Coverage for src / CSET / operators / read.py: 90%

1# © Crown copyright, Met Office (2022-2025) and CSET contributors. 

2# 

3# Licensed under the Apache License, Version 2.0 (the "License"); 

4# you may not use this file except in compliance with the License. 

5# You may obtain a copy of the License at 

6# 

7# http://www.apache.org/licenses/LICENSE-2.0 

8# 

9# Unless required by applicable law or agreed to in writing, software 

10# distributed under the License is distributed on an "AS IS" BASIS, 

11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 

12# See the License for the specific language governing permissions and 

13# limitations under the License. 

14 

15"""Operators for reading various types of files from disk.""" 

16 

17import ast 

18import datetime 

19import functools 

20import glob 

21import itertools 

22import logging 

23from pathlib import Path 

24from typing import Literal 

25 

26import iris 

27import iris.coord_systems 

28import iris.coords 

29import iris.cube 

30import iris.exceptions 

31import iris.util 

32import numpy as np 

33from iris.analysis.cartography import rotate_pole, rotate_winds 

34 

35from CSET._common import iter_maybe 

36from CSET.operators._stash_to_lfric import STASH_TO_LFRIC 

37from CSET.operators._utils import ( 

38 get_cube_coordindex, 

39 get_cube_yxcoordname, 

40 is_spatialdim, 

41) 

42 

43 

44class NoDataError(FileNotFoundError): 

45 """Error that no data has been loaded.""" 

46 

47 

48def read_cube( 

49 file_paths: list[str] | str, 

50 constraint: iris.Constraint = None, 

51 model_names: list[str] | str | None = None, 

52 subarea_type: str = None, 

53 subarea_extent: list[float] = None, 

54 **kwargs, 

55) -> iris.cube.Cube: 

56 """Read a single cube from files. 

57 

58 Read operator that takes a path string (can include shell-style glob 

59 patterns), and loads the cube matching the constraint. If any paths point to 

60 directory, all the files contained within are loaded. 

61 

62 Ensemble data can also be loaded. If it has a realization coordinate 

63 already, it will be directly used. If not, it will have its member number 

64 guessed from the filename, based on one of several common patterns. For 

65 example the pattern *emXX*, where XX is the realization. 

66 

67 Deterministic data will be loaded with a realization of 0, allowing it to be 

68 processed in the same way as ensemble data. 

69 

70 Arguments 

71 --------- 

72 file_paths: str | list[str] 

73 Path or paths to where .pp/.nc files are located 

74 constraint: iris.Constraint | iris.ConstraintCombination, optional 

75 Constraints to filter data by. Defaults to unconstrained. 

76 model_names: str | list[str], optional 

77 Names of the models that correspond to respective paths in file_paths. 

78 subarea_type: "gridcells" | "modelrelative" | "realworld", optional 

79 Whether to constrain data by model relative coordinates or real world 

80 coordinates. 

81 subarea_extent: list, optional 

82 List of coordinates to constraint data by, in order lower latitude, 

83 upper latitude, lower longitude, upper longitude. 

84 

85 Returns 

86 ------- 

87 cubes: iris.cube.Cube 

88 Cube loaded 

89 

90 Raises 

91 ------ 

92 FileNotFoundError 

93 If the provided path does not exist 

94 ValueError 

95 If the constraint doesn't produce a single cube. 

96 """ 

97 cubes = read_cubes( 

98 file_paths=file_paths, 

99 constraint=constraint, 

100 model_names=model_names, 

101 subarea_type=subarea_type, 

102 subarea_extent=subarea_extent, 

103 ) 

104 # Check filtered cubes is a CubeList containing one cube. 

105 if len(cubes) == 1: 

106 return cubes[0] 

107 else: 

108 raise ValueError( 

109 f"Constraint doesn't produce single cube: {constraint}\n{cubes}" 

110 ) 

111 

112 

113def read_cubes( 

114 file_paths: list[str] | str, 

115 constraint: iris.Constraint | None = None, 

116 model_names: str | list[str] | None = None, 

117 subarea_type: str = None, 

118 subarea_extent: list = None, 

119 **kwargs, 

120) -> iris.cube.CubeList: 

121 """Read cubes from files. 

122 

123 Read operator that takes a path string (can include shell-style glob 

124 patterns), and loads the cubes matching the constraint. If any paths point 

125 to directory, all the files contained within are loaded. 

126 

127 Ensemble data can also be loaded. If it has a realization coordinate 

128 already, it will be directly used. If not, it will have its member number 

129 guessed from the filename, based on one of several common patterns. For 

130 example the pattern *emXX*, where XX is the realization. 

131 

132 Deterministic data will be loaded with a realization of 0, allowing it to be 

133 processed in the same way as ensemble data. 

