Coverage for tests / test_image_fits.py: 18%

1# This file is part of afw. 

2# 

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5# (https://www.lsst.org). 

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

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8# 

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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 

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21 

22import platform 

23import unittest 

24from collections.abc import Iterator, Mapping, Sequence 

25from contextlib import contextmanager 

26 

27import astropy.io.fits 

28import numpy as np 

29 

30import lsst.afw.image.testUtils # for TestCase monkey-patch side-effects. # noqa: F401 

31from lsst.afw.fits import DitherAlgorithm 

32from lsst.afw.image import ( 

33 ExposureF, 

34 Image, 

35 ImageFitsReader, 

36 MaskedImage, 

37 MaskedImageFitsReader, 

38 makeMaskedImageFromArrays, 

39) 

40from lsst.geom import Box2I, Extent2I, Point2I 

41from lsst.utils.tests import TestCase, getTempFilePath 

42 

43 

44class ImageFitsTestCase(TestCase): 

45 """Tests for serializing Image, Mask, and MaskedImage to FITS.""" 

46 

47 def setUp(self) -> None: 

48 self.rng = np.random.default_rng(500) 

49 

50 def sprinkle(self, masked_image: MaskedImage, value: float | int, count: int, mask_plane: str) -> None: 

51 """set a small number of randomly-selected pixels to the given value. 

52 

53 Parameters 

54 ---------- 

55 masked_image : `MaskedImage` 

56 Masked image to modify in place. The image plane and mask plane 

57 are modified; the variance is not. 

58 value : `int` or `float` 

59 Value of the modified pixels. 

60 count : `int` 

61 Number of pixels to modify. 

62 mask_plane : `str` 

63 Name of a mask plane to add and set. 

64 """ 

65 bit = masked_image.mask.addMaskPlane(mask_plane) 

66 if not count: 

67 return 

68 x = self.rng.integers(0, masked_image.width, count, dtype=int) 

69 y = self.rng.integers(0, masked_image.height, count, dtype=int) 

70 masked_image.image.array[y, x] = value 

71 masked_image.mask.array[y, x] = 1 << bit 

72 

73 def set_nontrivial_xy0(self, image: MaskedImage) -> MaskedImage: 

74 """Set the origin of the given `Image` or `MaskedImage` to something 

75 other than (0, 0). 

76 """ 

77 image.setXY0(Point2I(image.width // 3, image.height // 2)) 

78 return image 

79 

80 def make_int_image( 

81 self, 

82 dtype: np.typing.DTypeLike, 

83 noise_min: int, 

84 noise_max: int, 

85 *, 

86 hot_value: int = 0, 

87 hot_count: int = 50, 

88 cold_value: int = 0, 

89 cold_count: int = 50, 

90 shape: tuple[int, int] = (256, 225), 

91 variance: float | None = None, 

92 ) -> MaskedImage: 

93 """Make an integer test image. 

94 

95 Parameters 

96 ---------- 

97 dtype : `numpy.dtype` 

98 Numpy type object for the image's pixels. 

99 noise_min : `int` 

100 Minimum value (inclusive) for the discrete uniform noise that fills 

101 most of the image. 

102 noise_max : `int` 

103 Maximum value (inclusive) for the discrete uniform noise that fills 

104 most of the image. 

105 hot_value : `int`, optional 

106 Value for hot pixels to `sprinkle` into the image. 

107 hot_count : `int`, optional 

108 Number of hot pixels to `sprinkle` into the image. 

109 cold_value : `int`, optional 

110 Value for cold pixels to `sprinkle` into the image. 

111 cold_count : `int`, optional 

112 Number of cold pixels to `sprinkle` into the image. 

113 shape : `tuple`, optional 

114 Dimensions of the image (y, x). 

115 variance : `float`, optional 

116 Constant value for the variance plane. Default is to use the 

117 actual variance of the added noise. 

118 

119 Returns 

120 ------- 

121 masked_image : `MaskedImage` 

122 Test masked image. Sprinkled pixels are masked as "HOT" or "COLD". 

123 """ 

124 array = self.rng.integers(noise_min, noise_max, shape, dtype, endpoint=True) 

125 masked_image = makeMaskedImageFromArrays(array) 

126 masked_image.variance.array = ( 

127 variance if variance is not None else (noise_max - noise_min) ** 2 / 12.0 

128 ) 

129 self.sprinkle(masked_image, hot_value, hot_count, mask_plane="HOT") 

130 self.sprinkle(masked_image, cold_value, cold_count, mask_plane="COLD") 

131 return self.set_nontrivial_xy0(masked_image) 

132 

133 def make_float_image( 

134 self, 

135 dtype: np.typing.DTypeLike, 

136 noise_sigma: float = 1.0, 

137 noise_mean: float = 0.0, 

138 *, 

139 hot_value: float = 20000.0, 

140 hot_count: int = 50, 

141 cold_value: float = -20000.0, 

142 cold_count: int = 50, 

143 nan_count: int = 50, 

144 shape: tuple[int, int] = (256, 256), 

145 ) -> MaskedImage: 

146 """Make a floating-point test image. 

