Coverage for python/lsst/pipe/tasks/healSparseMapping.py: 17%

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1# This file is part of pipe_tasks. 

2# 

3# Developed for the LSST Data Management System. 

4# This product includes software developed by the LSST Project 

5# (https://www.lsst.org). 

6# See the COPYRIGHT file at the top-level directory of this distribution 

7# for details of code ownership. 

8# 

9# This program is free software: you can redistribute it and/or modify 

10# it under the terms of the GNU General Public License as published by 

11# the Free Software Foundation, either version 3 of the License, or 

12# (at your option) any later version. 

13# 

14# This program is distributed in the hope that it will be useful, 

15# but WITHOUT ANY WARRANTY; without even the implied warranty of 

16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 

17# GNU General Public License for more details. 

18# 

19# You should have received a copy of the GNU General Public License 

20# along with this program. If not, see <https://www.gnu.org/licenses/>. 

21 

22__all__ = ["HealSparseInputMapTask", "HealSparseInputMapConfig", 

23 "HealSparseMapFormatter", "HealSparsePropertyMapConnections", 

24 "HealSparsePropertyMapConfig", "HealSparsePropertyMapTask", 

25 "ConsolidateHealSparsePropertyMapConnections", 

26 "ConsolidateHealSparsePropertyMapConfig", 

27 "ConsolidateHealSparsePropertyMapTask"] 

28 

29from collections import defaultdict 

30import warnings 

31import numbers 

32import numpy as np 

33import hpgeom as hpg 

34import healsparse as hsp 

35 

36import lsst.pex.config as pexConfig 

37import lsst.pipe.base as pipeBase 

38import lsst.geom 

39import lsst.afw.geom as afwGeom 

40from lsst.daf.butler import Formatter 

41from lsst.skymap import BaseSkyMap 

42from lsst.utils.timer import timeMethod 

43from .healSparseMappingProperties import (BasePropertyMap, BasePropertyMapConfig, 

44 PropertyMapMap, compute_approx_psf_size_and_shape) 

45 

46 

47class HealSparseMapFormatter(Formatter): 

48 """Interface for reading and writing healsparse.HealSparseMap files.""" 

49 unsupportedParameters = frozenset() 

50 supportedExtensions = frozenset({".hsp", ".fit", ".fits"}) 

51 extension = '.hsp' 

52 

53 def read(self, component=None): 

54 # Docstring inherited from Formatter.read. 

55 path = self.fileDescriptor.location.path 

56 

57 if component == 'coverage': 

58 try: 

59 data = hsp.HealSparseCoverage.read(path) 

60 except (OSError, RuntimeError): 

61 raise ValueError(f"Unable to read healsparse map with URI {self.fileDescriptor.location.uri}") 

62 

63 return data 

64 

65 if self.fileDescriptor.parameters is None: 

66 pixels = None 

67 degrade_nside = None 

68 else: 

69 pixels = self.fileDescriptor.parameters.get('pixels', None) 

70 degrade_nside = self.fileDescriptor.parameters.get('degrade_nside', None) 

71 try: 

72 data = hsp.HealSparseMap.read(path, pixels=pixels, degrade_nside=degrade_nside) 

73 except (OSError, RuntimeError): 

74 raise ValueError(f"Unable to read healsparse map with URI {self.fileDescriptor.location.uri}") 

75 

76 return data 

77 

78 def write(self, inMemoryDataset): 

79 # Docstring inherited from Formatter.write. 

80 # Update the location with the formatter-preferred file extension 

81 self.fileDescriptor.location.updateExtension(self.extension) 

82 inMemoryDataset.write(self.fileDescriptor.location.path, clobber=True) 

83 

84 

85def _is_power_of_two(value): 

86 """Check that value is a power of two. 

87 

88 Parameters 

89 ---------- 

90 value : `int` 

91 Value to check. 

92 

93 Returns 

94 ------- 

95 is_power_of_two : `bool` 

96 True if value is a power of two; False otherwise, or 

97 if value is not an integer. 

98 """ 

99 if not isinstance(value, numbers.Integral): 

100 return False 

101 

102 # See https://stackoverflow.com/questions/57025836 

103 # Every power of 2 has exactly 1 bit set to 1; subtracting 

104 # 1 therefore flips every preceding bit. If you and that 

105 # together with the original value it must be 0. 

106 return (value & (value - 1) == 0) and value != 0 

107 

108 

109class HealSparseInputMapConfig(pexConfig.Config): 

110 """Configuration parameters for HealSparseInputMapTask""" 

111 nside = pexConfig.Field( 

112 doc="Mapping healpix nside. Must be power of 2.", 

113 dtype=int, 

114 default=32768, 

115 check=_is_power_of_two, 

116 ) 

117 nside_coverage = pexConfig.Field( 

118 doc="HealSparse coverage map nside. Must be power of 2.", 

119 dtype=int, 

120 default=256, 

121 check=_is_power_of_two, 

122 ) 

123 bad_mask_min_coverage = pexConfig.Field( 

124 doc=("Minimum area fraction of a map healpixel pixel that must be " 

125 "covered by bad pixels to be removed from the input map. " 

126 "This is approximate."), 

127 dtype=float, 

128 default=0.5, 

129 ) 

130 

131 

132class HealSparseInputMapTask(pipeBase.Task): 

133 """Task for making a HealSparse input map.""" 

134 

135 ConfigClass = HealSparseInputMapConfig 

136 _DefaultName = "healSparseInputMap" 

137 

138 def __init__(self, **kwargs): 

139 pipeBase.Task.__init__(self, **kwargs) 

140 

141 self.ccd_input_map = None 

142 

143 def build_ccd_input_map(self, bbox, wcs, ccds): 

144 """Build a map from ccd valid polygons or bounding boxes. 

