Coverage for python/lsst/images/cells/

1# This file is part of lsst-images.

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.

9# Use of this source code is governed by a 3-clause BSD-style

10# license that can be found in the LICENSE file.

12from __future__ import annotations

14__all__ = ("CellCoadd", "CellCoaddSerializationModel")

16import functools

17from collections.abc import Mapping, Sequence

18from types import EllipsisType

19from typing import TYPE_CHECKING, Any, cast

21import astropy.io.fits

22import astropy.units

23import astropy.wcs

24import pydantic

26from .._backgrounds import BackgroundMap, BackgroundMapSerializationModel

27from .._cell_grid import CellGrid, CellGridBounds, PatchDefinition

28from .._geom import YX, Box

29from .._image import Image, ImageSerializationModel

30from .._mask import Mask, MaskPlane, MaskSchema, MaskSerializationModel

31from .._masked_image import MaskedImage, MaskedImageSerializationModel

32from .._transforms import Projection, ProjectionSerializationModel, TractFrame

33from ..serialization import InputArchive, InvalidParameterError, OutputArchive

34from ._provenance import CoaddProvenance, CoaddProvenanceSerializationModel

35from ._psf import CellPointSpreadFunction, CellPointSpreadFunctionSerializationModel

37if TYPE_CHECKING:

38 try:

39 from lsst.cell_coadds import MultipleCellCoadd

40 from lsst.skymap import TractInfo

41 except ImportError:

42 type MultipleCellCoadd = Any # type: ignore[no-redef]

43 type TractInfo = Any # type: ignore[no-redef]

46class CellCoadd(MaskedImage):

47 """A coadd comprised of cells on a regular grid.

49 Parameters

50 ----------

51 image

52 The main image plane. If this has a `.Projection`, it will be used

53 for all planes unless a ``projection`` is passed separately.

54 mask

55 A bitmask image that annotates the main image plane. Must have the

56 same bounding box as ``image`` if provided. Any attached projection

57 is replaced (possibly by `None`).

58 variance

59 The per-pixel uncertainty of the main image as an image of variance

60 values. Must have the same bounding box as ``image`` if provided, and

61 its units must be the square of ``image.unit`` or `None`.

62 Values default to ``1.0``. Any attached projection is replaced

63 (possibly by `None`).

64 mask_fractions

65 A mapping from an input-image mask plane name to an image of the

66 weights sums of that plane.

67 noise_realizations

68 A sequence of images with Monte Carlo realizations of the noise in

69 the coadd.

70 mask_schema

71 Schema for the mask plane. Must be provided if and only if ``mask`` is

72 not provided.

73 projection

74 Projection that maps the pixel grid to the sky. Can only be `None` if

75 a projection is already attached to ``image``.

76 band

77 Name of the band.

78 psf

79 Effective point-spread function for the coadd. The missing cells

80 reported by ``psf.bounds`` are assumed to apply to all image data for

81 that cell as well (i.e. there is a PSF for a cell if and only if

82 there is image data for that cell).

83 patch

84 Identifiers and geometry of the full patch, if the image is confined

85 to a single patch. When present, the cell grid of the PSF and

86 provenance (if provideD) must be the full patch grid, even if its

87 bounds select a subset of that area.

88 provenance

89 Information about the images that went into the coadd.

90 backgrounds

91 Background models associated with this image.

92 """

94 def __init__(

95 self,

96 image: Image,

97 *,

98 mask: Mask | None = None,

99 variance: Image | None = None,

100 mask_fractions: Mapping[str, Image] | None = None,

101 noise_realizations: Sequence[Image] = (),

102 mask_schema: MaskSchema | None = None,

103 projection: Projection[TractFrame] | None = None,

104 band: str | None = None,

105 psf: CellPointSpreadFunction,

106 patch: PatchDefinition | None = None,

107 provenance: CoaddProvenance | None = None,

108 backgrounds: BackgroundMap | None = None,

109 ):

110 super().__init__(

111 image,

112 mask=mask,

113 variance=variance,

114 mask_schema=mask_schema,

115 projection=projection,

116 )

117 if self.image.unit is None:

118 raise TypeError("The image component of a CellCoadd must have units.")

119 if self.image.projection is None:

120 raise TypeError("The projection component of a CellCoadd cannot be None.")

121 if not isinstance(self.image.projection.pixel_frame, TractFrame):

122 raise TypeError("The projection's pixel frame must be a TractFrame for CellCoadd.")

123 self._mask_fractions = dict(mask_fractions) if mask_fractions is not None else {}

124 self._noise_realizations = list(noise_realizations)

125 self._band = band

126 if psf.bounds.bbox != self.bbox:

127 psf = psf[self.bbox]

128 self._psf = psf

129 self._patch = patch

130 self._provenance = provenance

131 if self._provenance and not self._patch:

132 raise TypeError("A CellCoadd cannot carry provenance without a patch definition.")

