result : `None` or `numpy.array` 

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

226 input bbox, containing computed gradient values. 

227 """ 

228 

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

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

231 return 

232 

233 y, x = in_x[0, :], in_x[1, :] 

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

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

236 gradient += pars[1] * x 

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

238 gradient += pars[2] * y 

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

240 gradient += pars[3] * (x * y) 

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

242 gradient += pars[4] * (x * x) 

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

244 gradient += pars[5] * (y * y) 

245 

246 return gradient 

247 

248 def _generateXYGrid(self, bbox): 

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

250 

251 Parameters 

252 ---------- 

253 bbox : `lsst.geom.Box2I` 

254 input Bounding Box defining the coordinate limits 

255 

256 Returns 

257 ------- 

258 in_x : `numpy.array` 

259 (2, w, h)-dimensional numpy array containing the grid indexing over x- and 

260 y- coordinates 

261 """ 

262 

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

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

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

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

267 return in_x 

268 

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

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

271 as an `lsst.afw.image.ImageF`. 

272 

273 Parameters 

274 ---------- 

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

276 HeavyFootprint to use to generate the subimage 

277 badfill : `float`, optional 

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

279 grow : `int` 

280 Optionally grow the footprint by this amount before extraction 

281 

282 Returns 

283 ------- 

284 subim2 : `lsst.afw.image.ImageF` 

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

286 """ 

287 bbox = fp.getBBox() 

288 if grow > 0: 

289 bbox.grow(grow) 

290 

291 subim2 = afwImage.ImageF(bbox, badfill) 

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

293 return subim2 

294 

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

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

297 

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

299 

300 Parameters 

301 ---------- 

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

303 SourceRecord, the footprint of which is to be fit 

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

305 The exposure from which to extract the footprint subimage 

306 order : `int` 

307 Polynomial order of background gradient to fit. 

308 

309 Returns 

310 ------- 

311 pars : `tuple` of `float` 

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

313 containing the resulting fit parameters 

314 """ 

315 

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

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

318 fp = source.getFootprint() 

319 bbox = fp.getBBox() 

320 bbox.grow(3) 

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

322 

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

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

325 # fitting the background. 

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

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

328 

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

330 

331 data = posImg.array.ravel() 

332 data = data[isBg] 

333 B = data 

334 

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

336 x = x.astype(np.float64).ravel() 

337 x -= np.mean(x) 

338 x = x[isBg] 

339 y = y.astype(np.float64).ravel() 

340 y -= np.mean(y) 

341 y = y[isBg] 

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

343 

344 M = np.vstack([b]).T # order = 0 

345 if order == 1: 

346 M = np.vstack([b, x, y]).T 

347 elif order == 2: 

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

349 

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

351 return pars 

352 

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

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

355 

356 Parameters 

357 ---------- 

358 bbox : `lsst.geom.Box` 

359 Bounding box marking pixel coordinates for generated model 

360 psf : TODO: DM-17458 

361 Psf model used to generate the 'star' 

362 xcen : `float` 

363 Desired x-centroid of the 'star' 

364 ycen : `float` 

365 Desired y-centroid of the 'star' 

366 flux : `float` 

367 Desired flux of the 'star' 

368 

369 Returns 

370 ------- 

371 p_Im : `lsst.afw.image.Image` 

372 2-d stellar image of width/height matching input ``bbox``, 

373 containing PSF with given centroid and flux 

374 """ 

375 

376 # Generate the psf image, normalize to flux 

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

378 psf_img_sum = np.nansum(psf_img.array) 

379 psf_img *= (flux/psf_img_sum) 

380 

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

382 psf_box = psf_img.getBBox() 

383 psf_box.clip(bbox) 

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

385 

386 # Then actually crop the psf image. 

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

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

389 # see if it actually was clipped. 

390 p_Im = afwImage.ImageF(bbox) 

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

392 tmpSubim += psf_img 

393 

394 return p_Im 

395 

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

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

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

399 **kwargs): 

400 """Generate dipole model with given parameters. 

