Hide keyboard shortcuts

Hot-keys on this page

r m x p   toggle line displays

j k   next/prev highlighted chunk

0   (zero) top of page

1   (one) first highlighted chunk

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

327

328

329

330

331

332

333

334

335

336

337

338

339

340

341

342

343

344

345

346

347

348

349

350

351

352

353

354

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

378

379

380

381

382

383

384

385

386

387

388

389

390

391

392

393

394

395

396

397

398

399

400

401

402

403

404

405

406

407

408

409

410

411

412

413

414

415

416

417

418

419

420

421

422

423

424

425

426

427

428

429

430

431

432

433

434

435

436

437

438

439

440

441

442

443

444

445

446

447

448

449

450

451

452

453

454

455

456

457

458

459

460

461

462

463

464

465

466

467

468

469

470

471

472

473

474

475

476

477

478

479

480

481

482

483

484

485

486

487

488

489

490

491

492

493

494

495

496

497

498

499

500

501

502

503

504

505

506

507

508

509

510

511

512

513

514

515

516

517

518

519

520

521

522

523

524

525

526

527

528

529

530

531

532

533

534

535

536

537

538

539

540

541

542

543

544

545

546

547

548

549

550

551

552

553

554

555

556

557

558

559

560

561

562

563

564

565

566

567

568

569

570

571

572

573

574

575

576

577

578

579

580

581

582

583

584

585

586

587

588

589

590

591

592

593

594

595

596

597

598

599

600

601

602

603

604

605

606

607

608

609

610

611

612

613

614

615

616

617

618

619

620

621

622

623

624

625

626

627

628

629

630

631

632

633

634

635

636

637

638

639

640

641

642

643

644

645

646

647

648

649

650

651

652

653

654

655

656

657

658

659

660

661

662

663

664

665

666

667

668

669

670

671

672

673

674

675

676

677

678

679

680

681

682

683

684

685

686

687

688

689

690

# This file is part of ap_association. 

# 

# Developed for the LSST Data Management System. 

# This product includes software developed by the LSST Project 

# (https://www.lsst.org). 

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

# for details of code ownership. 

# 

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

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

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

# (at your option) any later version. 

# 

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

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

# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 

# GNU General Public License for more details. 

# 

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

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

 

from astropy.stats import median_absolute_deviation 

import numpy as np 

import pandas as pd 

from scipy.stats import skew 

import unittest 

 

from lsst.ap.association import ( 

MeanDiaPosition, MeanDiaPositionConfig, 

HTMIndexDiaPosition, HTMIndexDiaPositionConfig, 

NumDiaSourcesDiaPlugin, NumDiaSourcesDiaPluginConfig, 

SimpleSourceFlagDiaPlugin, SimpleSourceFlagDiaPluginConfig, 

WeightedMeanDiaPsFlux, WeightedMeanDiaPsFluxConfig, 

PercentileDiaPsFlux, PercentileDiaPsFluxConfig, 

SigmaDiaPsFlux, SigmaDiaPsFluxConfig, 

Chi2DiaPsFlux, Chi2DiaPsFluxConfig, 

MadDiaPsFlux, MadDiaPsFluxConfig, 

SkewDiaPsFlux, SkewDiaPsFluxConfig, 

MinMaxDiaPsFlux, MinMaxDiaPsFluxConfig, 

MaxSlopeDiaPsFlux, MaxSlopeDiaPsFluxConfig, 

ErrMeanDiaPsFlux, ErrMeanDiaPsFluxConfig, 

LinearFitDiaPsFlux, LinearFitDiaPsFluxConfig, 

StetsonJDiaPsFlux, StetsonJDiaPsFluxConfig, 

WeightedMeanDiaTotFlux, WeightedMeanDiaTotFluxConfig, 

SigmaDiaTotFlux, SigmaDiaTotFluxConfig) 

import lsst.utils.tests 

 

 

class TestMeanPosition(unittest.TestCase): 

 

def testCalculate(self): 

"""Test mean position calculation. 

