Coverage for src/CSET/operators/constraints.py: 93%
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15"""Operators to generate constraints to filter with."""
17import numbers
18import re
19from collections.abc import Iterable
20from datetime import timedelta
22import iris
23import iris.coords
24import iris.cube
26import CSET.operators._utils as operator_utils
27from CSET._common import iter_maybe
30def generate_stash_constraint(stash: str, **kwargs) -> iris.AttributeConstraint:
31 """Generate constraint from STASH code.
33 Operator that takes a stash string, and uses iris to generate a constraint
34 to be passed into the read operator to minimize the CubeList the read
35 operator loads and speed up loading.
37 Arguments
38 ---------
39 stash: str
40 stash code to build iris constraint, such as "m01s03i236"
42 Returns
43 -------
44 stash_constraint: iris.AttributeConstraint
45 """
46 # At a later stage str list an option to combine constraints. Arguments
47 # could be a list of stash codes that combined build the constraint.
48 stash_constraint = iris.AttributeConstraint(STASH=stash)
49 return stash_constraint
52def generate_var_constraint(varname: str, **kwargs) -> iris.Constraint:
53 """Generate constraint from variable name or STASH code.
55 Operator that takes a CF compliant variable name string, and generates an
56 iris constraint to be passed into the read or filter operator. Can also be
57 passed a STASH code to generate a STASH constraint.
59 Arguments
60 ---------
61 varname: str
62 CF compliant name of variable, or a UM STASH code such as "m01s03i236".
64 Returns
65 -------
66 varname_constraint: iris.Constraint
67 """
68 if re.match(r"m[0-9]{2}s[0-9]{2}i[0-9]{3}$", varname):
69 varname_constraint = iris.AttributeConstraint(STASH=varname)
70 else:
71 varname_constraint = iris.Constraint(name=varname)
73 # Ensure access to variable vector components for computed fields
74 if varname == "wind_speed_at_10m":
75 varname_constraint = iris.Constraint(
76 cube_func=lambda cube: (
77 cube.long_name
78 in ["wind_at_10m", "eastward_wind_at_10m", "northward_wind_at_10m"]
79 )
80 )
82 return varname_constraint
85def generate_level_constraint(
86 coordinate: str, levels: int | list[int] | str, **kwargs
87) -> iris.Constraint:
88 """Generate constraint for particular levels on the specified coordinate.
90 Operator that generates a constraint to constrain to specific model or
91 pressure levels. If no levels are specified then any cube with the specified
92 coordinate is rejected.
94 Typically ``coordinate`` will be ``"pressure"`` or ``"model_level_number"``
95 for UM, or ``"full_levels"`` or ``"half_levels"`` for LFRic.
97 Arguments
98 ---------
99 coordinate: str
100 Level coordinate name about which to constraint.
101 levels: int | list[int] | str
102 CF compliant level points, ``"*"`` for retrieving all levels, or
103 ``[]`` for no levels.
105 Returns
106 -------
107 constraint: iris.Constraint
109 Notes
110 -----
111 Due to the specification of ``coordinate`` as an argument any iterable
112 coordinate can be stratified with this function. Therefore,
113 ``"realization"`` is a valid option. Subsequently, ``levels`` specifies the
114 ensemble members, or group of ensemble members you wish to constrain your
115 results over.
116 """
117 # If asterisks, then return all levels for given coordinate.
118 if levels == "*":
119 return iris.Constraint(**{coordinate: lambda cell: True})
120 else:
121 # Ensure is iterable.
122 if not isinstance(levels, Iterable):
123 levels = [levels]
125 # When no levels specified reject cube with level coordinate.
126 if len(levels) == 0:
128 def no_levels(cube):
129 # Reject cubes for which coordinate exists.
130 return not cube.coords(coordinate)
132 return iris.Constraint(cube_func=no_levels)
134 # Filter the coordinate to the desired levels.
135 # Dictionary unpacking is used to provide programmatic keyword arguments.