401 

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

403 is minimized by `lmfit`. 

404 

405 x : TODO: DM-17458 

406 Input independent variable. Used here as the grid on 

407 which to compute the background gradient model. 

408 flux : `float` 

409 Desired flux of the positive lobe of the dipole 

410 xcenPos, ycenPos : `float` 

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

412 xcenNeg, ycenNeg : `float` 

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

414 fluxNeg : `float`, optional 

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

416 b, x1, y1, xy, x2, y2 : `float` 

417 Gradient parameters for positive lobe. 

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

419 Gradient parameters for negative lobe. 

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

421 

422 **kwargs : `dict` [`str`] 

423 Keyword arguments passed through ``lmfit`` and 

424 used by this function. These must include: 

425 

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

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

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

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

430 

431 Returns 

432 ------- 

433 zout : `numpy.array` 

434 Has width and height matching the input bbox, and 

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

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

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

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

439 respectively. 

440 """ 

441 

442 psf = kwargs.get('psf') 

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

444 fp = kwargs.get('footprint') 

445 bbox = fp.getBBox() 

446 

447 if fluxNeg is None: 

448 fluxNeg = flux 

449 

450 self.log.debug('flux: %.2f fluxNeg: %.2f x+: %.2f x-: %.2f y+: %.2f y-: %.2f ', 

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

452 if x1 is not None: 

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

454 if xy is not None: 

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

456 

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

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

459 

460 in_x = x 

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

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

463 in_x = np.array([x, y]) * 1. 

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

465 in_x[1, :] -= in_x[1, :].mean() 

466 

467 if b is not None: 

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

469 

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

471 if bNeg is not None: 

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

473 else: 

474 gradientNeg = gradient 

475 

476 posIm.array[:, :] += gradient 

477 negIm.array[:, :] += gradientNeg 

478 

479 # Generate the diffIm model 

480 diffIm = afwImage.ImageF(bbox) 

481 diffIm += posIm 

482 diffIm -= negIm 

483 

484 zout = diffIm.array 

485 if rel_weight > 0.: 

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

487 

488 return zout 

489 

490 

491class DipoleFitAlgorithm: 

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

493 

494 See also: 

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

496 """ 

497 

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

499 # using for algorithm development. 

500 _private_version_ = '0.0.5' 

501 

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

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

504 

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

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

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

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

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

510 # todo 6. better exception handling in the plugin 

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

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

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

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

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

516 

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

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

519 

520 Parameters 

521 ---------- 

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

523 Exposure on which the diaSources were detected 

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

525 "Positive" exposure from which the template was subtracted 

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

527 "Negative" exposure which was subtracted from the posImage 

528 """ 

529 

530 self.diffim = diffim 

531 self.posImage = posImage 

532 self.negImage = negImage 

533 self.psfSigma = None 

534 if diffim is not None: 

535 diffimPsf = diffim.getPsf() 

536 diffimAvgPos = diffimPsf.getAveragePosition() 

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

538 

539 self.log = logging.getLogger(__name__) 

540 

541 import lsstDebug 

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

543 

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

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

546 separateNegParams=True, verbose=False): 

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

548 

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

550 footprint) and optionally constrain the fit using the 

551 pre-subtraction images posImage and negImage. 

552 

553 Parameters 

554 ---------- 

555 source : TODO: DM-17458 

556 TODO: DM-17458 

557 tol : float, optional 

558 TODO: DM-17458 

559 rel_weight : `float`, optional 

560 TODO: DM-17458 

561 fitBackground : `int`, optional 

562 TODO: DM-17458 

563 bgGradientOrder : `int`, optional 

564 TODO: DM-17458 

565 maxSepInSigma : `float`, optional 

566 TODO: DM-17458 

567 separateNegParams : `bool`, optional 

568 TODO: DM-17458 

569 verbose : `bool`, optional 

570 TODO: DM-17458 

571 

572 Returns 

573 ------- 

574 result : `lmfit.MinimizerResult` 

575 return `lmfit.MinimizerResult` object containing the fit 

576 parameters and other information. 

