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1# 

2# Developed for the LSST Data Management System. 

3# This product includes software developed by the LSST Project 

4# (http://www.lsst.org). 

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

6# for details of code ownership. 

7# 

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

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

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

11# (at your option) any later version. 

12# 

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

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

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

16# GNU General Public License for more details. 

17# 

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

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

20# 

21 

22"""Classes for taking science pipeline outputs and creating data products for 

23use in ap_association and the alert production database (APDB). 

24""" 

25 

26__all__ = ["MapApDataConfig", "MapApDataTask", 

27 "MapDiaSourceConfig", "MapDiaSourceTask", 

28 "UnpackApdbFlags"] 

29 

30import numpy as np 

31import os 

32import yaml 

33 

34import lsst.afw.table as afwTable 

35from lsst.daf.base import DateTime 

36import lsst.pipe.base as pipeBase 

37import lsst.pex.config as pexConfig 

38from lsst.pex.exceptions import RuntimeError 

39from lsst.utils import getPackageDir 

40from .afwUtils import make_dia_source_schema 

41 

42 

43class MapApDataConfig(pexConfig.Config): 

44 """Configuration for the generic MapApDataTask class. 

45 """ 

46 copyColumns = pexConfig.DictField( 

47 keytype=str, 

48 itemtype=str, 

49 doc="Mapping of input SciencePipelines columns to output DPDD " 

50 "columns.", 

51 default={"id": "id", 

52 "parent": "parent", 

53 "coord_ra": "coord_ra", 

54 "coord_dec": "coord_dec"} 

55 ) 

56 

57 

58class MapApDataTask(pipeBase.Task): 

59 """Generic mapper class for copying values from a science pipelines catalog 

60 into a product for use in ap_association or the APDB. 

61 """ 

62 ConfigClass = MapApDataConfig 

63 _DefaultName = "mapApDataTask" 

64 

65 def __init__(self, inputSchema, outputSchema, **kwargs): 

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

67 self.inputSchema = inputSchema 

68 self.outputSchema = outputSchema 

69 

70 self.mapper = afwTable.SchemaMapper(inputSchema, outputSchema) 

71 

72 for inputName, outputName in self.config.copyColumns.items(): 

73 self.mapper.addMapping( 

74 self.inputSchema.find(inputName).key, 

75 outputName, 

76 True) 

77 

78 def run(self, inputCatalog, exposure=None): 

79 """Copy data from the inputCatalog into an output catalog with 

80 requested columns. 

81 

82 Parameters 

83 ---------- 

84 inputCatalog: `lsst.afw.table.SourceCatalog` 

85 Input catalog with data to be copied into new output catalog. 

86 

87 Returns 

88 ------- 

89 outputCatalog: `lsst.afw.table.SourceCatalog` 

90 Output catalog with data copied from input and new column names. 

91 """ 

92 outputCatalog = afwTable.SourceCatalog(self.outputSchema) 

93 outputCatalog.extend(inputCatalog, self.mapper) 

94 

95 if not outputCatalog.isContiguous(): 

96 raise RuntimeError("Output catalogs must be contiguous.") 

97 

98 return outputCatalog 

99 

100 

101class MapDiaSourceConfig(pexConfig.Config): 

102 """Config for the DiaSourceMapperTask 

103 """ 

104 copyColumns = pexConfig.DictField( 

105 keytype=str, 

106 itemtype=str, 

107 doc="Mapping of input SciencePipelines columns to output DPDD " 

108 "columns.", 

109 default={"id": "id", 

110 "parent": "parent", 

111 "coord_ra": "coord_ra", 

112 "coord_dec": "coord_dec", 

113 "slot_Centroid_x": "x", 

114 "slot_Centroid_xErr": "xErr", 

115 "slot_Centroid_y": "y", 

116 "slot_Centroid_yErr": "yErr", 

117 "slot_ApFlux_instFlux": "apFlux", 

118 "slot_ApFlux_instFluxErr": "apFluxErr", 

119 "slot_PsfFlux_instFlux": "psFlux", 

120 "slot_PsfFlux_instFluxErr": "psFluxErr", 

121 "ip_diffim_DipoleFit_orientation": "dipAngle", 

122 "ip_diffim_DipoleFit_chi2dof": "dipChi2", 

123 "ip_diffim_forced_PsfFlux_instFlux": "totFlux", 

124 "ip_diffim_forced_PsfFlux_instFluxErr": "totFluxErr", 

125 "ip_diffim_DipoleFit_flag_classification": "isDipole", 

126 "slot_Shape_xx": "ixx", 

127 "slot_Shape_xxErr": "ixxErr", 

128 "slot_Shape_yy": "iyy", 

129 "slot_Shape_yyErr": "iyyErr", 

130 "slot_Shape_xy": "ixy", 

131 "slot_Shape_xyErr": "ixyErr", 

132 "slot_PsfShape_xx": "ixxPSF", 

133 "slot_PsfShape_yy": "iyyPSF", 

134 "slot_PsfShape_xy": "ixyPSF"} 

135 ) 