""" 

n_sources = 10 

 

# Test expected means. 

diaObject = dict() 

diaSources = pd.DataFrame(data={"ra": np.linspace(-1, 1, n_sources), 

"decl": np.zeros(n_sources), 

"midPointTai": np.linspace(0, 

n_sources, 

n_sources)}) 

plug = MeanDiaPosition(MeanDiaPositionConfig(), 

"ap_meanPosition", 

None) 

plug.calculate(diaObject, diaSources) 

 

self.assertAlmostEqual(diaObject["ra"], 0.0) 

self.assertAlmostEqual(diaObject["decl"], 0.0) 

self.assertEqual(diaObject["radecTai"], 10) 

 

diaObject = dict() 

diaSources = pd.DataFrame(data={"ra": np.zeros(n_sources), 

"decl": np.linspace(-1, 1, n_sources), 

"midPointTai": np.linspace(0, 

n_sources, 

n_sources)}) 

plug.calculate(diaObject, diaSources) 

 

self.assertAlmostEqual(diaObject["ra"], 0.0) 

self.assertAlmostEqual(diaObject["decl"], 0.0) 

self.assertEqual(diaObject["radecTai"], 10) 

 

# Test failure modes. 

diaObject = dict() 

diaSources = pd.DataFrame(data={"ra": np.full(n_sources, np.nan), 

"decl": np.zeros(n_sources), 

"midPointTai": np.linspace(0, 

n_sources, 

n_sources)}) 

plug.calculate(diaObject, diaSources) 

 

self.assertTrue(np.isnan(diaObject["ra"])) 

self.assertTrue(np.isnan(diaObject["decl"])) 

self.assertTrue(np.isnan(diaObject["radecTai"])) 

 

diaObject = dict() 

diaSources = pd.DataFrame(data={"ra": np.zeros(n_sources), 

"decl": np.full(n_sources, np.nan), 

"midPointTai": np.linspace(0, 

n_sources, 

n_sources)}) 

plug.calculate(diaObject, diaSources) 

 

self.assertTrue(np.isnan(diaObject["ra"])) 

self.assertTrue(np.isnan(diaObject["decl"])) 

self.assertTrue(np.isnan(diaObject["radecTai"])) 

 

 

class TestHTMIndexPosition(unittest.TestCase): 

 

def testCalculate(self): 

"""Test HTMPixel assignment calculation. 

""" 

# Test expected pixelIds. 

diaObject = dict() 

diaObject["ra"] = 0. 

diaObject["decl"] = 0. 

plug = HTMIndexDiaPosition(HTMIndexDiaPositionConfig(), 

"ap_HTMIndex", 

None) 

plug.calculate(diaObject) 

self.assertAlmostEqual(diaObject["pixelId"], 131072) 

 

diaObject = dict() 

diaObject["ra"] = 45.37 

diaObject["decl"] = 13.67 

plug.calculate(diaObject) 

self.assertAlmostEqual(diaObject["pixelId"], 260033) 

 

 

class TestNDiaSourcesDiaPlugin(unittest.TestCase): 

 

def testCalculate(self): 

"""Test that the number of DiaSources is correct. 

""" 

 

for n_sources in [0, 8, 10]: 

# Test expected means. 

diaObject = dict() 

diaSources = pd.DataFrame(data={"ra": np.linspace(-1, 1, n_sources), 

"decl": np.zeros(n_sources)}) 

plug = NumDiaSourcesDiaPlugin(NumDiaSourcesDiaPluginConfig(), 

"ap_nDiaSources", 

None) 

plug.calculate(diaObject, diaSources) 

 

 

class TestSimpleSourceFlagDiaPlugin(unittest.TestCase): 

 

def testCalculate(self): 

"""Test that DiaObject flags are set. 

""" 

n_sources = 10 

 

# Test expected means. 

diaObject = dict() 

diaSources = pd.DataFrame( 

data={"flags": np.zeros(n_sources, dtype=np.uint64)}) 

plug = SimpleSourceFlagDiaPlugin(SimpleSourceFlagDiaPluginConfig(), 

"ap_diaObjectFlag", 

None) 

plug.calculate(diaObject, diaSources) 

self.assertEqual(diaObject["flags"], 0) 

 

diaObject = dict() 

diaSources = diaSources = pd.DataFrame( 

data={"flags": np.ones(n_sources, dtype=np.uint64)}) 

plug.calculate(diaObject, diaSources) 

self.assertEqual(diaObject["flags"], 1) 

