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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/>.
22"""Module defining connection classes for PipelineTask.
23"""
25__all__ = ["PipelineTaskConnections", "InputQuantizedConnection", "OutputQuantizedConnection",
26 "DeferredDatasetRef", "iterConnections"]
28from collections import UserDict, namedtuple
29from types import SimpleNamespace
30import typing
32import itertools
33import string
35from . import config as configMod
36from .connectionTypes import (InitInput, InitOutput, Input, PrerequisiteInput,
37 Output, BaseConnection)
38from lsst.daf.butler import DatasetRef, Quantum
40if typing.TYPE_CHECKING: 40 ↛ 41line 40 didn't jump to line 41, because the condition on line 40 was never true
41 from .config import PipelineTaskConfig
44class ScalarError(TypeError):
45 """Exception raised when dataset type is configured as scalar
46 but there are multiple DataIds in a Quantum for that dataset.
48 Parameters
49 ----------
50 key : `str`
51 Name of the configuration field for dataset type.
52 If ``numDataIds`` is not specified, it is assumed that this parameter
53 is the full message to be reported and not the key.
54 numDataIds : `int`, optional
55 Actual number of DataIds in a Quantum for this dataset type.
56 """
57 def __init__(self, key, numDataIds=None):
58 if numDataIds is None:
59 # Assume we are receiving a normal TypeError message
60 err_msg = key
61 else:
62 err_msg = f"Expected scalar for output dataset field {key}, " \
63 f"received {numDataIds} DataIds"
64 super().__init__(err_msg)
67class PipelineTaskConnectionDict(UserDict):
68 """This is a special dict class used by PipelineTaskConnectionMetaclass
70 This dict is used in PipelineTaskConnection class creation, as the
71 dictionary that is initially used as __dict__. It exists to
72 intercept connection fields declared in a PipelineTaskConnection, and
73 what name is used to identify them. The names are then added to class
74 level list according to the connection type of the class attribute. The
75 names are also used as keys in a class level dictionary associated with
76 the corresponding class attribute. This information is a duplicate of
77 what exists in __dict__, but provides a simple place to lookup and
78 iterate on only these variables.
79 """
80 def __init__(self, *args, **kwargs):
81 super().__init__(*args, **kwargs)
82 # Initialize class level variables used to track any declared
83 # class level variables that are instances of
84 # connectionTypes.BaseConnection
85 self.data['inputs'] = []
86 self.data['prerequisiteInputs'] = []
87 self.data['outputs'] = []
88 self.data['initInputs'] = []
89 self.data['initOutputs'] = []
90 self.data['allConnections'] = {}
92 def __setitem__(self, name, value):
93 if isinstance(value, Input):
94 self.data['inputs'].append(name)
95 elif isinstance(value, PrerequisiteInput): 95 ↛ 96line 95 didn't jump to line 96, because the condition on line 95 was never true
96 self.data['prerequisiteInputs'].append(name)
97 elif isinstance(value, Output):
98 self.data['outputs'].append(name)
99 elif isinstance(value, InitInput): 99 ↛ 100line 99 didn't jump to line 100, because the condition on line 99 was never true
100 self.data['initInputs'].append(name)
101 elif isinstance(value, InitOutput): 101 ↛ 102line 101 didn't jump to line 102, because the condition on line 101 was never true
102 self.data['initOutputs'].append(name)
103 # This should not be an elif, as it needs tested for
104 # everything that inherits from BaseConnection
105 if isinstance(value, BaseConnection):
106 object.__setattr__(value, 'varName', name)
107 self.data['allConnections'][name] = value
108 # defer to the default behavior
109 super().__setitem__(name, value)
112class PipelineTaskConnectionsMetaclass(type):
113 """Metaclass used in the declaration of PipelineTaskConnections classes
114 """
115 def __prepare__(name, bases, **kwargs): # noqa: 805
116 # Create an instance of our special dict to catch and track all
117 # variables that are instances of connectionTypes.BaseConnection
118 # Copy any existing connections from a parent class
119 dct = PipelineTaskConnectionDict()
120 for base in bases:
121 if isinstance(base, PipelineTaskConnectionsMetaclass): 121 ↛ 120line 121 didn't jump to line 120, because the condition on line 121 was never false
122 for name, value in base.allConnections.items(): 122 ↛ 123line 122 didn't jump to line 123, because the loop on line 122 never started
123 dct[name] = value
124 return dct
126 def __new__(cls, name, bases, dct, **kwargs):
127 dimensionsValueError = TypeError("PipelineTaskConnections class must be created with a dimensions "
128 "attribute which is an iterable of dimension names")
130 if name != 'PipelineTaskConnections':
131 # Verify that dimensions are passed as a keyword in class
132 # declaration
133 if 'dimensions' not in kwargs: 133 ↛ 134line 133 didn't jump to line 134, because the condition on line 133 was never true
134 for base in bases:
135 if hasattr(base, 'dimensions'):
136 kwargs['dimensions'] = base.dimensions
137 break
138 if 'dimensions' not in kwargs:
139 raise dimensionsValueError
140 try:
141 dct['dimensions'] = set(kwargs['dimensions'])
142 except TypeError as exc:
143 raise dimensionsValueError from exc
144 # Lookup any python string templates that may have been used in the
145 # declaration of the name field of a class connection attribute
146 allTemplates = set()
147 stringFormatter = string.Formatter()
148 # Loop over all connections
149 for obj in dct['allConnections'].values():
150 nameValue = obj.name
151 # add all the parameters to the set of templates
152 for param in stringFormatter.parse(nameValue):
153 if param[1] is not None: 153 ↛ 154line 153 didn't jump to line 154, because the condition on line 153 was never true
154 allTemplates.add(param[1])
156 # look up any template from base classes and merge them all
157 # together
158 mergeDict = {}
159 for base in bases[::-1]:
160 if hasattr(base, 'defaultTemplates'): 160 ↛ 161line 160 didn't jump to line 161, because the condition on line 160 was never true
161 mergeDict.update(base.defaultTemplates)
162 if 'defaultTemplates' in kwargs: 162 ↛ 163line 162 didn't jump to line 163, because the condition on line 162 was never true
163 mergeDict.update(kwargs['defaultTemplates'])
165 if len(mergeDict) > 0: 165 ↛ 166line 165 didn't jump to line 166, because the condition on line 165 was never true
166 kwargs['defaultTemplates'] = mergeDict
168 # Verify that if templated strings were used, defaults were
169 # supplied as an argument in the declaration of the connection
170 # class
171 if len(allTemplates) > 0 and 'defaultTemplates' not in kwargs: 171 ↛ 172line 171 didn't jump to line 172, because the condition on line 171 was never true
172 raise TypeError("PipelineTaskConnection class contains templated attribute names, but no "
173 "defaut templates were provided, add a dictionary attribute named "
174 "defaultTemplates which contains the mapping between template key and value")
175 if len(allTemplates) > 0: 175 ↛ 177line 175 didn't jump to line 177, because the condition on line 175 was never true
176 # Verify all templates have a default, and throw if they do not
177 defaultTemplateKeys = set(kwargs['defaultTemplates'].keys())
178 templateDifference = allTemplates.difference(defaultTemplateKeys)
179 if templateDifference:
180 raise TypeError(f"Default template keys were not provided for {templateDifference}")
181 # Verify that templates do not share names with variable names
182 # used for a connection, this is needed because of how
183 # templates are specified in an associated config class.
184 nameTemplateIntersection = allTemplates.intersection(set(dct['allConnections'].keys()))
185 if len(nameTemplateIntersection) > 0:
186 raise TypeError(f"Template parameters cannot share names with Class attributes")
187 dct['defaultTemplates'] = kwargs.get('defaultTemplates', {})
189 # Convert all the connection containers into frozensets so they cannot
190 # be modified at the class scope
191 for connectionName in ("inputs", "prerequisiteInputs", "outputs", "initInputs", "initOutputs"):
192 dct[connectionName] = frozenset(dct[connectionName])
193 # our custom dict type must be turned into an actual dict to be used in
194 # type.__new__
195 return super().__new__(cls, name, bases, dict(dct))
197 def __init__(cls, name, bases, dct, **kwargs):
198 # This overrides the default init to drop the kwargs argument. Python
199 # metaclasses will have this argument set if any kwargs are passes at
200 # class construction time, but should be consumed before calling
201 # __init__ on the type metaclass. This is in accordance with python
202 # documentation on metaclasses
203 super().__init__(name, bases, dct)
206class QuantizedConnection(SimpleNamespace):
207 """A Namespace to map defined variable names of connections to their
208 `lsst.daf.buter.DatasetRef`s
210 This class maps the names used to define a connection on a
211 PipelineTaskConnectionsClass to the corresponding
212 `lsst.daf.butler.DatasetRef`s provided by a `lsst.daf.butler.Quantum`
213 instance. This will be a quantum of execution based on the graph created
214 by examining all the connections defined on the
215 `PipelineTaskConnectionsClass`.
216 """
217 def __init__(self, **kwargs):
218 # Create a variable to track what attributes are added. This is used
219 # later when iterating over this QuantizedConnection instance
220 object.__setattr__(self, "_attributes", set())
222 def __setattr__(self, name: str, value: typing.Union[DatasetRef, typing.List[DatasetRef]]):
223 # Capture the attribute name as it is added to this object
224 self._attributes.add(name)
225 super().__setattr__(name, value)
227 def __delattr__(self, name):
228 object.__delattr__(self, name)
229 self._attributes.remove(name)
231 def __iter__(self) -> typing.Generator[typing.Tuple[str, typing.Union[DatasetRef,
232 typing.List[DatasetRef]]], None, None]:
233 """Make an Iterator for this QuantizedConnection
235 Iterating over a QuantizedConnection will yield a tuple with the name
236 of an attribute and the value associated with that name. This is
237 similar to dict.items() but is on the namespace attributes rather than
238 dict keys.
239 """
240 yield from ((name, getattr(self, name)) for name in self._attributes)
242 def keys(self) -> typing.Generator[str, None, None]:
243 """Returns an iterator over all the attributes added to a
244 QuantizedConnection class
245 """
246 yield from self._attributes
249class InputQuantizedConnection(QuantizedConnection):
250 pass
253class OutputQuantizedConnection(QuantizedConnection):
254 pass
257class DeferredDatasetRef(namedtuple("DeferredDatasetRefBase", "datasetRef")):
258 """Class which denotes that a datasetRef should be treated as deferred when
259 interacting with the butler
261 Parameters
262 ----------
263 datasetRef : `lsst.daf.butler.DatasetRef`
264 The `lsst.daf.butler.DatasetRef` that will be eventually used to
265 resolve a dataset
266 """
267 __slots__ = ()
270class PipelineTaskConnections(metaclass=PipelineTaskConnectionsMetaclass):
271 """PipelineTaskConnections is a class used to declare desired IO when a
272 PipelineTask is run by an activator
274 Parameters
275 ----------
276 config : `PipelineTaskConfig`
277 A `PipelineTaskConfig` class instance whose class has been configured
278 to use this `PipelineTaskConnectionsClass`
280 Notes
281 -----
282 ``PipelineTaskConnection`` classes are created by declaring class
283 attributes of types defined in `lsst.pipe.base.connectionTypes` and are
284 listed as follows:
286 * ``InitInput`` - Defines connections in a quantum graph which are used as
287 inputs to the ``__init__`` function of the `PipelineTask` corresponding
288 to this class
289 * ``InitOuput`` - Defines connections in a quantum graph which are to be
290 persisted using a butler at the end of the ``__init__`` function of the
291 `PipelineTask` corresponding to this class. The variable name used to
292 define this connection should be the same as an attribute name on the
293 `PipelineTask` instance. E.g. if an ``InitOutput`` is declared with
294 the name ``outputSchema`` in a ``PipelineTaskConnections`` class, then
295 a `PipelineTask` instance should have an attribute
296 ``self.outputSchema`` defined. Its value is what will be saved by the
297 activator framework.
298 * ``PrerequisiteInput`` - An input connection type that defines a
299 `lsst.daf.butler.DatasetType` that must be present at execution time,
300 but that will not be used during the course of creating the quantum
301 graph to be executed. These most often are things produced outside the
302 processing pipeline, such as reference catalogs.
303 * ``Input`` - Input `lsst.daf.butler.DatasetType` objects that will be used
304 in the ``run`` method of a `PipelineTask`. The name used to declare
305 class attribute must match a function argument name in the ``run``
306 method of a `PipelineTask`. E.g. If the ``PipelineTaskConnections``
307 defines an ``Input`` with the name ``calexp``, then the corresponding
308 signature should be ``PipelineTask.run(calexp, ...)``
309 * ``Output`` - A `lsst.daf.butler.DatasetType` that will be produced by an
310 execution of a `PipelineTask`. The name used to declare the connection
311 must correspond to an attribute of a `Struct` that is returned by a
312 `PipelineTask` ``run`` method. E.g. if an output connection is
313 defined with the name ``measCat``, then the corresponding
314 ``PipelineTask.run`` method must return ``Struct(measCat=X,..)`` where
315 X matches the ``storageClass`` type defined on the output connection.
317 The process of declaring a ``PipelineTaskConnection`` class involves
318 parameters passed in the declaration statement.
320 The first parameter is ``dimensions`` which is an iterable of strings which
321 defines the unit of processing the run method of a corresponding
322 `PipelineTask` will operate on. These dimensions must match dimensions that
323 exist in the butler registry which will be used in executing the
324 corresponding `PipelineTask`.
326 The second parameter is labeled ``defaultTemplates`` and is conditionally
327 optional. The name attributes of connections can be specified as python
328 format strings, with named format arguments. If any of the name parameters
329 on connections defined in a `PipelineTaskConnections` class contain a
330 template, then a default template value must be specified in the
331 ``defaultTemplates`` argument. This is done by passing a dictionary with
332 keys corresponding to a template identifier, and values corresponding to
333 the value to use as a default when formatting the string. For example if
334 ``ConnectionClass.calexp.name = '{input}Coadd_calexp'`` then
335 ``defaultTemplates`` = {'input': 'deep'}.
337 Once a `PipelineTaskConnections` class is created, it is used in the
338 creation of a `PipelineTaskConfig`. This is further documented in the
339 documentation of `PipelineTaskConfig`. For the purposes of this
340 documentation, the relevant information is that the config class allows
341 configuration of connection names by users when running a pipeline.
343 Instances of a `PipelineTaskConnections` class are used by the pipeline
344 task execution framework to introspect what a corresponding `PipelineTask`
345 will require, and what it will produce.
347 Examples
348 --------
349 >>> from lsst.pipe.base import connectionTypes as cT
350 >>> from lsst.pipe.base import PipelineTaskConnections
351 >>> from lsst.pipe.base import PipelineTaskConfig
352 >>> class ExampleConnections(PipelineTaskConnections,
353 ... dimensions=("A", "B"),
354 ... defaultTemplates={"foo": "Example"}):
355 ... inputConnection = cT.Input(doc="Example input",
356 ... dimensions=("A", "B"),
357 ... storageClass=Exposure,
358 ... name="{foo}Dataset")
359 ... outputConnection = cT.Output(doc="Example output",
360 ... dimensions=("A", "B"),
361 ... storageClass=Exposure,
362 ... name="{foo}output")
363 >>> class ExampleConfig(PipelineTaskConfig,
364 ... pipelineConnections=ExampleConnections):
365 ... pass
366 >>> config = ExampleConfig()
367 >>> config.connections.foo = Modified
368 >>> config.connections.outputConnection = "TotallyDifferent"
369 >>> connections = ExampleConnections(config=config)
370 >>> assert(connections.inputConnection.name == "ModifiedDataset")
371 >>> assert(connections.outputConnection.name == "TotallyDifferent")
372 """
374 def __init__(self, *, config: 'PipelineTaskConfig' = None):
375 self.inputs = set(self.inputs)
376 self.prerequisiteInputs = set(self.prerequisiteInputs)
377 self.outputs = set(self.outputs)
378 self.initInputs = set(self.initInputs)
379 self.initOutputs = set(self.initOutputs)
381 if config is None or not isinstance(config, configMod.PipelineTaskConfig):
382 raise ValueError("PipelineTaskConnections must be instantiated with"
383 " a PipelineTaskConfig instance")
384 self.config = config
385 # Extract the template names that were defined in the config instance
386 # by looping over the keys of the defaultTemplates dict specified at
387 # class declaration time
388 templateValues = {name: getattr(config.connections, name) for name in getattr(self,
389 'defaultTemplates').keys()}
390 # Extract the configured value corresponding to each connection
391 # variable. I.e. for each connection identifier, populate a override
392 # for the connection.name attribute
393 self._nameOverrides = {name: getattr(config.connections, name).format(**templateValues)
394 for name in self.allConnections.keys()}
396 # connections.name corresponds to a dataset type name, create a reverse
397 # mapping that goes from dataset type name to attribute identifier name
398 # (variable name) on the connection class
399 self._typeNameToVarName = {v: k for k, v in self._nameOverrides.items()}
401 def buildDatasetRefs(self, quantum: Quantum) -> typing.Tuple[InputQuantizedConnection,
402 OutputQuantizedConnection]:
403 """Builds QuantizedConnections corresponding to input Quantum
405 Parameters
406 ----------
407 quantum : `lsst.daf.butler.Quantum`
408 Quantum object which defines the inputs and outputs for a given
409 unit of processing
411 Returns
412 -------
413 retVal : `tuple` of (`InputQuantizedConnection`,
414 `OutputQuantizedConnection`) Namespaces mapping attribute names
415 (identifiers of connections) to butler references defined in the
416 input `lsst.daf.butler.Quantum`
417 """
418 inputDatasetRefs = InputQuantizedConnection()
419 outputDatasetRefs = OutputQuantizedConnection()
420 # operate on a reference object and an interable of names of class
421 # connection attributes
422 for refs, names in zip((inputDatasetRefs, outputDatasetRefs),
423 (itertools.chain(self.inputs, self.prerequisiteInputs), self.outputs)):
424 # get a name of a class connection attribute
425 for attributeName in names:
426 # get the attribute identified by name
427 attribute = getattr(self, attributeName)
428 # Branch if the attribute dataset type is an input
429 if attribute.name in quantum.predictedInputs:
430 # Get the DatasetRefs
431 quantumInputRefs = quantum.predictedInputs[attribute.name]
432 # if the dataset is marked to load deferred, wrap it in a
433 # DeferredDatasetRef
434 if attribute.deferLoad:
435 quantumInputRefs = [DeferredDatasetRef(datasetRef=ref) for ref in quantumInputRefs]
436 # Unpack arguments that are not marked multiples (list of
437 # length one)
438 if not attribute.multiple:
439 if len(quantumInputRefs) > 1:
440 raise ScalarError(attributeName, len(quantumInputRefs))
441 if len(quantumInputRefs) == 0:
442 continue
443 quantumInputRefs = quantumInputRefs[0]
444 # Add to the QuantizedConnection identifier
445 setattr(refs, attributeName, quantumInputRefs)
446 # Branch if the attribute dataset type is an output
447 elif attribute.name in quantum.outputs:
448 value = quantum.outputs[attribute.name]
449 # Unpack arguments that are not marked multiples (list of
450 # length one)
451 if not attribute.multiple:
452 value = value[0]
453 # Add to the QuantizedConnection identifier
454 setattr(refs, attributeName, value)
455 # Specified attribute is not in inputs or outputs dont know how
456 # to handle, throw
457 else:
458 raise ValueError(f"Attribute with name {attributeName} has no counterpoint "
459 "in input quantum")
460 return inputDatasetRefs, outputDatasetRefs
462 def adjustQuantum(self, datasetRefMap: InputQuantizedConnection):
463 """Override to make adjustments to `lsst.daf.butler.DatasetRef` objects
464 in the `lsst.daf.butler.core.Quantum` during the graph generation stage
465 of the activator.
467 Parameters
468 ----------
469 datasetRefMap : `dict`
470 Mapping with keys of dataset type name to `list` of
471 `lsst.daf.butler.DatasetRef` objects
473 Returns
474 -------
475 datasetRefMap : `dict`
476 Modified mapping of input with possible adjusted
477 `lsst.daf.butler.DatasetRef` objects
479 Raises
480 ------
481 Exception
482 Overrides of this function have the option of raising an Exception
483 if a field in the input does not satisfy a need for a corresponding
484 pipelineTask, i.e. no reference catalogs are found.
485 """
486 return datasetRefMap
489def iterConnections(connections: PipelineTaskConnections, connectionType: str) -> typing.Generator:
490 """Creates an iterator over the selected connections type which yields
491 all the defined connections of that type.
493 Parameters
494 ----------
495 connections: `PipelineTaskConnections`
496 An instance of a `PipelineTaskConnections` object that will be iterated
497 over.
498 connectionType: `str`
499 The type of connections to iterate over, valid values are inputs,
500 outputs, prerequisiteInputs, initInputs, initOutputs.
502 Yields
503 -------
504 connection: `BaseConnection`
505 A connection defined on the input connections object of the type
506 supplied. The yielded value Will be an derived type of
507 `BaseConnection`.
508 """
509 for name in getattr(connections, connectionType):
510 yield getattr(connections, name)