Coverage for python/lsst/daf/butler/core/datasets/type.py : 18%

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1# This file is part of daf_butler.
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (http://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This program is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
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
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
19# You should have received a copy of the GNU General Public License
20# along with this program. If not, see <http://www.gnu.org/licenses/>.
22from __future__ import annotations
24__all__ = ["DatasetType"]
26from copy import deepcopy
27import re
29from types import MappingProxyType
31from typing import (
32 TYPE_CHECKING,
33 Any,
34 Callable,
35 Dict,
36 Iterable,
37 List,
38 Mapping,
39 Optional,
40 Tuple,
41 Type,
42 Union,
43)
46from ..storageClass import StorageClass, StorageClassFactory
47from ..dimensions import DimensionGraph
48from ..configSupport import LookupKey
50if TYPE_CHECKING: 50 ↛ 51line 50 didn't jump to line 51, because the condition on line 50 was never true
51 from ..dimensions import Dimension, DimensionUniverse
54def _safeMakeMappingProxyType(data: Optional[Mapping]) -> Mapping:
55 if data is None:
56 data = {}
57 return MappingProxyType(data)
60class DatasetType:
61 r"""A named category of Datasets that defines how they are organized,
62 related, and stored.
64 A concrete, final class whose instances represent `DatasetType`\ s.
65 `DatasetType` instances may be constructed without a `Registry`,
66 but they must be registered
67 via `Registry.registerDatasetType()` before corresponding Datasets
68 may be added.
69 `DatasetType` instances are immutable.
71 Parameters
72 ----------
73 name : `str`
74 A string name for the Dataset; must correspond to the same
75 `DatasetType` across all Registries. Names must start with an
76 upper or lowercase letter, and may contain only letters, numbers,
77 and underscores. Component dataset types should contain a single
78 period separating the base dataset type name from the component name
79 (and may be recursive).
80 dimensions : `DimensionGraph` or iterable of `Dimension`
81 Dimensions used to label and relate instances of this `DatasetType`.
82 If not a `DimensionGraph`, ``universe`` must be provided as well.
83 storageClass : `StorageClass` or `str`
84 Instance of a `StorageClass` or name of `StorageClass` that defines
85 how this `DatasetType` is persisted.
86 parentStorageClass : `StorageClass` or `str`, optional
87 Instance of a `StorageClass` or name of `StorageClass` that defines
88 how the composite parent is persisted. Must be `None` if this
89 is not a component. Mandatory if it is a component but can be the
90 special temporary placeholder
91 (`DatasetType.PlaceholderParentStorageClass`) to allow
92 construction with an intent to finalize later.
93 universe : `DimensionUniverse`, optional
94 Set of all known dimensions, used to normalize ``dimensions`` if it
95 is not already a `DimensionGraph`.
96 isCalibration : `bool`, optional
97 If `True`, this dataset type may be included in
98 `~CollectionType.CALIBRATION` collections.
99 """
101 __slots__ = ("_name", "_dimensions", "_storageClass", "_storageClassName",
102 "_parentStorageClass", "_parentStorageClassName",
103 "_isCalibration")
105 VALID_NAME_REGEX = re.compile("^[a-zA-Z][a-zA-Z0-9_]*(\\.[a-zA-Z][a-zA-Z0-9_]*)*$")
107 PlaceholderParentStorageClass = StorageClass("PlaceHolder")
108 """Placeholder StorageClass that can be used temporarily for a
109 component.
111 This can be useful in pipeline construction where we are creating
112 dataset types without a registry.
113 """
115 @staticmethod
116 def nameWithComponent(datasetTypeName: str, componentName: str) -> str:
117 """Form a valid DatasetTypeName from a parent and component.
119 No validation is performed.
121 Parameters
122 ----------
123 datasetTypeName : `str`
124 Base type name.
125 componentName : `str`
126 Name of component.
128 Returns
129 -------
130 compTypeName : `str`
131 Name to use for component DatasetType.
132 """
133 return "{}.{}".format(datasetTypeName, componentName)
135 def __init__(self, name: str, dimensions: Union[DimensionGraph, Iterable[Dimension]],
136 storageClass: Union[StorageClass, str],
137 parentStorageClass: Optional[Union[StorageClass, str]] = None, *,
138 universe: Optional[DimensionUniverse] = None,
139 isCalibration: bool = False):
140 if self.VALID_NAME_REGEX.match(name) is None:
141 raise ValueError(f"DatasetType name '{name}' is invalid.")
