Converters¶
The Converter
interface defines a mapping between
tagged objects in the ASDF tree and their corresponding Python object(s).
Typically a Converter will map one YAML tag to one Python type, but
the interface also supports many-to-one and many-to-many mappings. A
Converter provides the software support for a tag and is responsible
for both converting from parsed YAML to more complex Python objects
and vice versa.
The Converter interface¶
Every Converter implementation must provide two required properties and two required methods:
Converter.tags
- a list of tag URIs or URI patterns handled by the converter.
Patterns may include the wildcard character *
, which matches any sequence of
characters up to a /
, or **
, which matches any sequence of characters.
The uri_match
method can be used to test URI patterns.
Converter.types
- a list of Python types or fully-qualified Python type names handled
by the converter. Note that a string name must reflect the actual location of the
class’s implementation and not just a module where it is imported for convenience.
For example, if class Foo
is implemented in example_package.foo.Foo
but
imported as example_package.Foo
for convenience, it is the former name that
must be used. The get_class_name
method will return the name that
asdf
expects.
The string type name is recommended over a type object for performance reasons, see Entry point performance considerations.
Converter.to_yaml_tree
- a method that accepts a complex Python object and returns
a simple node object (typically a dict
) suitable for serialization to YAML. The
node is permitted to contain nested complex objects; these will in turn
be passed to other to_yaml_tree
methods in other Converters.
Converter.from_yaml_tree
- a method that accepts a simple node object from parsed YAML and
returns the appropriate complex Python object. Nested nodes in the received node
will have already been converted to complex objects by other calls to from_yaml_tree
methods, except where reference cycles are present – see
Reference cycles for information on how to handle that
situation.
Additionally, the Converter interface includes a method that must be implemented
when some logic is required to select the tag to assign to a to_yaml_tree
result:
Converter.select_tag
- a method that accepts a complex Python object and a list
candidate tags and returns the tag that should be used to serialize the object.
A simple example¶
Say we have a Python class, Rectangle
, that we wish to serialize
to an ASDF file. A Rectangle
instance has two attributes, width
and height, and a convenient method that computes its area:
# in module example_package.shapes
class Rectangle:
def __init__(self, width, height):
self.width = width
self.height = height
def get_area(self):
return self.width * self.height
We’ll need to designate a tag URI to represent this object’s type
in the ASDF tree – let’s use asdf://example.com/example-project/tags/rectangle-1.0.0
.
Here is a simple Converter implementation for this type and tag:
from asdf.extension import Converter
class RectangleConverter(Converter):
tags = ["asdf://example.com/shapes/tags/rectangle-1.0.0"]
types = ["example_package.shapes.Rectangle"]
def to_yaml_tree(self, obj, tag, ctx):
return {
"width": obj.width,
"height": obj.height,
}
def from_yaml_tree(self, node, tag, ctx):
from example_package.shapes import Rectangle
return Rectangle(node["width"], node["height"])
Note that import of the Rectangle
class has been deferred to
inside the from_yaml_tree
method. This is a performance consideration
that is discussed in Entry point performance considerations.
In order to use this Converter, we’ll need to create a simple extension around it and install that extension:
import asdf
from asdf.extension import Extension
class ShapesExtension(Extension):
extension_uri = "asdf://example.com/shapes/extensions/shapes-1.0.0"
converters = [RectangleConverter()]
tags = ["asdf://example.com/shapes/tags/rectangle-1.0.0"]
asdf.get_config().add_extension(ShapesExtension())
Now we can include a Rectangle object in an AsdfFile
tree
and write out a file:
with asdf.AsdfFile() as af:
af["rect"] = Rectangle(5, 4)
af.write_to("test.asdf")
The portion of the ASDF file that represents the rectangle looks like this:
rect: !<asdf://example.com/shapes/tags/rectangle-1.0.0> {height: 4, width: 5}
Reference cycles¶
Special considerations must be made when deserializing a tagged object that
contains a reference to itself among its descendants. Consider a
fractions.Fraction
subclass that maintains a reference to its multiplicative
inverse:
# in the example_project.fractions module
class FractionWithInverse(fractions.Fraction):
def __init__(self, *args, **kwargs):
self._inverse = None
@property
def inverse(self):
return self._inverse
@inverse.setter
def inverse(self, value):
self._inverse = value
The inverse of the inverse of a fraction is the fraction itself, we might wish to construct the objects in the following way:
f1 = FractionWithInverse(3, 5)
f2 = FractionWithInverse(5, 3)
f1.inverse = f2
f2.inverse = f1
Which creates an “infinite loop” between the two fractions. An ordinary
Converter wouldn’t be able to deserialize this, since each fraction
requires that the other be deserialized first! Let’s see what happens
when we define our from_yaml_tree
method in a naive way:
class FractionWithInverseConverter(Converter):
tags = ["asdf://example.com/fractions/tags/fraction-1.0.0"]
types = ["example_project.fractions.FractionWithInverse"]
def to_yaml_tree(self, obj, tag, ctx):
return {
"numerator": obj.width,
"denominator": obj.height,
"inverse": obj.inverse,
}
def from_yaml_tree(self, node, tag, ctx):
from example_project.fractions import FractionWithInverse
obj = FractionWithInverse(tree["numerator"], tree["denominator"])
obj.inverse = tree["inverse"]
return obj
After adding this Converter to an Extension and installing it, the fraction will serialize correctly:
with asdf.AsdfFile({"fraction": f1}) as af:
af.write_to("with_inverse.asdf")
But upon deserialization, we notice a problem:
with asdf.open("with_inverse.asdf") as af:
reconstituted_f1 = af["fraction"]
assert reconstituted_f1.inverse.inverse is asdf.treeutil.PendingValue
The presence of _PendingValue
is asdf’s way of telling us
that the value corresponding to the key inverse
was not fully deserialized
at the time that we retrieved it. We can handle this situation by making our
from_yaml_tree
a generator function:
def from_yaml_tree(self, node, tag, ctx):
from example_project.fractions import FractionWithInverse
obj = FractionWithInverse(tree["numerator"], tree["denominator"])
yield obj
obj.inverse = tree["inverse"]
The generator version of from_yaml_tree
yields the partially constructed
FractionWithInverse
object before setting its inverse property. This allows
asdf
to proceed to constructing the inverse FractionWithInverse
object,
and resume the original from_yaml_tree
execution only when the inverse
is actually available.
With this modification we can successfully deserialize our ASDF file:
with asdf.open("with_inverse.asdf") as af:
reconstituted_f1 = ff["fraction"]
assert reconstituted_f1.inverse.inverse is reconstituted_f1
Entry point performance considerations¶
For the good of asdf
users everywhere, it’s important that entry point
methods load as quickly as possible. All extensions must be loaded before
reading an ASDF file, and therefore all converters are created as well. Any
converter module or __init__
method that lingers will introduce a delay
to the initial call to asdf.open
. For that reason, we recommend that converter
authors minimize the number of imports that occur in the module containing the
Converter implementation, and defer imports of serializable types to within the
from_yaml_tree
method. This will prevent the type from ever being imported
when reading ASDF files that do not contain the associated tag.