Coverage for python/lsst/pipe/base/pipelineIR.py: 21%
387 statements
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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__ = (
24 "ConfigIR",
25 "ContractError",
26 "ContractIR",
27 "ImportIR",
28 "LabeledSubset",
29 "ParametersIR",
30 "PipelineIR",
31 "TaskIR",
32)
34import copy
35import enum
36import os
37import re
38import warnings
39from collections import Counter
40from collections.abc import Generator, Hashable, Iterable, MutableMapping
41from dataclasses import dataclass, field
42from typing import Any, Literal
44import yaml
45from lsst.resources import ResourcePath, ResourcePathExpression
46from lsst.utils.introspection import find_outside_stacklevel
49class _Tags(enum.Enum):
50 KeepInstrument = enum.auto()
53class PipelineYamlLoader(yaml.SafeLoader):
54 """Specialized version of yaml's SafeLoader.
56 It checks and raises an exception if it finds that there are multiple
57 instances of the same key found inside a pipeline file at a given scope.
58 """
60 def construct_mapping(self, node: yaml.MappingNode, deep: bool = False) -> dict[Hashable, Any]:
61 # do the call to super first so that it can do all the other forms of
62 # checking on this node. If you check the uniqueness of keys first
63 # it would save the work that super does in the case of a failure, but
64 # it might fail in the case that the node was the incorrect node due
65 # to a parsing error, and the resulting exception would be difficult to
66 # understand.
67 mapping = super().construct_mapping(node, deep)
68 # Check if there are any duplicate keys
69 all_keys = Counter(key_node.value for key_node, _ in node.value)
70 duplicates = {k for k, i in all_keys.items() if i != 1}
71 if duplicates:
72 raise KeyError(
73 f"Pipeline files must not have duplicated keys, {duplicates} appeared multiple times"
74 )
75 return mapping
78class MultilineStringDumper(yaml.Dumper):
79 """Custom YAML dumper that makes multi-line strings use the '|'
80 continuation style instead of unreadable newlines and tons of quotes.
82 Basic approach is taken from
83 https://stackoverflow.com/questions/8640959/how-can-i-control-what-scalar-form-pyyaml-uses-for-my-data,
84 but is written as a Dumper subclass to make its effects non-global (vs
85 `yaml.add_representer`).
86 """
88 def represent_scalar(self, tag: str, value: Any, style: str | None = None) -> yaml.ScalarNode:
89 if style is None and tag == "tag:yaml.org,2002:str" and len(value.splitlines()) > 1:
90 style = "|"
91 return super().represent_scalar(tag, value, style)
94class ContractError(Exception):
95 """An exception that is raised when a pipeline contract is not satisfied"""
97 pass
100@dataclass
101class ContractIR:
102 """Intermediate representation of configuration contracts read from a
103 pipeline yaml file.
104 """
106 contract: str
107 """A string of python code representing one or more conditions on configs
108 in a pipeline. This code-as-string should, once evaluated, should be True
109 if the configs are fine, and False otherwise.
110 """
111 msg: str | None = None
112 """An optional message to be shown to the user if a contract fails
113 """
115 def to_primitives(self) -> dict[str, str]:
116 """Convert to a representation used in yaml serialization"""
117 accumulate = {"contract": self.contract}
118 if self.msg is not None:
119 accumulate["msg"] = self.msg
120 return accumulate
122 def __eq__(self, other: object) -> bool:
123 if not isinstance(other, ContractIR):
124 return False
125 return self.contract == other.contract and self.msg == other.msg
128@dataclass
129class LabeledSubset:
130 """Intermediate representation of named subset of task labels read from
131 a pipeline yaml file.
132 """
134 label: str
135 """The label used to identify the subset of task labels.
136 """
137 subset: set[str]
138 """A set of task labels contained in this subset.
139 """
140 description: str | None
141 """A description of what this subset of tasks is intended to do
142 """
144 @staticmethod
145 def from_primitives(label: str, value: list[str] | dict) -> LabeledSubset:
146 """Generate `LabeledSubset` objects given a properly formatted object
147 that as been created by a yaml loader.