136 return iris.Constraint(**{coordinate: levels})
139def generate_remove_single_level_constraint(
140 coord: str, level: int = 0, **kwargs
141) -> iris.Constraint:
142 """
143 Generate a constraint to remove a single model level number.
145 Operator that returns a constraint to remove the given level. By
146 default the first level is removed (assumed to be
147 level zero). However, any level can be removed.
149 Arguments
150 ---------
151 coord: str
152 The coordinate for which the level is to be removed.
153 level: int
154 Default is 0. The model level number to remove.
156 Returns
157 -------
158 iris.Constraint
160 Notes
161 -----
162 This operator is primarily used to ensure the levels are consistent
163 as some level sets (e.g. specific humidity) will be on the same level set
164 but have a different number of levels (e.g 71 instead of expected 70).
165 """
166 return iris.Constraint(**{coord: lambda m: m.point != level})
169def generate_cell_methods_constraint(
170 cell_methods: list,
171 varname: str | None = None,
172 coord: iris.coords.Coord | None = None,
173 interval: str | None = None,
174 comment: str | None = None,
175 **kwargs,
176) -> iris.Constraint:
177 """Generate constraint from cell methods.
179 Operator that takes a list of cell methods and generates a constraint from
180 that. Use [] to specify non-aggregated data.
182 Arguments
183 ---------
184 cell_methods: list
185 cube.cell_methods for filtering.
186 varname: str, optional
187 CF compliant name of variable.
188 coord: iris.coords.Coord, optional
189 iris.coords.Coord to which the cell method is applied to.
190 interval: str, optional
191 interval over which the cell method is applied to (e.g. 1 hour).
192 comment: str, optional
193 any comments in Cube meta data associated with the cell method.
195 Returns
196 -------
197 cell_method_constraint: iris.Constraint
198 """
199 if len(cell_methods) == 0:
201 def check_no_aggregation(cube: iris.cube.Cube) -> bool:
202 """Check that any cell methods are "point", meaning no aggregation."""
203 return set(cm.method for cm in cube.cell_methods) <= {"point"}
205 def check_cell_sum(cube: iris.cube.Cube) -> bool:
206 """Check that any cell methods are "sum"."""
207 return set(cm.method for cm in cube.cell_methods) == {"sum"}
209 def check_cell_mean(cube: iris.cube.Cube) -> bool:
210 """Check that any cell methods are "mean"."""
211 return set(cm.method for cm in cube.cell_methods) == {"mean"}
213 if varname:
214 # Require number_of_lightning_flashes to be "sum" cell_method input.
215 # Require surface_microphyisical_rainfall_amount and surface_microphysical_snowfall_amount to be "sum" cell_method inputs.
216 if ("lightning" in varname) or (
217 "surface_microphysical" in varname and "amount" in varname
218 ):
219 cell_methods_constraint = iris.Constraint(cube_func=check_cell_sum)
220 return cell_methods_constraint
221 # Require climatological ancillary as time-average mean.
222 if ("albedo" in varname) or ( 222 ↛ 229line 222 didn't jump to line 229 because the condition on line 222 was always true
223 "ocean" in varname and "chlorophyll" in varname
224 ):
225 cell_methods_constraint = iris.Constraint(cube_func=check_cell_mean)
226 return cell_methods_constraint
228 # If no variable name set, assume require instantaneous cube.
229 cell_methods_constraint = iris.Constraint(cube_func=check_no_aggregation)
231 else:
232 # If cell_method constraint set in recipe, check for required input.
233 def check_cell_methods(cube: iris.cube.Cube) -> bool:
234 return all(
235 iris.coords.CellMethod(
236 method=cm, coords=coord, intervals=interval, comments=comment
237 )
238 in cube.cell_methods
239 for cm in cell_methods
240 )
242 cell_methods_constraint = iris.Constraint(cube_func=check_cell_methods)
244 return cell_methods_constraint
247def generate_time_constraint(
248 time_start: str, time_end: str = None, **kwargs
249) -> iris.Constraint:
250 """Generate constraint between times.
252 Operator that takes one or two ISO 8601 date strings, and returns a
253 constraint that selects values between those dates (inclusive).
