Coverage for python/lsst/pipe/base/pipelineIR.py: 19%
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1# This file is part of pipe_base.
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/>.
21from __future__ import annotations
23__all__ = ("ConfigIR", "ContractError", "ContractIR", "ImportIR", "PipelineIR", "TaskIR", "LabeledSubset")
25import copy
26import enum
27import os
28import re
29import warnings
30from collections import Counter
31from collections.abc import Generator, Hashable, Iterable, MutableMapping
32from dataclasses import dataclass, field
33from typing import Any, Literal
35import yaml
36from lsst.resources import ResourcePath, ResourcePathExpression
39class _Tags(enum.Enum):
40 KeepInstrument = enum.auto()
43class PipelineYamlLoader(yaml.SafeLoader):
44 """Specialized version of yaml's SafeLoader.
46 It checks and raises an exception if it finds that there are multiple
47 instances of the same key found inside a pipeline file at a given scope.
48 """
50 def construct_mapping(self, node: yaml.MappingNode, deep: bool = False) -> dict[Hashable, Any]:
51 # do the call to super first so that it can do all the other forms of
52 # checking on this node. If you check the uniqueness of keys first
53 # it would save the work that super does in the case of a failure, but
54 # it might fail in the case that the node was the incorrect node due
55 # to a parsing error, and the resulting exception would be difficult to
56 # understand.
57 mapping = super().construct_mapping(node, deep)
58 # Check if there are any duplicate keys
59 all_keys = Counter(key_node.value for key_node, _ in node.value)
60 duplicates = {k for k, i in all_keys.items() if i != 1}
61 if duplicates:
62 raise KeyError(
63 f"Pipeline files must not have duplicated keys, {duplicates} appeared multiple times"
64 )
65 return mapping
68class MultilineStringDumper(yaml.Dumper):
69 """Custom YAML dumper that makes multi-line strings use the '|'
70 continuation style instead of unreadable newlines and tons of quotes.
72 Basic approach is taken from
73 https://stackoverflow.com/questions/8640959/how-can-i-control-what-scalar-form-pyyaml-uses-for-my-data,
74 but is written as a Dumper subclass to make its effects non-global (vs
75 `yaml.add_representer`).
76 """
78 def represent_scalar(self, tag: str, value: Any, style: str | None = None) -> yaml.ScalarNode:
79 if style is None and tag == "tag:yaml.org,2002:str" and len(value.splitlines()) > 1:
80 style = "|"
81 return super().represent_scalar(tag, value, style)
84class ContractError(Exception):
85 """An exception that is raised when a pipeline contract is not satisfied"""
87 pass
90@dataclass
91class ContractIR:
92 """Intermediate representation of configuration contracts read from a
93 pipeline yaml file.
94 """
96 contract: str
97 """A string of python code representing one or more conditions on configs
98 in a pipeline. This code-as-string should, once evaluated, should be True
99 if the configs are fine, and False otherwise.
100 """
101 msg: str | None = None
102 """An optional message to be shown to the user if a contract fails
103 """
105 def to_primitives(self) -> dict[str, str]:
106 """Convert to a representation used in yaml serialization"""
107 accumulate = {"contract": self.contract}
108 if self.msg is not None:
109 accumulate["msg"] = self.msg
110 return accumulate
112 def __eq__(self, other: object) -> bool:
113 if not isinstance(other, ContractIR):
114 return False
115 elif self.contract == other.contract and self.msg == other.msg:
116 return True
117 else:
118 return False
121@dataclass
122class LabeledSubset:
123 """Intermediate representation of named subset of task labels read from
124 a pipeline yaml file.
125 """
127 label: str
128 """The label used to identify the subset of task labels.
129 """
130 subset: set[str]
131 """A set of task labels contained in this subset.
132 """
133 description: str | None
134 """A description of what this subset of tasks is intended to do
135 """
137 @staticmethod
138 def from_primitives(label: str, value: list[str] | dict) -> LabeledSubset:
139 """Generate `LabeledSubset` objects given a properly formatted object
140 that as been created by a yaml loader.
