asdf

ASDF (Advanced Scientific Data Format) is a next generation interchange format for scientific data

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ASDF - Advanced Scientific Data Format

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.. _begin-summary-text:

The A\ dvanced S\ cientific D\ ata F\ ormat (ASDF) is a next-generation interchange format for scientific data. This package contains the Python implementation of the ASDF Standard. More information on the ASDF Standard itself can be found here <https://asdf-standard.readthedocs.io>__.

The ASDF format has the following features:

  • A hierarchical, human-readable metadata format (implemented using YAML <http://yaml.org>__)
  • Numerical arrays are stored as binary data blocks which can be memory mapped. Data blocks can optionally be compressed.
  • The structure of the data can be automatically validated using schemas (implemented using JSON Schema <http://json-schema.org>__)
  • Native Python data types (numerical types, strings, dicts, lists) are serialized automatically
  • ASDF can be extended to serialize custom data types

.. _end-summary-text:

ASDF is under active development on github <https://github.com/asdf-format/asdf>. More information on contributing can be found below <#contributing>.

Overview

This section outlines basic use cases of the ASDF package for creating and reading ASDF files.

Creating a file


.. _begin-create-file-text:

We're going to store several `numpy` arrays and other data to an ASDF file. We
do this by creating a "tree", which is simply a `dict`, and we provide it as
input to the constructor of `AsdfFile`:

.. code:: python

    import asdf
    import numpy as np

    # Create some data
    sequence = np.arange(100)
    squares  = sequence**2
    random = np.random.random(100)

    # Store the data in an arbitrarily nested dictionary
    tree = {
        'foo': 42,
        'name': 'Monty',
        'sequence': sequence,
        'powers': { 'squares' : squares },
        'random': random
    }

    # Create the ASDF file object from our data tree
    af = asdf.AsdfFile(tree)

    # Write the data to a new file
    af.write_to('example.asdf')

If we open the newly created file, we can see some of the key features
of ASDF on display:

::

    #ASDF 1.0.0
    #ASDF_STANDARD 1.2.0
    %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: 2.0.0}
    history:
      extensions:
      - !core/extension_metadata-1.0.0
        extension_class: asdf.extension.BuiltinExtension
        software: {name: asdf, version: 2.0.0}
    foo: 42
    name: Monty
    powers:
      squares: !core/ndarray-1.0.0
        source: 1
        datatype: int64
        byteorder: little
        shape: [100]
    random: !core/ndarray-1.0.0
      source: 2
      datatype: float64
      byteorder: little
      shape: [100]
    sequence: !core/ndarray-1.0.0
      source: 0
      datatype: int64
      byteorder: little
      shape: [100]
    ...

The metadata in the file mirrors the structure of the tree that was stored. It
is hierarchical and human-readable. Notice that metadata has been added to the
tree that was not explicitly given by the user. Notice also that the numerical
array data is not stored in the metadata tree itself. Instead, it is stored as
binary data blocks below the metadata section (not shown here).

It is possible to compress the array data when writing the file:

.. code:: python

    af.write_to('compressed.asdf', all_array_compression='zlib')

The built-in compression algorithms are ``'zlib'``, and ``'bzp2'``.  The
``'lz4'`` algorithm becomes available when the `lz4 <https://python-lz4.readthedocs.io/>`__ package
is installed.  Other compression algorithms may be available via extensions.

.. _end-create-file-text:

Reading a file
~~~~~~~~~~~~~~

.. _begin-read-file-text:

To read an existing ASDF file, we simply use the top-level `open` function of
the `asdf` package:

.. code:: python

    import asdf

    af = asdf.open('example.asdf')

The `open` function also works as a context handler:

.. code:: python

    with asdf.open('example.asdf') as af:
        ...

To get a quick overview of the data stored in the file, use the top-level
`AsdfFile.info()` method:

.. code:: python

    >>> import asdf
    >>> af = asdf.open('example.asdf')
    >>> af.info()
    root (AsdfObject)
    ├─asdf_library (Software)
     ├─author (str): The ASDF Developers
     ├─homepage (str): http://github.com/asdf-format/asdf
     ├─name (str): asdf
     └─version (str): 2.8.0
    ├─history (dict)
     └─extensions (list)
       └─[0] (ExtensionMetadata)
         ├─extension_class (str): asdf.extension.BuiltinExtension
         └─software (Software)
           ├─name (str): asdf
           └─version (str): 2.8.0
    ├─foo (int): 42
    ├─name (str): Monty
    ├─powers (dict)
     └─squares (NDArrayType): shape=(100,), dtype=int64
    ├─random (NDArrayType): shape=(100,), dtype=float64
    └─sequence (NDArrayType): shape=(100,), dtype=int64

The `AsdfFile` behaves like a Python `dict`, and nodes are accessed like
any other dictionary entry:

.. code:: python

    >>> af['name']
    'Monty'
    >>> af['powers']
    {'squares': <array (unloaded) shape: [100] dtype: int64>}

Array data remains unloaded until it is explicitly accessed:

.. code:: python

    >>> af['powers']['squares']
    array([   0,    1,    4,    9,   16,   25,   36,   49,   64,   81,  100,
            121,  144,  169,  196,  225,  256,  289,  324,  361,  400,  441,
            484,  529,  576,  625,  676,  729,  784,  841,  900,  961, 1024,
           1089, 1156, 1225, 1296, 1369, 1444, 1521, 1600, 1681, 1764, 1849,
           1936, 2025, 2116, 2209, 2304, 2401, 2500, 2601, 2704, 2809, 2916,
           3025, 3136, 3249, 3364, 3481, 3600, 3721, 3844, 3969, 4096, 4225,
           4356, 4489, 4624, 4761, 4900, 5041, 5184, 5329, 5476, 5625, 5776,
           5929, 6084, 6241, 6400, 6561, 6724, 6889, 7056, 7225, 7396, 7569,
           7744, 7921, 8100, 8281, 8464, 8649, 8836, 9025, 9216, 9409, 9604,
           9801])

    >>> import numpy as np
    >>> expected = [x**2 for x in range(100)]
    >>> np.equal(af['powers']['squares'], expected).all()
    True

By default, uncompressed data blocks are memory mapped for efficient
access. Memory mapping can be disabled by using the ``copy_arrays``
option of `open` when reading:

.. code:: python

    af = asdf.open('example.asdf', copy_arrays=True)

.. _end-read-file-text:

For more information and for advanced usage examples, see the
`documentation <#documentation>`__.

Extending ASDF
~~~~~~~~~~~~~~

Out of the box, the ``asdf`` package automatically serializes and
deserializes native Python types. It is possible to extend ``asdf`` by
implementing custom tags that correspond to custom user types. More
information on extending ASDF can be found in the `official
documentation <http://asdf.readthedocs.io/en/latest/#extending-asdf>`__.

Installation
------------

.. _begin-pip-install-text:

Stable releases of the ASDF Python package are registered `at
PyPi <https://pypi.python.org/pypi/asdf>`__. The latest stable version
can be installed using ``pip``:

::

    $ pip install asdf

.. _begin-source-install-text:

The latest development version of ASDF is available from the ``master`` branch
`on github <https://github.com/asdf-format/asdf>`__. To clone the project:

::

    $ git clone https://github.com/asdf-format/asdf

To install:

::

    $ cd asdf
    $ git submodule update --init
    $ pip install .

To install in `development
mode <https://packaging.python.org/tutorials/distributing-packages/#working-in-development-mode>`__::

    $ pip install -e .

.. note::

    The source repository makes use of a git submodule for referencing the
    schemas provided by the ASDF standard. While this submodule is
    automatically initialized when installing the package (including in
    development mode), it may be necessary for developers to manually update
    the submodule if changes are made upstream. See the `documentation on git
    submodules <https://git-scm.com/docs/git-submodule>`__ for more
    information.

.. _end-source-install-text:

Testing
-------

.. _begin-testing-text:

To install the test dependencies from a source checkout of the repository:

::

   $ pip install -e .[tests]

To run the unit tests from a source checkout of the repository:

::

    $ pytest

It is also possible to run the test suite from an installed version of
the package.

::

    pip install asdf[tests]
    pytest --pyargs asdf

It is also possible to run the tests using `tox
<https://tox.readthedocs.io/en/latest/>`__.

::

   $ pip install tox

To list all available environments:

::

   $ tox -va

To run a specific environment:

::

   $ tox -e <envname>


.. _end-testing-text:

Documentation
-------------

More detailed documentation on this software package can be found
`here <https://asdf.readthedocs.io>`__.

More information on the ASDF Standard itself can be found
`here <https://asdf-standard.readthedocs.io>`__.

There are two mailing lists for ASDF:

* `asdf-users <https://groups.google.com/forum/#!forum/asdf-users>`_
* `asdf-developers <https://groups.google.com/forum/#!forum/asdf-developers>`_

    If you are looking for the **A**\ daptable **S**\ eismic **D**\ ata
    **F**\ ormat, information can be found
    `here <https://seismic-data.org/>`__.

Contributing
------------

We welcome feedback and contributions to the project. Contributions of
code, documentation, or general feedback are all appreciated. Please
follow the `contributing guidelines <CONTRIBUTING.md>`__ to submit an
issue or a pull request.

We strive to provide a welcoming community to all of our users by
abiding to the `Code of Conduct <CODE_OF_CONDUCT.md>`__.

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