145 

146 Parameters 

147 ---------- 

148 bbox : `lsst.geom.Box2I` 

149 Bounding box for region to build input map. 

150 wcs : `lsst.afw.geom.SkyWcs` 

151 WCS object for region to build input map. 

152 ccds : `lsst.afw.table.ExposureCatalog` 

153 Exposure catalog with ccd data from coadd inputs. 

154 """ 

155 with warnings.catch_warnings(): 

156 # Healsparse will emit a warning if nside coverage is greater than 

157 # 128. In the case of generating patch input maps, and not global 

158 # maps, high nside coverage works fine, so we can suppress this 

159 # warning. 

160 warnings.simplefilter("ignore") 

161 self.ccd_input_map = hsp.HealSparseMap.make_empty(nside_coverage=self.config.nside_coverage, 

162 nside_sparse=self.config.nside, 

163 dtype=hsp.WIDE_MASK, 

164 wide_mask_maxbits=len(ccds)) 

165 self._wcs = wcs 

166 self._bbox = bbox 

167 self._ccds = ccds 

168 

169 pixel_scale = wcs.getPixelScale().asArcseconds() 

170 hpix_area_arcsec2 = hpg.nside_to_pixel_area(self.config.nside, degrees=True)*(3600.**2.) 

171 self._min_bad = self.config.bad_mask_min_coverage*hpix_area_arcsec2/(pixel_scale**2.) 

172 

173 metadata = {} 

174 self._bits_per_visit_ccd = {} 

175 self._bits_per_visit = defaultdict(list) 

176 for bit, ccd_row in enumerate(ccds): 

177 metadata[f"B{bit:04d}CCD"] = ccd_row["ccd"] 

178 metadata[f"B{bit:04d}VIS"] = ccd_row["visit"] 

179 metadata[f"B{bit:04d}WT"] = ccd_row["weight"] 

180 

181 self._bits_per_visit_ccd[(ccd_row["visit"], ccd_row["ccd"])] = bit 

182 self._bits_per_visit[ccd_row["visit"]].append(bit) 

183 

184 ccd_poly = ccd_row.getValidPolygon() 

185 if ccd_poly is None: 

186 ccd_poly = afwGeom.Polygon(lsst.geom.Box2D(ccd_row.getBBox())) 

187 # Detectors need to be rendered with their own wcs. 

188 ccd_poly_radec = self._pixels_to_radec(ccd_row.getWcs(), ccd_poly.convexHull().getVertices()) 

189 

190 # Create a ccd healsparse polygon 

191 poly = hsp.Polygon(ra=ccd_poly_radec[: -1, 0], 

192 dec=ccd_poly_radec[: -1, 1], 

193 value=[bit]) 

194 self.ccd_input_map.set_bits_pix(poly.get_pixels(nside=self.ccd_input_map.nside_sparse), 

195 [bit]) 

196 

197 # Cut down to the overall bounding box with associated wcs. 

198 bbox_afw_poly = afwGeom.Polygon(lsst.geom.Box2D(bbox)) 

199 bbox_poly_radec = self._pixels_to_radec(self._wcs, 

200 bbox_afw_poly.convexHull().getVertices()) 

201 bbox_poly = hsp.Polygon(ra=bbox_poly_radec[: -1, 0], dec=bbox_poly_radec[: -1, 1], 

202 value=np.arange(self.ccd_input_map.wide_mask_maxbits)) 

203 with warnings.catch_warnings(): 

204 warnings.simplefilter("ignore") 

205 bbox_poly_map = bbox_poly.get_map_like(self.ccd_input_map) 

206 self.ccd_input_map = hsp.and_intersection([self.ccd_input_map, bbox_poly_map]) 

207 self.ccd_input_map.metadata = metadata 

208 

209 # Create a temporary map to hold the count of bad pixels in each healpix pixel 

210 self._ccd_input_pixels = self.ccd_input_map.valid_pixels 

211 

212 dtype = [(f"v{visit}", np.int64) for visit in self._bits_per_visit.keys()] 

213 

214 with warnings.catch_warnings(): 

215 # Healsparse will emit a warning if nside coverage is greater than 

216 # 128. In the case of generating patch input maps, and not global 

217 # maps, high nside coverage works fine, so we can suppress this 

218 # warning. 

219 warnings.simplefilter("ignore") 

220 self._ccd_input_bad_count_map = hsp.HealSparseMap.make_empty( 

221 nside_coverage=self.config.nside_coverage, 

222 nside_sparse=self.config.nside, 

223 dtype=dtype, 

224 primary=dtype[0][0]) 

225 

226 # Don't set input bad map if there are no ccds which overlap the bbox. 

227 if len(self._ccd_input_pixels) > 0: 

228 self._ccd_input_bad_count_map[self._ccd_input_pixels] = np.zeros(1, dtype=dtype) 

229 

230 def mask_warp_bbox(self, bbox, visit, mask, bit_mask_value): 

231 """Mask a subregion from a visit. 

232 This must be run after build_ccd_input_map initializes 

233 the overall map. 

234 

235 Parameters 

236 ---------- 

237 bbox : `lsst.geom.Box2I` 

238 Bounding box from region to mask. 

239 visit : `int` 

240 Visit number corresponding to warp with mask. 

241 mask : `lsst.afw.image.MaskX` 

242 Mask plane from warp exposure. 

243 bit_mask_value : `int` 

244 Bit mask to check for bad pixels. 

245 

246 Raises 

247 ------ 

248 RuntimeError : Raised if build_ccd_input_map was not run first. 

249 """ 

250 if self.ccd_input_map is None: 

251 raise RuntimeError("Must run build_ccd_input_map before mask_warp_bbox") 

252 

253 # Find the bad pixels and convert to healpix 

254 bad_pixels = np.where(mask.array & bit_mask_value) 

255 if len(bad_pixels[0]) == 0: 

256 # No bad pixels 

257 return 

258 

259 # Bad pixels come from warps which use the overall wcs. 

