Core Features
This section discusses the core features of the ASDF data format, and provides examples and use cases that are specific to the Python implementation.
Data Model
The fundamental data object in ASDF is the tree
, which is a nested
combination of basic data structures: dictionaries, lists, strings and numbers.
In Python, these types correspond to dict
, list
,
str
, and int
, float
, and complex
,
respectively. The top-level tree object behaves like a Python dictionary and
supports arbitrary nesting of data structures. For simple examples of creating
and reading trees, see Overview.
Note
The ASDF Standard imposes a maximum size of 64-bit signed integers literals in the tree (see Literal integer values in the Tree for details and justification). Attempting to store a larger value as a YAML literal will result in a validation error.
For arbitrary precision integer support, see IntegerType
.
Integers and floats of up to 64 bits can be stored inside of numpy
arrays (see below).
Note
The ASDF standard does not have an immutable sequence type that maps directly
to Python’s tuple
. Following the behavior of
pyyaml, asdf writes tuples as YAML sequences, which when loaded
are converted to lists. If round-tripping of tuples is important
to your application see the Extending ASDF to write a custom extension
to save and load tuples.
One of the key features of asdf
is its ability to serialize numpy
arrays. This is discussed in detail in Array Data.
While the core asdf
package supports serialization of basic data types and
Numpy arrays, its true power comes from its ability to be extended to support
serialization of a wide range of custom data types. Details on using ASDF
extensions can be found in Using extensions. Details on creating custom
ASDF extensions to support custom data types can be found in Extending ASDF.
Array Data
Much of ASDF’s power and convenience comes from its ability to represent
multidimensional array data. The asdf
Python package provides native
support for numpy
arrays.
Using extensions
According to Wikipedia, serialization “is the process of translating data structures or object state into a format that can be stored…and reconstructed later” [1].
The power of ASDF is that it provides the ability to store, or serialize, the state of Python objects into a human-readable data format. The state of those objects can later be restored by another program in a process called deserialization.
While ASDF is capable of serializing basic Python types and Numpy arrays out of the box, it can also be extended to serialize arbitrary custom data types. This section discusses the extension mechanism from a user’s perspective. For documentation on creating extensions, see Extensions.
Even though this particular implementation of ASDF necessarily serializes Python data types, in theory an ASDF implementation in another language could read the resulting file and reconstruct an analogous type in that language. Conversely, this implementation can read ASDF files that were written by other implementations of ASDF as long as the proper extensions are available.
Schema validation
Schema validation is used to determine whether an ASDF file is well formed. All
ASDF files must conform to the schemas defined by the ASDF Standard. Schema validation can be run using AsdfFile.validate
and occurs when reading ASDF files (using asdf.open
) and writing them out
(using AsdfFile.write_to
or AsdfFile.update
).
Schema validation also plays a role when using custom extensions (see Using extensions and Extensions). Extensions must provide schemas for the types that they serialize. When writing a file with custom types, the output is validated against the schemas corresponding to those types. If the appropriate extension is installed when reading a file with custom types, then the types will be validated against the schemas provided by the corresponding extension.
Custom schemas
Every ASDF file is validated against the ASDF Standard, and also against any schemas provided by custom extensions. However, it is sometimes useful for particular applications to impose additional restrictions when deciding whether a given file is valid or not.
For example, consider an application that processes digital image data. The application expects the file to contain an image, and also some metadata about how the image was created. The following example schema reflects these expectations:
%YAML 1.1
---
id: "http://example.com/schemas/your-custom-schema"
$schema: "http://stsci.edu/schemas/yaml-schema/draft-01"
type: object
properties:
image:
description: An ndarray containing image data.
$ref: "ndarray-1.0.0"
metadata:
type: object
description: Metadata about the image
properties:
time:
description: |
A timestamp for when the image was created, in UTC.
type: string
format: date-time
resolution:
description: |
A 2D array representing the resolution of the image (N x M).
type: array
items:
type: integer
number: 2
required: [image, metadata]
additionalProperties: true
This schema restricts the kinds of files that will be accepted as valid to
those that contain a top-level image
property that is an ndarray
, and
a top-level metadata
property that contains information about the time the
image was taken and the resolution of the image.
In order to use this schema for a secondary validation pass, we pass the
custom_schema
argument to either asdf.open
or the AsdfFile
constructor.