134 

135 Data output by XIOS (such as LFRic) has its per-file metadata removed so 

136 that the cubes merge across files. 

137 

138 Arguments 

139 --------- 

140 file_paths: str | list[str] 

141 Path or paths to where .pp/.nc files are located. Can include globs. 

142 constraint: iris.Constraint | iris.ConstraintCombination, optional 

143 Constraints to filter data by. Defaults to unconstrained. 

144 model_names: str | list[str], optional 

145 Names of the models that correspond to respective paths in file_paths. 

146 subarea_type: str, optional 

147 Whether to constrain data by model relative coordinates or real world 

148 coordinates. 

149 subarea_extent: list[float], optional 

150 List of coordinates to constraint data by, in order lower latitude, 

151 upper latitude, lower longitude, upper longitude. 

152 

153 Returns 

154 ------- 

155 cubes: iris.cube.CubeList 

156 Cubes loaded after being merged and concatenated. 

157 

158 Raises 

159 ------ 

160 FileNotFoundError 

161 If the provided path does not exist 

162 """ 

163 # Get iterable of paths. Each path corresponds to 1 model. 

164 paths = iter_maybe(file_paths) 

165 model_names = iter_maybe(model_names) 

166 

167 # Check we have appropriate number of model names. 

168 if model_names != (None,) and len(model_names) != len(paths): 

169 raise ValueError( 

170 f"The number of model names ({len(model_names)}) should equal " 

171 f"the number of paths given ({len(paths)})." 

172 ) 

173 

174 # Load the data for each model into a CubeList per model. 

175 model_cubes = ( 

176 _load_model(path, name, constraint) 

177 for path, name in itertools.zip_longest(paths, model_names, fillvalue=None) 

178 ) 

179 

180 # Split out first model's cubes and mark it as the base for comparisons. 

181 cubes = next(model_cubes) 

182 for cube in cubes: 

183 # Use 1 to indicate True, as booleans can't be saved in NetCDF attributes. 

184 cube.attributes["cset_comparison_base"] = 1 

185 

186 # Load the rest of the models. 

187 cubes.extend(itertools.chain.from_iterable(model_cubes)) 

188 

189 # Unify time units so different case studies can merge. 

190 iris.util.unify_time_units(cubes) 

191 

192 # Select sub region. 

193 cubes = _cutout_cubes(cubes, subarea_type, subarea_extent) 

194 

195 # Merge and concatenate cubes now metadata has been fixed. 

196 cubes = cubes.merge() 

197 cubes = cubes.concatenate() 

198 

199 # Squeeze single valued coordinates into scalar coordinates. 

200 cubes = iris.cube.CubeList(iris.util.squeeze(cube) for cube in cubes) 

201 

202 # Ensure dimension coordinates are bounded. 

203 for cube in cubes: 

204 for dim_coord in cube.coords(dim_coords=True): 

205 # Iris can't guess the bounds of a scalar coordinate. 

206 if not dim_coord.has_bounds() and dim_coord.shape[0] > 1: 

207 dim_coord.guess_bounds() 

208 

209 logging.info("Loaded cubes: %s", cubes) 

210 if len(cubes) == 0: 

211 raise NoDataError("No cubes loaded, check your constraints!") 

212 return cubes 

213 

214 

215def _load_model( 

216 paths: str | list[str], 

217 model_name: str | None, 

218 constraint: iris.Constraint | None, 

219) -> iris.cube.CubeList: 

220 """Load a single model's data into a CubeList.""" 

221 input_files = _check_input_files(paths) 

222 # If unset, a constraint of None lets everything be loaded. 

223 logging.debug("Constraint: %s", constraint) 

224 cubes = iris.load(input_files, constraint, callback=_loading_callback) 

225 # Make the UM's winds consistent with LFRic. 

226 _fix_um_winds(cubes) 

227 

228 # Add model_name attribute to each cube to make it available at any further 

229 # step without needing to pass it as function parameter. 

230 if model_name is not None: 

231 for cube in cubes: 

232 cube.attributes["model_name"] = model_name 

233 return cubes 

234 

235 

236def _check_input_files(input_paths: str | list[str]) -> list[Path]: 

237 """Get an iterable of files to load, and check that they all exist. 

238 

239 Arguments 

240 --------- 

241 input_paths: list[str] 

242 List of paths to input files or directories. The path may itself contain 

243 glob patterns, but unlike in shells it will match directly first. 

244 

245 Returns 

246 ------- 

247 list[Path] 

248 A list of files to load. 

249 

250 Raises 

251 ------ 

252 FileNotFoundError: 

253 If the provided arguments don't resolve to at least one existing file. 

254 """ 

255 files = [] 

256 for raw_filename in iter_maybe(input_paths): 

257 # Match glob-like files first, if they exist. 