 

diaObject = dict() 

diaSources = diaSources = pd.DataFrame( 

data={"flags": np.random.randint(0, 2 ** 16, size=n_sources)}) 

plug.calculate(diaObject, diaSources) 

self.assertEqual(diaObject["flags"], 1) 

 

diaObject = dict() 

flag_array = np.zeros(n_sources, dtype=np.uint64) 

flag_array[4] = 256 

diaSources = diaSources = pd.DataFrame( 

data={"flags": flag_array}) 

plug.calculate(diaObject, diaSources) 

self.assertEqual(diaObject["flags"], 1) 

 

 

class TestWeightedMeanDiaPsFlux(unittest.TestCase): 

 

def testCalculate(self): 

"""Test mean value calculation. 

""" 

n_sources = 10 

diaObject = dict() 

diaSources = pd.DataFrame(data={"psFlux": np.linspace(-1, 1, n_sources), 

"psFluxErr": np.ones(n_sources)}) 

 

plug = WeightedMeanDiaPsFlux(WeightedMeanDiaPsFluxConfig(), 

"ap_meanFlux", 

None) 

plug.calculate(diaObject, diaSources, diaSources, "u") 

 

self.assertAlmostEqual(diaObject["uPSFluxMean"], 0.0) 

self.assertAlmostEqual(diaObject["uPSFluxMeanErr"], np.sqrt(1 / n_sources)) 

self.assertEqual(diaObject["uPSFluxNdata"], n_sources) 

 

diaObject = dict() 

plug.calculate(diaObject, [], [], "g") 

 

self.assertTrue(np.isnan(diaObject["gPSFluxMean"])) 

self.assertTrue(np.isnan(diaObject["gPSFluxMeanErr"])) 

self.assertEqual(diaObject["gPSFluxNdata"], 0) 

 

diaObject = dict() 

diaSources.loc[4, "psFlux"] = np.nan 

plug.calculate(diaObject, diaSources, diaSources, "r") 

 

self.assertTrue(~np.isnan(diaObject["rPSFluxMean"])) 

self.assertTrue(~np.isnan(diaObject["rPSFluxMeanErr"])) 

self.assertEqual(diaObject["rPSFluxNdata"], n_sources - 1) 

 

 

class TestPercentileDiaPsFlux(unittest.TestCase): 

 

def testCalculate(self): 

"""Test flux percentile calculation. 

""" 

n_sources = 10 

diaObject = dict() 

fluxes = np.linspace(-1, 1, n_sources) 

diaSources = pd.DataFrame(data={"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources)}) 

 

plug = PercentileDiaPsFlux(PercentileDiaPsFluxConfig(), 

"ap_percentileFlux", 

None) 

plug.calculate(diaObject, diaSources, diaSources, "u") 

for pTile, testVal in zip(plug.config.percentiles, 

np.nanpercentile( 

fluxes, 

plug.config.percentiles)): 

self.assertAlmostEqual( 

diaObject["uPSFluxPercentile{:02d}".format(pTile)], 

testVal) 

 

diaObject = dict() 

plug.calculate(diaObject, [], [], "g") 

for pTile in plug.config.percentiles: 

self.assertTrue(np.isnan( 

diaObject["gPSFluxPercentile{:02d}".format(pTile)])) 

 

diaObject = dict() 

diaSources.loc[4, "psFlux"] = np.nan 

fluxes[4] = np.nan 

plug.calculate(diaObject, diaSources, diaSources, "r") 

for pTile, testVal in zip(plug.config.percentiles, 

np.nanpercentile( 

fluxes, 

plug.config.percentiles)): 

self.assertAlmostEqual( 

diaObject["rPSFluxPercentile{:02d}".format(pTile)], 

testVal) 

 

 

class TestSigmaDiaPsFlux(unittest.TestCase): 

 

def testCalculate(self): 

"""Test flux scatter calculation. 