Assume that the schema file lives in image_schema.yaml
, and we wish to
open a file called image.asdf
. We would open the file with the following
code:
import asdf
af = asdf.open('image.asdf', custom_schema='image_schema.yaml')
Similarly, if we wished to use this schema when creating new files:
new_af = asdf.AsdfFile(custom_schema='image_schema.yaml')
...
If your custom schema is registered with ASDF in an extension, you may
pass the schema URI (http://example.com/schemas/your-custom-schema
, in this
case) instead of a file path.
Note
The top-level core schemas can be found here.
Versioning and Compatibility
There are several different versions to keep in mind when discussing ASDF:
The software package version
The ASDF Standard version
The ASDF file format version
Individual tag, schema, and extension versions
Each ASDF file contains information about the various versions that were used to create the file. The most important of these are the ASDF Standard version and the ASDF file format version. A particular version of the ASDF software package will explicitly provide support for specific combinations of these versions.
Tag, schema, and extension versions are also important for serializing and deserializing data types that are stored in ASDF files. A detailed discussion of these versions from a user perspective can be found in Custom types, extensions, and versioning.
Since ASDF is designed to serve as an archival format, this library is careful to maintain backwards compatibility with older versions of the ASDF Standard, ASDF file format, and core tags. However, since deserializing custom tags requires other software packages, backwards compatibility is often contingent on the available versions of such software packages.
In general, forward compatibility with newer versions of the ASDF Standard and ASDF file format is not supported by the software.
When creating new ASDF files, it is possible to control the version of the ASDF
standard that is used. This can be specified by passing the version
argument to
either the AsdfFile
constructor when the file object is created, or to the
AsdfFile.write_to
method when it is written. By default, the latest stable
version of the ASDF standard will be used.
Warning
Take care when providing version
to AsdfFile.write_to
to select a version
that is stable. Writing files with a development
(unstable) version may
produce files that will become unreadable as that version evolves. The default
version will always be stable and is often the best choice unless you are trying
to write out a file that is readable by older software (where you will want to
use an older, stable version).
External References
Tree References
ASDF files may reference items in the tree in other ASDF files. The
syntax used in the file for this is called “JSON Pointer”, but users
of asdf
can largely ignore that.
First, we’ll create a ASDF file with a couple of arrays in it:
import asdf
from asdf import AsdfFile
import numpy as np
tree = {
'a': np.arange(0, 10),
'b': np.arange(10, 20)
}
target = AsdfFile(tree)
target.write_to('target.asdf')
target.asdf
#ASDF 1.0.0
#ASDF_STANDARD 1.5.0
%YAML 1.1
%TAG ! tag:stsci.edu:asdf/
--- !core/asdf-1.1.0
asdf_library: !core/software-1.0.0 {author: The ASDF Developers, homepage: 'http://github.com/asdf-format/asdf',
name: asdf, version: 3.5.1.dev6+ga4e7961e}
history:
extensions:
- !core/extension_metadata-1.0.0
extension_class: asdf.extension._manifest.ManifestExtension
extension_uri: asdf://asdf-format.org/core/extensions/core-1.5.0
manifest_software: !core/software-1.0.0 {name: asdf_standard, version: 1.1.1}
software: !core/software-1.0.0 {name: asdf, version: 3.5.1.dev6+ga4e7961e}
a: !core/ndarray-1.0.0
source: 0
datatype: int64
byteorder: little
shape: [10]
b: !core/ndarray-1.0.0
source: 1
datatype: int64
byteorder: little
shape: [10]
...
BLOCK 0:
allocated_size: 80
used_size: 80
data_size: 80
data: b'0000000000000000010000000000000002000000...'
BLOCK 1:
allocated_size: 80
used_size: 80
data_size: 80
data: b'0a000000000000000b000000000000000c000000...'
#ASDF BLOCK INDEX
%YAML 1.1
---
- 779
- 913
...
Then we will reference those arrays in a couple of different ways.
First, we’ll load the source file in Python and use the
make_reference
method to generate a reference to array a
.
