ytree.frontends.consistent_trees_hdf5.fields.ConsistentTreesHDF5FieldInfo

class ytree.frontends.consistent_trees_hdf5.fields.ConsistentTreesHDF5FieldInfo(arbor)[source]

Methods

`__init__`(arbor)
`add_alias_field`(alias, field[, units, force_add])	Add an alias field.
`add_analysis_field`(name, units[, dtype, default])	Add an analysis field.
`add_derived_field`(name, function[, units, ...])	Add a derived field.
`add_vector_field`(fieldname)	Add vector and magnitude fields for a field with x/y/z components.
`clear`()
`copy`()
`fromkeys`([value])	Create a new dictionary with keys from iterable and values set to value.
`get`(key[, default])	Return the value for key if key is in the dictionary, else default.
`items`()
`keys`()
`pop`(k[,d])	If key is not found, default is returned if given, otherwise KeyError is raised
`popitem`()	Remove and return a (key, value) pair as a 2-tuple.
`resolve_field_dependencies`(fields[, fcache, ...])	Divide fields into those to be read and those to generate.
`setdefault`(key[, default])	Insert key with a value of default if key is not in the dictionary.
`setup_aliases`()	Add aliases defined in the alias_fields tuple for each frontend.
`setup_derived_fields`()	Add stock derived fields.
`setup_known_fields`()	Add units for fields on disk as defined in the known_fields tuple.
`setup_vector_fields`()	Add vector and magnitude fields.
`update`([E, ]**F)	If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
`values`()

Attributes