""" 

n_sources = 10 

diaObject = dict() 

fluxes = np.linspace(-1, 1, n_sources) 

diaSources = pd.DataFrame(data={"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources)}) 

 

plug = SigmaDiaPsFlux(SigmaDiaPsFluxConfig(), 

"ap_sigmaFlux", 

None) 

plug.calculate(diaObject, diaSources, diaSources, "u") 

self.assertAlmostEqual(diaObject["uPSFluxSigma"], 

np.nanstd(fluxes, ddof=1)) 

 

# test no inputs 

diaObject = dict() 

plug.calculate(diaObject, [], [], "g") 

self.assertTrue(np.isnan(diaObject["gPSFluxSigma"])) 

 

# test one input 

diaObject = dict() 

diaSources = pd.DataFrame(data={"psFlux": [fluxes[0]], 

"psFluxErr": [np.ones(n_sources)[0]]}) 

plug.calculate(diaObject, diaSources, diaSources, "g") 

self.assertTrue(np.isnan(diaObject["gPSFluxSigma"])) 

 

diaObject = dict() 

fluxes[4] = np.nan 

diaSources = pd.DataFrame(data={"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources)}) 

plug.calculate(diaObject, diaSources, diaSources, "r") 

self.assertAlmostEqual(diaObject["rPSFluxSigma"], 

np.nanstd(fluxes, ddof=1)) 

 

 

class TestChi2DiaPsFlux(unittest.TestCase): 

 

def testCalculate(self): 

"""Test flux chi2 calculation. 

""" 

n_sources = 10 

diaObject = dict() 

diaObject["uPSFluxMean"] = 0.0 

fluxes = np.linspace(-1, 1, n_sources) 

diaSources = pd.DataFrame(data={"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources)}) 

 

plug = Chi2DiaPsFlux(Chi2DiaPsFluxConfig(), 

"ap_chi2Flux", 

None) 

plug.calculate(diaObject, diaSources, diaSources, "u") 

self.assertAlmostEqual( 

diaObject["uPSFluxChi2"], 

np.nansum(((diaSources["psFlux"] - 

np.nanmean(diaSources["psFlux"])) / 

diaSources["psFluxErr"]) ** 2)) 

 

# test no inputs 

diaObject = dict() 

plug.calculate(diaObject, [], [], "g") 

self.assertTrue(np.isnan(diaObject["gPSFluxChi2"])) 

 

diaObject = dict() 

diaSources.loc[4, "psFlux"] = np.nan 

fluxes[4] = np.nan 

diaObject["rPSFluxMean"] = np.nanmean(fluxes) 

plug.calculate(diaObject, diaSources, diaSources, "r") 

self.assertAlmostEqual( 

diaObject["rPSFluxChi2"], 

np.nansum(((diaSources["psFlux"] - 

np.nanmean(diaSources["psFlux"])) / 

diaSources["psFluxErr"]) ** 2)) 

 

 

class TestMadDiaPsFlux(unittest.TestCase): 

 

def testCalculate(self): 

"""Test flux median absolute deviation calculation. 

""" 

n_sources = 10 

diaObject = dict() 

fluxes = np.linspace(-1, 1, n_sources) 

diaSources = pd.DataFrame(data={"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources)}) 

 

plug = MadDiaPsFlux(MadDiaPsFluxConfig(), 

"ap_madFlux", 

None) 

plug.calculate(diaObject, diaSources, diaSources, "u") 

self.assertAlmostEqual(diaObject["uPSFluxMAD"], 

median_absolute_deviation(fluxes, 

ignore_nan=True)) 

 

# test no inputs 

diaObject = dict() 

plug.calculate(diaObject, [], [], "g") 

self.assertTrue(np.isnan(diaObject["gPSFluxMAD"])) 

 

diaObject = dict() 

fluxes[4] = np.nan 

diaSources = pd.DataFrame(data={"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources)}) 

plug.calculate(diaObject, diaSources, diaSources, "r") 

self.assertAlmostEqual(diaObject["rPSFluxMAD"], 

median_absolute_deviation(fluxes, 

ignore_nan=True)) 

 

 

class TestSkewDiaPsFlux(unittest.TestCase): 

 

def testCalculate(self): 

"""Test flux skew calculation. 