147 

148 Parameters 

149 ---------- 

150 dtype : `numpy.dtype` 

151 Numpy type object for the image's pixels. 

152 noise_sigma : `float` 

153 Standard deviation of the Gaussian noise distribution that fills 

154 most of the image. 

155 noise_mean : `int` 

156 Mean of the Gaussian noise distribution that fills most of the 

157 image. 

158 hot_value : `float`, optional 

159 Value for hot pixels to `sprinkle` into the image. 

160 hot_count : `float`, optional 

161 Number of hot pixels to `sprinkle` into the image. 

162 cold_value : `float`, optional 

163 Value for cold pixels to `sprinkle` into the image. 

164 cold_count : `float`, optional 

165 Number of cold pixels to `sprinkle` into the image. 

166 nan_count : `float`, optional 

167 Number of NaN pixels to `sprinkle` into the image. 

168 shape : `tuple`, optional 

169 Dimensions of the image (y, x). 

170 

171 Returns 

172 ------- 

173 masked_image : `MaskedImage` 

174 Test masked image. Sprinkled pixels are masked as "HOT", "COLD", 

175 or `NaN`, and the variance plane is set to a constant 

176 ``nan_sigma**2``. 

177 """ 

178 array = self.rng.normal(noise_mean, noise_sigma, shape).astype(dtype) 

179 masked_image = makeMaskedImageFromArrays(array) 

180 masked_image.variance.array = noise_sigma**2 

181 self.sprinkle(masked_image, hot_value, hot_count, mask_plane="HOT") 

182 self.sprinkle(masked_image, cold_value, cold_count, mask_plane="COLD") 

183 self.sprinkle(masked_image, np.nan, nan_count, mask_plane="NAN") 

184 return self.set_nontrivial_xy0(masked_image) 

185 

186 @contextmanager 

187 def roundtrip_image_reader( 

188 self, image: Image, compression: Mapping[str, object] | None = None, original_fits: bool = False 

189 ) -> Iterator[tuple[ImageFitsReader, astropy.io.fits.HDUList]]: 

190 """Return a context manager that writes and reads a test image. 

191 

192 Parameters 

193 ---------- 

194 image : `Image` 

195 Image to write and then read. 

196 compression : `collections.abc.Mapping` or `None`, optional 

197 How to compress the image (`None` for no compression), as a 

198 mapping compatible with `Image.writeFitsWithOptions`. 

199 original_fits : `bool`, optional 

200 If `True`, open the astropy FITS object with 

201 ``disable_image_compression=True``, interpreting all compressed 

202 image HDUs as binary tables and leaving their headers unchanged. 

203 

204 Returns 

205 ------- 

206 context : `contextlib.AbstractContextManager 

207 A context manager that, when entered, returns: 

208 

209 - reader (`ImageFitsReader`): a reader for the saved image; 

210 - fits (`astropy.io.fits.HDUList`): an Astropy FITS object. 

211 """ 

212 with getTempFilePath(".fits") as filename: 

213 image.writeFitsWithOptions(filename, compression) 

214 with astropy.io.fits.open(filename, disable_image_compression=original_fits) as fits: 

215 yield ImageFitsReader(filename), fits 

216 

217 @contextmanager 

218 def roundtrip_masked_image_reader( 

219 self, 

220 masked_image: MaskedImage, 

221 compression: Mapping[str, object] | None = None, 

222 original_fits: bool = False, 

223 ) -> Iterator[tuple[MaskedImageFitsReader, astropy.io.fits.HDUList]]: 

224 """Return a context manager that writes and reads a test masked image. 

225 

226 Parameters 

227 ---------- 

228 image : `MaskedImage` 

229 Masked image to write and then read. 

230 compression : `collections.abc.Mapping` or `None`, optional 

231 How to compress the image (`None` for no compression), as a 

232 mapping compatible with `MaskedImage.writeFitsWithOptions`. 