258 raw_path = Path(raw_filename) 

259 if raw_path.is_file(): 

260 files.append(raw_path) 

261 else: 

262 for input_path in glob.glob(raw_filename): 

263 # Convert string paths into Path objects. 

264 input_path = Path(input_path) 

265 # Get the list of files in the directory, or use it directly. 

266 if input_path.is_dir(): 

267 logging.debug("Checking directory '%s' for files", input_path) 

268 files.extend(p for p in input_path.iterdir() if p.is_file()) 

269 else: 

270 files.append(input_path) 

271 

272 files.sort() 

273 logging.info("Loading files:\n%s", "\n".join(str(path) for path in files)) 

274 if len(files) == 0: 

275 raise FileNotFoundError(f"No files found for {input_paths}") 

276 return files 

277 

278 

279def _cutout_cubes( 

280 cubes: iris.cube.CubeList, 

281 subarea_type: Literal["gridcells", "realworld", "modelrelative"] | None, 

282 subarea_extent: list[float, float, float, float], 

283): 

284 """Cut out a subarea from a CubeList.""" 

285 if subarea_type is None: 

286 logging.debug("Subarea selection is disabled.") 

287 return cubes 

288 

289 # If selected, cutout according to number of grid cells to trim from each edge. 

290 cutout_cubes = iris.cube.CubeList() 

291 # Find spatial coordinates 

292 for cube in cubes: 

293 # Find dimension coordinates. 

294 lat_name, lon_name = get_cube_yxcoordname(cube) 

295 

296 # Compute cutout based on number of cells to trim from edges. 

297 if subarea_type == "gridcells": 

298 logging.debug( 

299 "User requested LowerTrim: %s LeftTrim: %s UpperTrim: %s RightTrim: %s", 

300 subarea_extent[0], 

301 subarea_extent[1], 

302 subarea_extent[2], 

303 subarea_extent[3], 

304 ) 

305 lat_points = np.sort(cube.coord(lat_name).points) 

306 lon_points = np.sort(cube.coord(lon_name).points) 

307 # Define cutout region using user provided cell points. 

308 lats = [lat_points[subarea_extent[0]], lat_points[-subarea_extent[2] - 1]] 

309 lons = [lon_points[subarea_extent[1]], lon_points[-subarea_extent[3] - 1]] 

310 

311 # Compute cutout based on specified coordinate values. 

312 elif subarea_type == "realworld" or subarea_type == "modelrelative": 

313 # If not gridcells, cutout by requested geographic area, 

314 logging.debug( 

315 "User requested LLat: %s ULat: %s LLon: %s ULon: %s", 

316 subarea_extent[0], 

317 subarea_extent[1], 

318 subarea_extent[2], 

319 subarea_extent[3], 

320 ) 

321 # Define cutout region using user provided coordinates. 

322 lats = np.array(subarea_extent[0:2]) 

323 lons = np.array(subarea_extent[2:4]) 

324 # Ensure cutout longitudes are within +/- 180.0 bounds. 

325 while lons[0] < -180.0: 

326 lons += 360.0 

327 while lons[1] > 180.0: 

328 lons -= 360.0 

329 # If the coordinate system is rotated we convert coordinates into 

330 # model-relative coordinates to extract the appropriate cutout. 

331 coord_system = cube.coord(lat_name).coord_system 

332 if subarea_type == "realworld" and isinstance( 

333 coord_system, iris.coord_systems.RotatedGeogCS 

334 ): 

335 lons, lats = rotate_pole( 

336 lons, 

337 lats, 

338 pole_lon=coord_system.grid_north_pole_longitude, 

339 pole_lat=coord_system.grid_north_pole_latitude, 

340 ) 

341 else: 

342 raise ValueError("Unknown subarea_type:", subarea_type) 

343 

344 # Do cutout and add to cutout_cubes. 

345 intersection_args = {lat_name: lats, lon_name: lons} 

346 logging.debug("Cutting out coords: %s", intersection_args) 

347 try: 

348 cutout_cubes.append(cube.intersection(**intersection_args)) 

349 except IndexError as err: 

350 raise ValueError( 

351 "Region cutout error. Check and update SUBAREA_EXTENT." 

352 "Cutout region requested should be contained within data area. " 

353 "Also check if cutout region requested is smaller than input grid spacing." 

354 ) from err 

355 

356 return cutout_cubes 

357 

358 

359def _loading_callback(cube: iris.cube.Cube, field, filename: str) -> iris.cube.Cube: 

360 """Compose together the needed callbacks into a single function.""" 

361 # Most callbacks operate in-place, but save the cube when returned! 