Second, we’ll work at the lower level by manually writing a JSON
Pointer to array b
, which doesn’t require loading or having access
to the target file.
ff = AsdfFile()
with asdf.open('target.asdf') as target:
ff.tree['my_ref_a'] = target.make_reference(['a'])
ff.tree['my_ref_b'] = {'$ref': 'target.asdf#b'}
ff.write_to('source.asdf')
source.asdf
#ASDF 1.0.0
#ASDF_STANDARD 1.5.0
%YAML 1.1
%TAG ! tag:stsci.edu:asdf/
--- !core/asdf-1.1.0
asdf_library: !core/software-1.0.0 {author: The ASDF Developers, homepage: 'http://github.com/asdf-format/asdf',
name: asdf, version: 3.5.1.dev6+ga4e7961e}
history:
extensions:
- !core/extension_metadata-1.0.0
extension_class: asdf.extension._manifest.ManifestExtension
extension_uri: asdf://asdf-format.org/core/extensions/core-1.5.0
manifest_software: !core/software-1.0.0 {name: asdf_standard, version: 1.1.1}
software: !core/software-1.0.0 {name: asdf, version: 3.5.1.dev6+ga4e7961e}
my_ref_a: {$ref: target.asdf#a}
my_ref_b: {$ref: target.asdf#b}
...
Calling find_references
will look up all of the
references so they can be used as if they were local to the tree. It
doesn’t actually move any of the data, and keeps the references as
references.
with asdf.open('source.asdf') as ff:
ff.find_references()
assert ff.tree['my_ref_b'].shape == (10,)
On the other hand, calling resolve_references
places all of the referenced content directly in the tree, so when we
write it out again, all of the external references are gone, with the
literal content in its place.
with asdf.open('source.asdf') as ff:
ff.resolve_references()
ff.write_to('resolved.asdf')
resolved.asdf
#ASDF 1.0.0
#ASDF_STANDARD 1.5.0
%YAML 1.1
%TAG ! tag:stsci.edu:asdf/
--- !core/asdf-1.1.0
asdf_library: !core/software-1.0.0 {author: The ASDF Developers, homepage: 'http://github.com/asdf-format/asdf',
name: asdf, version: 3.5.1.dev6+ga4e7961e}
history:
extensions:
- !core/extension_metadata-1.0.0
extension_class: asdf.extension._manifest.ManifestExtension
extension_uri: asdf://asdf-format.org/core/extensions/core-1.5.0
manifest_software: !core/software-1.0.0 {name: asdf_standard, version: 1.1.1}
software: !core/software-1.0.0 {name: asdf, version: 3.5.1.dev6+ga4e7961e}
my_ref_a: !core/ndarray-1.0.0
source: 0
datatype: int64
byteorder: little
shape: [10]
my_ref_b: !core/ndarray-1.0.0
source: 1
datatype: int64
byteorder: little
shape: [10]
...
BLOCK 0:
allocated_size: 80
used_size: 80
data_size: 80
data: b'0000000000000000010000000000000002000000...'
BLOCK 1:
allocated_size: 80
used_size: 80
data_size: 80
data: b'0a000000000000000b000000000000000c000000...'
#ASDF BLOCK INDEX
%YAML 1.1
---
- 793
- 927
...
A similar feature provided by YAML, anchors and aliases, also provides
a way to support references within the same file. These are supported
by asdf
, however the JSON Pointer approach is generally favored because:
It is possible to reference elements in another file
Elements are referenced by location in the tree, not an identifier, therefore, everything can be referenced.
Anchors and aliases are handled automatically by asdf
when the
data structure is recursive. For example here is a dictionary that is
included twice in the same tree:
d = {'foo': 'bar'}
d['baz'] = d
tree = {'d': d}
ff = AsdfFile(tree)
ff.write_to('anchors.asdf')
anchors.asdf
#ASDF 1.0.0
#ASDF_STANDARD 1.5.0
%YAML 1.1
%TAG ! tag:stsci.edu:asdf/
--- !core/asdf-1.1.0
asdf_library: !core/software-1.0.0 {author: The ASDF Developers, homepage: 'http://github.com/asdf-format/asdf',
name: asdf, version: 3.5.1.dev6+ga4e7961e}
history:
extensions:
- !core/extension_metadata-1.0.0
extension_class: asdf.extension._manifest.ManifestExtension
extension_uri: asdf://asdf-format.org/core/extensions/core-1.5.0
manifest_software: !core/software-1.0.0 {name: asdf_standard, version: 1.1.1}
software: !core/software-1.0.0 {name: asdf, version: 3.5.1.dev6+ga4e7961e}
d: &id001
baz: *id001
foo: bar
...