142 self._name = name
143 if not isinstance(dimensions, DimensionGraph):
144 if universe is None:
145 raise ValueError("If dimensions is not a normalized DimensionGraph, "
146 "a universe must be provided.")
147 dimensions = universe.extract(dimensions)
148 self._dimensions = dimensions
149 if name in self._dimensions.universe.getGovernorDimensions().names:
150 raise ValueError(f"Governor dimension name {name} cannot be used as a dataset type name.")
151 if not isinstance(storageClass, (StorageClass, str)):
152 raise ValueError("StorageClass argument must be StorageClass or str. "
153 f"Got {storageClass}")
154 self._storageClass: Optional[StorageClass]
155 if isinstance(storageClass, StorageClass):
156 self._storageClass = storageClass
157 self._storageClassName = storageClass.name
158 else:
159 self._storageClass = None
160 self._storageClassName = storageClass
162 self._parentStorageClass: Optional[StorageClass] = None
163 self._parentStorageClassName: Optional[str] = None
164 if parentStorageClass is not None:
165 if not isinstance(storageClass, (StorageClass, str)):
166 raise ValueError("Parent StorageClass argument must be StorageClass or str. "
167 f"Got {parentStorageClass}")
169 # Only allowed for a component dataset type
170 _, componentName = self.splitDatasetTypeName(self._name)
171 if componentName is None:
172 raise ValueError("Can not specify a parent storage class if this is not a component"
173 f" ({self._name})")
174 if isinstance(parentStorageClass, StorageClass):
175 self._parentStorageClass = parentStorageClass
176 self._parentStorageClassName = parentStorageClass.name
177 else:
178 self._parentStorageClassName = parentStorageClass
180 # Ensure that parent storage class is specified when we have
181 # a component and is not specified when we don't
182 _, componentName = self.splitDatasetTypeName(self._name)
183 if parentStorageClass is None and componentName is not None:
184 raise ValueError(f"Component dataset type '{self._name}' constructed without parent"
185 " storage class")
186 if parentStorageClass is not None and componentName is None:
187 raise ValueError(f"Parent storage class specified by {self._name} is not a composite")
188 self._isCalibration = isCalibration
190 def __repr__(self) -> str:
191 extra = ""
192 if self._parentStorageClassName:
193 extra = f", parentStorageClass={self._parentStorageClassName}"
194 if self._isCalibration:
195 extra += ", isCalibration=True"
196 return f"DatasetType({self.name!r}, {self.dimensions}, {self._storageClassName}{extra})"
198 def __eq__(self, other: Any) -> bool:
199 if not isinstance(other, type(self)):
200 return False
201 if self._name != other._name:
202 return False
203 if self._dimensions != other._dimensions:
204 return False
205 if self._storageClass is not None and other._storageClass is not None:
206 if self._storageClass != other._storageClass:
207 return False
208 else:
209 if self._storageClassName != other._storageClassName:
210 return False
211 if self._isCalibration != other._isCalibration:
212 return False
213 if self._parentStorageClass is not None and other._parentStorageClass is not None:
214 return self._parentStorageClass == other._parentStorageClass
215 else:
216 return self._parentStorageClassName == other._parentStorageClassName
218 def __hash__(self) -> int:
219 """Hash DatasetType instance.
221 This only uses StorageClass name which is it consistent with the
222 implementation of StorageClass hash method.
223 """
224 return hash((self._name, self._dimensions, self._storageClassName,
225 self._parentStorageClassName))
227 def __lt__(self, other: Any) -> bool:
228 """Sort using the dataset type name.
229 """
230 if not isinstance(other, type(self)):
231 return NotImplemented
232 return self.name < other.name
234 @property
235 def name(self) -> str:
236 """A string name for the Dataset; must correspond to the same
237 `DatasetType` across all Registries.
238 """
239 return self._name
241 @property
242 def dimensions(self) -> DimensionGraph:
243 r"""The `Dimension`\ s that label and relate instances of this
244 `DatasetType` (`DimensionGraph`).