149 Parameters
150 ----------
151 label : `str`
152 The label that will be used to identify this labeled subset.
153 value : `list` of `str` or `dict`
154 Object returned from loading a labeled subset section from a yaml
155 document.
157 Returns
158 -------
159 labeledSubset : `LabeledSubset`
160 A `LabeledSubset` object build from the inputs.
162 Raises
163 ------
164 ValueError
165 Raised if the value input is not properly formatted for parsing
166 """
167 if isinstance(value, MutableMapping):
168 subset = value.pop("subset", None)
169 if subset is None:
170 raise ValueError(
171 "If a labeled subset is specified as a mapping, it must contain the key 'subset'"
172 )
173 description = value.pop("description", None)
174 elif isinstance(value, Iterable):
175 subset = value
176 description = None
177 else:
178 raise ValueError(
179 f"There was a problem parsing the labeled subset {label}, make sure the "
180 "definition is either a valid yaml list, or a mapping with keys "
181 "(subset, description) where subset points to a yaml list, and description is "
182 "associated with a string"
183 )
184 return LabeledSubset(label, set(subset), description)
186 def to_primitives(self) -> dict[str, list[str] | str]:
187 """Convert to a representation used in yaml serialization."""
188 accumulate: dict[str, list[str] | str] = {"subset": list(self.subset)}
189 if self.description is not None:
190 accumulate["description"] = self.description
191 return accumulate
194@dataclass
195class ParametersIR:
196 """Intermediate representation of parameters that are global to a pipeline.
198 Parameters
199 ----------
200 mapping : `dict` [`str`, `str`]
201 A mutable mapping of identifiers as keys, and shared configuration
202 as values.
204 Notes
205 -----
206 These parameters are specified under a top level key named ``parameters``
207 and are declared as a yaml mapping. These entries can then be used inside
208 task configuration blocks to specify configuration values. They may not be
209 used in the special ``file`` or ``python`` blocks.
211 Examples
212 --------
213 .. code-block:: yaml
215 \u200bparameters:
216 shared_value: 14
217 tasks:
218 taskA:
219 class: modA
220 config:
221 field1: parameters.shared_value
222 taskB:
223 class: modB
224 config:
225 field2: parameters.shared_value
226 """
228 mapping: MutableMapping[str, Any]
229 """A mutable mapping of identifiers as keys, and shared configuration
230 as values.
231 """
233 def update(self, other: ParametersIR | None) -> None:
234 if other is not None:
235 self.mapping.update(other.mapping)
237 def to_primitives(self) -> MutableMapping[str, str]:
238 """Convert to a representation used in yaml serialization"""
239 return self.mapping
241 def __contains__(self, value: str) -> bool:
242 return value in self.mapping
244 def __getitem__(self, item: str) -> Any:
245 return self.mapping[item]
247 def __bool__(self) -> bool:
248 return bool(self.mapping)
251@dataclass
252class ConfigIR:
253 """Intermediate representation of configurations read from a pipeline yaml
254 file.
255 """
257 python: str | None = None
258 """A string of python code that is used to modify a configuration. This can
259 also be None if there are no modifications to do.
260 """
261 dataId: dict | None = None
262 """A dataId that is used to constrain these config overrides to only quanta
263 with matching dataIds. This field can be None if there is no constraint.
264 This is currently an unimplemented feature, and is placed here for future
265 use.
266 """
267 file: list[str] = field(default_factory=list)
268 """A list of paths which points to a file containing config overrides to be
269 applied. This value may be an empty list if there are no overrides to
270 apply.
271 """
272 rest: dict = field(default_factory=dict)
273 """This is a dictionary of key value pairs, where the keys are strings
274 corresponding to qualified fields on a config to override, and the values
275 are strings representing the values to apply.
