Coverage for python/lsst/pipe/base/pipelineIR.py : 15%

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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")
25from collections import Counter
26from collections.abc import Iterable as abcIterable
27from dataclasses import dataclass, field
28from typing import Any, List, Set, Union, Generator, MutableMapping, Optional, Dict, Type
30import copy
31import re
32import os
33import yaml
34import warnings
37class KeepInstrument:
38 pass
41class PipelineYamlLoader(yaml.SafeLoader):
42 """This is a specialized version of yaml's SafeLoader. It checks and raises
43 an exception if it finds that there are multiple instances of the same key
44 found inside a pipeline file at a given scope.
45 """
46 def construct_mapping(self, node, deep=False):
47 # do the call to super first so that it can do all the other forms of
48 # checking on this node. If you check the uniqueness of keys first
49 # it would save the work that super does in the case of a failure, but
50 # it might fail in the case that the node was the incorrect node due
51 # to a parsing error, and the resulting exception would be difficult to
52 # understand.
53 mapping = super().construct_mapping(node, deep)
54 # Check if there are any duplicate keys
55 all_keys = Counter(key_node.value for key_node, _ in node.value)
56 duplicates = {k for k, i in all_keys.items() if i != 1}
57 if duplicates:
58 raise KeyError("Pipeline files must not have duplicated keys, "
59 f"{duplicates} appeared multiple times")
60 return mapping
63class ContractError(Exception):
64 """An exception that is raised when a pipeline contract is not satisfied
65 """
66 pass
69@dataclass
70class ContractIR:
71 """Intermediate representation of contracts read from a pipeline yaml file.
72 """
73 contract: str
74 """A string of python code representing one or more conditions on configs
75 in a pipeline. This code-as-string should, once evaluated, should be True
76 if the configs are fine, and False otherwise.
77 """
78 msg: Union[str, None] = None
79 """An optional message to be shown to the user if a contract fails
80 """
82 def to_primitives(self) -> dict:
83 """Convert to a representation used in yaml serialization
84 """
85 accumulate = {"contract": self.contract}
86 if self.msg is not None:
87 accumulate['msg'] = self.msg
88 return accumulate
90 def __eq__(self, other: "ContractIR"):
91 if not isinstance(other, ContractIR):
92 return False
93 elif self.contract == other.contract and self.msg == other.msg:
94 return True
95 else:
96 return False
99@dataclass
100class LabeledSubset:
101 """Intermediate representation of named subset of task labels read from
102 a pipeline yaml file.
103 """
104 label: str
105 """The label used to identify the subset of task labels.
106 """
107 subset: Set[str]
108 """A set of task labels contained in this subset.
109 """
110 description: Optional[str]
111 """A description of what this subset of tasks is intended to do
112 """
114 @staticmethod
115 def from_primatives(label: str, value: Union[List[str], dict]) -> LabeledSubset:
116 """Generate `LabeledSubset` objects given a properly formatted object
117 that as been created by a yaml loader.
119 Parameters
120 ----------
121 label : `str`
122 The label that will be used to identify this labeled subset.
123 value : `list` of `str` or `dict`
124 Object returned from loading a labeled subset section from a yaml
125 document.
127 Returns
128 -------
129 labeledSubset : `LabeledSubset`
130 A `LabeledSubset` object build from the inputs.
132 Raises
133 ------
134 ValueError
135 Raised if the value input is not properly formatted for parsing
136 """
137 if isinstance(value, MutableMapping):
138 subset = value.pop("subset", None)
139 if subset is None:
140 raise ValueError("If a labeled subset is specified as a mapping, it must contain the key "
141 "'subset'")
142 description = value.pop("description", None)
143 elif isinstance(value, abcIterable):
144 subset = value
145 description = None
146 else:
147 raise ValueError(f"There was a problem parsing the labeled subset {label}, make sure the "
148 "definition is either a valid yaml list, or a mapping with keys "
149 "(subset, description) where subset points to a yaml list, and description is "
150 "associated with a string")
151 return LabeledSubset(label, set(subset), description)
153 def to_primitives(self) -> dict:
154 """Convert to a representation used in yaml serialization
155 """
156 accumulate: Dict[str, Any] = {"subset": list(self.subset)}
157 if self.description is not None:
158 accumulate["description"] = self.description
159 return accumulate
162@dataclass
163class ParametersIR:
164 """Intermediate representation of parameters that are global to a pipeline
166 These parameters are specified under a top level key named `parameters`
167 and are declared as a yaml mapping. These entries can then be used inside
168 task configuration blocks to specify configuration values. They may not be
169 used in the special ``file`` or ``python`` blocks.
171 Example:
172 paramters:
173 shared_value: 14
174 tasks:
175 taskA:
176 class: modA
177 config:
178 field1: parameters.shared_value
179 taskB:
180 class: modB
181 config:
182 field2: parameters.shared_value
183 """
184 mapping: MutableMapping[str, str]
185 """A mutable mapping of identifiers as keys, and shared configuration
186 as values.
187 """
188 def update(self, other: Optional[ParametersIR]):
189 if other is not None:
190 self.mapping.update(other.mapping)
192 def to_primitives(self) -> MutableMapping[str, str]:
193 """Convert to a representation used in yaml serialization
194 """
195 return self.mapping
197 def __contains__(self, value: str) -> bool:
198 return value in self.mapping
200 def __getitem__(self, item: str) -> Any:
201 return self.mapping[item]
203 def __bool__(self) -> bool:
204 return bool(self.mapping)
207@dataclass
208class ConfigIR:
209 """Intermediate representation of configurations read from a pipeline yaml
210 file.
211 """
212 python: Union[str, None] = None
213 """A string of python code that is used to modify a configuration. This can
214 also be None if there are no modifications to do.
215 """
216 dataId: Union[dict, None] = None
217 """A dataId that is used to constrain these config overrides to only quanta
218 with matching dataIds. This field can be None if there is no constraint.
219 This is currently an unimplemented feature, and is placed here for future
220 use.
221 """
222 file: List[str] = field(default_factory=list)
223 """A list of paths which points to a file containing config overrides to be
224 applied. This value may be an empty list if there are no overrides to
225 apply.
226 """
227 rest: dict = field(default_factory=dict)
228 """This is a dictionary of key value pairs, where the keys are strings
229 corresponding to qualified fields on a config to override, and the values
230 are strings representing the values to apply.
231 """
233 def to_primitives(self) -> dict:
234 """Convert to a representation used in yaml serialization
235 """
236 accumulate = {}
237 for name in ("python", "dataId", "file"):
238 # if this attribute is thruthy add it to the accumulation
239 # dictionary
240 if getattr(self, name):
241 accumulate[name] = getattr(self, name)
242 # Add the dictionary containing the rest of the config keys to the
243 # # accumulated dictionary
244 accumulate.update(self.rest)
245 return accumulate
247 def formatted(self, parameters: ParametersIR) -> ConfigIR:
248 """Returns a new ConfigIR object that is formatted according to the
249 specified parameters
251 Parameters
252 ----------
253 parameters : ParametersIR
254 Object that contains variable mappings used in substitution.
256 Returns
257 -------
258 config : ConfigIR
259 A new ConfigIR object formatted with the input parameters
260 """
261 new_config = copy.deepcopy(self)
262 for key, value in new_config.rest.items():
263 if not isinstance(value, str):
264 continue
265 match = re.match("parameters[.](.*)", value)
266 if match and match.group(1) in parameters:
267 new_config.rest[key] = parameters[match.group(1)]
268 if match and match.group(1) not in parameters:
269 warnings.warn(f"config {key} contains value {match.group(0)} which is formatted like a "
270 "Pipeline parameter but was not found within the Pipeline, if this was not "
271 "intentional, check for a typo")
272 return new_config
274 def maybe_merge(self, other_config: "ConfigIR") -> Generator["ConfigIR", None, None]:
275 """Merges another instance of a `ConfigIR` into this instance if
276 possible. This function returns a generator that is either self
277 if the configs were merged, or self, and other_config if that could
278 not be merged.
280 Parameters
281 ----------
282 other_config : `ConfigIR`
283 An instance of `ConfigIR` to merge into this instance.
285 Returns
286 -------
287 Generator : `ConfigIR`
288 A generator containing either self, or self and other_config if
289 the configs could be merged or not respectively.
290 """
291 # Verify that the config blocks can be merged
292 if self.dataId != other_config.dataId or self.python or other_config.python or\
293 self.file or other_config.file:
294 yield from (self, other_config)
295 return
297 # create a set of all keys, and verify two keys do not have different
298 # values
299 key_union = self.rest.keys() & other_config.rest.keys()
300 for key in key_union:
301 if self.rest[key] != other_config.rest[key]:
302 yield from (self, other_config)
303 return
304 self.rest.update(other_config.rest)
306 # Combine the lists of override files to load
307 self_file_set = set(self.file)
308 other_file_set = set(other_config.file)
309 self.file = list(self_file_set.union(other_file_set))
311 yield self
313 def __eq__(self, other: "ConfigIR"):
314 if not isinstance(other, ConfigIR):
315 return False
316 elif all(getattr(self, attr) == getattr(other, attr) for attr in
317 ("python", "dataId", "file", "rest")):
318 return True
319 else:
320 return False
323@dataclass
324class TaskIR:
325 """Intermediate representation of tasks read from a pipeline yaml file.
326 """
327 label: str
328 """An identifier used to refer to a task.
329 """
330 klass: str
331 """A string containing a fully qualified python class to be run in a
332 pipeline.
333 """
334 config: Union[List[ConfigIR], None] = None
335 """List of all configs overrides associated with this task, and may be
336 `None` if there are no config overrides.
337 """
339 def to_primitives(self) -> dict:
340 """Convert to a representation used in yaml serialization
341 """
342 accumulate = {'class': self.klass}
343 if self.config:
344 accumulate['config'] = [c.to_primitives() for c in self.config]
345 return accumulate
347 def add_or_update_config(self, other_config: ConfigIR):
348 """Adds a `ConfigIR` to this task if one is not present. Merges configs
349 if there is a `ConfigIR` present and the dataId keys of both configs
350 match, otherwise adds a new entry to the config list. The exception to
351 the above is that if either the last config or other_config has a
352 python block, then other_config is always added, as python blocks can
353 modify configs in ways that cannot be predicted.
355 Parameters
356 ----------
357 other_config : `ConfigIR`
358 A `ConfigIR` instance to add or merge into the config attribute of
359 this task.
360 """
361 if not self.config:
362 self.config = [other_config]
363 return
364 self.config.extend(self.config.pop().maybe_merge(other_config))
366 def __eq__(self, other: "TaskIR"):
367 if not isinstance(other, TaskIR):
368 return False
369 elif all(getattr(self, attr) == getattr(other, attr) for attr in
370 ("label", "klass", "config")):
371 return True
372 else:
373 return False
376@dataclass
377class ImportIR:
378 """An intermediate representation of imported pipelines
379 """
380 location: str
381 """This is the location of the pipeline to inherit. The path should be
382 specified as an absolute path. Environment variables may be used in the
383 path and should be specified as a python string template, with the name of
384 the environment variable inside braces.
385 """
386 include: Union[List[str], None] = None
387 """List of tasks that should be included when inheriting this pipeline.
388 Either the include or exclude attributes may be specified, but not both.
389 """
390 exclude: Union[List[str], None] = None
391 """List of tasks that should be excluded when inheriting this pipeline.
392 Either the include or exclude attributes may be specified, but not both.
393 """
394 importContracts: bool = True
395 """Boolean attribute to dictate if contracts should be inherited with the
396 pipeline or not.
397 """
398 instrument: Union[Type[KeepInstrument], str, None] = KeepInstrument
399 """Instrument to assign to the Pipeline at import. The default value of
400 KEEP_INSTRUMENT indicates that whatever instrument the pipeline is declared
401 with will not be modified. Setting this value to None will drop any
402 declared instrument prior to import.
403 """
405 def toPipelineIR(self) -> "PipelineIR":
406 """Load in the Pipeline specified by this object, and turn it into a
407 PipelineIR instance.
409 Returns
410 -------
411 pipeline : `PipelineIR`
412 A pipeline generated from the imported pipeline file
413 """
414 if self.include and self.exclude:
415 raise ValueError("Both an include and an exclude list cant be specified"
416 " when declaring a pipeline import")
417 tmp_pipeline = PipelineIR.from_file(os.path.expandvars(self.location))
418 if self.instrument is not KeepInstrument:
419 tmp_pipeline.instrument = self.instrument
421 included_labels = set()
422 for label in tmp_pipeline.tasks:
423 if (self.include and label in self.include) or (self.exclude and label not in self.exclude)\
424 or (self.include is None and self.exclude is None):
425 included_labels.add(label)
427 # Handle labeled subsets being specified in the include or exclude
428 # list, adding or removing labels.
429 if self.include is not None:
430 subsets_in_include = tmp_pipeline.labeled_subsets.keys() & self.include
431 for label in subsets_in_include:
432 included_labels.update(tmp_pipeline.labeled_subsets[label].subset)
434 elif self.exclude is not None:
435 subsets_in_exclude = tmp_pipeline.labeled_subsets.keys() & self.exclude
436 for label in subsets_in_exclude:
437 included_labels.difference_update(tmp_pipeline.labeled_subsets[label].subset)
439 tmp_pipeline = tmp_pipeline.subset_from_labels(included_labels)
441 if not self.importContracts:
442 tmp_pipeline.contracts = []
444 return tmp_pipeline
446 def __eq__(self, other: "ImportIR"):
447 if not isinstance(other, ImportIR):
448 return False
449 elif all(getattr(self, attr) == getattr(other, attr) for attr in
450 ("location", "include", "exclude", "importContracts")):
451 return True
452 else:
453 return False
456class PipelineIR:
457 """Intermediate representation of a pipeline definition
459 Parameters
460 ----------
461 loaded_yaml : `dict`
462 A dictionary which matches the structure that would be produced by a
463 yaml reader which parses a pipeline definition document
465 Raises
466 ------
467 ValueError :
468 - If a pipeline is declared without a description
469 - If no tasks are declared in a pipeline, and no pipelines are to be
470 inherited
471 - If more than one instrument is specified
472 - If more than one inherited pipeline share a label
473 """
474 def __init__(self, loaded_yaml):
475 # Check required fields are present
476 if "description" not in loaded_yaml:
477 raise ValueError("A pipeline must be declared with a description")
478 if "tasks" not in loaded_yaml and len({"imports", "inherits"} - loaded_yaml.keys()) == 2:
479 raise ValueError("A pipeline must be declared with one or more tasks")
481 # These steps below must happen in this call order
483 # Process pipeline description
484 self.description = loaded_yaml.pop("description")
486 # Process tasks
487 self._read_tasks(loaded_yaml)
489 # Process instrument keys
490 inst = loaded_yaml.pop("instrument", None)
491 if isinstance(inst, list):
492 raise ValueError("Only one top level instrument can be defined in a pipeline")
493 self.instrument = inst
495 # Process any contracts
496 self._read_contracts(loaded_yaml)
498 # Process any defined parameters
499 self._read_parameters(loaded_yaml)
501 # Process any named label subsets
502 self._read_labeled_subsets(loaded_yaml)
504 # Process any inherited pipelines
505 self._read_imports(loaded_yaml)
507 # verify named subsets, must be done after inheriting
508 self._verify_labeled_subsets()
510 def _read_contracts(self, loaded_yaml):
511 """Process the contracts portion of the loaded yaml document
513 Parameters
514 ---------
515 loaded_yaml : `dict`
516 A dictionary which matches the structure that would be produced by
517 a yaml reader which parses a pipeline definition document
518 """
519 loaded_contracts = loaded_yaml.pop("contracts", [])
520 if isinstance(loaded_contracts, str):
521 loaded_contracts = [loaded_contracts]
522 self.contracts = []
523 for contract in loaded_contracts:
524 if isinstance(contract, dict):
525 self.contracts.append(ContractIR(**contract))
526 if isinstance(contract, str):
527 self.contracts.append(ContractIR(contract=contract))
529 def _read_parameters(self, loaded_yaml):
530 """Process the parameters portion of the loaded yaml document
532 Parameters
533 ---------
534 loaded_yaml : `dict`
535 A dictionary which matches the structure that would be produced by
536 a yaml reader which parses a pipeline definition document
537 """
538 loaded_parameters = loaded_yaml.pop("parameters", {})
539 if not isinstance(loaded_parameters, dict):
540 raise ValueError("The parameters section must be a yaml mapping")
541 self.parameters = ParametersIR(loaded_parameters)
543 def _read_labeled_subsets(self, loaded_yaml: dict):
544 """Process the subsets portion of the loaded yaml document
546 Parameters
547 ----------
548 loaded_yaml: `MutableMapping`
549 A dictionary which matches the structure that would be produced
550 by a yaml reader which parses a pipeline definition document
551 """
552 loaded_subsets = loaded_yaml.pop("subsets", {})
553 self.labeled_subsets = {}
554 if not loaded_subsets and "subset" in loaded_yaml:
555 raise ValueError("Top level key should be subsets and not subset, add an s")
556 for key, value in loaded_subsets.items():
557 self.labeled_subsets[key] = LabeledSubset.from_primatives(key, value)
559 def _verify_labeled_subsets(self):
560 """Verifies that all the labels in each named subset exist within the
561 pipeline.
562 """
563 # Verify that all labels defined in a labeled subset are in the
564 # Pipeline
565 for labeled_subset in self.labeled_subsets.values():
566 if not labeled_subset.subset.issubset(self.tasks.keys()):
567 raise ValueError(f"Labels {labeled_subset.subset - self.tasks.keys()} were not found in the "
568 "declared pipeline")
569 # Verify subset labels are not already task labels
570 label_intersection = self.labeled_subsets.keys() & self.tasks.keys()
571 if label_intersection:
572 raise ValueError(f"Labeled subsets can not use the same label as a task: {label_intersection}")
574 def _read_imports(self, loaded_yaml):
575 """Process the inherits 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 def process_args(argument: Union[str, dict]) -> dict:
584 if isinstance(argument, str):
585 return {"location": argument}
586 elif isinstance(argument, dict):
587 if "exclude" in argument and isinstance(argument["exclude"], str):
588 argument["exclude"] = [argument["exclude"]]
589 if "include" in argument and isinstance(argument["include"], str):
590 argument["include"] = [argument["include"]]
591 if "instrument" in argument and argument["instrument"] == "None":
592 argument["instrument"] = None
593 return argument
594 if not {"inherits", "imports"} - loaded_yaml.keys():
595 raise ValueError("Cannot define both inherits and imports sections, use imports")
596 tmp_import = loaded_yaml.pop("inherits", None)
597 if tmp_import is None:
598 tmp_import = loaded_yaml.pop("imports", None)
599 else:
600 warnings.warn("The 'inherits' key is deprecated, and will be "
601 "removed around June 2021. Please use the key "
602 "'imports' instead")
603 if tmp_import is None:
604 self.imports = []
605 elif isinstance(tmp_import, list):
606 self.imports = [ImportIR(**process_args(args)) for args in tmp_import]
607 else:
608 self.imports = [ImportIR(**process_args(tmp_import))]
610 # integrate any imported pipelines
611 accumulate_tasks = {}
612 accumulate_labeled_subsets = {}
613 accumulated_parameters = ParametersIR({})
614 for other_pipeline in self.imports:
615 tmp_IR = other_pipeline.toPipelineIR()
616 if self.instrument is None:
617 self.instrument = tmp_IR.instrument
618 elif self.instrument != tmp_IR.instrument and tmp_IR.instrument is not None:
619 raise ValueError("Only one instrument can be declared in a pipeline or it's imports")
620 if accumulate_tasks.keys() & tmp_IR.tasks.keys():
621 raise ValueError("Task labels in the imported pipelines must "
622 "be unique")
623 accumulate_tasks.update(tmp_IR.tasks)
624 self.contracts.extend(tmp_IR.contracts)
625 # verify that tmp_IR has unique labels for named subset among
626 # existing labeled subsets, and with existing task labels.
627 overlapping_subsets = accumulate_labeled_subsets.keys() & tmp_IR.labeled_subsets.keys()
628 task_subset_overlap = ((accumulate_labeled_subsets.keys() | tmp_IR.labeled_subsets.keys())
629 & accumulate_tasks.keys())
630 if overlapping_subsets or task_subset_overlap:
631 raise ValueError("Labeled subset names must be unique amongst imports in both labels and "
632 f" named Subsets. Duplicate: {overlapping_subsets | task_subset_overlap}")
633 accumulate_labeled_subsets.update(tmp_IR.labeled_subsets)
634 accumulated_parameters.update(tmp_IR.parameters)
636 # verify that any accumulated labeled subsets dont clash with a label
637 # from this pipeline
638 if accumulate_labeled_subsets.keys() & self.tasks.keys():
639 raise ValueError("Labeled subset names must be unique amongst imports in both labels and "
640 " named Subsets")
641 # merge in the named subsets for self so this document can override any
642 # that have been delcared
643 accumulate_labeled_subsets.update(self.labeled_subsets)
644 self.labeled_subsets = accumulate_labeled_subsets
646 # merge the dict of label:TaskIR objects, preserving any configs in the
647 # imported pipeline if the labels point to the same class
648 for label, task in self.tasks.items():
649 if label not in accumulate_tasks:
650 accumulate_tasks[label] = task
651 elif accumulate_tasks[label].klass == task.klass:
652 if task.config is not None:
653 for config in task.config:
654 accumulate_tasks[label].add_or_update_config(config)
655 else:
656 accumulate_tasks[label] = task
657 self.tasks = accumulate_tasks
658 self.parameters.update(accumulated_parameters)
660 def _read_tasks(self, loaded_yaml):
661 """Process the tasks portion of the loaded yaml document
663 Parameters
664 ---------
665 loaded_yaml : `dict`
666 A dictionary which matches the structure that would be produced by
667 a yaml reader which parses a pipeline definition document
668 """
669 self.tasks = {}
670 tmp_tasks = loaded_yaml.pop("tasks", None)
671 if tmp_tasks is None:
672 tmp_tasks = {}
674 if "parameters" in tmp_tasks:
675 raise ValueError("parameters is a reserved word and cannot be used as a task label")
677 for label, definition in tmp_tasks.items():
678 if isinstance(definition, str):
679 definition = {"class": definition}
680 config = definition.get('config', None)
681 if config is None:
682 task_config_ir = None
683 else:
684 if isinstance(config, dict):
685 config = [config]
686 task_config_ir = []
687 for c in config:
688 file = c.pop("file", None)
689 if file is None:
690 file = []
691 elif not isinstance(file, list):
692 file = [file]
693 task_config_ir.append(ConfigIR(python=c.pop("python", None),
694 dataId=c.pop("dataId", None),
695 file=file,
696 rest=c))
697 self.tasks[label] = TaskIR(label, definition["class"], task_config_ir)
699 def _remove_contracts(self, label: str):
700 """Remove any contracts that contain the given label
702 String comparison used in this way is not the most elegant and may
703 have issues, but it is the only feasible way when users can specify
704 contracts with generic strings.
705 """
706 new_contracts = []
707 for contract in self.contracts:
708 # match a label that is not preceded by an ASCII identifier, or
709 # is the start of a line and is followed by a dot
710 if re.match(f".*([^A-Za-z0-9_]|^){label}[.]", contract.contract):
711 continue
712 new_contracts.append(contract)
713 self.contracts = new_contracts
715 def subset_from_labels(self, labelSpecifier: Set[str]) -> PipelineIR:
716 """Subset a pipelineIR to contain only labels specified in
717 labelSpecifier.
719 Parameters
720 ----------
721 labelSpecifier : `set` of `str`
722 Set containing labels that describes how to subset a pipeline.
724 Returns
725 -------
726 pipeline : `PipelineIR`
727 A new pipelineIR object that is a subset of the old pipelineIR
729 Raises
730 ------
731 ValueError
732 Raised if there is an issue with specified labels
734 Notes
735 -----
736 This method attempts to prune any contracts that contain labels which
737 are not in the declared subset of labels. This pruning is done using a
738 string based matching due to the nature of contracts and may prune more
739 than it should. Any labeled subsets defined that no longer have all
740 members of the subset present in the pipeline will be removed from the
741 resulting pipeline.
742 """
744 pipeline = copy.deepcopy(self)
746 # update the label specifier to expand any named subsets
747 toRemove = set()
748 toAdd = set()
749 for label in labelSpecifier:
750 if label in pipeline.labeled_subsets:
751 toRemove.add(label)
752 toAdd.update(pipeline.labeled_subsets[label].subset)
753 labelSpecifier.difference_update(toRemove)
754 labelSpecifier.update(toAdd)
755 # verify all the labels are in the pipeline
756 if not labelSpecifier.issubset(pipeline.tasks.keys()
757 | pipeline.labeled_subsets):
758 difference = labelSpecifier.difference(pipeline.tasks.keys())
759 raise ValueError("Not all supplied labels (specified or named subsets) are in the pipeline "
760 f"definition, extra labels: {difference}")
761 # copy needed so as to not modify while iterating
762 pipeline_labels = set(pipeline.tasks.keys())
763 # Remove the labels from the pipelineIR, and any contracts that contain
764 # those labels (see docstring on _remove_contracts for why this may
765 # cause issues)
766 for label in pipeline_labels:
767 if label not in labelSpecifier:
768 pipeline.tasks.pop(label)
769 pipeline._remove_contracts(label)
771 # create a copy of the object to iterate over
772 labeled_subsets = copy.copy(pipeline.labeled_subsets)
773 # remove any labeled subsets that no longer have a complete set
774 for label, labeled_subset in labeled_subsets.items():
775 if labeled_subset.subset - pipeline.tasks.keys():
776 pipeline.labeled_subsets.pop(label)
778 return pipeline
780 @classmethod
781 def from_string(cls, pipeline_string: str):
782 """Create a `PipelineIR` object from a string formatted like a pipeline
783 document
785 Parameters
786 ----------
787 pipeline_string : `str`
788 A string that is formatted according like a pipeline document
789 """
790 loaded_yaml = yaml.load(pipeline_string, Loader=PipelineYamlLoader)
791 return cls(loaded_yaml)
793 @classmethod
794 def from_file(cls, filename: str):
795 """Create a `PipelineIR` object from the document specified by the
796 input path.
798 Parameters
799 ----------
800 filename : `str`
801 Location of document to use in creating a `PipelineIR` object.
802 """
803 with open(filename, 'r') as f:
804 loaded_yaml = yaml.load(f, Loader=PipelineYamlLoader)
805 return cls(loaded_yaml)
807 def to_file(self, filename: str):
808 """Serialize this `PipelineIR` object into a yaml formatted string and
809 write the output to a file at the specified path.
811 Parameters
812 ----------
813 filename : `str`
814 Location of document to write a `PipelineIR` object.
815 """
816 with open(filename, 'w') as f:
817 yaml.dump(self.to_primitives(), f, sort_keys=False)
819 def to_primitives(self):
820 """Convert to a representation used in yaml serialization
821 """
822 accumulate = {"description": self.description}
823 if self.instrument is not None:
824 accumulate['instrument'] = self.instrument
825 if self.parameters:
826 accumulate['parameters'] = self.parameters.to_primitives()
827 accumulate['tasks'] = {m: t.to_primitives() for m, t in self.tasks.items()}
828 if len(self.contracts) > 0:
829 accumulate['contracts'] = [c.to_primitives() for c in self.contracts]
830 if self.labeled_subsets:
831 accumulate['subsets'] = {k: v.to_primitives() for k, v in self.labeled_subsets.items()}
832 return accumulate
834 def __str__(self) -> str:
835 """Instance formatting as how it would look in yaml representation
836 """
837 return yaml.dump(self.to_primitives(), sort_keys=False)
839 def __repr__(self) -> str:
840 """Instance formatting as how it would look in yaml representation
841 """
842 return str(self)
844 def __eq__(self, other: "PipelineIR"):
845 if not isinstance(other, PipelineIR):
846 return False
847 elif all(getattr(self, attr) == getattr(other, attr) for attr in
848 ("contracts", "tasks", "instrument")):
849 return True
850 else:
851 return False