Creating a file

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.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’s metadata section, 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 above).

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

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 package is installed. Other compression algorithms may be available via extensions.

Reading a file

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

>>> 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:

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

Array data remains unloaded until it is explicitly accessed:

>>> 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

Memory mapping can be enabled by providing memmap=True to open:

af = asdf.open("example.asdf", memmap=True)

For more information and for advanced usage examples, see the 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.