136 calibrateColumns = pexConfig.ListField( 

137 dtype=str, 

138 doc="Flux columns in the input catalog to calibrate.", 

139 default=["slot_ApFlux", "slot_PsfFlux", "ip_diffim_forced_PsfFlux"] 

140 ) 

141 flagMap = pexConfig.Field( 

142 dtype=str, 

143 doc="Yaml file specifying SciencePipelines flag fields to bit packs.", 

144 default=os.path.join(getPackageDir("ap_association"), 

145 "data", 

146 "association-flag-map.yaml"), 

147 ) 

148 dipFluxPrefix = pexConfig.Field( 

149 dtype=str, 

150 doc="Prefix of the Dipole measurement column containing negative and " 

151 "positive flux lobes.", 

152 default="ip_diffim_DipoleFit", 

153 ) 

154 dipSepColumn = pexConfig.Field( 

155 dtype=str, 

156 doc="Column of the separation of the negative and positive poles of " 

157 "the dipole.", 

158 default="ip_diffim_DipoleFit_separation" 

159 ) 

160 

161 

162class MapDiaSourceTask(MapApDataTask): 

163 """Task specific for copying columns from science pipelines catalogs, 

164 calibrating them, for use in ap_association and the APDB. 

165 

166 This task also copies information from the exposure such as the ExpsoureId 

167 and the exposure date as specified in the DPDD. 

168 """ 

169 

170 ConfigClass = MapDiaSourceConfig 

171 _DefaultName = "mapDiaSourceTask" 

172 

173 def __init__(self, inputSchema, **kwargs): 

174 MapApDataTask.__init__(self, 

175 inputSchema=inputSchema, 

176 outputSchema=make_dia_source_schema(), 

177 **kwargs) 

178 self._create_bit_pack_mappings() 

179 

180 def _create_bit_pack_mappings(self): 

181 """Setup all flag bit packings. 

182 """ 

183 self.bit_pack_columns = [] 

184 with open(self.config.flagMap) as yaml_stream: 

185 table_list = list(yaml.safe_load_all(yaml_stream)) 

186 for table in table_list: 

187 if table['tableName'] == 'DiaSource': 

188 self.bit_pack_columns = table['columns'] 

189 break 

190 

191 # Test that all flags requested are present in both the input and 

192 # output schemas. 

193 for outputFlag in self.bit_pack_columns: 

194 try: 

195 self.outputSchema.find(outputFlag['columnName']) 

196 except KeyError: 

197 raise KeyError( 

198 "Requested column %s not found in MapDiaSourceTask output " 

199 "schema. Please check that the requested output column " 

200 "exists." % outputFlag['columnName']) 

201 bitList = outputFlag['bitList'] 

202 for bit in bitList: 

203 try: 

204 self.inputSchema.find(bit['name']) 

205 except KeyError: 

206 raise KeyError( 

207 "Requested column %s not found in MapDiaSourceTask input " 

208 "schema. Please check that the requested input column " 

209 "exists." % outputFlag['columnName']) 

210 

211 def run(self, inputCatalog, exposure, return_pandas=False): 

212 """Copy data from the inputCatalog into an output catalog with 

213 requested columns. 

214 

215 Parameters 

216 ---------- 

217 inputCatalog : `lsst.afw.table.SourceCatalog` 

218 Input catalog with data to be copied into new output catalog. 

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

220 Exposure with containing the PhotoCalib object relevant to this 

221 catalog. 

222 return_pandas : `bool` 

223 Return `pandas.DataFrame` instead of `lsst.afw.table.SourceCatalog` 

224 

225 Returns 

226 ------- 

227 outputCatalog: `lsst.afw.table.SourceCatalog` or `pandas.DataFrame` 

228 Output catalog with data copied from input and new column names. 

229 """ 

230 visit_info = exposure.getInfo().getVisitInfo() 

231 ccdVisitId = visit_info.getExposureId() 

232 midPointTaiMJD = visit_info.getDate().get(system=DateTime.MJD) 

233 filterId = exposure.getFilter().getId() 

234 # canonical name should always be the abstract filter (in Gen 3 sense) 

235 # TODO DM-21333: Remove [0] (first character only) workaround 

236 filterName = exposure.getFilter().getCanonicalName()[0] 