276 """
278 def to_primitives(self) -> dict[str, str | dict | list[str]]:
279 """Convert to a representation used in yaml serialization"""
280 accumulate = {}
281 for name in ("python", "dataId", "file"):
282 # if this attribute is thruthy add it to the accumulation
283 # dictionary
284 if getattr(self, name):
285 accumulate[name] = getattr(self, name)
286 # Add the dictionary containing the rest of the config keys to the
287 # # accumulated dictionary
288 accumulate.update(self.rest)
289 return accumulate
291 def formatted(self, parameters: ParametersIR) -> ConfigIR:
292 """Return a new ConfigIR object that is formatted according to the
293 specified parameters
295 Parameters
296 ----------
297 parameters : `ParametersIR`
298 Object that contains variable mappings used in substitution.
300 Returns
301 -------
302 config : `ConfigIR`
303 A new ConfigIR object formatted with the input parameters
304 """
305 new_config = copy.deepcopy(self)
306 for key, value in new_config.rest.items():
307 if not isinstance(value, str):
308 continue
309 match = re.match("parameters[.](.*)", value)
310 if match and match.group(1) in parameters:
311 new_config.rest[key] = parameters[match.group(1)]
312 if match and match.group(1) not in parameters:
313 warnings.warn(
314 f"config {key} contains value {match.group(0)} which is formatted like a "
315 "Pipeline parameter but was not found within the Pipeline, if this was not "
316 "intentional, check for a typo",
317 stacklevel=find_outside_stacklevel("lsst.pipe.base"),
318 )
319 return new_config
321 def maybe_merge(self, other_config: "ConfigIR") -> Generator["ConfigIR", None, None]:
322 """Merge another instance of a `ConfigIR` into this instance if
323 possible. This function returns a generator that is either self
324 if the configs were merged, or self, and other_config if that could
325 not be merged.
327 Parameters
328 ----------
329 other_config : `ConfigIR`
330 An instance of `ConfigIR` to merge into this instance.
332 Returns
333 -------
334 Generator : `ConfigIR`
335 A generator containing either self, or self and other_config if
336 the configs could be merged or not respectively.
337 """
338 # Verify that the config blocks can be merged
339 if (
340 self.dataId != other_config.dataId
341 or self.python
342 or other_config.python
343 or self.file
344 or other_config.file
345 ):
346 yield from (self, other_config)
347 return
349 # create a set of all keys, and verify two keys do not have different
350 # values
351 key_union = self.rest.keys() & other_config.rest.keys()
352 for key in key_union:
353 if self.rest[key] != other_config.rest[key]:
354 yield from (self, other_config)
355 return
356 self.rest.update(other_config.rest)
358 # Combine the lists of override files to load
359 self_file_set = set(self.file)
360 other_file_set = set(other_config.file)
361 self.file = list(self_file_set.union(other_file_set))
363 yield self
365 def __eq__(self, other: object) -> bool:
366 if not isinstance(other, ConfigIR):
367 return False
368 return all(
369 getattr(self, attr) == getattr(other, attr) for attr in ("python", "dataId", "file", "rest")
370 )
373@dataclass
374class TaskIR:
375 """Intermediate representation of tasks read from a pipeline yaml file."""
377 label: str
378 """An identifier used to refer to a task.
379 """
380 klass: str
381 """A string containing a fully qualified python class to be run in a
382 pipeline.
383 """
384 config: list[ConfigIR] | None = None
385 """list of all configs overrides associated with this task, and may be
386 `None` if there are no config overrides.
387 """
389 def to_primitives(self) -> dict[str, str | list[dict]]:
390 """Convert to a representation used in yaml serialization"""
391 accumulate: dict[str, str | list[dict]] = {"class": self.klass}
392 if self.config:
393 accumulate["config"] = [c.to_primitives() for c in self.config]
394 return accumulate
396 def add_or_update_config(self, other_config: ConfigIR) -> None:
397 """Add a `ConfigIR` to this task if one is not present. Merges configs
398 if there is a `ConfigIR` present and the dataId keys of both configs
399 match, otherwise adds a new entry to the config list. The exception to
400 the above is that if either the last config or other_config has a
401 python block, then other_config is always added, as python blocks can
402 modify configs in ways that cannot be predicted.
404 Parameters
405 ----------
406 other_config : `ConfigIR`
407 A `ConfigIR` instance to add or merge into the config attribute of
408 this task.