260 bad_ra, bad_dec = self._wcs.pixelToSkyArray(bad_pixels[1].astype(np.float64), 

261 bad_pixels[0].astype(np.float64), 

262 degrees=True) 

263 bad_hpix = hpg.angle_to_pixel(self.config.nside, bad_ra, bad_dec) 

264 

265 # Count the number of bad image pixels in each healpix pixel 

266 min_bad_hpix = bad_hpix.min() 

267 bad_hpix_count = np.zeros(bad_hpix.max() - min_bad_hpix + 1, dtype=np.int32) 

268 np.add.at(bad_hpix_count, bad_hpix - min_bad_hpix, 1) 

269 

270 # Add these to the accumulator map. 

271 # We need to make sure that the "primary" array has valid values for 

272 # this pixel to be registered in the accumulator map. 

273 pix_to_add, = np.where(bad_hpix_count > 0) 

274 count_map_arr = self._ccd_input_bad_count_map[min_bad_hpix + pix_to_add] 

275 primary = self._ccd_input_bad_count_map.primary 

276 count_map_arr[primary] = np.clip(count_map_arr[primary], 0, None) 

277 

278 count_map_arr[f"v{visit}"] = np.clip(count_map_arr[f"v{visit}"], 0, None) 

279 count_map_arr[f"v{visit}"] += bad_hpix_count[pix_to_add] 

280 

281 self._ccd_input_bad_count_map[min_bad_hpix + pix_to_add] = count_map_arr 

282 

283 def finalize_ccd_input_map_mask(self): 

284 """Use accumulated mask information to finalize the masking of 

285 ccd_input_map. 

286 

287 Raises 

288 ------ 

289 RuntimeError : Raised if build_ccd_input_map was not run first. 

290 """ 

291 if self.ccd_input_map is None: 

292 raise RuntimeError("Must run build_ccd_input_map before finalize_ccd_input_map_mask.") 

293 

294 count_map_arr = self._ccd_input_bad_count_map[self._ccd_input_pixels] 

295 for visit in self._bits_per_visit: 

296 to_mask, = np.where(count_map_arr[f"v{visit}"] > self._min_bad) 

297 if to_mask.size == 0: 

298 continue 

299 self.ccd_input_map.clear_bits_pix(self._ccd_input_pixels[to_mask], 

300 self._bits_per_visit[visit]) 

301 

302 # Clear memory 

303 self._ccd_input_bad_count_map = None 

304 

305 def _pixels_to_radec(self, wcs, pixels): 

306 """Convert pixels to ra/dec positions using a wcs. 

307 

308 Parameters 

309 ---------- 

310 wcs : `lsst.afw.geom.SkyWcs` 

311 WCS object. 

312 pixels : `list` [`lsst.geom.Point2D`] 

313 List of pixels to convert. 

314 

315 Returns 

316 ------- 

317 radec : `numpy.ndarray` 

318 Nx2 array of ra/dec positions associated with pixels. 

319 """ 

320 sph_pts = wcs.pixelToSky(pixels) 

321 return np.array([(sph.getRa().asDegrees(), sph.getDec().asDegrees()) 

322 for sph in sph_pts]) 

323 

324 

325class HealSparsePropertyMapConnections(pipeBase.PipelineTaskConnections, 

326 dimensions=("tract", "band", "skymap",), 

327 defaultTemplates={"coaddName": "deep", 

328 "calexpType": ""}): 

329 input_maps = pipeBase.connectionTypes.Input( 

330 doc="Healsparse bit-wise coadd input maps", 

331 name="{coaddName}Coadd_inputMap", 

332 storageClass="HealSparseMap", 

333 dimensions=("tract", "patch", "skymap", "band"), 

334 multiple=True, 

335 deferLoad=True, 

336 ) 

337 coadd_exposures = pipeBase.connectionTypes.Input( 

338 doc="Coadded exposures associated with input_maps", 

339 name="{coaddName}Coadd", 

340 storageClass="ExposureF", 

341 dimensions=("tract", "patch", "skymap", "band"), 

342 multiple=True, 

343 deferLoad=True, 

344 ) 

345 visit_summaries = pipeBase.connectionTypes.Input( 

346 doc="Visit summary tables with aggregated statistics", 

347 name="finalVisitSummary", 

348 storageClass="ExposureCatalog", 

349 dimensions=("instrument", "visit"), 

350 multiple=True, 

351 deferLoad=True, 

352 ) 

353 sky_map = pipeBase.connectionTypes.Input( 

354 doc="Input definition of geometry/bbox and projection/wcs for coadded exposures", 

355 name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME, 

356 storageClass="SkyMap", 

357 dimensions=("skymap",), 

358 ) 

359 

360 # Create output connections for all possible maps defined in the 

361 # registry. The vars() trick used here allows us to set class attributes 

362 # programatically. Taken from 

363 # https://stackoverflow.com/questions/2519807/ 

364 # setting-a-class-attribute-with-a-given-name-in-python-while-defining-the-class 

365 for name in BasePropertyMap.registry: 

366 vars()[f"{name}_map_min"] = pipeBase.connectionTypes.Output( 

367 doc=f"Minimum-value map of {name}", 

368 name=f"{{coaddName}}Coadd_{name}_map_min", 

369 storageClass="HealSparseMap", 

370 dimensions=("tract", "skymap", "band"), 

371 ) 