Array References
ASDF files can refer to array data that is stored in other files using the
ExternalArrayReference
type.
External files need not be ASDF files: ASDF is completely agnostic as to the format of the external file. The ASDF external array reference does not define how the external data file will be resolved; in fact it does not even check for the existence of the external file. It simply provides a way for ASDF files to refer to arrays that exist in external files.
Creating an external array reference is simple. Only four pieces of information are required:
The name of the external file. Since ASDF does not itself resolve the file or check for its existence, the format of the name is not important. In most cases the name will be a path relative to the ASDF file itself, or a URI for a network resource.
The data type of the array data. This is a string representing any valid
numpy.dtype
.The shape of the data array. This is a tuple representing the dimensions of the array data.
The array data
target
. This is either an integer or a string that indicates to the user something about how the data array should be accessed in the external file. For example, if there are multiple data arrays in the external file, thetarget
might be an integer index. Or if the external file is an ASDF file, thetarget
might be a string indicating the key to use in the external file’s tree. The value and format of thetarget
field is completely arbitrary since ASDF will not use it itself.
As an example, we will create a reference to an external CSV file. We will assume that one of the rows of the CSV file contains the array data we care about:
import asdf
csv_data_row = 10 # The row of the CSV file containing the data we want
csv_row_size = 100 # The size of the array
extref = asdf.ExternalArrayReference('data.csv', csv_data_row, "int64", (csv_row_size,))
tree = {'csv_data': extref}
af = asdf.AsdfFile(tree)
af.write_to('external_array.asdf')
external_array.asdf
#ASDF 1.0.0
#ASDF_STANDARD 1.5.0
%YAML 1.1
%TAG ! tag:stsci.edu:asdf/
--- !core/asdf-1.1.0
asdf_library: !core/software-1.0.0 {author: The ASDF Developers, homepage: 'http://github.com/asdf-format/asdf',
name: asdf, version: 3.5.1.dev6+ga4e7961e}
history:
extensions:
- !core/extension_metadata-1.0.0
extension_class: asdf.extension._manifest.ManifestExtension
extension_uri: asdf://asdf-format.org/core/extensions/core-1.5.0
manifest_software: !core/software-1.0.0 {name: asdf_standard, version: 1.1.1}
software: !core/software-1.0.0 {name: asdf, version: 3.5.1.dev6+ga4e7961e}
csv_data: !core/externalarray-1.0.0
datatype: int64
fileuri: data.csv
shape: [100]
target: 10
...
When reading a file containing external references, the user is responsible for
using the information in the ExternalArrayReference
type to open the external
file and retrieve the associated array data.
Saving history entries
asdf
has a convenience method for notating the history of transformations
that have been performed on a file.
Given a AsdfFile
object, call add_history_entry
, given
a description of the change and optionally a description of the software (i.e.
your software, not asdf
) that performed the operation.
from asdf import AsdfFile
import numpy as np
tree = {
'a': np.random.rand(32, 32)
}
ff = AsdfFile(tree)
ff.add_history_entry(
"Initial random numbers",
{'name': 'asdf examples',
'author': 'John Q. Public',
'homepage': 'http://github.com/asdf-format/asdf',
'version': '0.1'})
ff.write_to('example.asdf')
example.asdf
#ASDF 1.0.0
#ASDF_STANDARD 1.5.0
%YAML 1.1
%TAG ! tag:stsci.edu:asdf/
--- !core/asdf-1.1.0
asdf_library: !core/software-1.0.0 {author: The ASDF Developers, homepage: 'http://github.com/asdf-format/asdf',
name: asdf, version: 3.5.1.dev6+ga4e7961e}
history:
entries:
- !core/history_entry-1.0.0
description: Initial random numbers
software: !core/software-1.0.0 {author: John Q. Public, homepage: 'http://github.com/asdf-format/asdf',
name: asdf examples, version: '0.1'}
time: 2024-10-11 15:13:41+00:00
extensions:
- !core/extension_metadata-1.0.0
extension_class: asdf.extension._manifest.ManifestExtension
extension_uri: asdf://asdf-format.org/core/extensions/core-1.5.0
manifest_software: !core/software-1.0.0 {name: asdf_standard, version: 1.1.1}
software: !core/software-1.0.0 {name: asdf, version: 3.5.1.dev6+ga4e7961e}
a: !core/ndarray-1.0.0
source: 0
datatype: float64
byteorder: little
shape: [32, 32]
...
