FitsInputArchive#

class lsst.images.fits.FitsInputArchive(stream: IO[bytes])#

Bases: InputArchive[TableCellReferenceModel]

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

Instances of this class should only be constructed via the open context manager.

Methods Summary

`deserialize_pointer`(pointer, model_type, ...)	Deserialize an object that was saved by `serialize_pointer`.
`get_array`(ref, *[, slices, strip_header])	Load an array from the archive.
`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`(ref[, strip_header])	Load a table from the archive as a structured array.
`get_table`(ref[, strip_header])	Load a table from the archive.
`get_tree`(model_type)	Read the JSON tree from the archive.
`open`(path, *[, page_size, partial])	Create an output archive that writes to the given file.

Methods Documentation

deserialize_pointer(pointer: TableCellReferenceModel, model_type: type[U], deserializer: Callable[[U, InputArchive[TableCellReferenceModel]], 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(ref: ~lsst.images.serialization._asdf_utils.ArrayReferenceModel, *, 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#

ref: A Pydantic model that references 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.

get_frame_set(ref: TableCellReferenceModel) → 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() → FitsOpaqueMetadata#

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(ref: ~lsst.images.serialization._tables.TableReferenceModel, 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#

ref: A Pydantic model that references 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(ref: ~lsst.images.serialization._tables.TableReferenceModel, strip_header: ~collections.abc.Callable[[~astropy.io.fits.header.Header], None] = <function no_header_updates>) → Table#

Load a table from the archive.

Parameters#

ref: A Pydantic model that references 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.

get_tree(model_type: type[T]) → T#

Read the JSON tree from the archive.

Parameters#

model_type: A Pydantic model type to use to validate the JSON.

Returns#

T: The validated Pydantic model.

classmethod open(path: str | ParseResult | ResourcePath | Path, *, page_size: int = 144000, partial: bool = False) → Iterator[Self]#

Create an output archive that writes to the given file.

Parameters#

path: File to read; convertible to lsst.resources.ResourcePath.
page_size: Minimum number of bytes to read at at once. Making this a multiple of the FITS block size (2880) is recommended.
partial: Whether we will be reading only some of the archive, or if memory pressure forces us to read it only a little at a time. If False (default), the entire raw file may be read into memory up front.

Returns#

contextlib.AbstractContextManager [FitsInputArchive]: A context manager that returns a FitsInputArchive when entered.