245 """
246 return self._dimensions
248 @property
249 def storageClass(self) -> StorageClass:
250 """`StorageClass` instance that defines how this `DatasetType`
251 is persisted. Note that if DatasetType was constructed with a name
252 of a StorageClass then Butler has to be initialized before using
253 this property.
254 """
255 if self._storageClass is None:
256 self._storageClass = StorageClassFactory().getStorageClass(self._storageClassName)
257 return self._storageClass
259 @property
260 def parentStorageClass(self) -> Optional[StorageClass]:
261 """`StorageClass` instance that defines how the composite associated
262 with this `DatasetType` is persisted.
264 Note that if DatasetType was constructed with a name of a
265 StorageClass then Butler has to be initialized before using this
266 property. Can be `None` if this is not a component of a composite.
267 Must be defined if this is a component.
268 """
269 if self._parentStorageClass is None and self._parentStorageClassName is None:
270 return None
271 if self._parentStorageClass is None and self._parentStorageClassName is not None:
272 self._parentStorageClass = StorageClassFactory().getStorageClass(self._parentStorageClassName)
273 return self._parentStorageClass
275 def isCalibration(self) -> bool:
276 """Return whether datasets of this type may be included in calibration
277 collections.
279 Returns
280 -------
281 flag : `bool`
282 `True` if datasets of this type may be included in calibration
283 collections.
284 """
285 return self._isCalibration
287 def finalizeParentStorageClass(self, newParent: StorageClass) -> None:
288 """Replace the current placeholder parent storage class with
289 the real parent.
291 Parameters
292 ----------
293 newParent : `StorageClass`
294 The new parent to be associated with this composite dataset
295 type. This replaces the temporary placeholder parent that
296 was specified during construction.
298 Raises
299 ------
300 ValueError
301 Raised if this dataset type is not a component of a composite.
302 Raised if a StorageClass is not given.
303 Raised if the parent currently associated with the dataset
304 type is not a placeholder.
305 """
306 if not self.isComponent():
307 raise ValueError("Can not set a parent storage class if this is not a component"
308 f" ({self.name})")
309 if self._parentStorageClass != self.PlaceholderParentStorageClass:
310 raise ValueError(f"This DatasetType has a parent of {self._parentStorageClassName} and"
311 " is not a placeholder.")
312 if not isinstance(newParent, StorageClass):
313 raise ValueError(f"Supplied parent must be a StorageClass. Got {newParent!r}")
314 self._parentStorageClass = newParent
315 self._parentStorageClassName = newParent.name
317 @staticmethod
318 def splitDatasetTypeName(datasetTypeName: str) -> Tuple[str, Optional[str]]:
319 """Given a dataset type name, return the root name and the component
320 name.
322 Parameters
323 ----------
324 datasetTypeName : `str`
325 The name of the dataset type, can include a component using
326 a "."-separator.
328 Returns
329 -------
330 rootName : `str`
331 Root name without any components.
332 componentName : `str`
333 The component if it has been specified, else `None`.
335 Notes
336 -----
337 If the dataset type name is ``a.b.c`` this method will return a
338 root name of ``a`` and a component name of ``b.c``.
339 """
340 comp = None
341 root = datasetTypeName
342 if "." in root:
343 # If there is doubt, the component is after the first "."
344 root, comp = root.split(".", maxsplit=1)
345 return root, comp
347 def nameAndComponent(self) -> Tuple[str, Optional[str]]:
348 """Return the root name of this dataset type and the component
349 name (if defined).
351 Returns
352 -------
353 rootName : `str`
354 Root name for this `DatasetType` without any components.
355 componentName : `str`
356 The component if it has been specified, else `None`.
357 """
358 return self.splitDatasetTypeName(self.name)
360 def component(self) -> Optional[str]:
361 """Component name (if defined)
363 Returns
364 -------
365 comp : `str`
366 Name of component part of DatasetType name. `None` if this
367 `DatasetType` is not associated with a component.
368 """
369 _, comp = self.nameAndComponent()
370 return comp
372 def componentTypeName(self, component: str) -> str:
373 """Given a component name, derive the datasetTypeName of that component
375 Parameters
376 ----------
377 component : `str`
378 Name of component
380 Returns
381 -------
382 derived : `str`
383 Compound name of this `DatasetType` and the component.
385 Raises
386 ------
387 KeyError
388 Requested component is not supported by this `DatasetType`.
389 """
390 if component in self.storageClass.allComponents():
391 return self.nameWithComponent(self.name, component)
392 raise KeyError("Requested component ({}) not understood by this DatasetType".format(component))
394 def makeComponentDatasetType(self, component: str) -> DatasetType:
395 """Return a DatasetType suitable for the given component, assuming the
396 same dimensions as the parent.
