.. currentmodule:: asdf ************* 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 :class:`dict`, :class:`list`, :class:`str`, and :class:`int`, :class:`float`, and :class:`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 :ref:`overview`. .. note:: The ASDF Standard imposes a maximum size of 64-bit signed integers literals in the tree (see `the docs `_ 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 :mod:`numpy` arrays (see below). One of the key features of `asdf` is its ability to serialize :mod:`numpy` arrays. This is discussed in detail in :ref:`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 :ref:`using_extensions`. Details on creating custom ASDF extensions to support custom data types can be found in :ref:`extending`. .. _array-data: Array Data ========== Much of ASDF's power and convenience comes from its ability to represent multidimensional array data. The :mod:`asdf` Python package provides native support for :mod:`numpy` arrays. .. toctree:: :maxdepth: 2 arrays .. _using_extensions: 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" [#wiki]_. 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 :ref:`extending_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. .. toctree:: :maxdepth: 2 using_extensions .. _schema_validation: 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 occurs when reading ASDF files (using `asdf.open`), and also when writing them out (using `AsdfFile.write_to` or `AsdfFile.update`). Schema validation also plays a role when using custom extensions (see :ref:`using_extensions` and :ref:`extending_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: 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: .. code:: yaml %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: .. 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: .. code:: 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. .. _top-level core schema: https://github.com/asdf-format/asdf-standard/blob/master/schemas/stsci.edu/asdf/core/asdf-1.1.0.yaml .. _version_and_compat: 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 :ref:`custom_type_versions`. 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 file format 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 version of the file format will be used. Note that this option has no effect on the versions of tags from custom extensions. 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: .. runcode:: 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') .. asdf:: target.asdf 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. .. runcode:: 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') .. asdf:: source.asdf Calling `~asdf.AsdfFile.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. .. runcode:: with asdf.open('source.asdf') as ff: ff.find_references() assert ff.tree['my_ref_b'].shape == (10,) On the other hand, calling `~asdf.AsdfFile.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. .. runcode:: with asdf.open('source.asdf') as ff: ff.resolve_references() ff.write_to('resolved.asdf') .. asdf:: resolved.asdf 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: .. runcode:: d = {'foo': 'bar'} d['baz'] = d tree = {'d': d} ff = AsdfFile(tree) ff.write_to('anchors.asdf') .. asdf:: anchors.asdf .. _array-references: 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, the ``target`` might be an integer index. Or if the external file is an ASDF file, the ``target`` might be a string indicating the key to use in the external file's tree. The value and format of the ``target`` 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: .. runcode:: 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') .. asdf:: external_array.asdf 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 `~asdf.AsdfFile` object, call `~asdf.AsdfFile.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. .. runcode:: 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') .. asdf:: example.asdf `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 :ref:`extension_checking` for more details). .. _asdf-in-fits: Saving ASDF in FITS =================== .. note:: This section is about packaging entire ASDF files inside of `FITS data format `_ files. This is probably only of interest to astronomers. Making use of this feature requires the `astropy` package to be installed. Sometimes you may need to store the structured data supported by ASDF inside of a FITS file in order to be compatible with legacy tools that support only FITS. First, create an `~astropy.io.fits.HDUList` object using `astropy.io.fits`. Here, we are building an `~astropy.io.fits.HDUList` from scratch, but it could also have been loaded from an existing file. We will create a FITS file that has two image extensions, SCI and DQ respectively. .. runcode:: from astropy.io import fits hdulist = fits.HDUList() hdulist.append(fits.ImageHDU(np.arange(512, dtype=float), name='SCI')) hdulist.append(fits.ImageHDU(np.arange(512, dtype=float), name='DQ')) Next we make a tree structure out of the data in the FITS file. Importantly, we use the *same* array references in the FITS `~astropy.io.fits.HDUList` and store them in the tree. By doing this, ASDF will automatically refer to the data in the regular FITS extensions. .. runcode:: tree = { 'model': { 'sci': { 'data': hdulist['SCI'].data, }, 'dq': { 'data': hdulist['DQ'].data, } } } Now we take both the FITS `~astropy.io.fits.HDUList` and the ASDF