233 original_fits : `bool`, optional 

234 If `True`, open the astropy FITS object with 

235 ``disable_image_compression=True``, interpreting all compressed 

236 image HDUs as binary tables and leaving their headers unchanged. 

237 

238 Returns 

239 ------- 

240 context : `contextlib.AbstractContextManager 

241 A context manager that, when entered, returns: 

242 

243 - reader (`MaskedImageFitsReader`): a reader for the saved image; 

244 - fits (`astropy.io.fits.HDUList`): an Astropy FITS object. 

245 """ 

246 with getTempFilePath(".fits") as filename: 

247 masked_image.writeFitsWithOptions(filename, compression) 

248 with astropy.io.fits.open(filename, disable_image_compression=original_fits) as fits: 

249 yield MaskedImageFitsReader(filename), fits 

250 

251 @contextmanager 

252 def check_roundtrip_image_invariants( 

253 self, 

254 image: Image, 

255 compression: Mapping[str, object] | None = None, 

256 safe_dtypes: Sequence[np.typing.DTypeLike] = (), 

257 *, 

258 rtol: float = 0.0, 

259 atol: float = 0.0, 

260 ) -> Iterator[Image]: 

261 """Return a context manager that writes and reads a test image and then 

262 runs a battery of tests. 

263 

264 Parameters 

265 ---------- 

266 image : `Image` 

267 Image to write and then read. 

268 compression : `collections.abc.Mapping` or `None`, optional 

269 How to compress the image (`None` for no compression), as a 

270 mapping compatible with `Image.writeFitsWithOptions`. 

271 safe_dtypes : `~collections.abc.Sequence` [`numpy.dtype`], optional 

272 Other pixel types that the image can be safely read back in as 

273 without any loss of precision or range. 

274 rtol : `float`, optional 

275 Relative tolerance for floating-point comparisons between our 

276 reader and astropy. Defaults to zero. 

277 atol : `float`, optional 

278 Absolute tolerance for floating-point comparisons between our 

279 reader and astropy. Defaults to zero. 

280 

281 Returns 

282 ------- 

283 context : `contextlib.AbstractContextManager 

284 A context manager that returns the roundtripped `Image` when 

285 entered. 

286 

287 Notes 

288 ----- 

289 This method tests: 

290 

291 - reading just the bounding box; 

292 - reading the pixel type; 

293 - reading a subimage; 

294 - consistency with reading with astropy (by comparing to the 

295 roundtripped image, not the original); 

296 - consistency with reading into other pixel types (``safe_dtypes``). 

297 

298 It does not test that the roundtripped image matches the original (that 

299 is for calling code to do). 

300 """ 

301 hdu = 0 if compression is None else 1 

302 with self.subTest(compression=compression): 

303 with self.roundtrip_image_reader(image, compression) as (reader, fits): 

304 self.assertEqual(reader.readDType(), image.array.dtype) 

305 self.assertEqual(reader.readBBox(), image.getBBox()) 

306 roundtripped = reader.read() 

307 np.testing.assert_allclose(roundtripped.array, fits[hdu].data, atol=atol, rtol=rtol) 

308 subbox = Box2I( 

309 image.getXY0() + Extent2I(image.width // 5, image.height // 6), 

310 image.getXY0() + Extent2I(4 * image.width // 5, 5 * image.height // 6), 

311 ) 

312 subrt = reader.read(bbox=subbox) 

313 self.assertEqual(subrt.getBBox(), subbox) 

314 self.assertImagesEqual(subrt, roundtripped[subbox]) 

315 for dtype in [image.dtype, *safe_dtypes]: 

316 np.testing.assert_array_equal( 

317 reader.readArray(dtype=np.dtype(dtype)), roundtripped.array.astype(dtype) 

318 ) 

319 for dtype in [image.dtype, *safe_dtypes]: 

320 np.testing.assert_array_equal( 

321 reader.readArray(dtype=np.dtype(dtype)), roundtripped.array.astype(dtype) 

322 ) 

323 yield roundtripped 

324 

325 @contextmanager 

326 def check_roundtrip_masked_image_invariants( 

327 self, 

328 masked_image: MaskedImage, 

329 compression: Mapping[str, object] | None = None, 

330 safe_dtypes: Sequence[np.typing.DTypeLike] = (), 

331 *, 

332 rtol: float = 0.0, 

333 atol: float = 0.0, 

334 ) -> Iterator[MaskedImage]: 

335 """Return a context manager that writes and reads a test masked image 

336 and then runs a battery of tests. 