409 """
410 if not self.config:
411 self.config = [other_config]
412 return
413 self.config.extend(self.config.pop().maybe_merge(other_config))
415 def __eq__(self, other: object) -> bool:
416 if not isinstance(other, TaskIR):
417 return False
418 return all(getattr(self, attr) == getattr(other, attr) for attr in ("label", "klass", "config"))
421@dataclass
422class ImportIR:
423 """An intermediate representation of imported pipelines"""
425 location: str
426 """This is the location of the pipeline to inherit. The path should be
427 specified as an absolute path. Environment variables may be used in the
428 path and should be specified as a python string template, with the name of
429 the environment variable inside braces.
430 """
431 include: list[str] | None = None
432 """list of tasks that should be included when inheriting this pipeline.
433 Either the include or exclude attributes may be specified, but not both.
434 """
435 exclude: list[str] | None = None
436 """list of tasks that should be excluded when inheriting this pipeline.
437 Either the include or exclude attributes may be specified, but not both.
438 """
439 importContracts: bool = True
440 """Boolean attribute to dictate if contracts should be inherited with the
441 pipeline or not.
442 """
443 instrument: Literal[_Tags.KeepInstrument] | str | None = _Tags.KeepInstrument
444 """Instrument to assign to the Pipeline at import. The default value of
445 `_Tags.KeepInstrument`` indicates that whatever instrument the pipeline is
446 declared with will not be modified. setting this value to None will drop
447 any declared instrument prior to import.
448 """
450 def toPipelineIR(self) -> "PipelineIR":
451 """Load in the Pipeline specified by this object, and turn it into a
452 PipelineIR instance.
454 Returns
455 -------
456 pipeline : `PipelineIR`
457 A pipeline generated from the imported pipeline file
458 """
459 if self.include and self.exclude:
460 raise ValueError(
461 "An include list and an exclude list cannot both be specified"
462 " when declaring a pipeline import."
463 )
464 tmp_pipeline = PipelineIR.from_uri(os.path.expandvars(self.location))
465 if self.instrument is not _Tags.KeepInstrument:
466 tmp_pipeline.instrument = self.instrument
468 included_labels = set()
469 for label in tmp_pipeline.tasks:
470 if (
471 (self.include and label in self.include)
472 or (self.exclude and label not in self.exclude)
473 or (self.include is None and self.exclude is None)
474 ):
475 included_labels.add(label)
477 # Handle labeled subsets being specified in the include or exclude
478 # list, adding or removing labels.
479 if self.include is not None:
480 subsets_in_include = tmp_pipeline.labeled_subsets.keys() & self.include
481 for label in subsets_in_include:
482 included_labels.update(tmp_pipeline.labeled_subsets[label].subset)
484 elif self.exclude is not None:
485 subsets_in_exclude = tmp_pipeline.labeled_subsets.keys() & self.exclude
486 for label in subsets_in_exclude:
487 included_labels.difference_update(tmp_pipeline.labeled_subsets[label].subset)
489 tmp_pipeline = tmp_pipeline.subset_from_labels(included_labels)
491 if not self.importContracts:
492 tmp_pipeline.contracts = []
494 return tmp_pipeline
496 def __eq__(self, other: object) -> bool:
497 if not isinstance(other, ImportIR):
498 return False
499 return all(
500 getattr(self, attr) == getattr(other, attr)
501 for attr in ("location", "include", "exclude", "importContracts")
502 )
505class PipelineIR:
506 """Intermediate representation of a pipeline definition
508 Parameters
509 ----------
510 loaded_yaml : `dict`
511 A dictionary which matches the structure that would be produced by a
512 yaml reader which parses a pipeline definition document
514 Raises
515 ------
516 ValueError
517 Raised if:
519 - a pipeline is declared without a description;
520 - no tasks are declared in a pipeline, and no pipelines are to be
521 inherited;
522 - more than one instrument is specified;
523 - more than one inherited pipeline share a label.