362 _realization_callback(cube, field, filename) 

363 _um_normalise_callback(cube, field, filename) 

364 _lfric_normalise_callback(cube, field, filename) 

365 cube = _lfric_time_coord_fix_callback(cube, field, filename) 

366 _normalise_var0_varname(cube) 

367 _fix_spatial_coords_callback(cube) 

368 _fix_pressure_coord_callback(cube) 

369 _fix_um_radtime(cube) 

370 _fix_cell_methods(cube) 

371 cube = _convert_cube_units_callback(cube) 

372 cube = _grid_longitude_fix_callback(cube) 

373 _fix_lfric_cloud_base_altitude(cube) 

374 _proleptic_gregorian_fix(cube) 

375 _lfric_time_callback(cube) 

376 _lfric_forecast_period_standard_name_callback(cube) 

377 _normalise_ML_varname(cube) 

378 return cube 

379 

380 

381def _realization_callback(cube, field, filename): 

382 """Give deterministic cubes a realization of 0. 

383 

384 This means they can be handled in the same way as ensembles through the rest 

385 of the code. 

386 """ 

387 # Only add if realization coordinate does not exist. 

388 if not cube.coords("realization"): 

389 cube.add_aux_coord( 

390 iris.coords.DimCoord(0, standard_name="realization", units="1") 

391 ) 

392 

393 

394@functools.lru_cache(None) 

395def _warn_once(msg): 

396 """Print a warning message, skipping recent duplicates.""" 

397 logging.warning(msg) 

398 

399 

400def _um_normalise_callback(cube: iris.cube.Cube, field, filename): 

401 """Normalise UM STASH variable long names to LFRic variable names. 

402 

403 Note standard names will remain associated with cubes where different. 

404 Long name will be used consistently in output filename and titles. 

405 """ 

406 # Convert STASH to LFRic variable name 

407 if "STASH" in cube.attributes: 

408 stash = cube.attributes["STASH"] 

409 try: 

410 (name, grid) = STASH_TO_LFRIC[str(stash)] 

411 cube.long_name = name 

412 except KeyError: 

413 # Don't change cubes with unknown stash codes. 

414 _warn_once( 

415 f"Unknown STASH code: {stash}. Please check file stash_to_lfric.py to update." 

416 ) 

417 

418 

419def _lfric_normalise_callback(cube: iris.cube.Cube, field, filename): 

420 """Normalise attributes that prevents LFRic cube from merging. 

421 

422 The uuid and timeStamp relate to the output file, as saved by XIOS, and has 

423 no relation to the data contained. These attributes are removed. 

424 

425 The um_stash_source is a list of STASH codes for when an LFRic field maps to 

426 multiple UM fields, however it can be encoded in any order. This attribute 

427 is sorted to prevent this. This attribute is only present in LFRic data that 

428 has been converted to look like UM data. 

429 """ 

430 # Remove unwanted attributes. 

431 cube.attributes.pop("timeStamp", None) 

432 cube.attributes.pop("uuid", None) 

433 cube.attributes.pop("name", None) 

434 

435 # Sort STASH code list. 

436 stash_list = cube.attributes.get("um_stash_source") 

437 if stash_list: 

438 # Parse the string as a list, sort, then re-encode as a string. 

439 cube.attributes["um_stash_source"] = str(sorted(ast.literal_eval(stash_list))) 

440 

441 

442def _lfric_time_coord_fix_callback( 

443 cube: iris.cube.Cube, field, filename 

444) -> iris.cube.Cube: 

445 """Ensure the time coordinate is a DimCoord rather than an AuxCoord. 

446 

447 The coordinate is converted and replaced if not. SLAMed LFRic data has this 

448 issue, though the coordinate satisfies all the properties for a DimCoord. 

449 Scalar time values are left as AuxCoords. 

450 """ 

451 # This issue seems to come from iris's handling of NetCDF files where time 

452 # always ends up as an AuxCoord. 

453 if cube.coords("time"): 

454 time_coord = cube.coord("time") 

455 if ( 

456 not isinstance(time_coord, iris.coords.DimCoord) 

457 and len(cube.coord_dims(time_coord)) == 1 

458 ): 

459 # Fudge the bounds to foil checking for strict monotonicity. 

460 if time_coord.has_bounds(): 460 ↛ 461line 460 didn't jump to line 461 because the condition on line 460 was never true

461 if (time_coord.bounds[-1][0] - time_coord.bounds[0][0]) < 1.0e-8: 

462 time_coord.bounds = [ 

463 [ 

464 time_coord.bounds[i][0] + 1.0e-8 * float(i), 

465 time_coord.bounds[i][1], 

466 ] 

467 for i in range(len(time_coord.bounds)) 

468 ] 

469 iris.util.promote_aux_coord_to_dim_coord(cube, time_coord) 

470 return cube 

471 

472 

473def _grid_longitude_fix_callback(cube: iris.cube.Cube) -> iris.cube.Cube: 

474 """Check grid_longitude coordinates are in the range -180 deg to 180 deg. 