133 self._backgrounds = backgrounds if backgrounds is not None else BackgroundMap()

134

135 @property

136 def skymap(self) -> str:

137 """Name of the skymap (`str`)."""

138 return self.projection.pixel_frame.skymap

139

140 @property

141 def tract(self) -> int:

142 """ID of the tract (`int`)."""

143 return self.projection.pixel_frame.tract

144

145 @property

146 def patch(self) -> PatchDefinition:

147 """Identifiers and geometry of the full patch, if the image is confined

148 to a single patch (`PatchDefinition`).

149 """

150 if self._patch is None:

151 raise AttributeError("Coadd has no patch information.")

152 return self._patch

153

154 @property

155 def band(self) -> str | None:

156 """Name of the band (`str` or `None`)."""

157 return self._band

158

159 @property

160 def mask_fractions(self) -> Mapping[str, Image]:

161 """A mapping from an input-image mask plane name to an image of the

162 weights sums of that plane

163 (`~collections.abc.Mapping` [`str`, `.Image`]).

164 """

165 return self._mask_fractions

166

167 @property

168 def noise_realizations(self) -> Sequence[Image]:

169 """A sequence of images with Monte Carlo realizations of the noise in

170 the coadd (`~collections.abc.Sequence` [`.Image`]).

171 """

172 return self._noise_realizations

173

174 @property

175 def unit(self) -> astropy.units.UnitBase:

176 """The units of the image plane (`astropy.units.Unit`)."""

177 return cast(astropy.units.UnitBase, super().unit)

178

179 @property

180 def projection(self) -> Projection[TractFrame]:

181 """The projection that maps the pixel grid to the sky

182 (`.Projection` [`.TractFrame`]).

183 """

184 return cast(Projection[TractFrame], super().projection)

185

186 @property

187 def psf(self) -> CellPointSpreadFunction:

188 """Effective point-spread function for the coadd

189 (`CellPointSpreadFunction`).

190 """

191 return self._psf

192

193 @property

194 def bounds(self) -> CellGridBounds:

195 """The grid of cells that overlap this coadd and a set of missing

196 cells (`CellGridBounds`).

197 """

198 return self._psf.bounds

199

200 @property

201 def grid(self) -> CellGrid:

202 """The grid of cells that overlap this coadd (`CellGrid`)."""

203 return self._psf.bounds.grid

204

205 @property

206 def provenance(self) -> CoaddProvenance:

207 """Information about the images that went into the coadd

208 (`CoaddProvenance` or `None`).

209 """

210 if self._provenance is None:

211 raise AttributeError("Coadd has no provenance information.")

212 return self._provenance

213

214 @property

215 def backgrounds(self) -> BackgroundMap:

216 """A mapping of backgrounds associated with this image

217 (`.BackgroundMap`).

218 """

219 return self._backgrounds

220

221 def __getitem__(self, bbox: Box | EllipsisType) -> CellCoadd:

222 if bbox is ...:

223 return self

224 super().__getitem__(bbox)

225 psf = self.psf[bbox]

226 return self._transfer_metadata(

227 CellCoadd(

228 self.image[bbox],

229 mask=self.mask[bbox],

230 variance=self.variance[bbox],

231 projection=self.projection,

232 mask_fractions={k: v[bbox] for k, v in self._mask_fractions.items()},

233 noise_realizations=[v[bbox] for v in self._noise_realizations],

234 band=self.band,

235 psf=psf,

236 patch=self._patch,

237 provenance=(

238 self._provenance.subset(psf.bounds.cell_indices())

239 if self._provenance is not None

240 else None

241 ),

242 backgrounds=self._backgrounds,

243 ),

244 bbox=bbox,

245 )

246

247 def __str__(self) -> str:

248 return f"CellCoadd({self.bbox!s}, tract={self.tract})"

249

250 def __repr__(self) -> str:

251 return str(self)

252

253 def copy(self) -> CellCoadd:

254 """Deep-copy the coadd."""

255 return self._transfer_metadata(

256 CellCoadd(

257 image=self._image.copy(),

258 mask=self._mask.copy(),

259 variance=self._variance.copy(),

260 projection=self.projection,

261 mask_fractions={k: v.copy() for k, v in self._mask_fractions.items()},

262 noise_realizations=[v.copy() for v in self._noise_realizations],

263 band=self.band,

264 psf=self.psf,

265 patch=self.patch,

266 provenance=self.provenance,

267 backgrounds=self._backgrounds.copy(),

268 ),

269 copy=True,

270 )

271

272 def serialize(self, archive: OutputArchive[Any]) -> CellCoaddSerializationModel:

273 """Serialize the image to an output archive.

