24 from builtins
import object
29 """This module defines the Mapper base class."""
33 """Mapper is a base class for all mappers.
35 Subclasses may define the following methods:
37 map_{datasetType}(self, dataId, write)
38 Map a dataset id for the given dataset type into a ButlerLocation.
39 If write=True, this mapping is for an output dataset.
41 query_{datasetType}(self, key, format, dataId)
42 Return the possible values for the format fields that would produce
43 datasets at the granularity of key in combination with the provided
46 std_{datasetType}(self, item)
47 Standardize an object of the given data set type.
49 Methods that must be overridden:
52 Return a list of the keys that can be used in data ids.
60 map(self, datasetType, dataId, write=False)
62 queryMetadata(self, datasetType, key, format, dataId)
64 canStandardize(self, datasetType)
66 standardize(self, datasetType, item, dataId)
68 validate(self, dataId)
73 '''Instantiate a Mapper from a configuration.
74 In come cases the cfg may have already been instantiated into a Mapper, this is allowed and
75 the input var is simply returned.
77 :param cfg: the cfg for this mapper. It is recommended this be created by calling
79 :return: a Mapper instance
81 if isinstance(cfg, Policy):
82 return cfg[
'cls'](cfg)
86 """Create a new Mapper, saving arguments for pickling.
88 This is in __new__ instead of __init__ to save the user
89 from having to save the arguments themselves (either explicitly,
90 or by calling the super's __init__ with all their
91 *args,**kwargs. The resulting pickling system (of __new__,
92 __getstate__ and __setstate__ is similar to how __reduce__
93 is usually used, except that we save the user from any
94 responsibility (except when overriding __new__, but that
97 self = super(Mapper, cls).
__new__(cls)
105 return self._arguments
113 raise NotImplementedError(
"keys() unimplemented")
116 """Get possible values for keys given a partial data id.
118 :param datasetType: see documentation about the use of datasetType
119 :param key: this is used as the 'level' parameter
121 :param dataId: see documentation about the use of dataId
124 func = getattr(self,
'query_' + datasetType)
126 val = func(format, self.
validate(dataId))
130 """Return a list of the mappable dataset types."""
133 for attr
in dir(self):
134 if attr.startswith(
"map_"):
135 list.append(attr[4:])
138 def map(self, datasetType, dataId, write=False):
139 """Map a data id using the mapping method for its dataset type.
144 The datasetType to map
145 dataId : DataId instance
146 The dataId to use when mapping
147 write : bool, optional
148 Indicates if the map is being performed for a read operation
149 (False) or a write operation (True)
153 ButlerLocation or a list of ButlerLocation
154 The location(s) found for the map operation. If write is True, a
155 list is returned. If write is False a single ButlerLocation is
161 If no locaiton was found for this map operation, the derived mapper
162 class may raise a lsst.daf.persistence.NoResults exception. Butler
163 catches this and will look in the next Repository if there is one.
165 func = getattr(self,
'map_' + datasetType)
166 return func(self.
validate(dataId), write)
169 """Return true if this mapper can standardize an object of the given
172 return hasattr(self,
'std_' + datasetType)
175 """Standardize an object using the standardization method for its data
176 set type, if it exists."""
178 if hasattr(self,
'std_' + datasetType):
179 func = getattr(self,
'std_' + datasetType)
180 return func(item, self.
validate(dataId))
184 """Validate a dataId's contents.
186 If the dataId is valid, return it. If an invalid component can be
187 transformed into a valid one, copy the dataId, fix the component, and
188 return the copy. Otherwise, raise an exception."""
193 """Rename any existing object with the given type and dataId.
195 Not implemented in the base mapper.
197 raise NotImplementedError(
"Base-class Mapper does not implement backups")
200 """Get the registry"""