JsonInputArchive#

class lsst.images.json.JsonInputArchive(indirect: list[Any] | None = None)#

Bases: InputArchive[JsonRef]

An implementation of the serialization.InputArchive interface that reads from JSON files.

Parameters#

indirect: The serialization.ArchiveTree.indirect attribute of the root serialization model.

Methods Summary

`deserialize_pointer`(pointer, model_type, ...)	Deserialize an object that was saved by `serialize_pointer`.
`get_array`(model, *[, slices, strip_header])	Load an array from the archive.
`get_basic_info`(path)	Read the top-level tree's `schema_url`; JSON has no container format version.
`get_frame_set`(ref)	Return an already-deserialized frame set from the archive.
`get_opaque_metadata`()	Return opaque metadata loaded from the file that should be saved if another version of the object is saved to the same file format.
`get_structured_array`(model[, strip_header])	Load a table from the archive as a structured array.
`get_table`(model[, strip_header])	Load a table from the archive.
`open_tree`(path, tree_cls, *[, partial])	Parse the JSON tree and yield `(archive, tree)`.

Methods Documentation

deserialize_pointer(pointer: JsonRef, model_type: type[U], deserializer: Callable[[U, InputArchive[JsonRef]], V]) → V#

Deserialize an object that was saved by serialize_pointer.

Parameters#

pointer: JSON Pointer model to dereference.
model_type: Pydantic model type that the pointer should dereference to.
deserializer: Callable that takes an instance of model_type and an input archive, and returns the deserialized object.

Returns#

V: The deserialized object.

Notes#

Implementations are required to remember previously-deserialized objects and return them when the same pointer is passed in multiple times.

There is no deserialize_direct (to pair with serialize_direct) because the caller can just call a deserializer function directly on a sub-model of its Pydantic tree.

get_array(model: ~lsst.images.serialization._asdf_utils.ArrayReferenceModel | ~lsst.images.serialization._asdf_utils.InlineArrayModel, *, slices: tuple[slice, ...] | ellipsis = Ellipsis, strip_header: ~collections.abc.Callable[[~astropy.io.fits.header.Header], None] = <function no_header_updates>) → ndarray#

Load an array from the archive.

Parameters#

model: A Pydantic model that references or holds the array.
slices: Slices that specify a subset of the original array to read.
strip_header: A callable that strips out any FITS header cards added by the update_header argument in the corresponding call to add_array.

classmethod get_basic_info(path: str | ParseResult | ResourcePath | Path) → ArchiveInfo#

Read the top-level tree’s schema_url; JSON has no container format version.

This parses the whole document. Unlike the FITS and NDF backends there is no cheap header to read: schema_url is a computed field serialized after the (potentially large) indirect payload, and nested trees carry their own schema_url, so a bounded prefix cannot identify the top-level tree reliably. JSON is not intended for large pixel archives, where FITS or NDF should be used instead.

get_frame_set(ref: JsonRef) → FrameSet#

Return an already-deserialized frame set from the archive.

Parameters#

ref: Implementation-specific reference to the frame set.

Returns#

FrameSet: Loaded frame set.

get_opaque_metadata() → OpaqueArchiveMetadata | None#

Return opaque metadata loaded from the file that should be saved if another version of the object is saved to the same file format.

Returns#

OpaqueArchiveMetadata: Opaque metadata specific to this archive type that should be round-tripped if it is saved in the same format.

get_structured_array(model: ~lsst.images.serialization._tables.TableModel, strip_header: ~collections.abc.Callable[[~astropy.io.fits.header.Header], None] = <function no_header_updates>) → ndarray#

Load a table from the archive as a structured array.

Parameters#

model: A Pydantic model that references or holds the table.
strip_header: A callable that strips out any FITS header cards added by the update_header argument in the corresponding call to add_structured_array.

Returns#

numpy.ndarray: The loaded table as a structured array.

get_table(model: ~lsst.images.serialization._tables.TableModel, strip_header: ~collections.abc.Callable[[~astropy.io.fits.header.Header], None] = <function no_header_updates>) → Table#

Load a table from the archive.

Parameters#

model: A Pydantic model that references or holds the table.
strip_header: A callable that strips out any FITS header cards added by the update_header argument in the corresponding call to add_table.

Returns#

astropy.table.Table: The loaded table.

classmethod open_tree(path: str | ParseResult | ResourcePath | Path, tree_cls: type[ArchiveTree], *, partial: bool = True, **backend_kwargs: Any) → Iterator[tuple[Self, ArchiveTree]]#

Parse the JSON tree and yield (archive, tree).

A no-resource context manager: JSON is fully in memory, so partial is a no-op. tree.indirect is released when the context exits.