398 Parameters
399 ----------
400 component : `str`
401 Name of component
403 Returns
404 -------
405 datasetType : `DatasetType`
406 A new DatasetType instance.
407 """
408 # The component could be a read/write or read component
409 return DatasetType(self.componentTypeName(component), dimensions=self.dimensions,
410 storageClass=self.storageClass.allComponents()[component],
411 parentStorageClass=self.storageClass)
413 def makeAllComponentDatasetTypes(self) -> List[DatasetType]:
414 """Return all the component dataset types assocaited with this
415 dataset type.
417 Returns
418 -------
419 all : `list` of `DatasetType`
420 All the component dataset types. If this is not a composite
421 then returns an empty list.
422 """
423 return [self.makeComponentDatasetType(componentName)
424 for componentName in self.storageClass.allComponents()]
426 def isComponent(self) -> bool:
427 """Boolean indicating whether this `DatasetType` refers to a
428 component of a composite.
430 Returns
431 -------
432 isComponent : `bool`
433 `True` if this `DatasetType` is a component, `False` otherwise.
434 """
435 if self.component():
436 return True
437 return False
439 def isComposite(self) -> bool:
440 """Boolean indicating whether this `DatasetType` is a composite type.
442 Returns
443 -------
444 isComposite : `bool`
445 `True` if this `DatasetType` is a composite type, `False`
446 otherwise.
447 """
448 return self.storageClass.isComposite()
450 def _lookupNames(self) -> Tuple[LookupKey, ...]:
451 """Name keys to use when looking up this datasetType in a
452 configuration.
454 The names are returned in order of priority.
456 Returns
457 -------
458 names : `tuple` of `LookupKey`
459 Tuple of the `DatasetType` name and the `StorageClass` name.
460 If the name includes a component the name with the component
461 is first, then the name without the component and finally
462 the storage class name.
463 """
464 rootName, componentName = self.nameAndComponent()
465 lookups: Tuple[LookupKey, ...] = (LookupKey(name=self.name),)
466 if componentName is not None:
467 lookups = lookups + (LookupKey(name=rootName),)
469 if self.dimensions:
470 # Dimensions are a lower priority than dataset type name
471 lookups = lookups + (LookupKey(dimensions=self.dimensions),)
473 return lookups + self.storageClass._lookupNames()
475 def __reduce__(self) -> Tuple[Callable, Tuple[Type[DatasetType],
476 Tuple[str, DimensionGraph, str, Optional[str]],
477 Dict[str, bool]]]:
478 """Support pickling.
480 StorageClass instances can not normally be pickled, so we pickle
481 StorageClass name instead of instance.
482 """
483 return _unpickle_via_factory, (self.__class__, (self.name, self.dimensions, self._storageClassName,
484 self._parentStorageClassName),
485 {"isCalibration": self._isCalibration})
487 def __deepcopy__(self, memo: Any) -> DatasetType:
488 """Support for deep copy method.
490 Normally ``deepcopy`` will use pickle mechanism to make copies.
491 We want to avoid that to support (possibly degenerate) use case when
492 DatasetType is constructed with StorageClass instance which is not
493 registered with StorageClassFactory (this happens in unit tests).
494 Instead we re-implement ``__deepcopy__`` method.
495 """
496 return DatasetType(name=deepcopy(self.name, memo),
497 dimensions=deepcopy(self.dimensions, memo),
498 storageClass=deepcopy(self._storageClass or self._storageClassName, memo),
499 parentStorageClass=deepcopy(self._parentStorageClass
500 or self._parentStorageClassName, memo),
501 isCalibration=deepcopy(self._isCalibration, memo))
504def _unpickle_via_factory(factory: Callable, args: Any, kwargs: Any) -> DatasetType:
505 """Unpickle something by calling a factory
507 Allows subclasses to unpickle using `__reduce__` with keyword
508 arguments as well as positional arguments.
509 """
510 return factory(*args, **kwargs)