142 Parameters
143 ----------
144 label : `str`
145 The label that will be used to identify this labeled subset.
146 value : `list` of `str` or `dict`
147 Object returned from loading a labeled subset section from a yaml
148 document.
150 Returns
151 -------
152 labeledSubset : `LabeledSubset`
153 A `LabeledSubset` object build from the inputs.
155 Raises
156 ------
157 ValueError
158 Raised if the value input is not properly formatted for parsing
159 """
160 if isinstance(value, MutableMapping):
161 subset = value.pop("subset", None)
162 if subset is None:
163 raise ValueError(
164 "If a labeled subset is specified as a mapping, it must contain the key 'subset'"
165 )
166 description = value.pop("description", None)
167 elif isinstance(value, Iterable):
168 subset = value
169 description = None
170 else:
171 raise ValueError(
172 f"There was a problem parsing the labeled subset {label}, make sure the "
173 "definition is either a valid yaml list, or a mapping with keys "
174 "(subset, description) where subset points to a yaml list, and description is "
175 "associated with a string"
176 )
177 return LabeledSubset(label, set(subset), description)
179 def to_primitives(self) -> dict[str, list[str] | str]:
180 """Convert to a representation used in yaml serialization."""
181 accumulate: dict[str, list[str] | str] = {"subset": list(self.subset)}
182 if self.description is not None:
183 accumulate["description"] = self.description
184 return accumulate
187@dataclass
188class ParametersIR:
189 """Intermediate representation of parameters that are global to a pipeline
191 These parameters are specified under a top level key named ``parameters``
192 and are declared as a yaml mapping. These entries can then be used inside
193 task configuration blocks to specify configuration values. They may not be
194 used in the special ``file`` or ``python`` blocks.
196 Examples
197 --------
198 .. code-block:: yaml
200 parameters:
201 shared_value: 14
202 tasks:
203 taskA:
204 class: modA
205 config:
206 field1: parameters.shared_value
207 taskB:
208 class: modB
209 config:
210 field2: parameters.shared_value
211 """
213 mapping: MutableMapping[str, str]
214 """A mutable mapping of identifiers as keys, and shared configuration
215 as values.
216 """
218 def update(self, other: ParametersIR | None) -> None:
219 if other is not None:
220 self.mapping.update(other.mapping)
222 def to_primitives(self) -> MutableMapping[str, str]:
223 """Convert to a representation used in yaml serialization"""
224 return self.mapping
226 def __contains__(self, value: str) -> bool:
227 return value in self.mapping
229 def __getitem__(self, item: str) -> Any:
230 return self.mapping[item]
232 def __bool__(self) -> bool:
233 return bool(self.mapping)
236@dataclass
237class ConfigIR:
238 """Intermediate representation of configurations read from a pipeline yaml
239 file.
240 """
242 python: str | None = None
243 """A string of python code that is used to modify a configuration. This can
244 also be None if there are no modifications to do.
245 """
246 dataId: dict | None = None
247 """A dataId that is used to constrain these config overrides to only quanta
248 with matching dataIds. This field can be None if there is no constraint.
249 This is currently an unimplemented feature, and is placed here for future
250 use.
251 """
252 file: list[str] = field(default_factory=list)
253 """A list of paths which points to a file containing config overrides to be
254 applied. This value may be an empty list if there are no overrides to
255 apply.
256 """
257 rest: dict = field(default_factory=dict)
258 """This is a dictionary of key value pairs, where the keys are strings
259 corresponding to qualified fields on a config to override, and the values
260 are strings representing the values to apply.
261 """
263 def to_primitives(self) -> dict[str, str | dict | list[str]]:
264 """Convert to a representation used in yaml serialization"""
265 accumulate = {}
266 for name in ("python", "dataId", "file"):
267 # if this attribute is thruthy add it to the accumulation
268 # dictionary
269 if getattr(self, name):
270 accumulate[name] = getattr(self, name)
271 # Add the dictionary containing the rest of the config keys to the
272 # # accumulated dictionary
273 accumulate.update(self.rest)
274 return accumulate
276 def formatted(self, parameters: ParametersIR) -> ConfigIR:
277 """Return a new ConfigIR object that is formatted according to the
278 specified parameters
280 Parameters
281 ----------
282 parameters : ParametersIR
283 Object that contains variable mappings used in substitution.
285 Returns
286 -------
287 config : ConfigIR
288 A new ConfigIR object formatted with the input parameters
289 """
290 new_config = copy.deepcopy(self)
291 for key, value in new_config.rest.items():
292 if not isinstance(value, str):
293 continue
294 match = re.match("parameters[.](.*)", value)
295 if match and match.group(1) in parameters:
296 new_config.rest[key] = parameters[match.group(1)]
297 if match and match.group(1) not in parameters:
298 warnings.warn(
299 f"config {key} contains value {match.group(0)} which is formatted like a "
300 "Pipeline parameter but was not found within the Pipeline, if this was not "
301 "intentional, check for a typo"
302 )
303 return new_config
305 def maybe_merge(self, other_config: "ConfigIR") -> Generator["ConfigIR", None, None]:
306 """Merge another instance of a `ConfigIR` into this instance if
307 possible. This function returns a generator that is either self
308 if the configs were merged, or self, and other_config if that could
309 not be merged.
311 Parameters
312 ----------
313 other_config : `ConfigIR`
314 An instance of `ConfigIR` to merge into this instance.
316 Returns
317 -------
318 Generator : `ConfigIR`
319 A generator containing either self, or self and other_config if
320 the configs could be merged or not respectively.
321 """
322 # Verify that the config blocks can be merged
323 if (
324 self.dataId != other_config.dataId
325 or self.python
326 or other_config.python
327 or self.file
328 or other_config.file
329 ):
330 yield from (self, other_config)
331 return
333 # create a set of all keys, and verify two keys do not have different
334 # values
335 key_union = self.rest.keys() & other_config.rest.keys()
336 for key in key_union:
337 if self.rest[key] != other_config.rest[key]:
338 yield from (self, other_config)
339 return
340 self.rest.update(other_config.rest)
342 # Combine the lists of override files to load
343 self_file_set = set(self.file)
344 other_file_set = set(other_config.file)
345 self.file = list(self_file_set.union(other_file_set))
347 yield self
349 def __eq__(self, other: object) -> bool:
350 if not isinstance(other, ConfigIR):
351 return False
352 elif all(
353 getattr(self, attr) == getattr(other, attr) for attr in ("python", "dataId", "file", "rest")
354 ):
355 return True
356 else:
357 return False
360@dataclass
361class TaskIR:
362 """Intermediate representation of tasks read from a pipeline yaml file."""
364 label: str
365 """An identifier used to refer to a task.
366 """
367 klass: str
368 """A string containing a fully qualified python class to be run in a
369 pipeline.
370 """
371 config: list[ConfigIR] | None = None
372 """list of all configs overrides associated with this task, and may be
373 `None` if there are no config overrides.
374 """
376 def to_primitives(self) -> dict[str, str | list[dict]]:
377 """Convert to a representation used in yaml serialization"""
378 accumulate: dict[str, str | list[dict]] = {"class": self.klass}
379 if self.config:
380 accumulate["config"] = [c.to_primitives() for c in self.config]
381 return accumulate
383 def add_or_update_config(self, other_config: ConfigIR) -> None:
384 """Add a `ConfigIR` to this task if one is not present. Merges configs
385 if there is a `ConfigIR` present and the dataId keys of both configs
386 match, otherwise adds a new entry to the config list. The exception to
387 the above is that if either the last config or other_config has a
388 python block, then other_config is always added, as python blocks can
389 modify configs in ways that cannot be predicted.
391 Parameters
392 ----------
393 other_config : `ConfigIR`
394 A `ConfigIR` instance to add or merge into the config attribute of
395 this task.
396 """
397 if not self.config:
398 self.config = [other_config]
399 return
400 self.config.extend(self.config.pop().maybe_merge(other_config))
402 def __eq__(self, other: object) -> bool:
403 if not isinstance(other, TaskIR):
404 return False
405 elif all(getattr(self, attr) == getattr(other, attr) for attr in ("label", "klass", "config")):
406 return True
407 else:
408 return False
411@dataclass
412class ImportIR:
413 """An intermediate representation of imported pipelines"""
415 location: str
416 """This is the location of the pipeline to inherit. The path should be
417 specified as an absolute path. Environment variables may be used in the
418 path and should be specified as a python string template, with the name of
419 the environment variable inside braces.