""" 

n_sources = 10 

diaObject = dict() 

fluxes = np.linspace(-1, 1, n_sources) 

diaSources = pd.DataFrame(data={"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources)}) 

 

plug = SkewDiaPsFlux(SkewDiaPsFluxConfig(), 

"ap_skewFlux", 

None) 

plug.calculate(diaObject, diaSources, diaSources, "u") 

self.assertAlmostEqual(diaObject["uPSFluxSkew"], skew(fluxes)) 

 

# test no inputs 

diaObject = dict() 

plug.calculate(diaObject, [], [], "g") 

self.assertTrue(np.isnan(diaObject["gPSFluxSkew"])) 

 

diaObject = dict() 

fluxes[4] = np.nan 

diaSources = pd.DataFrame(data={"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources)}) 

plug.calculate(diaObject, diaSources, diaSources, "r") 

cutFluxes = fluxes[~np.isnan(fluxes)] 

self.assertAlmostEqual(diaObject["rPSFluxSkew"], skew(cutFluxes)) 

 

 

class TestMinMaxDiaPsFlux(unittest.TestCase): 

 

def testCalculate(self): 

"""Test flux min/max calculation. 

""" 

n_sources = 10 

diaObject = dict() 

fluxes = np.linspace(-1, 1, n_sources) 

diaSources = pd.DataFrame(data={"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources)}) 

 

plug = MinMaxDiaPsFlux(MinMaxDiaPsFluxConfig(), 

"ap_minMaxFlux", 

None) 

plug.calculate(diaObject, diaSources, diaSources, "u") 

self.assertEqual(diaObject["uPSFluxMin"], -1) 

self.assertEqual(diaObject["uPSFluxMax"], 1) 

 

# test no inputs 

diaObject = dict() 

plug.calculate(diaObject, [], [], "g") 

self.assertTrue(np.isnan(diaObject["gPSFluxMin"])) 

self.assertTrue(np.isnan(diaObject["gPSFluxMax"])) 

 

diaObject = dict() 

fluxes[4] = np.nan 

diaSources = pd.DataFrame(data={"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources)}) 

plug.calculate(diaObject, diaSources, diaSources, "r") 

self.assertEqual(diaObject["rPSFluxMin"], -1) 

self.assertEqual(diaObject["rPSFluxMax"], 1) 

 

 

class TestMaxSlopeDiaPsFlux(unittest.TestCase): 

 

def testCalculate(self): 

"""Test flux maximum slope. 

""" 

n_sources = 10 

diaObject = dict() 

fluxes = np.linspace(-1, 1, n_sources) 

times = np.linspace(0, 1, n_sources) 

diaSources = pd.DataFrame(data={"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources), 

"midPointTai": times}) 

 

plug = MaxSlopeDiaPsFlux(MaxSlopeDiaPsFluxConfig(), 

"ap_maxSlopeFlux", 

None) 

plug.calculate(diaObject, diaSources, diaSources, "u") 

self.assertEqual(diaObject["uPSFluxMaxSlope"], 2.) 

 

# test no inputs 

diaObject = dict() 

plug.calculate(diaObject, [], [], "g") 

self.assertTrue(np.isnan(diaObject["gPSFluxMaxSlope"])) 

 

# test one input 

diaObject = dict() 

diaSources = pd.DataFrame(data={"psFlux": [fluxes[0]], 

"psFluxErr": [np.ones(n_sources)[0]], 

"midPointTai": [times[0]]}) 

plug.calculate(diaObject, diaSources, diaSources, "g") 

self.assertTrue(np.isnan(diaObject["gPSFluxMaxSlope"])) 

 

diaObject = dict() 

fluxes[4] = np.nan 

diaSources = pd.DataFrame(data={"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources), 

"midPointTai": times}) 

plug.calculate(diaObject, diaSources, diaSources, "r") 

self.assertEqual(diaObject["rPSFluxMaxSlope"], 2.) 

 

 

class TestErrMeanDiaPsFlux(unittest.TestCase): 

 

def testCalculate(self): 

"""Test error mean calculation. 

