LSSTApplications  19.0.0-14-gb0260a2+72efe9b372,20.0.0+7927753e06,20.0.0+8829bf0056,20.0.0+995114c5d2,20.0.0+b6f4b2abd1,20.0.0+bddc4f4cbe,20.0.0-1-g253301a+8829bf0056,20.0.0-1-g2b7511a+0d71a2d77f,20.0.0-1-g5b95a8c+7461dd0434,20.0.0-12-g321c96ea+23efe4bbff,20.0.0-16-gfab17e72e+fdf35455f6,20.0.0-2-g0070d88+ba3ffc8f0b,20.0.0-2-g4dae9ad+ee58a624b3,20.0.0-2-g61b8584+5d3db074ba,20.0.0-2-gb780d76+d529cf1a41,20.0.0-2-ged6426c+226a441f5f,20.0.0-2-gf072044+8829bf0056,20.0.0-2-gf1f7952+ee58a624b3,20.0.0-20-geae50cf+e37fec0aee,20.0.0-25-g3dcad98+544a109665,20.0.0-25-g5eafb0f+ee58a624b3,20.0.0-27-g64178ef+f1f297b00a,20.0.0-3-g4cc78c6+e0676b0dc8,20.0.0-3-g8f21e14+4fd2c12c9a,20.0.0-3-gbd60e8c+187b78b4b8,20.0.0-3-gbecbe05+48431fa087,20.0.0-38-ge4adf513+a12e1f8e37,20.0.0-4-g97dc21a+544a109665,20.0.0-4-gb4befbc+087873070b,20.0.0-4-gf910f65+5d3db074ba,20.0.0-5-gdfe0fee+199202a608,20.0.0-5-gfbfe500+d529cf1a41,20.0.0-6-g64f541c+d529cf1a41,20.0.0-6-g9a5b7a1+a1cd37312e,20.0.0-68-ga3f3dda+5fca18c6a4,20.0.0-9-g4aef684+e18322736b,w.2020.45
LSSTDataManagementBasePackage
_implDetails.py
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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/>.
21 from __future__ import annotations
22 from collections import defaultdict
23 
24 __all__ = ("_DatasetTracker", "DatasetTypeName")
25 
26 from dataclasses import dataclass, field
27 import networkx as nx
28 from typing import (DefaultDict, Generic, Optional, Set, TypeVar, Generator, Tuple, NewType,)
29 
30 from lsst.daf.butler import DatasetRef
31 
32 from .quantumNode import QuantumNode
33 from ..pipeline import TaskDef
34 
35 # NewTypes
36 DatasetTypeName = NewType("DatasetTypeName", str)
37 
38 # Generic type parameters
39 _T = TypeVar("_T", DatasetTypeName, DatasetRef)
40 _U = TypeVar("_U", TaskDef, QuantumNode)
41 
42 
43 @dataclass
44 class _DatasetTrackerElement(Generic[_U]):
45  inputs: Set[_U] = field(default_factory=set)
46  output: Optional[_U] = None
47 
48 
49 class _DatasetTracker(Generic[_T, _U]):
50  def __init__(self):
51  self._container: DefaultDict[_T, _DatasetTrackerElement[_U]] = defaultdict(_DatasetTrackerElement)
52 
53  def addInput(self, key: _T, value: _U):
54  self._container[key].inputs.add(value)
55 
56  def addOutput(self, key: _T, value: _U):
57  element = self._container[key]
58  if element.output is not None:
59  raise ValueError(f"Only one output for key {key} is allowed, "
60  f"the current output is set to {element.output}")
61  element.output = value
62 
63  def getInputs(self, key: _T) -> Set[_U]:
64  return self._container[key].inputs
65 
66  def getOutput(self, key: _T) -> Optional[_U]:
67  return self._container[key].output
68 
69  def getAll(self, key: _T) -> Set[_U]:
70  output = self._container[key].output
71  if output is not None:
72  return self._container[key].inputs.union((output,))
73  return set(self._container[key].inputs)
74 
75  def makeNetworkXGraph(self) -> nx.DiGraph:
76  graph = nx.DiGraph()
77  graph.add_edges_from(self._datasetDictToEdgeIterator())
78  if None in graph.nodes():
79  graph.remove_node(None)
80  return graph
81 
82  def _datasetDictToEdgeIterator(self) -> Generator[Tuple[Optional[_U], Optional[_U]], None, None]:
83  """Helper function designed to be used in conjunction with
84  `networkx.DiGraph.add_edges_from`. This takes a mapping of keys to
85  `_DatasetTrackers` and yields successive pairs of elements that are to
86  be considered connected by the graph.
87  """
88  for entry in self._container.values():
89  # If there is no inputs and only outputs (likely in test cases or
90  # building inits or something) use None as a Node, that will then
91  # be removed later
92  inputs = entry.inputs or (None,)
93  for inpt in inputs:
94  yield (entry.output, inpt)
95 
96  def keys(self) -> Set[_T]:
97  return set(self._container.keys())
lsst.pipe.base.graph._implDetails._DatasetTracker.getOutput
Optional[_U] getOutput(self, _T key)
Definition: _implDetails.py:66
lsst.pipe.base.graph._implDetails._DatasetTracker.makeNetworkXGraph
nx.DiGraph makeNetworkXGraph(self)
Definition: _implDetails.py:75
lsst.pipe.base.graph._implDetails._DatasetTracker.getInputs
Set[_U] getInputs(self, _T key)
Definition: _implDetails.py:63
lsst.pipe.base.graph._implDetails._DatasetTrackerElement
Definition: _implDetails.py:44
field
lsst.pipe.base.graph._implDetails._DatasetTracker.keys
Set[_T] keys(self)
Definition: _implDetails.py:96
lsst.pipe.base.graph._implDetails._DatasetTracker.addInput
def addInput(self, _T key, _U value)
Definition: _implDetails.py:53
lsst.pipe.base.graph._implDetails._DatasetTracker.addOutput
def addOutput(self, _T key, _U value)
Definition: _implDetails.py:56
lsst.pipe.base.graph._implDetails._DatasetTracker._datasetDictToEdgeIterator
Generator[Tuple[Optional[_U], Optional[_U]], None, None] _datasetDictToEdgeIterator(self)
Definition: _implDetails.py:82
lsst.pipe.base.graph._implDetails._DatasetTracker.getAll
Set[_U] getAll(self, _T key)
Definition: _implDetails.py:69
set
daf::base::PropertySet * set
Definition: fits.cc:912
lsst.pipe.base.graph._implDetails._DatasetTracker
Definition: _implDetails.py:49
lsst.pipe.base.graph._implDetails._DatasetTracker.__init__
def __init__(self)
Definition: _implDetails.py:50