475 

476 This is necessary if comparing two models with different conventions -- 

477 for example, models where the prime meridian is defined as 0 deg or 

478 360 deg. If not in the range -180 deg to 180 deg, we wrap the grid_longitude 

479 so that it falls in this range. Checks are for near-180 bounds given 

480 model data bounds may not extend exactly to 0. or 360. 

481 Input cubes on non-rotated grid coordinates are not impacted. 

482 """ 

483 try: 

484 y, x = get_cube_yxcoordname(cube) 

485 except ValueError: 

486 # Don't modify non-spatial cubes. 

487 return cube 

488 

489 long_coord = cube.coord(x) 

490 # Wrap longitudes if rotated pole coordinates 

491 coord_system = long_coord.coord_system 

492 if x == "grid_longitude" and isinstance( 

493 coord_system, iris.coord_systems.RotatedGeogCS 

494 ): 

495 long_points = long_coord.points.copy() 

496 long_centre = np.median(long_points) 

497 while long_centre < -175.0: 

498 long_centre += 360.0 

499 long_points += 360.0 

500 while long_centre >= 175.0: 

501 long_centre -= 360.0 

502 long_points -= 360.0 

503 long_coord.points = long_points 

504 

505 # Update coord bounds to be consistent with wrapping. 

506 if long_coord.has_bounds(): 

507 long_coord.bounds = None 

508 long_coord.guess_bounds() 

509 

510 return cube 

511 

512 

513def _fix_spatial_coords_callback(cube: iris.cube.Cube): 

514 """Check latitude and longitude coordinates name. 

515 

516 This is necessary as some models define their grid as on rotated 

517 'grid_latitude' and 'grid_longitude' coordinates while others define 

518 the grid on non-rotated 'latitude' and 'longitude'. 

519 Cube dimensions need to be made consistent to avoid recipe failures, 

520 particularly where comparing multiple input models with differing spatial 

521 coordinates. 

522 """ 

523 # Check if cube is spatial. 

524 if not is_spatialdim(cube): 

525 # Don't modify non-spatial cubes. 

526 return 

527 

528 # Get spatial coords and dimension index. 

529 y_name, x_name = get_cube_yxcoordname(cube) 

530 ny = get_cube_coordindex(cube, y_name) 

531 nx = get_cube_coordindex(cube, x_name) 

532 

533 # Remove spatial coords bounds if erroneous values detected. 

534 # Aims to catch some errors in input coord bounds by setting 

535 # invalid threshold of 10000.0 

536 if cube.coord(x_name).has_bounds() and cube.coord(y_name).has_bounds(): 

537 bx_max = np.max(np.abs(cube.coord(x_name).bounds)) 

538 by_max = np.max(np.abs(cube.coord(y_name).bounds)) 

539 if bx_max > 10000.0 or by_max > 10000.0: 

540 cube.coord(x_name).bounds = None 

541 cube.coord(y_name).bounds = None 

542 

543 # Translate [grid_latitude, grid_longitude] to an unrotated 1-d DimCoord 

544 # [latitude, longitude] for instances where rotated_pole=90.0 

545 if "grid_latitude" in [coord.name() for coord in cube.coords(dim_coords=True)]: 

546 coord_system = cube.coord("grid_latitude").coord_system 

547 pole_lat = getattr(coord_system, "grid_north_pole_latitude", None) 

548 if pole_lat == 90.0: 548 ↛ 549line 548 didn't jump to line 549 because the condition on line 548 was never true

549 lats = cube.coord("grid_latitude").points 

550 lons = cube.coord("grid_longitude").points 

551 

552 cube.remove_coord("grid_latitude") 

553 cube.add_dim_coord( 

554 iris.coords.DimCoord( 

555 lats, 

556 standard_name="latitude", 

557 var_name="latitude", 

558 units="degrees", 

559 coord_system=iris.coord_systems.GeogCS(6371229.0), 

560 circular=True, 

561 ), 

562 ny, 

563 ) 

564 y_name = "latitude" 

565 cube.remove_coord("grid_longitude") 

566 cube.add_dim_coord( 

567 iris.coords.DimCoord( 

568 lons, 

569 standard_name="longitude", 

570 var_name="longitude", 

571 units="degrees", 

572 coord_system=iris.coord_systems.GeogCS(6371229.0), 

573 circular=True, 

574 ), 

575 nx, 

576 ) 

577 x_name = "longitude" 