420 """
421 include: list[str] | None = None
422 """list of tasks that should be included when inheriting this pipeline.
423 Either the include or exclude attributes may be specified, but not both.
424 """
425 exclude: list[str] | None = None
426 """list of tasks that should be excluded when inheriting this pipeline.
427 Either the include or exclude attributes may be specified, but not both.
428 """
429 importContracts: bool = True
430 """Boolean attribute to dictate if contracts should be inherited with the
431 pipeline or not.
432 """
433 instrument: Literal[_Tags.KeepInstrument] | str | None = _Tags.KeepInstrument
434 """Instrument to assign to the Pipeline at import. The default value of
435 `_Tags.KeepInstrument`` indicates that whatever instrument the pipeline is
436 declared with will not be modified. setting this value to None will drop
437 any declared instrument prior to import.
438 """
440 def toPipelineIR(self) -> "PipelineIR":
441 """Load in the Pipeline specified by this object, and turn it into a
442 PipelineIR instance.
444 Returns
445 -------
446 pipeline : `PipelineIR`
447 A pipeline generated from the imported pipeline file
448 """
449 if self.include and self.exclude:
450 raise ValueError(
451 "An include list and an exclude list cannot both be specified"
452 " when declaring a pipeline import."
453 )
454 tmp_pipeline = PipelineIR.from_uri(os.path.expandvars(self.location))
455 if self.instrument is not _Tags.KeepInstrument:
456 tmp_pipeline.instrument = self.instrument
458 included_labels = set()
459 for label in tmp_pipeline.tasks:
460 if (
461 (self.include and label in self.include)
462 or (self.exclude and label not in self.exclude)
463 or (self.include is None and self.exclude is None)
464 ):
465 included_labels.add(label)
467 # Handle labeled subsets being specified in the include or exclude
468 # list, adding or removing labels.
469 if self.include is not None:
470 subsets_in_include = tmp_pipeline.labeled_subsets.keys() & self.include
471 for label in subsets_in_include:
472 included_labels.update(tmp_pipeline.labeled_subsets[label].subset)
474 elif self.exclude is not None:
475 subsets_in_exclude = tmp_pipeline.labeled_subsets.keys() & self.exclude
476 for label in subsets_in_exclude:
477 included_labels.difference_update(tmp_pipeline.labeled_subsets[label].subset)
479 tmp_pipeline = tmp_pipeline.subset_from_labels(included_labels)
481 if not self.importContracts:
482 tmp_pipeline.contracts = []
484 return tmp_pipeline
486 def __eq__(self, other: object) -> bool:
487 if not isinstance(other, ImportIR):
488 return False
489 elif all(
490 getattr(self, attr) == getattr(other, attr)
491 for attr in ("location", "include", "exclude", "importContracts")
492 ):
493 return True
494 else:
495 return False
498class PipelineIR:
499 """Intermediate representation of a pipeline definition
501 Parameters
502 ----------
503 loaded_yaml : `dict`
504 A dictionary which matches the structure that would be produced by a
505 yaml reader which parses a pipeline definition document
507 Raises
508 ------
509 ValueError
510 Raised if:
512 - a pipeline is declared without a description;
513 - no tasks are declared in a pipeline, and no pipelines are to be
514 inherited;
515 - more than one instrument is specified;
516 - more than one inherited pipeline share a label.