BLOCK 0:
allocated_size: 8192
used_size: 8192
data_size: 8192
data: b'38630bee93abd43ffc2f0356e8a3c83f3c331a69...'
#ASDF BLOCK INDEX
%YAML 1.1
---
- 966
...
asdf
automatically saves history metadata about the extensions that were used
to create the file. This information is used when opening files to determine if
the proper extensions are installed (see Extension checking for more
details).
Footnotes
Rendering ASDF trees
The asdf.info
function prints a representation of an ASDF
tree to stdout. For example:
>>> asdf.info("path/to/some/file.asdf")
root (AsdfObject)
├─asdf_library (Software)
│ ├─author (str): The ASDF Developers
│ ├─homepage (str): http://github.com/asdf-format/asdf
│ ├─name (str): asdf
│ └─version (str): 2.5.1
├─history (dict)
│ └─extensions (list) ...
└─data (dict)
└─example_key (str): example value
The first argument may be a str
or pathlib.Path
filesystem path,
or an AsdfFile
or sub-node of an ASDF tree.
By default, asdf.info
limits the number of lines, and line length,
of the displayed tree. The max_rows
parameter controls the number of
lines, and max_cols
controls the line length. Set either to None
to
disable that limit.
An integer max_rows
will be interpreted as an overall limit on the
number of displayed lines. If max_rows
is a tuple, then each member
limits lines per node at the depth corresponding to its tuple index.
For example, to show all top-level nodes and 5 of each’s children:
If the attribute is described in a schema, the info functionality will see if it has an associated title and if it does, display it as a comment on the same line. This provides a way for users to see more information about the the attribute in a similar way that FITS header comments are used.
>>> asdf.info("file.asdf", max_rows=(None, 5))
The AsdfFile.info
method behaves similarly to asdf.info
, rendering
the tree of the associated AsdfFile
.
Normally asdf.info
will not show the contents of asdf nodes turned
into Python custom objects, but if that object supports a special
method, you may see the contents of such objects.
See Making converted object’s contents visible to info and search for how
to implement such support for asdf.info
and asdf.search
.
Searching the ASDF tree
The AsdfFile
search interface provides a way to interactively discover the
locations and values of nodes within the ASDF tree. We can search for
nodes by key/index, type, or value.
Basic usage
Initiate a search by calling AsdfFile.search
on an open file:
>>> af.search()
root (AsdfObject)
├─asdf_library (Software)
│ ├─author (str): The ASDF Developers
│ ├─homepage (str): http://github.com/asdf-format/asdf
│ ├─name (str): asdf
│ └─version (str): 2.5.1
├─history (dict)
│ └─extensions (list) ...
└─data (dict)
└─example_key (str): example value
>>> af.search("example")
root (AsdfObject)
└─data (dict)
└─example_key (str): example value
The search returns an AsdfSearchResult
object that displays in
the Python console as a rendered tree. For single-node search
results, the AsdfSearchResult.path
property contains the Python code required to
reference that node directly:
>>> af.search("example").path
"root['data']['example_key']"
While the AsdfSearchResult.node
property contains the actual value of the node:
>>> af.search("example").node
'example value'
For searches with multiple matching nodes, use the AsdfSearchResult.paths
and AsdfSearchResult.nodes
properties instead:
>>> af.search("duplicate_key").paths
["root['data']['duplicate_key']", "root['other_data']['duplicate_key']"]
>>> af.search("duplicate_key").nodes
["value 1", "value 2"]
To replace matching nodes with a new value, use the AsdfSearchResult.replace
method:
>>> af.search("example").replace("replacement value")
>>> af.search("example").node
'replacement value'
The first argument to AsdfFile.search
searches by dict key or list/tuple index. We can
also search by type, value, or any combination thereof:
>>> af.search("foo") # Find nodes with key containing the string 'foo'
>>> af.search(type=int) # Find nodes that are instances of int
>>> af.search(value=10) # Find nodes whose value is equal to 10
>>> af.search(
... "foo", type=int, value=10
... ) # Find the intersection of the above
Chaining searches
The return value of AsdfFile.search
, asdf.search.AsdfSearchResult
, has its own search method,
so it’s possible to chain searches together. This is useful when you need
to see intermediate results before deciding how to further narrow the search.
