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LSSTDataManagementBasePackage
coaddHelpers.py
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2 # LSST Data Management System
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22 
23 from lsst.pipe.base import Struct
24 
25 """Helper functions for coaddition.
26 
27 We often want to use a data reference as a key in a dict (e.g., inputs as a
28 function of data reference for a warp/tempExp), but neither data references
29 (lsst.daf.persistence.ButlerDataRef) nor data identifiers (dict) are hashable.
30 One solution is to use tuples (which are hashable) of the data identifier
31 values, and carry the data identifier keys separately. Doing the key/value
32 gymnastics can be annoying, so we provide these helper functions to do this.
33 """
34 
35 def groupDataRefs(keys, dataRefIterable):
36  """Group data references by data identifier value-tuple.
37 
38  Value-tuples are built from the values of the given keys.
39  The effect is that the data references in each group have the same
40  values for the provided keys.
41 
42  @param keys: List of keys to consider when grouping (order is important)
43  @param dataRefIterable: Iterable of data references to group
44  @return Dict of <value-tuple>: <list of data references for group>
45  """
46  groupDict = dict()
47  for dataRef in dataRefIterable:
48  dataId = dataRef.dataId
49  values = tuple(dataId[key] for key in keys) # NOT dataId.values() as we must preserve order
50  group = groupDict.get(values)
51  if group:
52  group.append(dataRef)
53  else:
54  groupDict[values] = [dataRef]
55 
56  return groupDict
57 
58 def groupPatchExposures(patchDataRef, calexpDataRefList, coaddDatasetType="deepCoadd",
59  tempExpDatasetType="deepCoadd_tempExp"):
60  """Group calibrated exposures overlapping a patch by the warped
61  (temporary) exposure they contribute to.
62 
63  For example, if the instrument has a mosaic camera, each group would
64  consist of the subset of CCD exposures from a single camera exposure
65  that potentially overlap the patch.
66 
67  @return Struct with:
68  - groups: Dict of <group tuple>: <list of data references for group>
69  - keys: List of keys for group tuple
70  """
71  butler = patchDataRef.getButler()
72  tempExpKeys = butler.getKeys(datasetType=tempExpDatasetType)
73  coaddKeys = sorted(butler.getKeys(datasetType=coaddDatasetType))
74  keys = sorted(set(tempExpKeys) - set(coaddKeys)) # Keys that will specify an exposure
75  patchId = patchDataRef.dataId
76  groups = groupDataRefs(keys, calexpDataRefList)
77 
78  # Supplement the groups with the coadd-specific information (e.g., tract, patch; these are constant)
79  coaddValues = tuple(patchId[k] for k in coaddKeys)
80  groups = dict((k + coaddValues, v) for k,v in groups.iteritems())
81  keys += tuple(coaddKeys)
82 
83  return Struct(groups=groups, keys=keys)
84 
85 def getGroupDataId(groupTuple, keys):
86  """Reconstitute a data identifier from a tuple and corresponding keys
87 
88  @param groupTuple: Tuple with values specifying a group
89  @param keys: List of keys for group tuple
90  @return Data identifier dict
91  """
92  if len(groupTuple) != len(keys):
93  raise RuntimeError("Number of values (%d) and keys (%d) do not match" % (len(groupTuple), len(keys)))
94  return dict(zip(keys, groupTuple))
95 
96 def getGroupDataRef(butler, datasetType, groupTuple, keys):
97  """Construct a data reference from a tuple and corresponding keys
98 
99  @param butler: Data butler
100  @param datasetType: Name of dataset
101  @param groupTuple: Tuple with values specifying a group
102  @param keys: List of keys for group tuple
103  @return Data reference
104  """
105  dataId = getGroupDataId(groupTuple, keys)
106  return butler.dataRef(datasetType=datasetType, dataId=dataId)