Overview • Installation • Testing • Documentation • Contributing
The Advanced Scientific Data Format (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
ASDF is under active development on github. More information on contributing can be found below.
This section outlines basic use cases of the ASDF package for creating and reading ASDF files.
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:
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
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: Space Telescope Science Institute, homepage: 'http://github.com/spacetelescope/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:
af.write_to('compressed.asdf', all_array_compression='zlib')
Available compression algorithms are 'zlib'
, 'bzp2'
, and
'lz4'
.
To read an existing ASDF file, we simply use the top-level open function of the asdf package:
import asdf
af = asdf.open('example.asdf')
The open function also works as a context handler:
with asdf.open('example.asdf') as af:
...
To access the data stored in the file, use the top-level AsdfFile.tree attribute:
>>> import asdf
>>> af = asdf.open('example.asdf')
>>> af.tree
{'asdf_library': {'author': 'Space Telescope Science Institute',
'homepage': 'http://github.com/spacetelescope/asdf',
'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:
>>> af.tree['name']
'Monty'
>>> af.tree['powers']
{'squares': <array (unloaded) shape: [100] dtype: int64>}
Array data remains unloaded until it is explicitly accessed:
>>> 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,
9801])
>>> import numpy as np
>>> expected = [x**2 for x in range(100)]
>>> np.equal(af.tree['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:
af = asdf.open('example.asdf', copy_arrays=True)
For more information and for advanced usage examples, see the documentation.
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.
Stable releases of the ASDF Python package are registered at
PyPi. The latest stable version
can be installed using pip
:
$ pip install asdf
The latest development version of ASDF is available from the master
branch
on github. To clone the project:
$ git clone https://github.com/spacetelescope/asdf
To install:
$ cd asdf $ git submodule update --init $ pip install .
To install 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 for more information.
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. In a Python interpreter:
import asdf
asdf.test()
Please note that the astropy package must be installed to run the tests.
It is also possible to run the tests using tox. It is first necessary to install
tox
and tox-conda:
$ pip install tox tox-conda
To list all available environments:
$ tox -va
To run a specific environment:
$ tox -e <envname>
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 Adaptable Seismic Data Format, 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.