>>> af.search() # See an overview of the entire ASDF tree
>>> af.search().search(type="NDArrayType") # Find only ndarrays
>>> af.search().search(type="NDArrayType").search(
... "err"
... ) # Only ndarrays with 'err' in the key
Descending into child nodes
Another way to narrow the search is to use the index operator to descend into a child node of the current tree root:
>>> af.search()["data"] # Restrict search to the 'data' child
>>> af.search()["data"].search(
... type=int
... ) # Find integer descendants of 'data'
Regular expression searches
Any string argument to search is interpreted as a regular expression. For example, we can search for nodes whose keys start with a particular string:
>>> af.search("foo") # Find nodes with 'foo' anywhere in the key
>>> af.search("^foo") # Find only nodes whose keys start with 'foo'
Note that all node keys (even list indices) will be converted to string before the regular expression is matched:
>>> af.search("^7$") # Returns all nodes with key '7' or index 7
When the type
argument is a string, the search compares against the fully-qualified
class name of each node:
>>> af.search(
... type="asdf.tags.core.Software"
... ) # Find instances of ASDF's Software type
>>> af.search(type="^asdf\.") # Find all ASDF objects
When the value
argument is a string, the search compares against the string
representation of each node’s value.
>>> af.search(
... value="^[0-9]{4}-[0-9]{2}-[0-9]{2}$"
... ) # Find values that look like dates
Arbitrary search criteria
If key
, type
, and value
aren’t sufficient, we can also provide a callback
function to search by arbitrary criteria. The filter
parameter accepts
a callable that receives the node under consideration, and returns True
to keep it or False
to reject it from the search results. For example,
to search for NDArrayType with a particular shape:
>>> af.search(type="NDArrayType", filter=lambda n: n.shape[0] == 1024)
Formatting search results
The AsdfSearchResult
object displays its content as a rendered tree with
reasonable defaults for maximum number of lines and columns displayed. To
change those values, we call AsdfSearchResult.format
:
>>> af.search(type=float) # Displays limited rows
>>> af.search(type=float).format(max_rows=None) # Show all matching rows
Like AsdfSearchResult.search
, calls to format may be chained:
>>> af.search("time").format(max_rows=10).search(type=str).format(
... max_rows=None
... )
Searching Schema information
In some cases, one may wish to include information and/or documentation about an object defined by a tagged schema within the schema itself. It can be useful to directly access this information relative to a given ASDF file. For example one may wish to examine:
The
title
of a value to get a short description of it.The
description
of a value to get the longer description of it.
In other cases, it maybe useful to store general descriptive information such as
specific archival information about a given value in the file so that an archive
can easily ingest the file into the archive, such as what is done with the archive_catalog
information in the rad schemas for the
Nancy Grace Roman Space Telescope.
The AsdfFile.schema_info
method provides a way to access this information. This
method returns a nested tree of dictionaries which contains tuples consisting of
the information from the schema requested together with the value stored in the
ASDF file itself.
One needs to provide a key
, which corresponds the to the keyword the information
is stored under inside the schema, by default this is description
. One can also
provide a path
in the form of a dot-separated string of the keys in the
ASDF file that lead to the value(s) of interest. For example:
>>> af.schema_info("archive_catalog", "foo.bar")
{'thing1': {'archive_catalog': 'Thing 1 Archive catalog information'},
'thing2': {'archive_catalog': 'Thing 2 Archive catalog information'}}
Or one can provide a path
as an asdf.search.AsdfSearchResult
object:
>>> af.schema_info("archive_catalog", af.search("bar"))
{'thing1': {'archive_catalog': 'Thing 1 Archive catalog information'},
'thing2': {'archive_catalog': 'Thing 2 Archive catalog information'}}
Note
The there is also the asdf.search.AsdfSearchResult.schema_info
method,
which can be directly called on an asdf.search.AsdfSearchResult
object.
instead of having to pass the search through AsdfFile.schema_info
.