577 """ 

578 

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

580 import lmfit 

581 

582 fp = source.getFootprint() 

583 bbox = fp.getBBox() 

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

585 

586 z = diArr = subim.image.array 

587 # Make sure we don't overwrite buffers. 

588 z = z.copy() 

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

590 

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

592 if self.negImage is not None: 

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

594 if self.posImage is not None: 

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

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

597 posSubim = subim.clone() 

598 posSubim += negSubim 

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

600 negSubim = posSubim.clone() 

601 negSubim -= subim 

602 

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

604 negSubim.image.array], axis=0) 

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

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

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

608 else: 

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

610 

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

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

613 # makes this possible. 

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

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

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

617 **kwargs): 

618 """Generate dipole model with given parameters. 

619 

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

621 out of `kwargs['modelObj']`. 

622 """ 

623 modelObj = kwargs.pop('modelObj') 

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

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

626 bNeg=bNeg, x1Neg=x1Neg, y1Neg=y1Neg, xyNeg=xyNeg, 

627 x2Neg=x2Neg, y2Neg=y2Neg, **kwargs) 

628 

629 dipoleModel = DipoleModel() 

630 

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

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

633 # We have to (later) filter out the nans by hand in our input to gmod.fit(). 

634 gmod = lmfit.Model(modelFunctor, verbose=verbose) 

635 

636 # Add the constraints for centroids, fluxes. 

637 # starting constraint - near centroid of footprint 

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

639 cenNeg = cenPos = fpCentroid 

640 

641 pks = fp.getPeaks() 

642 

643 if len(pks) >= 1: 

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

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

646 cenNeg = pks[-1].getF() 

647 

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

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

650 maxSep = self.psfSigma * maxSepInSigma 

651 

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

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

654 cenPos = fpCentroid 

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

656 cenPos = fpCentroid 

657 

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

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

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

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

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

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

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

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

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

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

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

669 

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

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

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

673 posFlux = negFlux = startingFlux 

674 

675 # TBD: set max. flux limit? 

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

677 

678 if separateNegParams: 

679 # TBD: set max negative lobe flux limit? 

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

681 

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

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

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

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

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

687 # but might be desirable in some cases. 

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

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

690 pbg = 0. 

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

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

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

694 order=bgGradientOrder) 

695 

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

697 if fitBackground == 1: 

698 in_x = dipoleModel._generateXYGrid(bbox) 

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

700 z[1, :] -= pbg 

701 z[1, :] -= np.nanmedian(z[1, :]) 

702 posFlux = np.nansum(z[1, :]) 

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

704 

705 if separateNegParams and self.negImage is not None: 

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

707 order=bgGradientOrder) 

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

709 z[2, :] -= pbg 

710 z[2, :] -= np.nanmedian(z[2, :]) 

711 if separateNegParams: 

712 negFlux = np.nansum(z[2, :]) 

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

714 

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

716 if fitBackground == 2: 

717 if bgGradientOrder >= 0: 

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

719 if separateNegParams: 

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

721 if bgGradientOrder >= 1: 

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

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

724 if separateNegParams: 

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

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

727 if bgGradientOrder >= 2: 

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

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

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

731 if separateNegParams: 

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

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

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

735 

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

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

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

739 in_x[1, :] -= in_x[1, :].mean() 

740 

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

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

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

744 

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

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

747 # (override weights computed above). 

748 weights = mask.astype(np.float64) 

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

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

751 np.ones_like(diArr)*rel_weight]) 

752 

753 # Set the weights to zero if mask is False 

754 if np.any(~mask): 

755 weights[~mask] = 0. 

756 

757 # Filter out any nans, and make the weights 0. 

758 nans = (np.isnan(z) | np.isnan(weights)) 

759 nNans = nans.sum() 

760 if nNans > 0: 

761 if nNans < len(z): 

762 z[nans] = np.nanmedian(z) 

763 else: 

764 z[nans] = 0 

765 weights[nans] = 0 

766 

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

768 # since we set their param_hint's above. 