524 """
526 def __init__(self, loaded_yaml: dict[str, Any]):
527 # Check required fields are present
528 if "description" not in loaded_yaml:
529 raise ValueError("A pipeline must be declared with a description")
530 if "tasks" not in loaded_yaml and len({"imports", "inherits"} - loaded_yaml.keys()) == 2:
531 raise ValueError("A pipeline must be declared with one or more tasks")
533 # These steps below must happen in this call order
535 # Process pipeline description
536 self.description = loaded_yaml.pop("description")
538 # Process tasks
539 self._read_tasks(loaded_yaml)
541 # Process instrument keys
542 inst = loaded_yaml.pop("instrument", None)
543 if isinstance(inst, list):
544 raise ValueError("Only one top level instrument can be defined in a pipeline")
545 self.instrument: str | None = inst
547 # Process any contracts
548 self._read_contracts(loaded_yaml)
550 # Process any defined parameters
551 self._read_parameters(loaded_yaml)
553 # Process any named label subsets
554 self._read_labeled_subsets(loaded_yaml)
556 # Process any inherited pipelines
557 self._read_imports(loaded_yaml)
559 # verify named subsets, must be done after inheriting
560 self._verify_labeled_subsets()
562 def _read_contracts(self, loaded_yaml: dict[str, Any]) -> None:
563 """Process the contracts portion of the loaded yaml document
565 Parameters
566 ----------
567 loaded_yaml : `dict`
568 A dictionary which matches the structure that would be produced by
569 a yaml reader which parses a pipeline definition document
570 """
571 loaded_contracts = loaded_yaml.pop("contracts", [])
572 if isinstance(loaded_contracts, str):
573 loaded_contracts = [loaded_contracts]
574 self.contracts: list[ContractIR] = []
575 for contract in loaded_contracts:
576 if isinstance(contract, dict):
577 self.contracts.append(ContractIR(**contract))
578 if isinstance(contract, str):
579 self.contracts.append(ContractIR(contract=contract))
581 def _read_parameters(self, loaded_yaml: dict[str, Any]) -> None:
582 """Process the parameters portion of the loaded yaml document
584 Parameters
585 ----------
586 loaded_yaml : `dict`
587 A dictionary which matches the structure that would be produced by
588 a yaml reader which parses a pipeline definition document
589 """
590 loaded_parameters = loaded_yaml.pop("parameters", {})
591 if not isinstance(loaded_parameters, dict):
592 raise ValueError("The parameters section must be a yaml mapping")
593 self.parameters = ParametersIR(loaded_parameters)
595 def _read_labeled_subsets(self, loaded_yaml: dict[str, Any]) -> None:
596 """Process the subsets portion of the loaded yaml document
598 Parameters
599 ----------
600 loaded_yaml: `MutableMapping`
601 A dictionary which matches the structure that would be produced
602 by a yaml reader which parses a pipeline definition document
603 """
604 loaded_subsets = loaded_yaml.pop("subsets", {})
605 self.labeled_subsets: dict[str, LabeledSubset] = {}
606 if not loaded_subsets and "subset" in loaded_yaml:
607 raise ValueError("Top level key should be subsets and not subset, add an s")
608 for key, value in loaded_subsets.items():
609 self.labeled_subsets[key] = LabeledSubset.from_primitives(key, value)
611 def _verify_labeled_subsets(self) -> None:
612 """Verify that all the labels in each named subset exist within the
613 pipeline.
614 """
615 # Verify that all labels defined in a labeled subset are in the
616 # Pipeline
617 for labeled_subset in self.labeled_subsets.values():
618 if not labeled_subset.subset.issubset(self.tasks.keys()):
619 raise ValueError(
620 f"Labels {labeled_subset.subset - self.tasks.keys()} were not found in the "
621 "declared pipeline"
622 )
623 # Verify subset labels are not already task labels
624 label_intersection = self.labeled_subsets.keys() & self.tasks.keys()
625 if label_intersection:
626 raise ValueError(f"Labeled subsets can not use the same label as a task: {label_intersection}")
628 def _read_imports(self, loaded_yaml: dict[str, Any]) -> None:
629 """Process the inherits portion of the loaded yaml document
631 Parameters
632 ----------
633 loaded_yaml : `dict`
634 A dictionary which matches the structure that would be produced by
635 a yaml reader which parses a pipeline definition document
636 """
638 def process_args(argument: str | dict) -> dict:
639 if isinstance(argument, str):
640 return {"location": argument}
641 elif isinstance(argument, dict):
642 if "exclude" in argument and isinstance(argument["exclude"], str):
643 argument["exclude"] = [argument["exclude"]]
644 if "include" in argument and isinstance(argument["include"], str):
645 argument["include"] = [argument["include"]]
646 if "instrument" in argument and argument["instrument"] == "None":
647 argument["instrument"] = None
648 return argument
650 if not {"inherits", "imports"} - loaded_yaml.keys():
651 raise ValueError("Cannot define both inherits and imports sections, use imports")
652 tmp_import = loaded_yaml.pop("inherits", None)
653 if tmp_import is None:
654 tmp_import = loaded_yaml.pop("imports", None)
655 else:
656 raise ValueError("The 'inherits' key is not supported. Please use the key 'imports' instead")
657 if tmp_import is None:
658 self.imports: list[ImportIR] = []
659 elif isinstance(tmp_import, list):
660 self.imports = [ImportIR(**process_args(args)) for args in tmp_import]
661 else:
662 self.imports = [ImportIR(**process_args(tmp_import))]
664 self.merge_pipelines([fragment.toPipelineIR() for fragment in self.imports])
666 def merge_pipelines(self, pipelines: Iterable[PipelineIR]) -> None:
667 """Merge one or more other `PipelineIR` objects into this object.