274

275 Parameters

276 ----------

277 archive

278 Archive to write to.

279 """

280 serialized_image = archive.serialize_direct(

281 "image", functools.partial(self.image.serialize, save_projection=False)

282 )

283 serialized_mask = archive.serialize_direct(

284 "mask", functools.partial(self.mask.serialize, save_projection=False)

285 )

286 serialized_variance = archive.serialize_direct(

287 "variance", functools.partial(self.variance.serialize, save_projection=False)

288 )

289 serialized_projection = archive.serialize_direct("projection", self.projection.serialize)

290 serialized_mask_fractions = {

291 k: archive.serialize_direct(f"mask_fractions/{k}", v.serialize)

292 for k, v in self.mask_fractions.items()

293 }

294 serialized_noise_realizations = [

295 archive.serialize_direct(f"noise_realizations/{n}", v.serialize)

296 for n, v in enumerate(self.noise_realizations)

297 ]

298 serialized_psf = archive.serialize_direct("psf", self.psf.serialize)

299 serialized_provenance = (

300 archive.serialize_direct("provenance", self._provenance.serialize)

301 if self._provenance is not None

302 else None

303 )

304 serialized_backgrounds = archive.serialize_direct("background", self._backgrounds.serialize)

305 return CellCoaddSerializationModel(

306 image=serialized_image,

307 mask=serialized_mask,

308 variance=serialized_variance,

309 projection=serialized_projection,

310 mask_fractions=serialized_mask_fractions,

311 noise_realizations=serialized_noise_realizations,

312 band=self._band,

313 psf=serialized_psf,

314 patch=self._patch,

315 provenance=serialized_provenance,

316 backgrounds=serialized_backgrounds,

317 metadata=self.metadata,

318 )

319

320 @staticmethod

321 def _get_archive_tree_type[P: pydantic.BaseModel](

322 pointer_type: type[P],

323 ) -> type[CellCoaddSerializationModel[P]]:

324 """Return the serialization model type for this object for an archive

325 type that uses the given pointer type.

326 """

327 return CellCoaddSerializationModel[pointer_type] # type: ignore

328

329 # TODO: write_fits and read_fits inherited from MaskedImage, but that

330 # write_fits doesn't have compression-option kwargs for all of the new

331 # planes that CellCoadd adds. This makes me lean towards dropping the

332 # custom read_fits and write_fits in favor of the generic free functions

333 # in the fits subpackage, even though those aren't ideal either.

334

335 @staticmethod

336 def from_legacy( # type: ignore[override]

337 legacy: MultipleCellCoadd,

338 *,

339 plane_map: Mapping[str, MaskPlane] | None = None,

340 tract_info: TractInfo,

341 ) -> CellCoadd:

342 """Convert from an `lsst.cell_coadds.MultipleCellCoadd` instance.

343

344 Parameters

345 ----------

346 legacy

347 A `lsst.cell_coadds.MultipleCellCoadd` instance to convert.

348 plane_map

349 A mapping from legacy mask plane name to the new plane name and

350 description.

351 tract_info

352 Information about the full tract.

353 """

354 from lsst.geom import Box2I

355

356 legacy_bbox = Box2I()

357 for single_cell in legacy.cells.values():

358 legacy_bbox.include(single_cell.inner.bbox)

359 legacy_stitched = legacy.stitch(legacy_bbox)

360 unit = astropy.units.Unit(legacy.units.value)

361 tract_bbox = Box.from_legacy(tract_info.getBBox())

362 projection = Projection.from_legacy(

363 legacy.wcs,

364 TractFrame(

365 skymap=legacy.identifiers.skymap,

366 tract=legacy.identifiers.tract,

367 bbox=tract_bbox,

368 ),

369 pixel_bounds=tract_bbox,

370 )

371 band = legacy.identifiers.band

372 image = Image.from_legacy(legacy_stitched.image, unit=unit)

373 mask = Mask.from_legacy(legacy_stitched.mask, plane_map=plane_map)

374 variance = Image.from_legacy(legacy_stitched.variance, unit=unit**2)

375 noise_realizations = [

376 Image.from_legacy(noise_image) for noise_image in legacy_stitched.noise_realizations

377 ]

378 mask_fractions = (

379 {"rejected": Image.from_legacy(legacy_stitched.mask_fractions)}

380 if legacy_stitched.mask_fractions is not None

381 else {}

382 )

383 psf = CellPointSpreadFunction.from_legacy(legacy_stitched.psf, image.bbox)

384 patch_info = tract_info[legacy.identifiers.patch]

385 patch = PatchDefinition(

386 id=patch_info.getSequentialIndex(),

387 index=YX(y=legacy.identifiers.patch.y, x=legacy.identifiers.patch.x),

388 inner_bbox=Box.from_legacy(patch_info.getInnerBBox()),

389 cells=CellGrid.from_legacy(legacy.grid),

390 )

391 provenance = CoaddProvenance.from_legacy(legacy)

392 return CellCoadd(

393 image=image,

394 mask=mask,

395 variance=variance,

396 mask_fractions=mask_fractions,

397 noise_realizations=noise_realizations,

398 projection=projection,

399 band=band,

400 psf=psf,

401 patch=patch,

402 provenance=provenance,

403 )

404

405

406class CellCoaddSerializationModel[P: pydantic.BaseModel](MaskedImageSerializationModel[P]):

407 """A Pydantic model used to represent a serialized `CellCoadd`."""