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

770 with warnings.catch_warnings(): 

771 # Ignore lmfit unknown argument warnings: 

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

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

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

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

776 verbose=verbose, 

777 # see scipy docs for the meaning of these keywords 

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

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

780 'epsfcn': 1e-8}, 

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

782 rel_weight=rel_weight, 

783 footprint=fp, 

784 modelObj=dipoleModel) 

785 

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

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

788 # This is how to get confidence intervals out: 

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

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

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

792 if separateNegParams: 

793 print(result.ci_report()) 

794 

795 return result 

796 

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

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

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

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

801 

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

803 footprint) and optionally constrain the fit using the 

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

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

806 `pipeBase.Struct` named tuple after computing additional 

807 statistics such as orientation and SNR. 

808 

809 Parameters 

810 ---------- 

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

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

813 tol : `float`, optional 

814 Tolerance parameter for scipy.leastsq() optimization 

815 rel_weight : `float`, optional 

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

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

818 How to fit linear background gradient in posImage/negImage 

819 

820 - 0: do not fit background at all 

821 - 1 (default): pre-fit the background using linear least squares and then do not fit it 

822 as part of the dipole fitting optimization 

823 - 2: pre-fit the background using linear least squares (as in 1), and use the parameter 

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

825 background as part of the overall dipole fitting optimization. 

826 maxSepInSigma : `float`, optional 

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

828 separateNegParams : `bool`, optional 

829 Fit separate parameters to the flux and background gradient in 

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

831 Desired polynomial order of background gradient 

832 verbose: `bool`, optional 

833 Be verbose 

834 display 

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

836 

837 Returns 

838 ------- 

839 result : `struct` 

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

841 

842 result : `callable` 

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

844 

845 Notes 

846 ----- 

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

848 

849 """ 

850 

851 fitResult = self.fitDipoleImpl( 

852 source, tol=tol, rel_weight=rel_weight, fitBackground=fitBackground, 

853 maxSepInSigma=maxSepInSigma, separateNegParams=separateNegParams, 

854 bgGradientOrder=bgGradientOrder, verbose=verbose) 

855 

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

857 if display: 

858 fp = source.getFootprint() 

859 self.displayFitResults(fp, fitResult) 

860 

861 fitParams = fitResult.best_values 

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

863 return None, fitResult 

864 

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

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

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

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

869 

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

871 # Also extract the stderr of flux estimate. 

872 def computeSumVariance(exposure, footprint): 

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

874 

875 fluxVal = fluxVar = fitParams['flux'] 

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

877 if self.posImage is not None: 

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

879 else: 

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

881 

882 fluxValNeg, fluxVarNeg = fluxVal, fluxVar 

883 if separateNegParams: 

884 fluxValNeg = fitParams['fluxNeg'] 

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

886 if self.negImage is not None: 

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

888 

889 try: 

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

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

892 signalToNoise = np.nan 

893 

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

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

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

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

898 signalToNoise=signalToNoise, chi2=fitResult.chisqr, redChi2=fitResult.redchi, 

899 nData=fitResult.ndata) 

900 

901 # fitResult may be returned for debugging 

902 return out, fitResult 

903 

904 def displayFitResults(self, footprint, result): 

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

906 

907 Parameters 

908 ---------- 

909 footprint : `lsst.afw.detection.Footprint` 

910 Footprint containing the dipole that was fit 

911 result : `lmfit.MinimizerResult` 

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

913 """ 

914 try: 

915 import matplotlib.pyplot as plt 

916 except ImportError as err: 

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

918 raise err 

919 

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

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

922 """ 

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

924 plt.title(title) 

925 plt.colorbar(fig, cmap='gray') 

926 return fig 

927 

928 z = result.data 

929 fit = result.best_fit 

930 bbox = footprint.getBBox() 

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

932 if z.shape[0] == 3: 

933 plt.figure(figsize=(8, 8)) 