669 Parameters
670 ----------
671 pipelines : `~collections.abc.Iterable` of `PipelineIR` objects
672 An `~collections.abc.Iterable` that contains one or more
673 `PipelineIR` objects to merge into this object.
675 Raises
676 ------
677 ValueError
678 Raised if there is a conflict in instrument specifications.
679 Raised if a task label appears in more than one of the input
680 `PipelineIR` objects which are to be merged.
681 Raised if a labeled subset appears in more than one of the input
682 `PipelineIR` objects which are to be merged, and with any subset
683 existing in this object.
684 """
685 # integrate any imported pipelines
686 accumulate_tasks: dict[str, TaskIR] = {}
687 accumulate_labeled_subsets: dict[str, LabeledSubset] = {}
688 accumulated_parameters = ParametersIR({})
690 for tmp_IR in pipelines:
691 if self.instrument is None:
692 self.instrument = tmp_IR.instrument
693 elif self.instrument != tmp_IR.instrument and tmp_IR.instrument is not None:
694 msg = (
695 "Only one instrument can be declared in a pipeline or its imports. "
696 f"Top level pipeline defines {self.instrument} but pipeline to merge "
697 f"defines {tmp_IR.instrument}."
698 )
699 raise ValueError(msg)
700 if duplicate_labels := accumulate_tasks.keys() & tmp_IR.tasks.keys():
701 msg = (
702 "Task labels in the imported pipelines must be unique. "
703 f"These labels appear multiple times: {duplicate_labels}"
704 )
705 raise ValueError(msg)
706 accumulate_tasks.update(tmp_IR.tasks)
707 self.contracts.extend(tmp_IR.contracts)
708 # verify that tmp_IR has unique labels for named subset among
709 # existing labeled subsets, and with existing task labels.