255 Arguments
256 ---------
257 time_start: str | datetime.datetime | cftime.datetime
258 ISO date for lower bound
260 time_end: str | datetime.datetime | cftime.datetime
261 ISO date for upper bound. If omitted it defaults to the same as
262 time_start
264 Returns
265 -------
266 time_constraint: iris.Constraint
267 """
268 if isinstance(time_start, str):
269 pdt_start, offset_start = operator_utils.pdt_fromisoformat(time_start)
270 else:
271 pdt_start, offset_start = time_start, timedelta(0)
273 if time_end is None:
274 pdt_end, offset_end = time_start, offset_start
275 elif isinstance(time_end, str):
276 pdt_end, offset_end = operator_utils.pdt_fromisoformat(time_end)
277 print(pdt_end)
278 print(offset_end)
279 else:
280 pdt_end, offset_end = time_end, timedelta(0)
282 if offset_start is None:
283 offset_start = timedelta(0)
284 if offset_end is None:
285 offset_end = timedelta(0)
287 time_constraint = iris.Constraint(
288 time=lambda t: (
289 (pdt_start <= (t.point - offset_start))
290 and ((t.point - offset_end) <= pdt_end)
291 )
292 )
294 return time_constraint
297def generate_area_constraint(
298 lat_start: float | None,
299 lat_end: float | None,
300 lon_start: float | None,
301 lon_end: float | None,
302 **kwargs,
303) -> iris.Constraint:
304 """Generate an area constraint between latitude/longitude limits.
306 Operator that takes a set of latitude and longitude limits and returns a
307 constraint that selects grid values only inside that area. Works with the
308 data's native grid so is defined within the rotated pole CRS.
310 Alternatively, all arguments may be None to indicate the area should not be
311 constrained. This is useful to allow making subsetting an optional step in a
312 processing pipeline.
314 Arguments
315 ---------
316 lat_start: float | None
317 Latitude value for lower bound
318 lat_end: float | None
319 Latitude value for top bound
320 lon_start: float | None
321 Longitude value for left bound
322 lon_end: float | None
323 Longitude value for right bound
325 Returns
326 -------
327 area_constraint: iris.Constraint
328 """
329 # Check all arguments are defined, or all are None.
330 if not (
331 all(
332 (
333 isinstance(lat_start, numbers.Real),
334 isinstance(lat_end, numbers.Real),
335 isinstance(lon_start, numbers.Real),
336 isinstance(lon_end, numbers.Real),
337 )
338 )
339 or all((lat_start is None, lat_end is None, lon_start is None, lon_end is None))
340 ):
341 raise TypeError("Bounds must real numbers, or all None.")
343 # Don't constrain area if all arguments are None.
344 if lat_start is None: # Only need to check once, as they will be the same.
345 # An empty constraint allows everything.
346 return iris.Constraint()
348 # Handle bounds crossing the date line.
349 if lon_end < lon_start: 349 ↛ 350line 349 didn't jump to line 350 because the condition on line 349 was never true
350 lon_end = lon_end + 360
352 def bound_lat(cell: iris.coords.Cell) -> bool:
353 return lat_start < cell < lat_end
355 def bound_lon(cell: iris.coords.Cell) -> bool:
356 # Adjust cell values to handle crossing the date line.
357 if cell < lon_start:
358 cell = cell + 360
359 return lon_start < cell < lon_end
361 area_constraint = iris.Constraint(
362 coord_values={"grid_latitude": bound_lat, "grid_longitude": bound_lon}
363 )
364 return area_constraint
367def generate_remove_single_ensemble_member_constraint(
368 ensemble_member: int = 0, **kwargs
369) -> iris.Constraint:
370 """
371 Generate a constraint to remove a single ensemble member.
373 Operator that returns a constraint to remove the given ensemble member. By
374 default the ensemble member removed is the control member (assumed to have
375 a realization of zero). However, any ensemble member can be removed, thus
376 allowing a non-zero control member to be removed if the control is a
377 different member.
379 Arguments
380 ---------
381 ensemble_member: int
382 Default is 0. The ensemble member realization to remove.
384 Returns
385 -------
386 iris.Constraint
388 Notes
389 -----
390 This operator is primarily used to remove the control member to allow
391 ensemble metrics to be calculated without the control member. For
392 example, the ensemble mean is not normally calculated including the
393 control member. It is particularly useful to remove the control member
394 when it is not an equally-likely member of the ensemble.