372 vars()[f"{name}_map_max"] = pipeBase.connectionTypes.Output( 

373 doc=f"Maximum-value map of {name}", 

374 name=f"{{coaddName}}Coadd_{name}_map_max", 

375 storageClass="HealSparseMap", 

376 dimensions=("tract", "skymap", "band"), 

377 ) 

378 vars()[f"{name}_map_mean"] = pipeBase.connectionTypes.Output( 

379 doc=f"Mean-value map of {name}", 

380 name=f"{{coaddName}}Coadd_{name}_map_mean", 

381 storageClass="HealSparseMap", 

382 dimensions=("tract", "skymap", "band"), 

383 ) 

384 vars()[f"{name}_map_weighted_mean"] = pipeBase.connectionTypes.Output( 

385 doc=f"Weighted mean-value map of {name}", 

386 name=f"{{coaddName}}Coadd_{name}_map_weighted_mean", 

387 storageClass="HealSparseMap", 

388 dimensions=("tract", "skymap", "band"), 

389 ) 

390 vars()[f"{name}_map_sum"] = pipeBase.connectionTypes.Output( 

391 doc=f"Sum-value map of {name}", 

392 name=f"{{coaddName}}Coadd_{name}_map_sum", 

393 storageClass="HealSparseMap", 

394 dimensions=("tract", "skymap", "band"), 

395 ) 

396 

397 def __init__(self, *, config=None): 

398 super().__init__(config=config) 

399 

400 # Not all possible maps in the registry will be configured to run. 

401 # Here we remove the unused connections. 

402 for name in BasePropertyMap.registry: 

403 if name not in config.property_maps: 

404 prop_config = BasePropertyMapConfig() 

405 prop_config.do_min = False 

406 prop_config.do_max = False 

407 prop_config.do_mean = False 

408 prop_config.do_weighted_mean = False 

409 prop_config.do_sum = False 

410 else: 

411 prop_config = config.property_maps[name] 

412 

413 if not prop_config.do_min: 

414 self.outputs.remove(f"{name}_map_min") 

415 if not prop_config.do_max: 

416 self.outputs.remove(f"{name}_map_max") 

417 if not prop_config.do_mean: 

418 self.outputs.remove(f"{name}_map_mean") 

419 if not prop_config.do_weighted_mean: 

420 self.outputs.remove(f"{name}_map_weighted_mean") 

421 if not prop_config.do_sum: 

422 self.outputs.remove(f"{name}_map_sum") 

423 

424 

425class HealSparsePropertyMapConfig(pipeBase.PipelineTaskConfig, 

426 pipelineConnections=HealSparsePropertyMapConnections): 

427 """Configuration parameters for HealSparsePropertyMapTask""" 

428 property_maps = BasePropertyMap.registry.makeField( 

429 multi=True, 

430 default=["exposure_time", 

431 "psf_size", 

432 "psf_e1", 

433 "psf_e2", 

434 "psf_maglim", 

435 "sky_noise", 

436 "sky_background", 

437 "dcr_dra", 

438 "dcr_ddec", 

439 "dcr_e1", 

440 "dcr_e2", 

441 "epoch"], 

442 doc="Property map computation objects", 

443 ) 

444 

445 def setDefaults(self): 

446 self.property_maps["exposure_time"].do_sum = True 

447 self.property_maps["psf_size"].do_weighted_mean = True 

448 self.property_maps["psf_e1"].do_weighted_mean = True 

449 self.property_maps["psf_e2"].do_weighted_mean = True 

450 self.property_maps["psf_maglim"].do_weighted_mean = True 

451 self.property_maps["sky_noise"].do_weighted_mean = True 

452 self.property_maps["sky_background"].do_weighted_mean = True 

453 self.property_maps["dcr_dra"].do_weighted_mean = True 

454 self.property_maps["dcr_ddec"].do_weighted_mean = True 

455 self.property_maps["dcr_e1"].do_weighted_mean = True 

456 self.property_maps["dcr_e2"].do_weighted_mean = True 

457 self.property_maps["epoch"].do_mean = True 

458 self.property_maps["epoch"].do_min = True 

459 self.property_maps["epoch"].do_max = True 

460 

461 

462class HealSparsePropertyMapTask(pipeBase.PipelineTask): 

463 """Task to compute Healsparse property maps. 

464 

465 This task will compute individual property maps (per tract, per 

466 map type, per band). These maps cover the full coadd tract, and 

467 are not truncated to the inner tract region. 

468 """ 

469 ConfigClass = HealSparsePropertyMapConfig 

470 _DefaultName = "healSparsePropertyMapTask" 

471 

472 def __init__(self, **kwargs): 

473 super().__init__(**kwargs) 

474 self.property_maps = PropertyMapMap() 

475 for name, config, PropertyMapClass in self.config.property_maps.apply(): 

476 self.property_maps[name] = PropertyMapClass(config, name) 

477 

478 @timeMethod 

479 def runQuantum(self, butlerQC, inputRefs, outputRefs): 

480 inputs = butlerQC.get(inputRefs) 

481 

482 sky_map = inputs.pop("sky_map") 

483 

484 tract = butlerQC.quantum.dataId["tract"] 

485 band = butlerQC.quantum.dataId["band"] 

486 

487 input_map_dict = {ref.dataId["patch"]: ref for ref in inputs["input_maps"]} 

488 coadd_dict = {ref.dataId["patch"]: ref for ref in inputs["coadd_exposures"]} 

489 

490 visit_summary_dict = {ref.dataId["visit"]: ref.get() 

491 for ref in inputs["visit_summaries"]} 

492 

493 self.run(sky_map, tract, band, coadd_dict, input_map_dict, visit_summary_dict) 

494 

495 # Write the outputs 

496 for name, property_map in self.property_maps.items(): 

497 if property_map.config.do_min: 

498 butlerQC.put(property_map.min_map, 

499 getattr(outputRefs, f"{name}_map_min")) 

500 if property_map.config.do_max: 

501 butlerQC.put(property_map.max_map, 

502 getattr(outputRefs, f"{name}_map_max")) 

503 if property_map.config.do_mean: 

504 butlerQC.put(property_map.mean_map, 

505 getattr(outputRefs, f"{name}_map_mean")) 

506 if property_map.config.do_weighted_mean: 

507 butlerQC.put(property_map.weighted_mean_map, 

508 getattr(outputRefs, f"{name}_map_weighted_mean")) 

509 if property_map.config.do_sum: 

510 butlerQC.put(property_map.sum_map, 

511 getattr(outputRefs, f"{name}_map_sum")) 

512 

513 def run(self, sky_map, tract, band, coadd_dict, input_map_dict, visit_summary_dict): 

514 """Run the healsparse property task. 

515 

516 Parameters 

517 ---------- 

518 sky_map : Sky map object 

519 tract : `int` 

520 Tract number. 

521 band : `str` 

522 Band name for logging. 

523 coadd_dict : `dict` [`int`: `lsst.daf.butler.DeferredDatasetHandle`] 

524 Dictionary of coadd exposure references. Keys are patch numbers. 

525 input_map_dict : `dict` [`int`: `lsst.daf.butler.DeferredDatasetHandle`] 

526 Dictionary of input map references. Keys are patch numbers. 

527 visit_summary_dict : `dict` [`int`: `lsst.afw.table.ExposureCatalog`] 

528 Dictionary of visit summary tables. Keys are visit numbers. 

529 

530 Raises 

531 ------ 

532 RepeatableQuantumError 

533 If visit_summary_dict is missing any visits or detectors found in an 

534 input map. This leads to an inconsistency between what is in the coadd 

535 (via the input map) and the visit summary tables which contain data 

536 to compute the maps. 

537 """ 

538 tract_info = sky_map[tract] 

539 

540 tract_maps_initialized = False 

541 

542 for patch in input_map_dict.keys(): 

543 self.log.info("Making maps for band %s, tract %d, patch %d.", 

544 band, tract, patch) 

545 

546 patch_info = tract_info[patch] 

547 

548 input_map = input_map_dict[patch].get() 

549 

550 # Initialize the tract maps as soon as we have the first input 

551 # map for getting nside information. 

552 if not tract_maps_initialized: 

553 # We use the first input map nside information to initialize 

554 # the tract maps 

555 nside_coverage = self._compute_nside_coverage_tract(tract_info) 

556 nside = input_map.nside_sparse 

557 

558 do_compute_approx_psf = False 

559 # Initialize the tract maps 

560 for property_map in self.property_maps: 

561 property_map.initialize_tract_maps(nside_coverage, nside) 

562 if property_map.requires_psf: 

563 do_compute_approx_psf = True 

564 

565 tract_maps_initialized = True 

566 

567 if input_map.valid_pixels.size == 0: 

568 self.log.warning("No valid pixels for band %s, tract %d, patch %d; skipping.", 

569 band, tract, patch) 

570 continue 

571 

572 coadd_photo_calib = coadd_dict[patch].get(component="photoCalib") 

573 coadd_inputs = coadd_dict[patch].get(component="coaddInputs") 

574 

575 coadd_zeropoint = 2.5*np.log10(coadd_photo_calib.getInstFluxAtZeroMagnitude()) 

576 

577 # Crop input_map to the inner polygon of the patch 

578 poly_vertices = patch_info.getInnerSkyPolygon(tract_info.getWcs()).getVertices() 

579 patch_radec = self._vertices_to_radec(poly_vertices) 

580 patch_poly = hsp.Polygon(ra=patch_radec[:, 0], dec=patch_radec[:, 1], 

581 value=np.arange(input_map.wide_mask_maxbits)) 

582 with warnings.catch_warnings(): 

583 # Healsparse will emit a warning if nside coverage is greater than 

584 # 128. In the case of generating patch input maps, and not global 

585 # maps, high nside coverage works fine, so we can suppress this 

586 # warning. 

587 warnings.simplefilter("ignore") 

588 patch_poly_map = patch_poly.get_map_like(input_map) 

589 input_map = hsp.and_intersection([input_map, patch_poly_map]) 

590 

591 valid_pixels, vpix_ra, vpix_dec = input_map.valid_pixels_pos(return_pixels=True) 

592 

593 # Check if there are no valid pixels for the inner (unique) patch region 

594 if valid_pixels.size == 0: 

595 continue 

596 

597 # Initialize the value accumulators 

598 for property_map in self.property_maps: 

599 property_map.initialize_values(valid_pixels.size) 

600 property_map.zeropoint = coadd_zeropoint 

601 

602 # Initialize the weight and counter accumulators 

603 total_weights = np.zeros(valid_pixels.size) 

604 total_inputs = np.zeros(valid_pixels.size, dtype=np.int32) 

605 

606 for bit, ccd_row in enumerate(coadd_inputs.ccds): 

607 # Which pixels in the map are used by this visit/detector 

608 inmap, = np.where(input_map.check_bits_pix(valid_pixels, [bit])) 

609 

610 # Check if there are any valid pixels in the map from this deteector. 

611 if inmap.size == 0: 

612 continue 

613 

614 # visit, detector_id, weight = input_dict[bit] 

615 visit = ccd_row["visit"] 

616 detector_id = ccd_row["ccd"] 

617 weight = ccd_row["weight"] 

618 

619 x, y = ccd_row.getWcs().skyToPixelArray(vpix_ra[inmap], vpix_dec[inmap], degrees=True) 

620 scalings = self._compute_calib_scale(ccd_row, x, y) 

621 

622 if do_compute_approx_psf: 

623 psf_array = compute_approx_psf_size_and_shape(ccd_row, vpix_ra[inmap], vpix_dec[inmap]) 

624 else: 

625 psf_array = None 

626 

627 total_weights[inmap] += weight 

628 total_inputs[inmap] += 1 

629 

630 # Retrieve the correct visitSummary row 

631 if visit not in visit_summary_dict: 

632 msg = f"Visit {visit} not found in visit_summaries." 

633 raise pipeBase.RepeatableQuantumError(msg) 

634 row = visit_summary_dict[visit].find(detector_id) 

635 if row is None: 

636 msg = f"Visit {visit} / detector_id {detector_id} not found in visit_summaries." 

637 raise pipeBase.RepeatableQuantumError(msg) 

638 

639 # Accumulate the values 

640 for property_map in self.property_maps: 

641 property_map.accumulate_values(inmap, 

642 vpix_ra[inmap], 

643 vpix_dec[inmap], 

644 weight, 

645 scalings, 

646 row, 

647 psf_array=psf_array) 

648 

649 # Finalize the mean values and set the tract maps 

650 for property_map in self.property_maps: 

651 property_map.finalize_mean_values(total_weights, total_inputs) 

652 property_map.set_map_values(valid_pixels) 

653 

654 def _compute_calib_scale(self, ccd_row, x, y): 

655 """Compute calibration scaling values. 

656 

657 Parameters 

658 ---------- 

659 ccd_row : `lsst.afw.table.ExposureRecord` 

660 Exposure metadata for a given detector exposure. 

661 x : `np.ndarray` 

662 Array of x positions. 

663 y : `np.ndarray` 

664 Array of y positions. 

665 

666 Returns 

667 ------- 

668 calib_scale : `np.ndarray` 

669 Array of calibration scale values. 

670 """ 

671 photo_calib = ccd_row.getPhotoCalib() 

672 bf = photo_calib.computeScaledCalibration() 

673 if bf.getBBox() == ccd_row.getBBox(): 

674 # Track variable calibration over the detector 

675 calib_scale = photo_calib.getCalibrationMean()*bf.evaluate(x, y) 

676 else: 

677 # Spatially constant calibration 

678 calib_scale = photo_calib.getCalibrationMean() 

679 

680 return calib_scale 

681 

682 def _vertices_to_radec(self, vertices): 

683 """Convert polygon vertices to ra/dec. 

684 

685 Parameters 

686 ---------- 

687 vertices : `list` [ `lsst.sphgeom.UnitVector3d` ] 

688 Vertices for bounding polygon. 

689 

690 Returns 

691 ------- 

692 radec : `numpy.ndarray` 

693 Nx2 array of ra/dec positions (in degrees) associated with vertices. 

694 """ 

695 lonlats = [lsst.sphgeom.LonLat(x) for x in vertices] 

696 radec = np.array([(x.getLon().asDegrees(), x.getLat().asDegrees()) for 

697 x in lonlats]) 

698 return radec 

699 

700 def _compute_nside_coverage_tract(self, tract_info): 

701 """Compute the optimal coverage nside for a tract. 

702 

703 Parameters 

704 ---------- 

705 tract_info : `lsst.skymap.tractInfo.ExplicitTractInfo` 

706 Tract information object. 

707 

708 Returns 

709 ------- 

710 nside_coverage : `int` 

711 Optimal coverage nside for a tract map. 

712 """ 

713 num_patches = tract_info.getNumPatches() 

714 

715 # Compute approximate patch area 

716 patch_info = tract_info.getPatchInfo(0) 

717 vertices = patch_info.getInnerSkyPolygon(tract_info.getWcs()).getVertices() 

718 radec = self._vertices_to_radec(vertices) 

719 delta_ra = np.max(radec[:, 0]) - np.min(radec[:, 0]) 

720 delta_dec = np.max(radec[:, 1]) - np.min(radec[:, 1]) 

721 patch_area = delta_ra*delta_dec*np.cos(np.deg2rad(np.mean(radec[:, 1]))) 

722 

723 tract_area = num_patches[0]*num_patches[1]*patch_area 

724 # Start with a fairly low nside and increase until we find the approximate area. 

725 nside_coverage_tract = 32 

726 while hpg.nside_to_pixel_area(nside_coverage_tract, degrees=True) > tract_area: 

727 nside_coverage_tract = 2*nside_coverage_tract 

728 # Step back one, but don't go bigger pixels than nside=32 or smaller 

729 # than 128 (recommended by healsparse). 

730 nside_coverage_tract = int(np.clip(nside_coverage_tract/2, 32, 128)) 

731 

732 return nside_coverage_tract 

733 

734 

735class ConsolidateHealSparsePropertyMapConnections(pipeBase.PipelineTaskConnections, 

736 dimensions=("band", "skymap",), 

737 defaultTemplates={"coaddName": "deep"}): 

738 sky_map = pipeBase.connectionTypes.Input( 

739 doc="Input definition of geometry/bbox and projection/wcs for coadded exposures", 

740 name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME, 

741 storageClass="SkyMap", 

742 dimensions=("skymap",), 

743 ) 

744 

745 # Create output connections for all possible maps defined in the 

746 # registry. The vars() trick used here allows us to set class attributes 

747 # programatically. Taken from 

748 # https://stackoverflow.com/questions/2519807/ 

749 # setting-a-class-attribute-with-a-given-name-in-python-while-defining-the-class 

750 for name in BasePropertyMap.registry: 

751 vars()[f"{name}_map_min"] = pipeBase.connectionTypes.Input( 

752 doc=f"Minimum-value map of {name}", 

753 name=f"{{coaddName}}Coadd_{name}_map_min", 

754 storageClass="HealSparseMap", 

755 dimensions=("tract", "skymap", "band"), 

756 multiple=True, 

757 deferLoad=True, 

758 ) 

759 vars()[f"{name}_consolidated_map_min"] = pipeBase.connectionTypes.Output( 

760 doc=f"Minumum-value map of {name}", 

761 name=f"{{coaddName}}Coadd_{name}_consolidated_map_min", 

762 storageClass="HealSparseMap", 

763 dimensions=("skymap", "band"), 

764 ) 

765 vars()[f"{name}_map_max"] = pipeBase.connectionTypes.Input( 

766 doc=f"Maximum-value map of {name}", 

767 name=f"{{coaddName}}Coadd_{name}_map_max", 

768 storageClass="HealSparseMap", 

769 dimensions=("tract", "skymap", "band"), 

770 multiple=True, 

771 deferLoad=True, 

772 ) 

773 vars()[f"{name}_consolidated_map_max"] = pipeBase.connectionTypes.Output( 

774 doc=f"Minumum-value map of {name}", 

775 name=f"{{coaddName}}Coadd_{name}_consolidated_map_max", 

776 storageClass="HealSparseMap", 

777 dimensions=("skymap", "band"), 

778 ) 

779 vars()[f"{name}_map_mean"] = pipeBase.connectionTypes.Input( 

780 doc=f"Mean-value map of {name}", 

781 name=f"{{coaddName}}Coadd_{name}_map_mean", 

782 storageClass="HealSparseMap", 

783 dimensions=("tract", "skymap", "band"), 

784 multiple=True, 

785 deferLoad=True, 

786 ) 

787 vars()[f"{name}_consolidated_map_mean"] = pipeBase.connectionTypes.Output( 

788 doc=f"Minumum-value map of {name}", 

789 name=f"{{coaddName}}Coadd_{name}_consolidated_map_mean", 

790 storageClass="HealSparseMap", 

791 dimensions=("skymap", "band"), 

792 ) 

793 vars()[f"{name}_map_weighted_mean"] = pipeBase.connectionTypes.Input( 

794 doc=f"Weighted mean-value map of {name}", 

795 name=f"{{coaddName}}Coadd_{name}_map_weighted_mean", 

796 storageClass="HealSparseMap", 

797 dimensions=("tract", "skymap", "band"), 

798 multiple=True, 

799 deferLoad=True, 

800 ) 

801 vars()[f"{name}_consolidated_map_weighted_mean"] = pipeBase.connectionTypes.Output( 

802 doc=f"Minumum-value map of {name}", 

803 name=f"{{coaddName}}Coadd_{name}_consolidated_map_weighted_mean", 

804 storageClass="HealSparseMap", 

805 dimensions=("skymap", "band"), 

806 ) 

807 vars()[f"{name}_map_sum"] = pipeBase.connectionTypes.Input( 

808 doc=f"Sum-value map of {name}", 

809 name=f"{{coaddName}}Coadd_{name}_map_sum", 

810 storageClass="HealSparseMap", 

811 dimensions=("tract", "skymap", "band"), 

812 multiple=True, 

813 deferLoad=True, 

814 ) 

815 vars()[f"{name}_consolidated_map_sum"] = pipeBase.connectionTypes.Output( 

816 doc=f"Minumum-value map of {name}", 

817 name=f"{{coaddName}}Coadd_{name}_consolidated_map_sum", 

818 storageClass="HealSparseMap", 

819 dimensions=("skymap", "band"), 

820 ) 

821 

822 def __init__(self, *, config=None): 

823 super().__init__(config=config) 

824 

825 # Not all possible maps in the registry will be configured to run. 

826 # Here we remove the unused connections. 

827 for name in BasePropertyMap.registry: 

828 if name not in config.property_maps: 

829 prop_config = BasePropertyMapConfig() 

830 prop_config.do_min = False 

831 prop_config.do_max = False 

832 prop_config.do_mean = False 

833 prop_config.do_weighted_mean = False 

834 prop_config.do_sum = False 

835 else: 

836 prop_config = config.property_maps[name] 

837 

838 if not prop_config.do_min: 

839 self.inputs.remove(f"{name}_map_min") 

840 self.outputs.remove(f"{name}_consolidated_map_min") 

841 if not prop_config.do_max: 

842 self.inputs.remove(f"{name}_map_max") 

843 self.outputs.remove(f"{name}_consolidated_map_max") 

844 if not prop_config.do_mean: 

845 self.inputs.remove(f"{name}_map_mean") 

846 self.outputs.remove(f"{name}_consolidated_map_mean") 

847 if not prop_config.do_weighted_mean: 

848 self.inputs.remove(f"{name}_map_weighted_mean") 

849 self.outputs.remove(f"{name}_consolidated_map_weighted_mean") 

850 if not prop_config.do_sum: 

851 self.inputs.remove(f"{name}_map_sum") 

852 self.outputs.remove(f"{name}_consolidated_map_sum") 

853 

854 

855class ConsolidateHealSparsePropertyMapConfig(pipeBase.PipelineTaskConfig, 

856 pipelineConnections=ConsolidateHealSparsePropertyMapConnections): 

857 """Configuration parameters for ConsolidateHealSparsePropertyMapTask""" 

858 property_maps = BasePropertyMap.registry.makeField( 

859 multi=True, 

860 default=["exposure_time", 

861 "psf_size", 

862 "psf_e1", 

863 "psf_e2", 

864 "psf_maglim", 

865 "sky_noise", 

866 "sky_background", 

867 "dcr_dra", 

868 "dcr_ddec", 

869 "dcr_e1", 

870 "dcr_e2", 

871 "epoch"], 

872 doc="Property map computation objects", 

873 ) 