710 overlapping_subsets = accumulate_labeled_subsets.keys() & tmp_IR.labeled_subsets.keys()
711 task_subset_overlap = (
712 accumulate_labeled_subsets.keys() | tmp_IR.labeled_subsets.keys()
713 ) & accumulate_tasks.keys()
714 if overlapping_subsets or task_subset_overlap:
715 raise ValueError(
716 "Labeled subset names must be unique amongst imports in both labels and "
717 f" named Subsets. Duplicate: {overlapping_subsets | task_subset_overlap}"
718 )
719 accumulate_labeled_subsets.update(tmp_IR.labeled_subsets)
720 accumulated_parameters.update(tmp_IR.parameters)
722 # verify that any accumulated labeled subsets dont clash with a label
723 # from this pipeline
724 if accumulate_labeled_subsets.keys() & self.tasks.keys():
725 raise ValueError(
726 "Labeled subset names must be unique amongst imports in both labels and named Subsets"
727 )
728 # merge in the named subsets for self so this document can override any
729 # that have been delcared
730 accumulate_labeled_subsets.update(self.labeled_subsets)
731 self.labeled_subsets = accumulate_labeled_subsets
733 # merge the dict of label:TaskIR objects, preserving any configs in the
734 # imported pipeline if the labels point to the same class
735 for label, task in self.tasks.items():
736 if label not in accumulate_tasks:
737 accumulate_tasks[label] = task
738 elif accumulate_tasks[label].klass == task.klass:
739 if task.config is not None:
740 for config in task.config:
741 accumulate_tasks[label].add_or_update_config(config)
742 else:
743 accumulate_tasks[label] = task
744 self.tasks: dict[str, TaskIR] = accumulate_tasks
745 accumulated_parameters.update(self.parameters)
746 self.parameters = accumulated_parameters
748 def _read_tasks(self, loaded_yaml: dict[str, Any]) -> None:
749 """Process the tasks portion of the loaded yaml document
751 Parameters
752 ----------
753 loaded_yaml : `dict`
754 A dictionary which matches the structure that would be produced by
755 a yaml reader which parses a pipeline definition document
756 """
757 self.tasks = {}
758 tmp_tasks = loaded_yaml.pop("tasks", None)
759 if tmp_tasks is None:
760 tmp_tasks = {}
762 if "parameters" in tmp_tasks:
763 raise ValueError("parameters is a reserved word and cannot be used as a task label")
765 for label, definition in tmp_tasks.items():
766 if isinstance(definition, str):
767 definition = {"class": definition}
768 config = definition.get("config", None)
769 if config is None:
770 task_config_ir = None
771 else:
772 if isinstance(config, dict):
773 config = [config]
774 task_config_ir = []
775 for c in config:
776 file = c.pop("file", None)
777 if file is None:
778 file = []
779 elif not isinstance(file, list):
780 file = [file]
781 task_config_ir.append(
782 ConfigIR(
783 python=c.pop("python", None), dataId=c.pop("dataId", None), file=file, rest=c
784 )
785 )
786 self.tasks[label] = TaskIR(label, definition["class"], task_config_ir)
788 def _remove_contracts(self, label: str) -> None:
789 """Remove any contracts that contain the given label
791 String comparison used in this way is not the most elegant and may
792 have issues, but it is the only feasible way when users can specify
793 contracts with generic strings.
794 """
795 new_contracts = []
796 for contract in self.contracts:
797 # match a label that is not preceded by an ASCII identifier, or
798 # is the start of a line and is followed by a dot
799 if re.match(f".*([^A-Za-z0-9_]|^){label}[.]", contract.contract):
800 continue
801 new_contracts.append(contract)
802 self.contracts = new_contracts
804 def subset_from_labels(self, labelSpecifier: set[str]) -> PipelineIR:
805 """Subset a pipelineIR to contain only labels specified in
806 labelSpecifier.
808 Parameters
809 ----------
810 labelSpecifier : `set` of `str`
811 set containing labels that describes how to subset a pipeline.
813 Returns
814 -------
815 pipeline : `PipelineIR`
816 A new pipelineIR object that is a subset of the old pipelineIR
818 Raises
819 ------
820 ValueError
821 Raised if there is an issue with specified labels
823 Notes
824 -----
825 This method attempts to prune any contracts that contain labels which
826 are not in the declared subset of labels. This pruning is done using a
827 string based matching due to the nature of contracts and may prune more
828 than it should. Any labeled subsets defined that no longer have all
829 members of the subset present in the pipeline will be removed from the
830 resulting pipeline.