395 """
396 return iris.Constraint(realization=lambda m: m.point != ensemble_member)
399def generate_realization_constraint(
400 ensemble_members: int | list[int], **kwargs
401) -> iris.Constraint:
402 """
403 Generate a constraint to subset ensemble members.
405 Operator that is given a list of ensemble members and returns a constraint
406 to select those ensemble members. This operator is particularly useful for
407 subsetting ensembles.
409 Arguments
410 ---------
411 ensemble_members: int | list[int]
412 The ensemble members to be subsetted over.
414 Returns
415 -------
416 iris.Constraint
417 """
418 # Ensure ensemble_members is iterable.
419 ensemble_members = iter_maybe(ensemble_members)
420 return iris.Constraint(realization=ensemble_members)
423def generate_hour_constraint(
424 hour_start: int,
425 hour_end: int = None,
426 **kwargs,
427) -> iris.Constraint:
428 """Generate an hour constraint between hour of day limits.
430 Operator that takes a set of hour of day limits and returns a constraint that
431 selects only hours within that time frame regardless of day.
433 Alternatively, the result can be constrained to a single hour by just entering
434 a starting hour.
436 Should any sub-hourly data be given these will have the same hour coordinate
437 (e.g., 12:00 and 12:05 both have an hour coordinate of 12) all
438 times will be selected with this constraint.
440 Arguments
441 ---------
442 hour_start: int
443 The hour of day for the lower bound, within 0 to 23.
444 hour_end: int | None
445 The hour of day for the upper bound, within 0 to 23. Alternatively,
446 set to None if only one hour required.
448 Returns
449 -------
450 hour_constraint: iris.Constraint
452 Raises
453 ------
454 ValueError
455 If the provided arguments are outside of the range 0 to 23.
456 """
457 if hour_end is None:
458 hour_end = hour_start
460 if (hour_start < 0) or (hour_start > 23) or (hour_end < 0) or (hour_end > 23):
461 raise ValueError("Hours must be between 0 and 23 inclusive.")
463 hour_constraint = iris.Constraint(hour=lambda h: hour_start <= h.point <= hour_end)
464 return hour_constraint
467def combine_constraints(
468 constraint: iris.Constraint = None, **kwargs
469) -> iris.Constraint:
470 """
471 Operator that combines multiple constraints into one.
473 Arguments
474 ---------
475 constraint: iris.Constraint
476 First constraint to combine.
477 additional_constraint_1: iris.Constraint
478 Second constraint to combine. This must be a named argument.
479 additional_constraint_2: iris.Constraint
480 There can be any number of additional constraint, they just need unique
481 names.
482 ...
484 Returns
485 -------
486 combined_constraint: iris.Constraint
488 Raises
489 ------
490 TypeError
491 If the provided arguments are not constraints.
492 """
493 # If the first argument is not a constraint, it is ignored. This handles the
494 # automatic passing of the previous step's output.
495 if isinstance(constraint, iris.Constraint):
496 combined_constraint = constraint
497 else:
498 combined_constraint = iris.Constraint()
500 for constr in kwargs.values():
501 combined_constraint = combined_constraint & constr
502 return combined_constraint
505def generate_attribute_constraint(
506 attribute: str, value: str = None, **kwargs
507) -> iris.AttributeConstraint:
508 """Generate constraint on cube attributes.
510 Constrains based on the presence of an attribute, and that attribute having
511 a particular value.
513 Arguments
514 ---------
515 attribute: str
516 Attribute to constraint on.
518 value: str
519 Attribute value to constrain on. If omitted the constraint merely checks
520 for the presence of an attribute.
522 Returns
523 -------
524 attribute_constraint: iris.Constraint
525 """
526 if value is None:
527 attribute_constraint = iris.Constraint(
528 cube_func=lambda cube: attribute in cube.attributes
529 )
530 else:
531 attribute_constraint = iris.AttributeConstraint(**{attribute: value})
532 return attribute_constraint