934 for i in range(3): 

935 plt.subplot(3, 3, i*3+1) 

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

937 plt.subplot(3, 3, i*3+2) 

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

939 plt.subplot(3, 3, i*3+3) 

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

941 else: 

942 plt.figure(figsize=(8, 2.5)) 

943 plt.subplot(1, 3, 1) 

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

945 plt.subplot(1, 3, 2) 

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

947 plt.subplot(1, 3, 3) 

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

949 

950 plt.show() 

951 

952 

953@measBase.register("ip_diffim_DipoleFit") 

954class DipoleFitPlugin(measBase.SingleFramePlugin): 

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

956 

957 This measurement plugin accepts up to three input images in 

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

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

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

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

962 

963 Notes 

964 ----- 

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

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

967 

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

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

970 are deemed to be replaceable by this. 

971 """ 

972 

973 ConfigClass = DipoleFitPluginConfig 

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

975 

976 FAILURE_EDGE = 1 # too close to the edge 

977 FAILURE_FIT = 2 # failure in the fitting 

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

979 FAILURE_TOO_LARGE = 8 # input source is too large to be fit 

980 

981 @classmethod 

982 def getExecutionOrder(cls): 

983 """This algorithm simultaneously fits the centroid and flux, and does 

984 not require any previous centroid fit. 

985 """ 

986 return cls.CENTROID_ORDER 

987 

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

989 if logName is None: 

990 logName = name 

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

992 

993 self.log = logging.getLogger(logName) 

994 

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

996 

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

998 """Add fields for the outputs, and save the keys for fast assignment. 

999 """ 

1000 self.posFluxKey = measBase.FluxResultKey.addFields(schema, 

1001 schema.join(name, "pos"), 

1002 "Dipole positive lobe instrumental flux.") 

1003 self.negFluxKey = measBase.FluxResultKey.addFields(schema, 

1004 schema.join(name, "neg"), 

1005 "Dipole negative lobe instrumental flux.") 

1006 doc = "Dipole overall instrumental flux (mean of absolute value of positive and negative lobes)." 

1007 self.fluxKey = measBase.FluxResultKey.addFields(schema, name, doc) 

1008 

1009 self.posCentroidKey = measBase.CentroidResultKey.addFields(schema, 

1010 schema.join(name, "pos"), 

1011 "Dipole positive lobe centroid position.", 

1012 measBase.UncertaintyEnum.NO_UNCERTAINTY) 

1013 self.negCentroidKey = measBase.CentroidResultKey.addFields(schema, 

1014 schema.join(name, "neg"), 

1015 "Dipole negative lobe centroid position.", 

1016 measBase.UncertaintyEnum.NO_UNCERTAINTY) 

1017 self.centroidKey = measBase.CentroidResultKey.addFields(schema, 

1018 name, 

1019 "Dipole centroid position.", 

1020 measBase.UncertaintyEnum.SIGMA_ONLY) 

1021 

1022 self.orientationKey = schema.addField( 

1023 schema.join(name, "orientation"), type=float, units="deg", 

1024 doc="Dipole orientation") 

1025 

1026 self.separationKey = schema.addField( 

1027 schema.join(name, "separation"), type=float, units="pixel", 

1028 doc="Pixel separation between positive and negative lobes of dipole") 

1029 

1030 self.chi2dofKey = schema.addField( 

1031 schema.join(name, "chi2dof"), type=float, 

1032 doc="Chi2 per degree of freedom (chi2/(nData-nVariables)) of dipole fit") 

1033 

1034 self.nDataKey = schema.addField( 

1035 schema.join(name, "nData"), type=np.int64, 

1036 doc="Number of data points in the dipole fit") 

1037 

1038 self.signalToNoiseKey = schema.addField( 

1039 schema.join(name, "signalToNoise"), type=float, 

1040 doc="Estimated signal-to-noise of dipole fit") 