408

409 # Inherited attributes are duplicated because that improves the docs

410 # (some limitation in the sphinx/pydantic integration), and these are

411 # important docs.

412

413 image: ImageSerializationModel[P] = pydantic.Field(description="The main data image.")

414 mask: MaskSerializationModel[P] = pydantic.Field(

415 description="Bitmask that annotates the main image's pixels."

416 )

417 variance: ImageSerializationModel[P] = pydantic.Field(

418 description="Per-pixel variance estimates for the main image."

419 )

420 projection: ProjectionSerializationModel[P] = pydantic.Field(

421 description="Projection that maps the pixel grid to the sky.",

422 )

423 mask_fractions: dict[str, ImageSerializationModel[P]] = pydantic.Field(

424 description=(

425 "A mapping from an input-image mask plane name to an image of the weights sums of that plane."

426 )

427 )

428 noise_realizations: list[ImageSerializationModel[P]] = pydantic.Field(

429 description=(

430 "A mapping from an input-image mask plane name to an image of the weights sums of that plane."

431 )

432 )

433 band: str | None = pydantic.Field(description="Name of the band.")

434 psf: CellPointSpreadFunctionSerializationModel = pydantic.Field(

435 description="Effective point-spread function model for the coadd."

436 )

437 patch: PatchDefinition | None = pydantic.Field(description="Identifiers and geometry for the patch.")

438 provenance: CoaddProvenanceSerializationModel | None = pydantic.Field(

439 description="Information about the images that went into the coadd."

440 )

441 backgrounds: BackgroundMapSerializationModel = pydantic.Field(

442 default_factory=BackgroundMapSerializationModel,

443 description="Background models associated with this image.",

444 )

445

446 def deserialize( # type: ignore[override]

447 self,

448 archive: InputArchive[Any],

449 *,

450 bbox: Box | None = None,

451 provenance: bool = True,

452 **kwargs: Any,

453 ) -> CellCoadd:

454 """Deserialize an image from an input archive.

455

456 Parameters

457 ----------

458 archive

459 Archive to read from.

460 bbox

461 Bounding box of a subimage to read instead.

462 provenance

463 Whether to read and attach provenance information.

464 **kwargs

465 Unsupported keyword arguments are accepted only to provide better

466 error messages (raising `.serialization.InvalidParameterError`).

467 """

468 if kwargs:

469 raise InvalidParameterError(f"Unrecognized parameters for CellCoadd: {set(kwargs.keys())}.")

470 masked_image = super().deserialize(archive, bbox=bbox)

471 mask_fractions = {

472 k.removeprefix("mask_fractions/"): v.deserialize(archive) for k, v in self.mask_fractions.items()

473 }

474 noise_realizations = [v.deserialize(archive) for v in self.noise_realizations]

475 projection = self.projection.deserialize(archive)

476 psf = self.psf.deserialize(archive, bbox=bbox)

477 coadd_provenance: CoaddProvenance | None = None

478 if self.provenance is not None and provenance:

479 coadd_provenance = self.provenance.deserialize(archive)

480 if bbox is not None:

481 coadd_provenance = coadd_provenance.subset(psf.bounds.cell_indices())

482 backgrounds = self.backgrounds.deserialize(archive)

483 return CellCoadd(

484 masked_image.image,

485 mask=masked_image.mask,

486 variance=masked_image.variance,

487 mask_fractions=mask_fractions,

488 noise_realizations=noise_realizations,

489 projection=projection,

490 band=self.band,

491 psf=psf,

492 patch=self.patch,

493 provenance=coadd_provenance,

494 backgrounds=backgrounds,

495 )._finish_deserialize(self)

496

497 def deserialize_component(self, component: str, archive: InputArchive[Any], **kwargs: Any) -> Any:

498 match component:

499 case "mask_fractions":

500 return {

501 name: image_model.deserialize(archive, **kwargs)

502 for name, image_model in self.mask_fractions.items()

503 }

504 case "noise_realizations":

505 return [image_model.deserialize(archive, **kwargs) for image_model in self.noise_realizations]

506 return super().deserialize_component(component, archive, **kwargs)

Coverage for python/lsst/images/cells/_coadd.py: 37%

164 statements