""" 

n_sources = 10 

diaObject = dict() 

fluxes = np.linspace(-1, 1, n_sources) 

errors = np.linspace(1, 2, n_sources) 

diaSources = pd.DataFrame(data={"psFlux": fluxes, 

"psFluxErr": errors}) 

 

plug = ErrMeanDiaPsFlux(ErrMeanDiaPsFluxConfig(), 

"ap_errMeanFlux", 

None) 

plug.calculate(diaObject, diaSources, diaSources, "u") 

self.assertEqual(diaObject["uPSFluxErrMean"], np.nanmean(errors)) 

 

# test no inputs 

diaObject = dict() 

plug.calculate(diaObject, [], [], "g") 

self.assertTrue(np.isnan(diaObject["gPSFluxErrMean"])) 

 

diaObject = dict() 

errors[4] = np.nan 

diaSources = pd.DataFrame(data={"psFlux": fluxes, 

"psFluxErr": errors}) 

plug.calculate(diaObject, diaSources, diaSources, "r") 

self.assertEqual(diaObject["rPSFluxErrMean"], np.nanmean(errors)) 

 

 

class TestLinearFitDiaPsFlux(unittest.TestCase): 

 

def testCalculate(self): 

"""Test a linear fit to flux vs time. 

""" 

n_sources = 10 

diaObject = dict() 

fluxes = np.linspace(-1, 1, n_sources) 

errors = np.linspace(1, 2, n_sources) 

times = np.linspace(0, 1, n_sources) 

diaSources = pd.DataFrame(data={"psFlux": fluxes, 

"psFluxErr": errors, 

"midPointTai": times}) 

 

plug = LinearFitDiaPsFlux(LinearFitDiaPsFluxConfig(), 

"ap_LinearFit", 

None) 

plug.calculate(diaObject, diaSources, diaSources, "u") 

self.assertAlmostEqual(diaObject["uPSFluxLinearSlope"], 2.) 

self.assertAlmostEqual(diaObject["uPSFluxLinearIntercept"], -1.) 

 

# test no inputs 

diaObject = dict() 

plug.calculate(diaObject, [], [], "g") 

self.assertTrue(np.isnan(diaObject["gPSFluxLinearSlope"])) 

self.assertTrue(np.isnan(diaObject["gPSFluxLinearIntercept"])) 

 

# test no inputs 

diaObject = dict() 

diaSources = pd.DataFrame(data={"psFlux": [fluxes[0]], 

"psFluxErr": [errors[0]], 

"midPointTai": [times[0]]}) 

plug.calculate(diaObject, diaSources, diaSources, "g") 

self.assertTrue(np.isnan(diaObject["gPSFluxLinearSlope"])) 

self.assertTrue(np.isnan(diaObject["gPSFluxLinearIntercept"])) 

 

diaObject = dict() 

fluxes[7] = np.nan 

errors[4] = np.nan 

diaSources = pd.DataFrame(data={"psFlux": fluxes, 

"psFluxErr": errors, 

"midPointTai": times}) 

plug.calculate(diaObject, diaSources, diaSources, "r") 

self.assertAlmostEqual(diaObject["rPSFluxLinearSlope"], 2.) 

self.assertAlmostEqual(diaObject["rPSFluxLinearIntercept"], -1.) 

 

 

class TestStetsonJDiaPsFlux(unittest.TestCase): 

 

def testCalculate(self): 

"""Test the stetsonJ statistic. 

""" 

n_sources = 10 

diaObject = dict(uPSFluxMean=0) 

fluxes = np.linspace(-1, 1, n_sources) 

errors = np.ones(n_sources) 

times = np.linspace(0, 1, n_sources) 

diaSources = pd.DataFrame(data={"psFlux": fluxes, 

"psFluxErr": errors, 

"midPointTai": times}) 

 

plug = StetsonJDiaPsFlux(StetsonJDiaPsFluxConfig(), 

"ap_LinearFit", 

None) 

plug.calculate(diaObject, diaSources, diaSources, "u") 

# Expected StetsonJ for the values created. Confirmed using Cesimum's 

# implementation. http://github.com/cesium-ml/cesium 

self.assertAlmostEqual(diaObject["uPSFluxStetsonJ"], 

-0.5958393936080928) 

 

# test no inputs 

diaObject = dict(gPSFluxMean=0) 

plug.calculate(diaObject, [], [], "g") 

self.assertTrue(np.isnan(diaObject["gPSFluxStetsonJ"])) 

 

# test no inputs 

diaObject = dict(gPSFluxMean=0) 

diaSources = pd.DataFrame(data={"psFlux": [fluxes[0]], 

"psFluxErr": [errors[0]], 

"midPointTai": [times[0]]}) 

plug.calculate(diaObject, diaSources, diaSources, "g") 

self.assertTrue(np.isnan(diaObject["gPSFluxStetsonJ"])) 