578 

579 # Create additional AuxCoord [grid_latitude, grid_longitude] with 

580 # rotated pole attributes for cases with [lat, lon] inputs 

581 if y_name in ["latitude"] and cube.coord(y_name).units in [ 

582 "degrees", 

583 "degrees_north", 

584 "degrees_south", 

585 ]: 

586 # Add grid_latitude AuxCoord 

587 if "grid_latitude" not in [ 587 ↛ 600line 587 didn't jump to line 600 because the condition on line 587 was always true

588 coord.name() for coord in cube.coords(dim_coords=False) 

589 ]: 

590 cube.add_aux_coord( 

591 iris.coords.AuxCoord( 

592 cube.coord(y_name).points, 

593 var_name="grid_latitude", 

594 units="degrees", 

595 ), 

596 ny, 

597 ) 

598 # Ensure input latitude DimCoord has CoordSystem 

599 # This attribute is sometimes lost on iris.save 

600 if not cube.coord(y_name).coord_system: 

601 cube.coord(y_name).coord_system = iris.coord_systems.GeogCS(6371229.0) 

602 

603 if x_name in ["longitude"] and cube.coord(x_name).units in [ 

604 "degrees", 

605 "degrees_west", 

606 "degrees_east", 

607 ]: 

608 # Add grid_longitude AuxCoord 

609 if "grid_longitude" not in [ 609 ↛ 623line 609 didn't jump to line 623 because the condition on line 609 was always true

610 coord.name() for coord in cube.coords(dim_coords=False) 

611 ]: 

612 cube.add_aux_coord( 

613 iris.coords.AuxCoord( 

614 cube.coord(x_name).points, 

615 var_name="grid_longitude", 

616 units="degrees", 

617 ), 

618 nx, 

619 ) 

620 

621 # Ensure input longitude DimCoord has CoordSystem 

622 # This attribute is sometimes lost on iris.save 

623 if not cube.coord(x_name).coord_system: 

624 cube.coord(x_name).coord_system = iris.coord_systems.GeogCS(6371229.0) 

625 

626 

627def _fix_pressure_coord_callback(cube: iris.cube.Cube): 

628 """Rename pressure coordinate to "pressure" if it exists and ensure hPa units. 

629 

630 This problem was raised because the AIFS model data from ECMWF 

631 defines the pressure coordinate with the name "pressure_level" rather 

632 than compliant CF coordinate names. 

633 

634 Additionally, set the units of pressure to be hPa to be consistent with the UM, 

635 and approach the coordinates in a unified way. 

636 """ 

637 for coord in cube.dim_coords: 

638 if coord.name() in ["pressure_level", "pressure_levels"]: 

639 coord.rename("pressure") 

640 

641 if coord.name() == "pressure": 

642 if str(cube.coord("pressure").units) != "hPa": 

643 cube.coord("pressure").convert_units("hPa") 

644 

645 

646def _fix_um_radtime(cube: iris.cube.Cube): 

647 """Move radiation diagnostics from timestamps which are output N minutes or seconds past every hour. 

648 

649 This callback does not have any effect for output diagnostics with 

650 timestamps exactly 00 or 30 minutes past the hour. Only radiation 

651 diagnostics are checked. 

652 Note this callback does not interpolate the data in time, only adjust 

653 timestamps to sit on the hour to enable time-to-time difference plotting 

654 with models which may output radiation data on the hour. 

655 """ 

656 try: 

657 if cube.attributes["STASH"] in [ 

658 "m01s01i207", 

659 "m01s01i208", 

660 "m01s02i205", 

661 "m01s02i201", 

662 "m01s01i207", 

663 "m01s02i207", 

664 "m01s01i235", 

665 ]: 

666 time_coord = cube.coord("time") 

667 

668 # Convert time points to datetime objects 

669 time_unit = time_coord.units 

670 time_points = time_unit.num2date(time_coord.points) 

671 # Skip if times don't need fixing. 

672 if time_points[0].minute == 0 and time_points[0].second == 0: 

673 return 

674 if time_points[0].minute == 30 and time_points[0].second == 0: 674 ↛ 675line 674 didn't jump to line 675 because the condition on line 674 was never true

675 return 

676 

677 # Subtract time difference from the hour from each time point 

678 n_minute = time_points[0].minute 

679 n_second = time_points[0].second 

680 # If times closer to next hour, compute difference to add on to following hour 

681 if n_minute > 30: 

682 n_minute = n_minute - 60 

683 # Compute new diagnostic time stamp 

684 new_time_points = ( 

685 time_points 

686 - datetime.timedelta(minutes=n_minute) 

687 - datetime.timedelta(seconds=n_second) 

688 ) 

689 

690 # Convert back to numeric values using the original time unit. 

691 new_time_values = time_unit.date2num(new_time_points) 

692 

693 # Replace the time coordinate with updated values. 