337 

338 Parameters 

339 ---------- 

340 masked_image : `MaskedImage` 

341 Masked image to write and then read. 

342 compression : `collections.abc.Mapping` or `None`, optional 

343 How to compress the image (`None` for no compression), as a 

344 mapping compatible with `Image.writeFitsWithOptions`. 

345 safe_dtypes : `~collections.abc.Sequence` [`numpy.dtype`], optional 

346 Other image plane pixel types that the image can be safely read 

347 back in as without any loss of precision or range. 

348 rtol : `float`, optional 

349 Relative tolerance for floating-point comparisons between our 

350 reader and astropy. Defaults to zero. 

351 atol : `float`, optional 

352 Absolute tolerance for floating-point comparisons between our 

353 reader and astropy. Defaults to zero. 

354 

355 Returns 

356 ------- 

357 context : `contextlib.AbstractContextManager 

358 A context manager that returns the roundtripped `Image` when 

359 entered. 

360 

361 Notes 

362 ----- 

363 This method tests: 

364 

365 - reading just the bounding box; 

366 - reading the pixel type; 

367 - reading a subimage; 

368 - consistency with reading with astropy (by comparing to the 

369 roundtripped image, not the original); 

370 - consistency with reading into other pixel types (``safe_dtypes``). 

371 

372 It does not test that the roundtripped image matches the original (that 

373 is for calling code to do). 

374 """ 

375 with self.subTest(compression=compression): 

376 with self.roundtrip_masked_image_reader(masked_image, compression) as (reader, fits): 

377 self.assertEqual(reader.readImageDType(), masked_image.image.array.dtype) 

378 self.assertEqual(reader.readMaskDType(), masked_image.mask.array.dtype) 

379 self.assertEqual(reader.readVarianceDType(), masked_image.variance.array.dtype) 

380 self.assertEqual(reader.readBBox(), masked_image.getBBox()) 

381 roundtripped = reader.read() 

382 np.testing.assert_allclose(roundtripped.image.array, fits["IMAGE"].data, rtol=rtol, atol=atol) 

383 np.testing.assert_allclose(roundtripped.mask.array, fits["MASK"].data, rtol=rtol, atol=atol) 

384 np.testing.assert_allclose( 

385 roundtripped.variance.array, fits["VARIANCE"].data, rtol=rtol, atol=atol 

386 ) 

387 subbox = Box2I( 

388 masked_image.getXY0() + Extent2I(masked_image.width // 5, masked_image.height // 6), 

389 masked_image.getXY0() 

390 + Extent2I(4 * masked_image.width // 5, 5 * masked_image.height // 6), 

391 ) 

392 subrt = reader.read(bbox=subbox) 

393 self.assertEqual(subrt.getBBox(), subbox) 

394 self.assertMaskedImagesEqual(subrt, roundtripped[subbox]) 

395 for dtype in [masked_image.image.dtype, *safe_dtypes]: 

396 np.testing.assert_array_equal( 

397 reader.readImageArray(dtype=np.dtype(dtype)), roundtripped.image.array.astype(dtype) 

398 ) 

399 for dtype in [masked_image.image.dtype, *safe_dtypes]: 

400 np.testing.assert_array_equal( 

401 reader.readImageArray(dtype=np.dtype(dtype)), 

402 roundtripped.image.array.astype(dtype), 

403 ) 

404 yield roundtripped 

405 

406 def check_exact_roundtrip( 

407 self, 

408 masked_image: MaskedImage, 

409 compression: Mapping[str, object] | None = None, 

410 *, 

411 safe_dtypes: Sequence[np.typing.DTypeLike] = (), 

412 ) -> None: 

413 """Test that a masked image round-trips through serialization exactly. 

414 

415 Parameters 

416 ---------- 

417 masked_image : `MaskedImage` 

418 Masked image to write and then read. Roundtripping just the image 

419 plane on its own is also tested. 

420 compression : `collections.abc.Mapping` or `None`, optional 

421 How to compress the image (`None` for no compression), as a 

422 mapping compatible with `MaskedImage.writeFitsWithOptions`. 

423 safe_dtypes : `~collections.abc.Sequence` [`numpy.dtype`], optional 

424 Other image plane pixel types that the image can be safely read 

425 back in as without any loss of precision or range. 