 

fluxes[7] = np.nan 

errors[4] = np.nan 

nonNanMask = np.logical_and(~np.isnan(fluxes), 

~np.isnan(errors)) 

diaObject = dict(rPSFluxMean=np.average(fluxes[nonNanMask], 

weights=errors[nonNanMask])) 

diaSources = pd.DataFrame(data={"psFlux": fluxes, 

"psFluxErr": errors, 

"midPointTai": times}) 

plug.calculate(diaObject, diaSources, diaSources, "r") 

self.assertAlmostEqual(diaObject["rPSFluxStetsonJ"], 

-0.5412797916187173) 

 

 

class TestWeightedMeanDiaTotFlux(unittest.TestCase): 

 

def testCalculate(self): 

"""Test mean value calculation. 

""" 

n_sources = 10 

diaObject = dict() 

diaSources = pd.DataFrame(data={"totFlux": np.linspace(-1, 1, n_sources), 

"totFluxErr": np.ones(n_sources)}) 

 

plug = WeightedMeanDiaTotFlux(WeightedMeanDiaTotFluxConfig(), 

"ap_meanTotFlux", 

None) 

plug.calculate(diaObject, diaSources, diaSources, "u") 

 

self.assertAlmostEqual(diaObject["uTOTFluxMean"], 0.0) 

self.assertAlmostEqual(diaObject["uTOTFluxMeanErr"], np.sqrt(1 / n_sources)) 

 

diaObject = dict() 

plug.calculate(diaObject, [], [], "g") 

 

self.assertTrue(np.isnan(diaObject["gTOTFluxMean"])) 

self.assertTrue(np.isnan(diaObject["gTOTFluxMeanErr"])) 

 

diaObject = dict() 

diaSources.loc[4, "totFlux"] = np.nan 

plug.calculate(diaObject, diaSources, diaSources, "r") 

 

self.assertTrue(~np.isnan(diaObject["rTOTFluxMean"])) 

self.assertTrue(~np.isnan(diaObject["rTOTFluxMeanErr"])) 

 

 

class TestSigmaDiaTotFlux(unittest.TestCase): 

 

def testCalculate(self): 

"""Test flux scatter calculation. 

""" 

n_sources = 10 

diaObject = dict() 

fluxes = np.linspace(-1, 1, n_sources) 

diaSources = pd.DataFrame(data={"totFlux": fluxes, 

"totFluxErr": np.ones(n_sources)}) 

 

plug = SigmaDiaTotFlux(SigmaDiaTotFluxConfig(), 

"ap_sigmaTotFlux", 

None) 

plug.calculate(diaObject, diaSources, diaSources, "u") 

self.assertAlmostEqual(diaObject["uTOTFluxSigma"], 

np.nanstd(fluxes, ddof=1)) 

 

# test no inputs 

diaObject = dict() 

plug.calculate(diaObject, [], [], "g") 

self.assertTrue(np.isnan(diaObject["gTOTFluxSigma"])) 

 

# test one input 

diaObject = dict() 

diaSources = pd.DataFrame(data={"totFlux": [fluxes[0]], 

"totFluxErr": [np.ones(n_sources)[0]]}) 

plug.calculate(diaObject, diaSources, diaSources, "g") 

self.assertTrue(np.isnan(diaObject["gTOTFluxSigma"])) 

 

diaObject = dict() 

fluxes[4] = np.nan 

diaSources = pd.DataFrame(data={"totFlux": fluxes, 

"totFluxErr": np.ones(n_sources)}) 

plug.calculate(diaObject, diaSources, diaSources, "r") 

self.assertAlmostEqual(diaObject["rTOTFluxSigma"], 

np.nanstd(fluxes, ddof=1)) 

 

 

class MemoryTester(lsst.utils.tests.MemoryTestCase): 

pass 

 

 

def setup_module(module): 

lsst.utils.tests.init() 

 

 

688 ↛ 689line 688 didn't jump to line 689, because the condition on line 688 was never trueif __name__ == "__main__": 

lsst.utils.tests.init() 

unittest.main()