tree and create an `AsdfInFits` object. .. runcode:: from asdf import fits_embed ff = fits_embed.AsdfInFits(hdulist, tree) ff.write_to('embedded_asdf.fits') .. runcode:: hidden from astropy.io import fits with fits.open('embedded_asdf.fits') as new_hdulist: with open('content.asdf', 'wb') as fd: fd.write(new_hdulist['ASDF'].data.tobytes()) The special ASDF extension in the resulting FITS file contains the following data. Note that the data source of the arrays uses the ``fits:`` prefix to indicate that the data comes from a FITS extension: .. asdf:: content.asdf To load an ASDF-in-FITS file, simply open it using `asdf.open`. The returned value will be an `AsdfInFits` object, which can be used in the same way as any other `AsdfFile` object. .. runcode:: with asdf.open('embedded_asdf.fits') as asdf_in_fits: science = asdf_in_fits.tree['model']['sci'] .. rubric:: Footnotes .. [#wiki] https://en.wikipedia.org/wiki/Serialization Rendering ASDF trees ==================== The `asdf.info` function prints a representation of an ASDF tree to stdout. For example: .. code:: python >>> asdf.info('path/to/some/file.asdf') # doctest: +SKIP 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: .. code:: python >>> asdf.info('file.asdf', max_rows=(None, 5)) # doctest: +SKIP ... 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 :ref:`exposing_extension_object_internals` 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: .. code:: python >>> af.search() # doctest: +SKIP 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') # doctest: +SKIP root (AsdfObject) └─data (dict) └─example_key (str): example value .. currentmodule:: asdf.search 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: .. code:: python >>> af.search('example').path # doctest: +SKIP "root['data']['example_key']" While the `AsdfSearchResult.node` property contains the actual value of the node: .. code:: python >>> af.search('example').node # doctest: +SKIP 'example value' For searches with multiple matching nodes, use the `AsdfSearchResult.paths` and `AsdfSearchResult.nodes` properties instead: .. code:: python >>> af.search('duplicate_key').paths # doctest: +SKIP ["root['data']['duplicate_key']", "root['other_data']['duplicate_key']"] >>> af.search('duplicate_key').nodes # doctest: +SKIP ["value 1", "value 2"] To replace matching nodes with a new value, use the `AsdfSearchResult.replace` method: .. code:: python >>> af.search('example').replace('replacement value') # doctest: +SKIP >>> af.search('example').node # doctest: +SKIP 'replacement value' .. currentmodule:: asdf 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: .. code:: python >>> af.search('foo') # Find nodes with key containing the string 'foo' # doctest: +SKIP ... >>> af.search(type=int) # Find nodes that are instances of int # doctest: +SKIP ... >>> af.search(value=10) # Find nodes whose value is equal to 10 # doctest: +SKIP ... >>> af.search('foo', type=int, value=10) # Find the intersection of the above # doctest: +SKIP 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. .. code:: python >>> af.search() # See an overview of the entire ASDF tree # doctest: +SKIP ... >>> af.search().search(type='NDArrayType') # Find only ndarrays # doctest: +SKIP ... >>> af.search().search(type='NDArrayType').search('err') # Only ndarrays with 'err' in the key # doctest: +SKIP 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: .. code:: python >>> af.search()['data'] # Restrict search to the 'data' child # doctest: +SKIP ... >>> af.search()['data'].search(type=int) # Find integer descendants of 'data' # doctest: +SKIP Regular expression searches --------------------------- Any string argument to search is interpeted as a regular expression. For example, we can search for nodes whose keys start with a particular string: .. code:: python >>> af.search('foo') # Find nodes with 'foo' anywhere in the key # doctest: +SKIP ... >>> af.search('^foo') # Find only nodes whose keys start with 'foo' # doctest: +SKIP ... Note that all node keys (even list indices) will be converted to string before the regular expression is matched: .. code:: python >>> af.search('^7$') # Returns all nodes with key '7' or index 7 # doctest: +SKIP ... When the ``type`` argument is a string, the search compares against the fully-qualified class name of each node: .. code:: python >>> af.search(type='asdf.tags.core.Software') # Find instances of ASDF's Software type # doctest: +SKIP ... >>> af.search(type='^asdf\.') # Find all ASDF objects # doctest: +SKIP ... When the ``value`` argument is a string, the search compares against the string representation of each node's value. .. code:: python >>> af.search(value='^[0-9]{4}-[0-9]{2}-[0-9]{2}$') # Find values that look like dates # doctest: +SKIP ... 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: .. code:: python >>> af.search(type='NDArrayType', filter=lambda n: n.shape[0] == 1024) # doctest: +SKIP ... Formatting search results ------------------------- .. currentmodule:: asdf.search 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`: .. code:: python >>> af.search(type=float) # Displays limited rows # doctest: +SKIP ... >>> af.search(type=float).format(max_rows=None) # Show all matching rows # doctest: +SKIP ... Like `AsdfSearchResult.search`, calls to format may be chained: .. code:: python >>> af.search('time').format(max_rows=10).search(type=str).format(max_rows=None) # doctest: +SKIP ...