694 time_coord.points = new_time_values 

695 

696 # Recompute forecast_period with corrected values. 

697 if cube.coord("forecast_period"): 697 ↛ exitline 697 didn't return from function '_fix_um_radtime' because the condition on line 697 was always true

698 fcst_prd_points = cube.coord("forecast_period").points 

699 new_fcst_points = ( 

700 time_unit.num2date(fcst_prd_points) 

701 - datetime.timedelta(minutes=n_minute) 

702 - datetime.timedelta(seconds=n_second) 

703 ) 

704 cube.coord("forecast_period").points = time_unit.date2num( 

705 new_fcst_points 

706 ) 

707 except KeyError: 

708 pass 

709 

710 

711def _fix_cell_methods(cube: iris.cube.Cube): 

712 """To fix the assumed cell_methods in accumulation STASH from UM. 

713 

714 Lightning (m01s21i104), rainfall amount (m01s04i201, m01s05i201) and snowfall amount 

715 (m01s04i202, m01s05i202) in UM is being output as a time accumulation, 

716 over each hour (TAcc1hr), but input cubes show cell_methods as "mean". 

717 For UM and LFRic inputs to be compatible, we assume accumulated cell_methods are 

718 "sum". This callback changes "mean" cube attribute cell_method to "sum", 

719 enabling the cell_method constraint on reading to select correct input. 

720 """ 

721 # Shift "mean" cell_method to "sum" for selected UM inputs. 

722 if cube.attributes.get("STASH") in [ 

723 "m01s21i104", 

724 "m01s04i201", 

725 "m01s04i202", 

726 "m01s05i201", 

727 "m01s05i202", 

728 ]: 

729 # Check if input cell_method contains "mean" time-processing. 

730 if set(cm.method for cm in cube.cell_methods) == {"mean"}: 730 ↛ exitline 730 didn't return from function '_fix_cell_methods' because the condition on line 730 was always true

731 # Retrieve interval and any comment information. 

732 for cell_method in cube.cell_methods: 

733 interval_str = cell_method.intervals 

734 comment_str = cell_method.comments 

735 

736 # Remove input aggregation method. 

737 cube.cell_methods = () 

738 

739 # Replace "mean" with "sum" cell_method to indicate aggregation. 

740 cube.add_cell_method( 

741 iris.coords.CellMethod( 

742 method="sum", 

743 coords="time", 

744 intervals=interval_str, 

745 comments=comment_str, 

746 ) 

747 ) 

748 

749 

750def _convert_cube_units_callback(cube: iris.cube.Cube): 

751 """Adjust diagnostic units for specific variables. 

752 

753 Some precipitation diagnostics are output with unit kg m-2 s-1 and are 

754 converted here to mm hr-1. 

755 

756 Visibility diagnostics are converted here from m to km to improve output 

757 formatting. 

758 """ 

759 # Convert precipitation diagnostic units if required. 

760 varnames = filter(None, [cube.long_name, cube.standard_name, cube.var_name]) 

761 if any("surface_microphysical" in name for name in varnames): 

762 if cube.units == "kg m-2 s-1": 

763 logging.debug( 

764 "Converting precipitation rate units from kg m-2 s-1 to mm hr-1" 

765 ) 

766 # Convert from kg m-2 s-1 to mm s-1 assuming 1kg water = 1l water = 1dm^3 water. 

767 # This is a 1:1 conversion, so we just change the units. 

768 cube.units = "mm s-1" 

769 # Convert the units to per hour. 

770 cube.convert_units("mm hr-1") 

771 elif cube.units == "kg m-2": 771 ↛ 778line 771 didn't jump to line 778 because the condition on line 771 was always true

772 logging.debug("Converting precipitation amount units from kg m-2 to mm") 

773 # Convert from kg m-2 to mm assuming 1kg water = 1l water = 1dm^3 water. 

774 # This is a 1:1 conversion, so we just change the units. 

775 cube.units = "mm" 

776 

777 # Convert visibility diagnostic units if required. 

778 varnames = filter(None, [cube.long_name, cube.standard_name, cube.var_name]) 

779 if any("visibility" in name for name in varnames): 

780 if cube.units == "m": 780 ↛ 785line 780 didn't jump to line 785 because the condition on line 780 was always true

781 logging.debug("Converting visibility units m to km.") 

782 # Convert the units to km. 

783 cube.convert_units("km") 

784 

785 return cube 

786 

787 

788def _fix_lfric_cloud_base_altitude(cube: iris.cube.Cube): 

789 """Mask cloud_base_altitude diagnostic in regions with no cloud.""" 

