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Python tools to handle ASDF files

Project Description

ASDF - Advanced Scientific Data Format

|Build Status| |Documentation Status| |Coverage Status| |stsci|

.. figure:: docs/_static/stsci_logo.png
:alt: STScI Logo

.. _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 <>`__.

The ASDF format has the following features:

* A hierarchical, human-readable metadata format (implemented using `YAML
* 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 <>`__)
* 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
<>`__. More information on contributing
can be found `below <#contributing>`__.


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.array([x for x in range(100)])
squares = np.array([x**2 for x in range(100)])
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

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


#ASDF 1.0.0
%YAML 1.1
%TAG !
--- !core/asdf-1.1.0
asdf_library: !core/software-1.0.0 {author: Space Telescope Science Institute, homepage: '',
name: asdf, version: 2.0.0}
- !core/extension_metadata-1.0.0
extension_class: asdf.extension.BuiltinExtension
software: {name: asdf, version: 2.0.0}
foo: 42
name: Monty
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')

Available compression algorithms are ``'zlib'``, ``'bzp2'``, and

.. _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 ='example.asdf')

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

.. code:: python

with'example.asdf') as af:

To access the data stored in the file, use the top-level :attr:`AsdfFile.tree`

.. code:: python

>>> import asdf
>>> af ='example.asdf')
>>> af.tree
{'asdf_library': {'author': 'Space Telescope Science Institute',
'homepage': '',
'name': 'asdf',
'version': '1.3.1'},
'foo': 42,
'name': 'Monty',
'powers': {'squares': <array (unloaded) shape: [100] dtype: int64>},
'random': <array (unloaded) shape: [100] dtype: float64>,
'sequence': <array (unloaded) shape: [100] dtype: int64>}

The tree is simply a Python `dict`, and nodes are accessed like any other
dictionary entry:

.. code:: python

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

Array data remains unloaded until it is explicitly accessed:

.. code:: python

>>> af.tree['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,

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

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 ='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 tag types that correspond to custom user types. More
information on extending ASDF can be found in the `official
documentation <>`__.


.. _begin-pip-install-text

Stable releases of the ASDF Python package are registered `at
PyPi <>`__. 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 <>`__. To clone the project:


$ git clone

To install:


$ cd asdf
$ python3 install

To install in `development
mode <>`__::

$ python3 develop

.. 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 <>`__ for more

.. _end-source-install-text


.. _begin-testing-text

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


$ python3 test

It is also possible to run the test suite from an installed version of
the package. In a Python interpreter:

.. code:: python

import asdf

Please note that the `astropy <>`__
package must be installed to run the tests.

.. _end-testing-text


More detailed documentation on this software package can be found
`here <>`__.

More information on the ASDF Standard itself can be found
`here <>`__.

If you are looking for the **A**\ daptable **S**\ eismic **D**\ ata
**F**\ ormat, information can be found
`here <>`__.


We welcome feedback and contributions to the project. Contributions of
code, documentation, or general feedback are all appreciated. Please
follow the `contributing guidelines <>`__ 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 <>`__.

.. |Build Status| image::
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.. |Coverage Status| image::
.. |stsci| image::
.. |astropy| image::

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