1041 

1042 self.classificationFlagKey = schema.addField( 

1043 schema.join(name, "flag", "classification"), type="Flag", 

1044 doc="Flag indicating diaSource is classified as a dipole") 

1045 

1046 self.classificationAttemptedFlagKey = schema.addField( 

1047 schema.join(name, "flag", "classificationAttempted"), type="Flag", 

1048 doc="Flag indicating diaSource was attempted to be classified as a dipole") 

1049 

1050 self.flagKey = schema.addField( 

1051 schema.join(name, "flag"), type="Flag", 

1052 doc="General failure flag for dipole fit") 

1053 

1054 self.edgeFlagKey = schema.addField( 

1055 schema.join(name, "flag", "edge"), type="Flag", 

1056 doc="Flag set when dipole is too close to edge of image") 

1057 

1058 def measureDipoles(self, measRecord, exposure, posExp=None, negExp=None): 

1059 """Perform the non-linear least squares minimization on the putative dipole source. 

1060 

1061 Parameters 

1062 ---------- 

1063 measRecord : `lsst.afw.table.SourceRecord` 

1064 diaSources that will be measured using dipole measurement 

1065 exposure : `lsst.afw.image.Exposure` 

1066 Difference exposure on which the diaSources were detected; `exposure = posExp-negExp` 

1067 If both `posExp` and `negExp` are `None`, will attempt to fit the 

1068 dipole to just the `exposure` with no constraint. 

1069 posExp : `lsst.afw.image.Exposure`, optional 

1070 "Positive" exposure, typically a science exposure, or None if unavailable 

1071 When `posExp` is `None`, will compute `posImage = exposure + negExp`. 

1072 negExp : `lsst.afw.image.Exposure`, optional 

1073 "Negative" exposure, typically a template exposure, or None if unavailable 

1074 When `negExp` is `None`, will compute `negImage = posExp - exposure`. 

1075 

1076 Notes 

1077 ----- 

1078 The main functionality of this routine was placed outside of 

1079 this plugin (into `DipoleFitAlgorithm.fitDipole()`) so that 

1080 `DipoleFitAlgorithm.fitDipole()` can be called separately for 

1081 testing (@see `tests/testDipoleFitter.py`) 

1082 """ 

1083 result = None 

1084 pks = measRecord.getFootprint().getPeaks() 

1085 

1086 # Check if the footprint consists of a putative dipole - else don't fit it. 

1087 if ( 

1088 # One peak in the footprint (not a dipole) 

1089 ((nPeaks := len(pks)) <= 1) 

1090 # Peaks are the same sign (not a dipole); peaks are ordered 

1091 # from highest to lowest. 

1092 or (nPeaks > 1 and (np.sign(pks[0].getPeakValue()) 

1093 == np.sign(pks[-1].getPeakValue()))) 

1094 ): 

1095 if not self.config.fitAllDiaSources: 

1096 # Non-dipoles fall back on the centroid slot for positions, 

1097 # errors, and the failure flag, if we're not fitting them. 

1098 measRecord[self.centroidKey.getX()] = measRecord.getX() 

1099 measRecord[self.centroidKey.getY()] = measRecord.getY() 

1100 self.centroidKey.getCentroidErr().set(measRecord, measRecord.getCentroidErr()) 

1101 measRecord[self.flagKey] = measRecord.getCentroidFlag() 

1102 return 

1103 

1104 # Footprint is too large (not a dipole). 

1105 if ((area := measRecord.getFootprint().getArea()) > self.config.maxFootprintArea): 

1106 self.fail(measRecord, measBase.MeasurementError(f"{area} > {self.config.maxFootprintArea}", 

1107 self.FAILURE_TOO_LARGE)) 

1108 

1109 try: 

1110 alg = self.DipoleFitAlgorithmClass(exposure, posImage=posExp, negImage=negExp) 

1111 result, _ = alg.fitDipole( 

1112 measRecord, rel_weight=self.config.relWeight, 

1113 tol=self.config.tolerance, 

1114 maxSepInSigma=self.config.maxSeparation, 

1115 fitBackground=self.config.fitBackground, 

1116 separateNegParams=self.config.fitSeparateNegParams, 

1117 verbose=False, display=False) 