790 varnames = filter(None, [cube.long_name, cube.standard_name, cube.var_name]) 

791 if any("cloud_base_altitude" in name for name in varnames): 

792 # Mask cube where set > 144kft to catch default 144.35695538058164 

793 cube.data = np.ma.masked_array(cube.data) 

794 cube.data[cube.data > 144.0] = np.ma.masked 

795 

796 

797def _fix_um_winds(cubes: iris.cube.CubeList): 

798 """To make winds from the UM consistent with those from LFRic. 

799 

800 Diagnostics of wind are not always consistent between the UM 

801 and LFric. Here, winds from the UM are adjusted to make them i 

802 consistent with LFRic. 

803 """ 

804 # Check whether we have components of the wind identified by STASH, 

805 # (so this will apply only to cubes from the UM), but not the 

806 # wind speed and calculate it if it is missing. Note that 

807 # this will be biased low in general because the components will mostly 

808 # be time averages. For simplicity, we do this only if there is just one 

809 # cube of a component. A more complicated approach would be to consider 

810 # the cell methods, but it may not be warranted. 

811 u_constr = iris.AttributeConstraint(STASH="m01s03i225") 

812 v_constr = iris.AttributeConstraint(STASH="m01s03i226") 

813 speed_constr = iris.AttributeConstraint(STASH="m01s03i227") 

814 try: 

815 if cubes.extract(u_constr) and cubes.extract(v_constr): 815 ↛ 816line 815 didn't jump to line 816 because the condition on line 815 was never true

816 if len(cubes.extract(u_constr)) == 1 and not cubes.extract(speed_constr): 

817 _add_wind_speed_um(cubes) 

818 # Convert winds in the UM to be relative to true east and true north. 

819 _convert_wind_true_dirn_um(cubes) 

820 except (KeyError, AttributeError): 

821 pass 

822 

823 

824def _add_wind_speed_um(cubes: iris.cube.CubeList): 

825 """Add windspeeds to cubes from the UM.""" 

826 wspd10 = ( 

827 cubes.extract_cube(iris.AttributeConstraint(STASH="m01s03i225"))[0] ** 2 

828 + cubes.extract_cube(iris.AttributeConstraint(STASH="m01s03i226"))[0] ** 2 

829 ) ** 0.5 

830 wspd10.attributes["STASH"] = "m01s03i227" 

831 wspd10.standard_name = "wind_speed" 

832 wspd10.long_name = "wind_speed_at_10m" 

833 cubes.append(wspd10) 

834 

835 

836def _convert_wind_true_dirn_um(cubes: iris.cube.CubeList): 

837 """To convert winds to true directions. 

838 

839 Convert from the components relative to the grid to true directions. 

840 This functionality only handles the simplest case. 

841 """ 

842 u_grids = cubes.extract(iris.AttributeConstraint(STASH="m01s03i225")) 

843 v_grids = cubes.extract(iris.AttributeConstraint(STASH="m01s03i226")) 

844 for u, v in zip(u_grids, v_grids, strict=True): 

845 true_u, true_v = rotate_winds(u, v, iris.coord_systems.GeogCS(6371229.0)) 

846 u.data = true_u.data 

847 v.data = true_v.data 

848 

849 

850def _normalise_var0_varname(cube: iris.cube.Cube): 

851 """Fix varnames for consistency to allow merging. 

852 

853 Some model data netCDF sometimes have a coordinate name end in 

854 "_0" etc, where duplicate coordinates of same name are defined but 

855 with different attributes. This can be inconsistently managed in 

856 different model inputs and can cause cubes to fail to merge. 

857 """ 

858 for coord in cube.coords(): 

859 if coord.var_name and coord.var_name.endswith("_0"): 

860 coord.var_name = coord.var_name.removesuffix("_0") 

861 if coord.var_name and coord.var_name.endswith("_1"): 

862 coord.var_name = coord.var_name.removesuffix("_1") 

863 if coord.var_name and coord.var_name.endswith("_2"): 863 ↛ 864line 863 didn't jump to line 864 because the condition on line 863 was never true

864 coord.var_name = coord.var_name.removesuffix("_2") 

865 if coord.var_name and coord.var_name.endswith("_3"): 865 ↛ 866line 865 didn't jump to line 866 because the condition on line 865 was never true

866 coord.var_name = coord.var_name.removesuffix("_3") 

867 

868 if cube.var_name and cube.var_name.endswith("_0"): 

869 cube.var_name = cube.var_name.removesuffix("_0") 

870 

871 

872def _proleptic_gregorian_fix(cube: iris.cube.Cube): 

873 """Convert the calendars of time units to use a standard calendar.""" 

874 try: 

875 time_coord = cube.coord("time") 

876 if time_coord.units.calendar == "proleptic_gregorian": 

877 logging.debug( 

878 "Changing proleptic Gregorian calendar to standard calendar for %s", 

879 repr(time_coord.units),