426 """ 

427 with self.check_roundtrip_image_invariants(masked_image.image, compression, safe_dtypes) as image_rt: 

428 self.assertImagesEqual(image_rt, masked_image.image) 

429 with self.check_roundtrip_masked_image_invariants( 

430 masked_image, compression, safe_dtypes=safe_dtypes 

431 ) as masked_image_rt: 

432 self.assertMaskedImagesEqual(masked_image_rt, masked_image) 

433 

434 def check_quantized_roundtrip( 

435 self, 

436 masked_image: MaskedImage, 

437 *, 

438 compression: Mapping[str, object], 

439 roundtrip_atol: float, 

440 safe_dtypes: Sequence[np.typing.DTypeLike] = (), 

441 ) -> None: 

442 """Test that a masked image round-trips through serialization with some 

443 loss of precision expected. 

444 

445 Parameters 

446 ---------- 

447 masked_image : `MaskedImage` 

448 Masked image to write and then read. Roundtripping just the image 

449 plane on its own is also tested. 

450 compression : `collections.abc.Mapping` 

451 How to compress the image, as a mapping compatible with 

452 `MaskedImage.writeFitsWithOptions`. 

453 roundtrip_atol : `float` 

454 Absolute tolerance for comparisons between the input and output 

455 image. 

456 safe_dtypes : `~collections.abc.Sequence` [`numpy.dtype`], optional 

457 Other image plane pixel types that the image can be safely read 

458 back in as without any loss of precision or range. 

459 """ 

460 atol = 0.0 

461 rtol = 0.0 

462 if platform.machine() in ("aarch64", "arm64"): 

463 # CFITSIO on Arm64 (both Linux and macOS reads quantized images 

464 # back slightly but significantly differently from CFITSIO on 

465 # x86_64, Astropy on x86_64, and Astropy on aarch64 (which all 

466 # agree). This has been reported upstream. The difference is 

467 # typically smaller than the change due to quantization but larger 

468 # than expected from floating-point round-off error from just 

469 # switching around the order of operations in applying 

470 # ZZERO/ZSCALE, especially for float64. 

471 atol = 1E-4 

472 rtol = 1E-6 

473 with self.check_roundtrip_image_invariants( 

474 masked_image.image, compression, safe_dtypes, atol=atol, rtol=rtol 

475 ) as image_rt: 

476 self.assertImagesAlmostEqual(image_rt, masked_image.image, atol=roundtrip_atol) 

477 with self.check_roundtrip_masked_image_invariants( 

478 masked_image, compression, safe_dtypes=safe_dtypes, atol=atol, rtol=rtol 

479 ) as masked_image_rt: 

480 self.assertMaskedImagesAlmostEqual(masked_image_rt, masked_image, atol=roundtrip_atol) 

481 

482 def test_u16(self) -> None: 

483 """Test FITS serialization (including compression) for uint16.""" 

484 masked_image = self.make_int_image( 

485 np.uint16, noise_min=256, noise_max=512, cold_value=1, hot_value=32750 

486 ) 

487 safe_dtypes = (np.int32, np.float32, np.float64) 

488 # uint64 is only safe when not decompressing; otherwise CFITSIO can't 

489 # handle it. 

490 self.check_exact_roundtrip(masked_image, safe_dtypes=safe_dtypes + (np.uint64,)) 

491 self.check_exact_roundtrip( 

492 masked_image, {"image": {}, "mask": {}, "variance": {}}, safe_dtypes=safe_dtypes 

493 ) 

494 self.check_exact_roundtrip( 

495 masked_image, 

496 { 

497 "image": {"algorithm": "GZIP_1"}, 

498 "mask": {"algorithm": "GZIP_1"}, 

499 "variance": {"algorithm": "GZIP_1"}, 

500 }, 

501 safe_dtypes=safe_dtypes, 

502 ) 

503 self.check_exact_roundtrip( 

504 masked_image, 

505 { 

506 "image": {"algorithm": "GZIP_2"}, 

507 "mask": {"algorithm": "GZIP_2"}, 

508 "variance": {"algorithm": "GZIP_2"}, 

509 }, 

510 safe_dtypes=safe_dtypes, 

511 ) 

512 self.check_exact_roundtrip( 

513 masked_image, 

514 { 

515 "image": {"algorithm": "RICE_1"}, 

516 "mask": {"algorithm": "RICE_1"}, 

517 "variance": {}, 

518 }, 

519 safe_dtypes=safe_dtypes, 

520 ) 

521 

522 def test_i32(self) -> None: 

523 """Test FITS serialization (including compression) for int32.""" 