517 """
519 def __init__(self, loaded_yaml: dict[str, Any]):
520 # Check required fields are present
521 if "description" not in loaded_yaml:
522 raise ValueError("A pipeline must be declared with a description")
523 if "tasks" not in loaded_yaml and len({"imports", "inherits"} - loaded_yaml.keys()) == 2:
524 raise ValueError("A pipeline must be declared with one or more tasks")
526 # These steps below must happen in this call order
528 # Process pipeline description
529 self.description = loaded_yaml.pop("description")
531 # Process tasks
532 self._read_tasks(loaded_yaml)
534 # Process instrument keys
535 inst = loaded_yaml.pop("instrument", None)
536 if isinstance(inst, list):
537 raise ValueError("Only one top level instrument can be defined in a pipeline")
538 self.instrument: str | None = inst
540 # Process any contracts
541 self._read_contracts(loaded_yaml)
543 # Process any defined parameters
544 self._read_parameters(loaded_yaml)
546 # Process any named label subsets
547 self._read_labeled_subsets(loaded_yaml)
549 # Process any inherited pipelines
550 self._read_imports(loaded_yaml)
552 # verify named subsets, must be done after inheriting
553 self._verify_labeled_subsets()
555 def _read_contracts(self, loaded_yaml: dict[str, Any]) -> None:
556 """Process the contracts portion of the loaded yaml document
558 Parameters
559 ----------
560 loaded_yaml : `dict`
561 A dictionary which matches the structure that would be produced by
562 a yaml reader which parses a pipeline definition document
563 """
564 loaded_contracts = loaded_yaml.pop("contracts", [])
565 if isinstance(loaded_contracts, str):
566 loaded_contracts = [loaded_contracts]
567 self.contracts: list[ContractIR] = []
568 for contract in loaded_contracts:
569 if isinstance(contract, dict):
570 self.contracts.append(ContractIR(**contract))
571 if isinstance(contract, str):
572 self.contracts.append(ContractIR(contract=contract))
574 def _read_parameters(self, loaded_yaml: dict[str, Any]) -> None:
575 """Process the parameters portion of the loaded yaml document
577 Parameters
578 ----------
579 loaded_yaml : `dict`
580 A dictionary which matches the structure that would be produced by
581 a yaml reader which parses a pipeline definition document
582 """
583 loaded_parameters = loaded_yaml.pop("parameters", {})
584 if not isinstance(loaded_parameters, dict):
585 raise ValueError("The parameters section must be a yaml mapping")
586 self.parameters = ParametersIR(loaded_parameters)
588 def _read_labeled_subsets(self, loaded_yaml: dict[str, Any]) -> None:
589 """Process the subsets portion of the loaded yaml document
591 Parameters
592 ----------
593 loaded_yaml: `MutableMapping`
594 A dictionary which matches the structure that would be produced
595 by a yaml reader which parses a pipeline definition document
596 """
597 loaded_subsets = loaded_yaml.pop("subsets", {})
598 self.labeled_subsets: dict[str, LabeledSubset] = {}
599 if not loaded_subsets and "subset" in loaded_yaml:
600 raise ValueError("Top level key should be subsets and not subset, add an s")
601 for key, value in loaded_subsets.items():
602 self.labeled_subsets[key] = LabeledSubset.from_primitives(key, value)
604 def _verify_labeled_subsets(self) -> None:
605 """Verify that all the labels in each named subset exist within the
606 pipeline.
607 """
608 # Verify that all labels defined in a labeled subset are in the
609 # Pipeline
610 for labeled_subset in self.labeled_subsets.values():
611 if not labeled_subset.subset.issubset(self.tasks.keys()):
612 raise ValueError(
613 f"Labels {labeled_subset.subset - self.tasks.keys()} were not found in the "
614 "declared pipeline"
615 )
616 # Verify subset labels are not already task labels
617 label_intersection = self.labeled_subsets.keys() & self.tasks.keys()
618 if label_intersection:
619 raise ValueError(f"Labeled subsets can not use the same label as a task: {label_intersection}")
621 def _read_imports(self, loaded_yaml: dict[str, Any]) -> None:
622 """Process the inherits portion of the loaded yaml document
624 Parameters
625 ----------
626 loaded_yaml : `dict`
627 A dictionary which matches the structure that would be produced by
628 a yaml reader which parses a pipeline definition document
629 """
631 def process_args(argument: str | dict) -> dict:
632 if isinstance(argument, str):
633 return {"location": argument}
634 elif isinstance(argument, dict):
635 if "exclude" in argument and isinstance(argument["exclude"], str):
636 argument["exclude"] = [argument["exclude"]]
637 if "include" in argument and isinstance(argument["include"], str):
638 argument["include"] = [argument["include"]]
639 if "instrument" in argument and argument["instrument"] == "None":
640 argument["instrument"] = None
641 return argument
643 if not {"inherits", "imports"} - loaded_yaml.keys():
644 raise ValueError("Cannot define both inherits and imports sections, use imports")
645 tmp_import = loaded_yaml.pop("inherits", None)
646 if tmp_import is None:
647 tmp_import = loaded_yaml.pop("imports", None)
648 else:
649 raise ValueError("The 'inherits' key is not supported. Please use the key 'imports' instead")
650 if tmp_import is None:
651 self.imports: list[ImportIR] = []
652 elif isinstance(tmp_import, list):
653 self.imports = [ImportIR(**process_args(args)) for args in tmp_import]
654 else:
655 self.imports = [ImportIR(**process_args(tmp_import))]
657 self.merge_pipelines([fragment.toPipelineIR() for fragment in self.imports])
659 def merge_pipelines(self, pipelines: Iterable[PipelineIR]) -> None:
660 """Merge one or more other `PipelineIR` objects into this object.