831 """
832 pipeline = copy.deepcopy(self)
834 # update the label specifier to expand any named subsets
835 toRemove = set()
836 toAdd = set()
837 for label in labelSpecifier:
838 if label in pipeline.labeled_subsets:
839 toRemove.add(label)
840 toAdd.update(pipeline.labeled_subsets[label].subset)
841 labelSpecifier.difference_update(toRemove)
842 labelSpecifier.update(toAdd)
843 # verify all the labels are in the pipeline
844 if not labelSpecifier.issubset(pipeline.tasks.keys() | pipeline.labeled_subsets):
845 difference = labelSpecifier.difference(pipeline.tasks.keys())
846 raise ValueError(
847 "Not all supplied labels (specified or named subsets) are in the pipeline "
848 f"definition, extra labels: {difference}"
849 )
850 # copy needed so as to not modify while iterating
851 pipeline_labels = set(pipeline.tasks.keys())
852 # Remove the labels from the pipelineIR, and any contracts that contain
853 # those labels (see docstring on _remove_contracts for why this may
854 # cause issues)
855 for label in pipeline_labels:
856 if label not in labelSpecifier:
857 pipeline.tasks.pop(label)
858 pipeline._remove_contracts(label)
860 # create a copy of the object to iterate over
861 labeled_subsets = copy.copy(pipeline.labeled_subsets)
862 # remove any labeled subsets that no longer have a complete set
863 for label, labeled_subset in labeled_subsets.items():
864 if labeled_subset.subset - pipeline.tasks.keys():
865 pipeline.labeled_subsets.pop(label)
867 return pipeline
869 @classmethod
870 def from_string(cls, pipeline_string: str) -> PipelineIR:
871 """Create a `PipelineIR` object from a string formatted like a pipeline
872 document
874 Parameters
875 ----------
876 pipeline_string : `str`
877 A string that is formatted according like a pipeline document
878 """
879 loaded_yaml = yaml.load(pipeline_string, Loader=PipelineYamlLoader)
880 return cls(loaded_yaml)
882 @classmethod
883 def from_uri(cls, uri: ResourcePathExpression) -> PipelineIR:
884 """Create a `PipelineIR` object from the document specified by the
885 input uri.
887 Parameters
888 ----------
889 uri: convertible to `~lsst.resources.ResourcePath`
890 Location of document to use in creating a `PipelineIR` object.
892 Returns
893 -------
894 pipelineIR : `PipelineIR`
895 The loaded pipeline
896 """
897 loaded_uri = ResourcePath(uri)
898 with loaded_uri.open("r") as buffer:
899 loaded_yaml = yaml.load(buffer, Loader=PipelineYamlLoader)
900 return cls(loaded_yaml)
902 def write_to_uri(self, uri: ResourcePathExpression) -> None:
903 """Serialize this `PipelineIR` object into a yaml formatted string and
904 write the output to a file at the specified uri.
906 Parameters
907 ----------
908 uri: convertible to `~lsst.resources.ResourcePath`
909 Location of document to write a `PipelineIR` object.
910 """
911 with ResourcePath(uri).open("w") as buffer:
912 yaml.dump(self.to_primitives(), buffer, sort_keys=False, Dumper=MultilineStringDumper)
914 def to_primitives(self) -> dict[str, Any]:
915 """Convert to a representation used in yaml serialization
917 Returns
918 -------
919 primitives : `dict`
920 dictionary that maps directly to the serialized YAML form.
921 """
922 accumulate = {"description": self.description}
923 if self.instrument is not None:
924 accumulate["instrument"] = self.instrument
925 if self.parameters:
926 accumulate["parameters"] = self.parameters.to_primitives()
927 accumulate["tasks"] = {m: t.to_primitives() for m, t in self.tasks.items()}
928 if len(self.contracts) > 0:
929 # sort contracts lexicographical order by the contract string in
930 # absence of any other ordering principle
931 contracts_list = [c.to_primitives() for c in self.contracts]
932 contracts_list.sort(key=lambda x: x["contract"])
933 accumulate["contracts"] = contracts_list
934 if self.labeled_subsets:
935 accumulate["subsets"] = {k: v.to_primitives() for k, v in self.labeled_subsets.items()}
936 return accumulate
938 def __str__(self) -> str:
939 """Instance formatting as how it would look in yaml representation"""
940 return yaml.dump(self.to_primitives(), sort_keys=False, Dumper=MultilineStringDumper)
942 def __repr__(self) -> str:
943 """Instance formatting as how it would look in yaml representation"""
944 return str(self)
946 def __eq__(self, other: object) -> bool:
947 if not isinstance(other, PipelineIR):
948 return False
949 # special case contracts because it is a list, but order is not
950 # important
951 return (
952 all(
953 getattr(self, attr) == getattr(other, attr)
954 for attr in ("tasks", "instrument", "labeled_subsets", "parameters")
955 )
956 and len(self.contracts) == len(other.contracts)
957 and all(c in self.contracts for c in other.contracts)
958 )