524 masked_image = self.make_int_image( 

525 np.int32, noise_min=-(1 << 21), noise_max=(1 << 22), cold_value=-(1 << 24), hot_value=(1 << 25) 

526 ) 

527 safe_dtypes = (np.float64,) 

528 self.check_exact_roundtrip(masked_image, safe_dtypes=safe_dtypes) 

529 self.check_exact_roundtrip( 

530 masked_image, {"image": {}, "mask": {}, "variance": {}}, safe_dtypes=safe_dtypes 

531 ) 

532 self.check_exact_roundtrip( 

533 masked_image, 

534 { 

535 "image": {"algorithm": "GZIP_1"}, 

536 "mask": {"algorithm": "GZIP_1"}, 

537 "variance": {"algorithm": "GZIP_1"}, 

538 }, 

539 safe_dtypes=safe_dtypes, 

540 ) 

541 self.check_exact_roundtrip( 

542 masked_image, 

543 { 

544 "image": {"algorithm": "GZIP_2"}, 

545 "mask": {"algorithm": "GZIP_2"}, 

546 "variance": {"algorithm": "GZIP_2"}, 

547 }, 

548 safe_dtypes=safe_dtypes, 

549 ) 

550 self.check_exact_roundtrip( 

551 masked_image, 

552 { 

553 "image": {"algorithm": "RICE_1"}, 

554 "mask": {"algorithm": "RICE_1"}, 

555 "variance": {}, 

556 }, 

557 safe_dtypes=safe_dtypes, 

558 ) 

559 

560 def test_u64(self) -> None: 

561 """Test FITS serialization for uint64.""" 

562 masked_image = self.make_int_image( 

563 np.uint64, 

564 noise_min=256, 

565 noise_max=(1 << 42), 

566 cold_value=1, 

567 hot_value=(1 << 50), 

568 # We need to lie about the variance because the actual variance 

569 # of this uniform distribution is too big to be represented as 

570 # float32. 

571 variance=50.0, 

572 ) 

573 self.check_exact_roundtrip(masked_image) 

574 # CFITSIO does not support compressing uint64, so neither do we. 

575 

576 def test_f32_lossless(self) -> None: 

577 """Test uncompressed FITS serialization and lossless compression for 

578 float32. 

579 """ 

580 masked_image = self.make_float_image( 

581 np.float32, noise_mean=100.0, noise_sigma=15.0, cold_value=-(1 << 24), hot_value=(1 << 25) 

582 ) 

583 safe_dtypes = (np.float64,) 

584 self.check_exact_roundtrip(masked_image, compression=None, safe_dtypes=safe_dtypes) 

585 self.check_exact_roundtrip( 

586 masked_image, {"image": {}, "mask": {}, "variance": {}}, safe_dtypes=safe_dtypes 

587 ) 

588 self.check_exact_roundtrip( 

589 masked_image, 

590 { 

591 "image": {"algorithm": "GZIP_1"}, 

592 "mask": {"algorithm": "GZIP_1"}, 

593 "variance": {"algorithm": "GZIP_1"}, 

594 }, 

595 safe_dtypes=safe_dtypes, 

596 ) 

597 self.check_exact_roundtrip( 

598 masked_image, 

599 { 

600 "image": {"algorithm": "GZIP_2"}, 

601 "mask": {"algorithm": "GZIP_2"}, 

602 "variance": {"algorithm": "GZIP_2"}, 

603 }, 

604 safe_dtypes=safe_dtypes, 

605 ) 

606 

607 def test_f32_lossy_no_mask(self) -> None: 

608 """Test quantized compression of float32 with no bad pixels other than 

609 NaNs. 

610 """ 

611 masked_image = self.make_float_image( 

612 # Don't add hot or cold pixels because we won't be using a mask to 

613 # keep them out of the stdev estimation. 

614 np.float32, 

615 noise_mean=100.0, 

616 noise_sigma=15.0, 

617 cold_count=0, 

618 hot_count=0, 

619 ) 

620 lossy = {"algorithm": "RICE_1", "quantization": {"seed": 1}} 

621 lossless = {"algorithm": "GZIP_2"} 

622 for dither in DitherAlgorithm.__members__.keys(): 

623 lossy["quantization"]["dither"] = dither 

624 lossy["quantization"]["level"] = 30.0 

625 lossy["quantization"]["scaling"] = "STDEV_CFITSIO" 

626 self.check_quantized_roundtrip( 

627 masked_image, 

628 compression={"image": lossy, "mask": lossless, "variance": lossy}, 

629 roundtrip_atol=0.5, # target is 30 steps per sigma, and sigma=15. 

