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def | __new__ (cls, *args, **kwargs) |
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def | __init__ (self, **kwargs) |
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def | __getstate__ (self) |
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def | __setstate__ (self, state) |
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def | keys (self) |
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def | queryMetadata (self, datasetType, format, dataId) |
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def | getDatasetTypes (self) |
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def | map (self, datasetType, dataId, write=False) |
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def | canStandardize (self, datasetType) |
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def | standardize (self, datasetType, item, dataId) |
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def | validate (self, dataId) |
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def | backup (self, datasetType, dataId) |
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def | getRegistry (self) |
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Mapper is a base class for all mappers.
Subclasses may define the following methods:
map_{datasetType}(self, dataId, write)
Map a dataset id for the given dataset type into a ButlerLocation.
If write=True, this mapping is for an output dataset.
query_{datasetType}(self, key, format, dataId)
Return the possible values for the format fields that would produce
datasets at the granularity of key in combination with the provided
partial dataId.
std_{datasetType}(self, item)
Standardize an object of the given data set type.
Methods that must be overridden:
keys(self)
Return a list of the keys that can be used in data ids.
Other public methods:
__init__(self)
getDatasetTypes(self)
map(self, datasetType, dataId, write=False)
queryMetadata(self, datasetType, key, format, dataId)
canStandardize(self, datasetType)
standardize(self, datasetType, item, dataId)
validate(self, dataId)
Definition at line 29 of file mapper.py.
def lsst.daf.persistence.mapper.Mapper.__new__ |
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cls, |
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args, |
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kwargs |
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Create a new Mapper, saving arguments for pickling.
This is in __new__ instead of __init__ to save the user
from having to save the arguments themselves (either explicitly,
or by calling the super's __init__ with all their
*args,**kwargs. The resulting pickling system (of __new__,
__getstate__ and __setstate__ is similar to how __reduce__
is usually used, except that we save the user from any
responsibility (except when overriding __new__, but that
is not common).
Definition at line 82 of file mapper.py.