237 wcs = exposure.getWcs() 

238 

239 photoCalib = exposure.getPhotoCalib() 

240 

241 outputCatalog = afwTable.SourceCatalog(self.outputSchema) 

242 outputCatalog.reserve(len(inputCatalog)) 

243 

244 for inputRecord in inputCatalog: 

245 outputRecord = outputCatalog.addNew() 

246 outputRecord.assign(inputRecord, self.mapper) 

247 self.calibrateFluxes(inputRecord, outputRecord, photoCalib) 

248 self.computeDipoleFluxes(inputRecord, outputRecord, photoCalib) 

249 self.computeDipoleSep(inputRecord, outputRecord, wcs) 

250 self.bitPackFlags(inputRecord, outputRecord) 

251 self.computeBBoxSize(inputRecord, outputRecord) 

252 outputRecord.set("ccdVisitId", ccdVisitId) 

253 outputRecord.set("midPointTai", midPointTaiMJD) 

254 outputRecord.set("filterId", filterId) 

255 outputRecord.set("filterName", filterName) 

256 

257 if not outputCatalog.isContiguous(): 

258 raise RuntimeError("Output catalogs must be contiguous.") 

259 

260 if return_pandas: 

261 return self._convert_to_pandas(outputCatalog) 

262 return outputCatalog 

263 

264 def calibrateFluxes(self, inputRecord, outputRecord, photoCalib): 

265 """Copy flux values into an output record and calibrate them. 

266 

267 Parameters 

268 ---------- 

269 inputRecord : `lsst.afw.table.SourceRecord` 

270 Record to copy flux values from. 

271 outputRecord : `lsst.afw.table.SourceRecord` 

272 Record to copy and calibrate values into. 

273 photoCalib : `lsst.afw.image.PhotoCalib` 

274 Calibration object from the difference exposure. 

275 """ 

276 for col_name in self.config.calibrateColumns: 

277 meas = photoCalib.instFluxToNanojansky(inputRecord, col_name) 

278 outputRecord.set(self.config.copyColumns[col_name + "_instFlux"], 

279 meas.value) 

280 outputRecord.set( 

281 self.config.copyColumns[col_name + "_instFluxErr"], 

282 meas.error) 

283 

284 def computeDipoleFluxes(self, inputRecord, outputRecord, photoCalib): 

285 """Calibrate and compute dipole mean flux and diff flux. 

286 

287 Parameters 

288 ---------- 

289 inputRecord : `lsst.afw.table.SourceRecord` 

290 Record to copy flux values from. 

291 outputRecord : `lsst.afw.table.SourceRecord` 

292 Record to copy and calibrate values into. 

293 photoCalib `lsst.afw.image.PhotoCalib` 

294 Calibration object from the difference exposure. 

295 """ 

296 

297 neg_meas = photoCalib.instFluxToNanojansky( 

298 inputRecord, self.config.dipFluxPrefix + "_neg") 

299 pos_meas = photoCalib.instFluxToNanojansky( 

300 inputRecord, self.config.dipFluxPrefix + "_pos") 

301 outputRecord.set( 

302 "dipMeanFlux", 

303 0.5 * (np.abs(neg_meas.value) + np.abs(pos_meas.value))) 

304 outputRecord.set( 

305 "dipMeanFluxErr", 

306 0.5 * np.sqrt(neg_meas.error ** 2 + pos_meas.error ** 2)) 

307 outputRecord.set( 

308 "dipFluxDiff", 

309 np.abs(pos_meas.value) - np.abs(neg_meas.value)) 

310 outputRecord.set( 

311 "dipFluxDiffErr", 

312 np.sqrt(neg_meas.error ** 2 + pos_meas.error ** 2)) 

313 

314 def computeDipoleSep(self, inputRecord, outputRecord, wcs): 

315 """Convert the dipole separation from pixels to arcseconds. 

316 

317 Parameters 

318 ---------- 

319 inputRecord : `lsst.afw.table.SourceRecord` 

320 Record to copy flux values from. 

321 outputRecord : `lsst.afw.table.SourceRecord` 

322 Record to copy and calibrate values into. 

323 wcs : `lsst.afw.geom.SkyWcs` 

324 Wcs of image inputRecords was observed. 

325 """ 

326 pixScale = wcs.getPixelScale(inputRecord.getCentroid()) 

327 dipSep = pixScale * inputRecord.get(self.config.dipSepColumn) 

328 outputRecord.set("dipLength", dipSep.asArcseconds()) 

329 

330 def bitPackFlags(self, inputRecord, outputRecord): 

331 """Pack requested flag columns in inputRecord into single columns in 

332 outputRecord. 