630 ) 

631 lossy["quantization"]["scaling"] = "MANUAL" 

632 lossy["quantization"]["level"] = 0.25 

633 self.check_quantized_roundtrip( 

634 masked_image, 

635 compression={"image": lossy, "mask": lossless, "variance": lossy}, 

636 roundtrip_atol=0.25, 

637 ) 

638 

639 def test_f32_lossy_masked(self) -> None: 

640 """Test quantized compression of float 32 with hot and cold pixels.""" 

641 masked_image = self.make_float_image( 

642 np.float32, noise_mean=100.0, noise_sigma=15.0, hot_value=1e5, cold_value=-1e4 

643 ) 

644 lossy = { 

645 "algorithm": "RICE_1", 

646 "quantization": {"level": 30.0, "mask_planes": ["HOT", "COLD", "NAN"], "seed": 1}, 

647 } 

648 lossless = {"algorithm": "GZIP_2"} 

649 for dither in DitherAlgorithm.__members__.keys(): 

650 lossy["quantization"]["dither"] = dither 

651 lossy["quantization"]["scaling"] = "STDEV_MASKED" 

652 self.check_quantized_roundtrip( 

653 masked_image, 

654 compression={"image": lossy, "mask": lossless, "variance": lossy}, 

655 roundtrip_atol=0.5, # target is 30 steps per sigma, and sigma=15. 

656 ) 

657 lossy["quantization"]["scaling"] = "RANGE" 

658 with self.subTest(dither=dither, scaling="RANGE"): 

659 self.check_quantized_roundtrip( 

660 masked_image, 

661 compression={"image": lossy, "mask": lossless, "variance": lossy}, 

662 roundtrip_atol=1e-7, # since the dynamic range is tiny, this is actually barely lossy 

663 ) 

664 

665 def test_f64_lossless(self) -> None: 

666 """Test uncompressed FITS serialization and lossless compression for 

667 float36. 

668 """ 

669 masked_image = self.make_float_image( 

670 np.float64, noise_mean=100.0, noise_sigma=15.0, cold_value=-(1 << 24), hot_value=(1 << 25) 

671 ) 

672 self.check_exact_roundtrip(masked_image, compression=None) 

673 self.check_exact_roundtrip(masked_image, {"image": {}, "mask": {}, "variance": {}}) 

674 self.check_exact_roundtrip( 

675 masked_image, 

676 { 

677 "image": {"algorithm": "GZIP_1"}, 

678 "mask": {"algorithm": "GZIP_1"}, 

679 "variance": {"algorithm": "GZIP_1"}, 

680 }, 

681 ) 

682 self.check_exact_roundtrip( 

683 masked_image, 

684 { 

685 "image": {"algorithm": "GZIP_2"}, 

686 "mask": {"algorithm": "GZIP_2"}, 

687 "variance": {"algorithm": "GZIP_2"}, 

688 }, 

689 ) 

690 

691 def test_f64_lossy_no_mask(self) -> None: 

692 """Test quantized compression of float64 with no bad pixels other than 

693 NaNs. 

694 """ 

695 masked_image = self.make_float_image( 

696 # Don't add hot or cold pixels because we won't be using a mask to 

697 # keep them out of the stdev estimation. 

698 np.float64, 

699 noise_mean=100.0, 

700 noise_sigma=15.0, 

701 cold_count=0, 

702 hot_count=0, 

703 ) 

704 lossy = {"algorithm": "RICE_1", "quantization": {"seed": 1}} 

705 lossless = {"algorithm": "GZIP_2"} 

706 for dither in DitherAlgorithm.__members__.keys(): 

707 lossy["quantization"]["dither"] = dither 

708 lossy["quantization"]["level"] = 30.0 

709 lossy["quantization"]["scaling"] = "STDEV_CFITSIO" 

710 self.check_quantized_roundtrip( 

711 masked_image, 

712 compression={"image": lossy, "mask": lossless, "variance": lossy}, 

713 roundtrip_atol=0.5, # target is 30 steps per sigma, and sigma=15. 

714 ) 

715 lossy["quantization"]["scaling"] = "MANUAL" 

716 lossy["quantization"]["level"] = 0.25 

717 self.check_quantized_roundtrip( 

718 masked_image, 

719 compression={"image": lossy, "mask": lossless, "variance": lossy}, 

720 roundtrip_atol=0.25, 

721 ) 

722 

723 def test_f64_lossy_masked(self) -> None: 

724 """Test quantized compression of float64 with hot and cold pixels.""" 