662 Parameters
663 ----------
664 pipelines : `~collections.abc.Iterable` of `PipelineIR` objects
665 An `~collections.abc.Iterable` that contains one or more
666 `PipelineIR` objects to merge into this object.
668 Raises
669 ------
670 ValueError
671 Raised if there is a conflict in instrument specifications.
672 Raised if a task label appears in more than one of the input
673 `PipelineIR` objects which are to be merged.
674 Raised if a labeled subset appears in more than one of the input
675 `PipelineIR` objects which are to be merged, and with any subset
676 existing in this object.
677 """
678 # integrate any imported pipelines
679 accumulate_tasks: dict[str, TaskIR] = {}
680 accumulate_labeled_subsets: dict[str, LabeledSubset] = {}
681 accumulated_parameters = ParametersIR({})
683 for tmp_IR in pipelines:
684 if self.instrument is None:
685 self.instrument = tmp_IR.instrument
686 elif self.instrument != tmp_IR.instrument and tmp_IR.instrument is not None:
687 msg = (
688 "Only one instrument can be declared in a pipeline or its imports. "
689 f"Top level pipeline defines {self.instrument} but pipeline to merge "
690 f"defines {tmp_IR.instrument}."
691 )
692 raise ValueError(msg)
693 if duplicate_labels := accumulate_tasks.keys() & tmp_IR.tasks.keys():
694 msg = (
695 "Task labels in the imported pipelines must be unique. "
696 f"These labels appear multiple times: {duplicate_labels}"
697 )
698 raise ValueError(msg)
699 accumulate_tasks.update(tmp_IR.tasks)
700 self.contracts.extend(tmp_IR.contracts)
701 # verify that tmp_IR has unique labels for named subset among
702 # existing labeled subsets, and with existing task labels.
703 overlapping_subsets = accumulate_labeled_subsets.keys() & tmp_IR.labeled_subsets.keys()
704 task_subset_overlap = (
705 accumulate_labeled_subsets.keys() | tmp_IR.labeled_subsets.keys()
706 ) & accumulate_tasks.keys()
707 if overlapping_subsets or task_subset_overlap:
708 raise ValueError(
709 "Labeled subset names must be unique amongst imports in both labels and "
710 f" named Subsets. Duplicate: {overlapping_subsets | task_subset_overlap}"
711 )
712 accumulate_labeled_subsets.update(tmp_IR.labeled_subsets)
713 accumulated_parameters.update(tmp_IR.parameters)
715 # verify that any accumulated labeled subsets dont clash with a label
716 # from this pipeline
717 if accumulate_labeled_subsets.keys() & self.tasks.keys():
718 raise ValueError(
719 "Labeled subset names must be unique amongst imports in both labels and named Subsets"
720 )
721 # merge in the named subsets for self so this document can override any
722 # that have been delcared
723 accumulate_labeled_subsets.update(self.labeled_subsets)
724 self.labeled_subsets = accumulate_labeled_subsets
726 # merge the dict of label:TaskIR objects, preserving any configs in the
727 # imported pipeline if the labels point to the same class
728 for label, task in self.tasks.items():
729 if label not in accumulate_tasks:
730 accumulate_tasks[label] = task
731 elif accumulate_tasks[label].klass == task.klass:
732 if task.config is not None:
733 for config in task.config:
734 accumulate_tasks[label].add_or_update_config(config)
735 else:
736 accumulate_tasks[label] = task
737 self.tasks: dict[str, TaskIR] = accumulate_tasks
738 accumulated_parameters.update(self.parameters)
739 self.parameters = accumulated_parameters
741 def _read_tasks(self, loaded_yaml: dict[str, Any]) -> None:
742 """Process the tasks portion of the loaded yaml document
744 Parameters
745 ----------
746 loaded_yaml : `dict`
747 A dictionary which matches the structure that would be produced by
748 a yaml reader which parses a pipeline definition document
749 """
750 self.tasks = {}
751 tmp_tasks = loaded_yaml.pop("tasks", None)
752 if tmp_tasks is None:
753 tmp_tasks = {}
755 if "parameters" in tmp_tasks:
756 raise ValueError("parameters is a reserved word and cannot be used as a task label")
758 for label, definition in tmp_tasks.items():
759 if isinstance(definition, str):
760 definition = {"class": definition}
761 config = definition.get("config", None)
762 if config is None:
763 task_config_ir = None
764 else:
765 if isinstance(config, dict):
766 config = [config]
767 task_config_ir = []
768 for c in config:
769 file = c.pop("file", None)
770 if file is None:
771 file = []
772 elif not isinstance(file, list):
773 file = [file]
774 task_config_ir.append(
775 ConfigIR(
776 python=c.pop("python", None), dataId=c.pop("dataId", None), file=file, rest=c
777 )
778 )
779 self.tasks[label] = TaskIR(label, definition["class"], task_config_ir)
781 def _remove_contracts(self, label: str) -> None:
782 """Remove any contracts that contain the given label
784 String comparison used in this way is not the most elegant and may
785 have issues, but it is the only feasible way when users can specify
786 contracts with generic strings.
787 """
788 new_contracts = []
789 for contract in self.contracts:
790 # match a label that is not preceded by an ASCII identifier, or
791 # is the start of a line and is followed by a dot
792 if re.match(f".*([^A-Za-z0-9_]|^){label}[.]", contract.contract):
793 continue
794 new_contracts.append(contract)
795 self.contracts = new_contracts
797 def subset_from_labels(self, labelSpecifier: set[str]) -> PipelineIR:
798 """Subset a pipelineIR to contain only labels specified in
799 labelSpecifier.
801 Parameters
802 ----------
803 labelSpecifier : `set` of `str`
804 set containing labels that describes how to subset a pipeline.
806 Returns
807 -------
808 pipeline : `PipelineIR`
809 A new pipelineIR object that is a subset of the old pipelineIR
811 Raises
812 ------
813 ValueError
814 Raised if there is an issue with specified labels
816 Notes
817 -----
818 This method attempts to prune any contracts that contain labels which
819 are not in the declared subset of labels. This pruning is done using a
820 string based matching due to the nature of contracts and may prune more
821 than it should. Any labeled subsets defined that no longer have all
822 members of the subset present in the pipeline will be removed from the
823 resulting pipeline.