333 

334 Parameters 

335 ---------- 

336 inputRecord : `lsst.afw.table.SourceRecord` 

337 Record to copy flux values from. 

338 outputRecord : `lsst.afw.table.SourceRecord` 

339 Record to copy and calibrate values into. 

340 """ 

341 for outputFlag in self.bit_pack_columns: 

342 bitList = outputFlag['bitList'] 

343 value = 0 

344 for bit in bitList: 

345 value += inputRecord[bit['name']] * 2 ** bit['bit'] 

346 outputRecord.set(outputFlag['columnName'], value) 

347 

348 def computeBBoxSize(self, inputRecord, outputRecord): 

349 """Compute the size of a square bbox that fully contains the detection 

350 footprint. 

351 

352 Parameters 

353 ---------- 

354 inputRecord : `lsst.afw.table.SourceRecord` 

355 Record to copy flux values from. 

356 outputRecord : `lsst.afw.table.SourceRecord` 

357 Record to copy and calibrate values into. 

358 """ 

359 footprintBBox = inputRecord.getFootprint().getBBox() 

360 recX = inputRecord.getCentroid().x 

361 recY = inputRecord.getCentroid().y 

362 bboxSize = int( 

363 np.ceil(2 * np.max(np.fabs([footprintBBox.maxX - recX, 

364 footprintBBox.minX - recX, 

365 footprintBBox.maxY - recY, 

366 footprintBBox.minY - recY])))) 

367 outputRecord.set("bboxSize", bboxSize) 

368 

369 def _convert_to_pandas(self, inputCatalog): 

370 """Convert input afw table to pandas. 

371 

372 Using afwTable.toAstropy().to_pandas() alone is not sufficient to 

373 properly store data in the Apdb. We must also convert the RA/DEC values 

374 from radians to degrees and rename several columns. 

375 

376 Parameters 

377 ---------- 

378 inputCatalog : `lsst.afw.table.SourceCatalog` 

379 Catalog to convert to panads and rename columns. 

380 

381 Returns 

382 ------- 

383 catalog : `pandas.DataFrame` 

384 """ 

385 catalog = inputCatalog.asAstropy().to_pandas() 

386 catalog.rename(columns={"coord_ra": "ra", 

387 "coord_dec": "decl", 

388 "id": "diaSourceId", 

389 "parent": "parentDiaSourceId"}, 

390 inplace=True) 

391 catalog["ra"] = np.degrees(catalog["ra"]) 

392 catalog["decl"] = np.degrees(catalog["decl"]) 

393 

394 return catalog 

395 

396 

397class UnpackApdbFlags: 

398 """Class for unpacking bits from integer flag fields stored in the Apdb. 

399 

400 Attributes 

401 ---------- 

402 flag_map_file : `str` 

403 Absolute or relative path to a yaml file specifiying mappings of flags 

404 to integer bits. 

405 table_name : `str` 

406 Name of the Apdb table the integer bit data are coming from. 

407 """ 

408 

409 def __init__(self, flag_map_file, table_name): 

410 self.bit_pack_columns = [] 

411 with open(flag_map_file) as yaml_stream: 

412 table_list = list(yaml.safe_load_all(yaml_stream)) 

413 for table in table_list: 

414 if table['tableName'] == table_name: 

415 self.bit_pack_columns = table['columns'] 

416 break 

417 

418 self.output_flag_columns = {} 

419 

420 for column in self.bit_pack_columns: 

421 names = [] 

422 for bit in column["bitList"]: 

423 names.append((bit["name"], np.bool)) 

424 self.output_flag_columns[column["columnName"]] = names 

425 

426 def unpack(self, input_flag_values, flag_name): 

427 """Determine individual boolean flags from an input array of unsigned 

428 ints. 

429 

430 Parameters 

431 ---------- 

432 input_flag_values : array-like of type uint 

433 Input integer flags to unpack. 

434 flag_name : `str` 

435 Apdb column name of integer flags to unpack. Names of packed int 

436 flags are given by the flag_map_file. 

437 

438 Returns 

439 ------- 

440 output_flags : `numpy.ndarray` 

441 Numpy named tuple of booleans. 

442 """ 

443 bit_names_types = self.output_flag_columns[flag_name] 

444 output_flags = np.zeros(len(input_flag_values), dtype=bit_names_types) 

445 

446 for bit_idx, (bit_name, dtypes) in enumerate(bit_names_types): 

447 masked_bits = np.bitwise_and(input_flag_values, 2 ** bit_idx) 

448 output_flags[bit_name] = masked_bits 

449 

450 return output_flags