725 masked_image = self.make_float_image( 

726 np.float64, noise_mean=100.0, noise_sigma=15.0, hot_value=1e5, cold_value=-1e4 

727 ) 

728 lossy = { 

729 "algorithm": "RICE_1", 

730 "quantization": {"level": 30.0, "mask_planes": ["HOT", "COLD", "NAN"], "seed": 1}, 

731 } 

732 lossless = {"algorithm": "GZIP_2"} 

733 for dither in DitherAlgorithm.__members__.keys(): 

734 lossy["quantization"]["dither"] = dither 

735 lossy["quantization"]["scaling"] = "STDEV_MASKED" 

736 self.check_quantized_roundtrip( 

737 masked_image, 

738 compression={"image": lossy, "mask": lossless, "variance": lossy}, 

739 roundtrip_atol=0.5, # target is 30 steps per sigma, and sigma=15. 

740 ) 

741 lossy["quantization"]["scaling"] = "RANGE" 

742 with self.subTest(dither=dither, scaling="RANGE"): 

743 self.check_quantized_roundtrip( 

744 masked_image, 

745 compression={"image": lossy, "mask": lossless, "variance": lossy}, 

746 roundtrip_atol=1e-7, # since the dynamic range is tiny, this is actually barely lossy 

747 ) 

748 

749 def check_tile_shape( 

750 self, image: Image, compression: Mapping[str, object], ztile1: int, ztile2: int 

751 ) -> None: 

752 with self.roundtrip_image_reader(image, compression=compression, original_fits=True) as (_, fits): 

753 hdu = fits[1] 

754 self.assertEqual(hdu.header["ZTILE1"], ztile1) 

755 self.assertEqual(hdu.header["ZTILE2"], ztile2) 

756 

757 def test_tile_shapes(self) -> None: 

758 """Test customizing the tile size in FITS compression.""" 

759 masked_image = self.make_float_image( 

760 # Don't add hot or cold pixels because we won't be using a mask to 

761 # keep them out of the stdev estimation. 

762 np.float32, 

763 noise_mean=100.0, 

764 noise_sigma=15.0, 

765 cold_count=0, 

766 hot_count=0, 

767 ) 

768 lossy = { 

769 "image": { 

770 "algorithm": "RICE_1", 

771 "quantization": { 

772 "dither": "NO_DITHER", 

773 "scaling": "STDEV_CFITSIO", 

774 "level": 30.0, 

775 "seed": 1, 

776 }, 

777 } 

778 } 

779 self.check_tile_shape(masked_image.image, lossy, ztile1=masked_image.width, ztile2=1) 

780 lossy["image"]["tile_width"] = 1 

781 lossy["image"]["tile_height"] = 0 

782 self.check_tile_shape(masked_image.image, lossy, ztile1=1, ztile2=masked_image.height) 

783 lossy["image"]["tile_width"] = 25 

784 lossy["image"]["tile_height"] = 17 

785 self.check_tile_shape(masked_image.image, lossy, ztile1=25, ztile2=17) 

786 

787 def test_uzscale_header(self) -> None: 

788 """Test that we write the UZSCALE header card into the header when 

789 ZSCALE is universal. 

790 """ 

791 masked_image = self.make_float_image( 

792 # Don't add hot or cold pixels because we won't be using a mask to 

793 # keep them out of the stdev estimation. 

794 np.float32, 

795 noise_mean=100.0, 

796 noise_sigma=15.0, 

797 cold_count=0, 

798 hot_count=0, 

799 ) 

800 lossy = { 

801 "algorithm": "RICE_1", 

802 "quantization": {"level": 30.0, "mask_planes": ["HOT", "COLD", "NAN"], "seed": 1}, 

803 } 

804 lossy = { 

805 "image": { 

806 "algorithm": "RICE_1", 

807 "quantization": { 

808 "dither": "NO_DITHER", 

809 "scaling": "STDEV_MASKED", 

810 "level": 30.0, 

811 "seed": 1, 

812 }, 

813 } 

814 } 

815 with self.roundtrip_image_reader( 

816 masked_image.image, compression=lossy, original_fits=True 

817 ) as (_, fits): 

818 self.assertFloatsAlmostEqual(fits[1].header["UZSCALE"], 0.5, rtol=1E-2) 

819 

820 # I can't get CFITSIO to make the correction described by this test; if we 

821 # update the header key after asking it to write the image, it gets into 

822 # a weird state that prevents (at least) appending binary table HDUs. 

823 @unittest.expectedFailure 

824 def test_no_rice_one(self) -> None: