LSSTApplications  18.1.0
LSSTDataManagementBasePackage
calibRepoConverter.py
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21 
22 __all__ = ("CalibRepoConverter",)
23 
24 import sqlite3
25 import os
26 from datetime import datetime, timedelta
27 
28 from lsst.daf.butler.gen2convert import makeCalibrationLabel
29 from .repoConverter import RepoConverter
30 
31 
33  """A helper class that ingests (some of) the contents of a Gen2 calibration
34  data repository into a Gen3 data repository.
35 
36  This class must be used instead of the base `RepoConverter` to process
37  dataset types that are associated with validity ranges.
38 
39  Parameters
40  ----------
41  root : `str`
42  Root of the Gen2 data repository.
43  universe : `lsst.daf.butler.DimensionUniverse`
44  Object containing all dimension definitions.
45  baseDataId : `dict`
46  Key-value pairs that may need to appear in the Gen3 data ID, but can
47  never be inferred from a Gen2 filename. This should always include
48  the instrument name (even Gen3 data IDs that don't involve the
49  instrument dimension have instrument-dependent Gen2 filenames).
50  mapper : `lsst.obs.base.CameraMapper`, optional
51  Object that defines Gen2 filename templates. Will be identified,
52  imported, and constructed from ``root`` if not provided.
53  """
54 
55  def __init__(self, root, *, universe, baseDataId, mapper=None):
56  super().__init__(root, universe=universe, baseDataId=baseDataId, mapper=mapper)
57  self.db = sqlite3.connect(os.path.join(root, "calibRegistry.sqlite3"))
58  self.db.row_factory = sqlite3.Row
59  self.dimensionEntries = []
60  self.baseDataId = baseDataId
61 
62  def addDatasetType(self, datasetTypeName, storageClass):
63  # Docstring inherited from RepoConverter.addDatasetType
64  extractor = super().addDatasetType(datasetTypeName, storageClass)
65  if "calibration_label" not in extractor.datasetType.dimensions:
66  return extractor
67  fields = ["validStart", "validEnd", "calibDate"]
68  if "detector" in extractor.datasetType.dimensions.names:
69  fields.append("ccd")
70  else:
71  fields.append("NULL AS ccd")
72  if "physical_filter" in extractor.datasetType.dimensions.names:
73  fields.append("filter")
74  else:
75  fields.append("NULL AS filter")
76  query = f"SELECT DISTINCT {', '.join(fields)} FROM {datasetTypeName};"
77  for row in self.db.execute(query):
78  label = makeCalibrationLabel(datasetTypeName, row["calibDate"],
79  ccd=row["ccd"], filter=row["filter"])
81  "instrument": self.baseDataId["instrument"],
82  "calibration_label": label,
83  "valid_first": datetime.strptime(row["validStart"], "%Y-%m-%d"),
84  "valid_last": datetime.strptime(row["validEnd"], "%Y-%m-%d") + timedelta(days=1),
85  })
86  return extractor
87 
88  def convertRepo(self, butler, *, directory=None, transfer=None, formatter=None, skipDirs=()):
89  # Docstring inherited from RepoConverter.convertRepo.
90  butler.registry.addDimensionEntryList(butler.registry.dimensions["calibration_label"],
91  self.dimensionEntries)
92  super().convertRepo(butler, directory=directory, transfer=transfer, formatter=formatter,
93  skipDirs=tuple(skipDirs) + ("focalplane",))
def convertRepo(self, butler, directory=None, transfer=None, formatter=None, skipDirs=())
std::shared_ptr< FrameSet > append(FrameSet const &first, FrameSet const &second)
Construct a FrameSet that performs two transformations in series.
Definition: functional.cc:33
def __init__(self, root, universe, baseDataId, mapper=None)
def addDatasetType(self, datasetTypeName, storageClass)