1118 except pexExcept.LengthError: 

1119 self.fail(measRecord, measBase.MeasurementError('edge failure', self.FAILURE_EDGE)) 

1120 except Exception as e: 

1121 errorMessage = f"Exception in dipole fit. {e.__class__.__name__}: {e}" 

1122 self.fail(measRecord, measBase.MeasurementError(errorMessage, self.FAILURE_FIT)) 

1123 

1124 self.log.debug("Dipole fit result: %d %s", measRecord.getId(), str(result)) 

1125 

1126 if result is None: 

1127 self.fail(measRecord, measBase.MeasurementError("bad dipole fit", self.FAILURE_FIT)) 

1128 return 

1129 

1130 # add chi2, coord/flux uncertainties (TBD), dipole classification 

1131 # Add the relevant values to the measRecord 

1132 measRecord[self.posFluxKey.getInstFlux()] = result.posFlux 

1133 measRecord[self.posFluxKey.getInstFluxErr()] = result.signalToNoise # to be changed to actual sigma! 

1134 measRecord[self.posCentroidKey.getX()] = result.posCentroidX 

1135 measRecord[self.posCentroidKey.getY()] = result.posCentroidY 

1136 

1137 measRecord[self.negFluxKey.getInstFlux()] = result.negFlux 

1138 measRecord[self.negFluxKey.getInstFluxErr()] = result.signalToNoise # to be changed to actual sigma! 

1139 measRecord[self.negCentroidKey.getX()] = result.negCentroidX 

1140 measRecord[self.negCentroidKey.getY()] = result.negCentroidY 

1141 

1142 # Dia source flux: average of pos+neg 

1143 measRecord[self.fluxKey.getInstFlux()] = (abs(result.posFlux) + abs(result.negFlux))/2. 

1144 measRecord[self.orientationKey] = result.orientation 

1145 measRecord[self.separationKey] = np.sqrt((result.posCentroidX - result.negCentroidX)**2. 

1146 + (result.posCentroidY - result.negCentroidY)**2.) 

1147 measRecord[self.centroidKey.getX()] = result.centroidX 

1148 measRecord[self.centroidKey.getY()] = result.centroidY 

1149 

1150 measRecord[self.signalToNoiseKey] = result.signalToNoise 

1151 measRecord[self.chi2dofKey] = result.redChi2 

1152 

1153 if result.nData >= 1: 

1154 measRecord[self.nDataKey] = result.nData 

1155 else: 

1156 measRecord[self.nDataKey] = 0 

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.getInstFlux()]) 

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

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

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

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

1190 passesFluxNeg &= (abs(measRecord[self.negFluxKey.getInstFlux()]) >= 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 Fallback on the current slot centroid positions, but set the dipole 

1216 failure flag, since we attempted to fit the source. 

1217 """ 

1218 measRecord[self.centroidKey.getX()] = measRecord.getX() 

1219 measRecord[self.centroidKey.getY()] = measRecord.getY() 

1220 self.centroidKey.getCentroidErr().set(measRecord, measRecord.getCentroidErr()) 

1221 

1222 measRecord.set(self.flagKey, True) 

1223 if error is not None: 

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

1225 self.log.debug('DipoleFitPlugin not run on record %d: %s', measRecord.getId(), str(error)) 

1226 measRecord.set(self.edgeFlagKey, True) 

1227 if error.getFlagBit() == self.FAILURE_FIT: 

1228 self.log.warning('DipoleFitPlugin failed on record %d: %s', measRecord.getId(), str(error)) 

1229 if error.getFlagBit() == self.FAILURE_TOO_LARGE: 

1230 self.log.debug('DipoleFitPlugin not run on record with too large footprint %d: %s', 

1231 measRecord.getId(), str(error)) 

1232 else: 

1233 self.log.warning('DipoleFitPlugin failed on record %d', measRecord.getId())