824 """
825 pipeline = copy.deepcopy(self)
827 # update the label specifier to expand any named subsets
828 toRemove = set()
829 toAdd = set()
830 for label in labelSpecifier:
831 if label in pipeline.labeled_subsets:
832 toRemove.add(label)
833 toAdd.update(pipeline.labeled_subsets[label].subset)
834 labelSpecifier.difference_update(toRemove)
835 labelSpecifier.update(toAdd)
836 # verify all the labels are in the pipeline
837 if not labelSpecifier.issubset(pipeline.tasks.keys() | pipeline.labeled_subsets):
838 difference = labelSpecifier.difference(pipeline.tasks.keys())
839 raise ValueError(
840 "Not all supplied labels (specified or named subsets) are in the pipeline "
841 f"definition, extra labels: {difference}"
842 )
843 # copy needed so as to not modify while iterating
844 pipeline_labels = set(pipeline.tasks.keys())
845 # Remove the labels from the pipelineIR, and any contracts that contain
846 # those labels (see docstring on _remove_contracts for why this may
847 # cause issues)
848 for label in pipeline_labels:
849 if label not in labelSpecifier:
850 pipeline.tasks.pop(label)
851 pipeline._remove_contracts(label)
853 # create a copy of the object to iterate over
854 labeled_subsets = copy.copy(pipeline.labeled_subsets)
855 # remove any labeled subsets that no longer have a complete set
856 for label, labeled_subset in labeled_subsets.items():
857 if labeled_subset.subset - pipeline.tasks.keys():
858 pipeline.labeled_subsets.pop(label)
860 return pipeline
862 @classmethod
863 def from_string(cls, pipeline_string: str) -> PipelineIR:
864 """Create a `PipelineIR` object from a string formatted like a pipeline
865 document
867 Parameters
868 ----------
869 pipeline_string : `str`
870 A string that is formatted according like a pipeline document
871 """
872 loaded_yaml = yaml.load(pipeline_string, Loader=PipelineYamlLoader)
873 return cls(loaded_yaml)
875 @classmethod
876 def from_uri(cls, uri: ResourcePathExpression) -> PipelineIR:
877 """Create a `PipelineIR` object from the document specified by the
878 input uri.
880 Parameters
881 ----------
882 uri: convertible to `~lsst.resources.ResourcePath`
883 Location of document to use in creating a `PipelineIR` object.
885 Returns
886 -------
887 pipelineIR : `PipelineIR`
888 The loaded pipeline
889 """
890 loaded_uri = ResourcePath(uri)
891 with loaded_uri.open("r") as buffer:
892 loaded_yaml = yaml.load(buffer, Loader=PipelineYamlLoader)
893 return cls(loaded_yaml)
895 def write_to_uri(self, uri: ResourcePathExpression) -> None:
896 """Serialize this `PipelineIR` object into a yaml formatted string and
897 write the output to a file at the specified uri.
899 Parameters
900 ----------
901 uri: convertible to `~lsst.resources.ResourcePath`
902 Location of document to write a `PipelineIR` object.
903 """
904 with ResourcePath(uri).open("w") as buffer:
905 yaml.dump(self.to_primitives(), buffer, sort_keys=False, Dumper=MultilineStringDumper)
907 def to_primitives(self) -> dict[str, Any]:
908 """Convert to a representation used in yaml serialization
910 Returns
911 -------
912 primitives : `dict`
913 dictionary that maps directly to the serialized YAML form.
914 """
915 accumulate = {"description": self.description}
916 if self.instrument is not None:
917 accumulate["instrument"] = self.instrument
918 if self.parameters:
919 accumulate["parameters"] = self.parameters.to_primitives()
920 accumulate["tasks"] = {m: t.to_primitives() for m, t in self.tasks.items()}
921 if len(self.contracts) > 0:
922 # sort contracts lexicographical order by the contract string in
923 # absence of any other ordering principle
924 contracts_list = [c.to_primitives() for c in self.contracts]
925 contracts_list.sort(key=lambda x: x["contract"])
926 accumulate["contracts"] = contracts_list
927 if self.labeled_subsets:
928 accumulate["subsets"] = {k: v.to_primitives() for k, v in self.labeled_subsets.items()}
929 return accumulate
931 def __str__(self) -> str:
932 """Instance formatting as how it would look in yaml representation"""
933 return yaml.dump(self.to_primitives(), sort_keys=False, Dumper=MultilineStringDumper)
935 def __repr__(self) -> str:
936 """Instance formatting as how it would look in yaml representation"""
937 return str(self)
939 def __eq__(self, other: object) -> bool:
940 if not isinstance(other, PipelineIR):
941 return False
942 # special case contracts because it is a list, but order is not
943 # important
944 elif (
945 all(
946 getattr(self, attr) == getattr(other, attr)
947 for attr in ("tasks", "instrument", "labeled_subsets", "parameters")
948 )
949 and len(self.contracts) == len(other.contracts)
950 and all(c in self.contracts for c in other.contracts)
951 ):
952 return True
953 else:
954 return False