874 nside_coverage = pexConfig.Field( 

875 doc="Consolidated HealSparse coverage map nside. Must be power of 2.", 

876 dtype=int, 

877 default=32, 

878 check=_is_power_of_two, 

879 ) 

880 

881 def setDefaults(self): 

882 self.property_maps["exposure_time"].do_sum = True 

883 self.property_maps["psf_size"].do_weighted_mean = True 

884 self.property_maps["psf_e1"].do_weighted_mean = True 

885 self.property_maps["psf_e2"].do_weighted_mean = True 

886 self.property_maps["psf_maglim"].do_weighted_mean = True 

887 self.property_maps["sky_noise"].do_weighted_mean = True 

888 self.property_maps["sky_background"].do_weighted_mean = True 

889 self.property_maps["dcr_dra"].do_weighted_mean = True 

890 self.property_maps["dcr_ddec"].do_weighted_mean = True 

891 self.property_maps["dcr_e1"].do_weighted_mean = True 

892 self.property_maps["dcr_e2"].do_weighted_mean = True 

893 self.property_maps["epoch"].do_mean = True 

894 self.property_maps["epoch"].do_min = True 

895 self.property_maps["epoch"].do_max = True 

896 

897 

898class ConsolidateHealSparsePropertyMapTask(pipeBase.PipelineTask): 

899 """Task to consolidate HealSparse property maps. 

900 

901 This task will take all the individual tract-based maps (per map type, 

902 per band) and consolidate them into one survey-wide map (per map type, 

903 per band). Each tract map is truncated to its inner region before 

904 consolidation. 

905 """ 

906 ConfigClass = ConsolidateHealSparsePropertyMapConfig 

907 _DefaultName = "consolidateHealSparsePropertyMapTask" 

908 

909 def __init__(self, **kwargs): 

910 super().__init__(**kwargs) 

911 self.property_maps = PropertyMapMap() 

912 for name, config, PropertyMapClass in self.config.property_maps.apply(): 

913 self.property_maps[name] = PropertyMapClass(config, name) 

914 

915 @timeMethod 

916 def runQuantum(self, butlerQC, inputRefs, outputRefs): 

917 inputs = butlerQC.get(inputRefs) 

918 

919 sky_map = inputs.pop("sky_map") 

920 

921 # These need to be consolidated one at a time to conserve memory. 

922 for name in self.config.property_maps.names: 

923 for type_ in ['min', 'max', 'mean', 'weighted_mean', 'sum']: 

924 map_type = f"{name}_map_{type_}" 

925 if map_type in inputs: 

926 input_refs = {ref.dataId['tract']: ref 

927 for ref in inputs[map_type]} 

928 consolidated_map = self.consolidate_map(sky_map, input_refs) 

929 butlerQC.put(consolidated_map, 

930 getattr(outputRefs, f"{name}_consolidated_map_{type_}")) 

931 

932 def consolidate_map(self, sky_map, input_refs): 

933 """Consolidate the healsparse property maps. 

934 

935 Parameters 

936 ---------- 

937 sky_map : Sky map object 

938 input_refs : `dict` [`int`: `lsst.daf.butler.DeferredDatasetHandle`] 

939 Dictionary of tract_id mapping to dataref. 

940 

941 Returns 

942 ------- 

943 consolidated_map : `healsparse.HealSparseMap` 

944 Consolidated HealSparse map. 

945 """ 

946 # First, we read in the coverage maps to know how much memory 

947 # to allocate 

948 cov_mask = None 

949 nside_coverage_inputs = None 

950 for tract_id in input_refs: 

951 cov = input_refs[tract_id].get(component='coverage') 

952 if cov_mask is None: 

953 cov_mask = cov.coverage_mask 

954 nside_coverage_inputs = cov.nside_coverage 

955 else: 

956 cov_mask |= cov.coverage_mask 

957 

958 cov_pix_inputs, = np.where(cov_mask) 

959 

960 # Compute the coverage pixels for the desired nside_coverage 

961 if nside_coverage_inputs == self.config.nside_coverage: 

962 cov_pix = cov_pix_inputs 

963 elif nside_coverage_inputs > self.config.nside_coverage: 

964 # Converting from higher resolution coverage to lower 

965 # resolution coverage. 

966 bit_shift = hsp.utils._compute_bitshift(self.config.nside_coverage, 

967 nside_coverage_inputs) 

968 cov_pix = np.right_shift(cov_pix_inputs, bit_shift) 

969 else: 

970 # Converting from lower resolution coverage to higher 

971 # resolution coverage. 

972 bit_shift = hsp.utils._compute_bitshift(nside_coverage_inputs, 

973 self.config.nside_coverage) 

974 cov_pix = np.left_shift(cov_pix_inputs, bit_shift) 

975 

976 # Now read in each tract map and build the consolidated map. 

977 consolidated_map = None 

978 for tract_id in input_refs: 

979 input_map = input_refs[tract_id].get() 

980 if consolidated_map is None: 

981 consolidated_map = hsp.HealSparseMap.make_empty( 

982 self.config.nside_coverage, 

983 input_map.nside_sparse, 

984 input_map.dtype, 

985 sentinel=input_map._sentinel, 

986 cov_pixels=cov_pix, 

987 metadata=input_map.metadata, 

988 ) 

989 

990 # Only use pixels that are properly inside the tract. 

991 vpix, ra, dec = input_map.valid_pixels_pos(return_pixels=True) 

992 vpix_tract_ids = sky_map.findTractIdArray(ra, dec, degrees=True) 

993 

994 in_tract = (vpix_tract_ids == tract_id) 

995 

996 consolidated_map[vpix[in_tract]] = input_map[vpix[in_tract]] 

997 

998 return consolidated_map