Specializing dantro Classes

This page shows a few examples of how to specialize dantro classes to your liking.

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Specializing a data container

As an example, let’s look at the implementation of the MutableSequenceContainer, a container that is meant to store mutable sequences:

# Import the python abstract base class we want to adhere to
from collections.abc import MutableSequence

# Import base mixin classes (others can be found in the mixin module)
from dantro.base import (BaseDataContainer, ItemAccessMixin,
                         CollectionMixin, CheckDataMixin)


class MutableSequenceContainer(CheckDataMixin,
                               ItemAccessMixin,
                               CollectionMixin,
                               BaseDataContainer,
                               MutableSequence):
    """The MutableSequenceContainer stores data that is sequence-like"""
    # ...

The steps to arrive at this point are as follows:

The collections.abc python module is also used by python to specify the interfaces for python-internal classes. In the documentation it says that the MutableSequence inherits from Sequence and has the following abstract methods: __getitem__, __setitem__, __delitem__, __len__, and insert.

As we want the resulting container to adhere to this interface, we set MutableSequence as the first class to inherit from. The BaseDataContainer is what makes this object a dantro data container. It implements some of the required methods to concur to the MutableSequence interface, but leaves others abstract.

Now, we need to supply implementations of these abstract methods. That is the job of the following two (reading from right to left) mixin classes. In this case, the Sequence interface has to be fulfilled. As a Sequence is nothing more than a Collection with item access, we can fulfill this by inheriting from the CollectionMixin and the ItemAccessMixin.

The CheckDataMixin is an example of how functionality can be added to the container while still adhering to the interface. This mixin checks the provided data before storing it and allows specifying whether unexpected data should lead to warnings or exceptions.

Some methods will still remain abstract, in this case: insert. These need to be manually defined; the MutableSequenceContainer’s insert() method does exactly that, thus becoming a fully non-abstract class.

Using a specialized data container

Once defined, instantiation of a custom container works the same way as for other data containers:

dc = MutableSequenceContainer(name="my_mutable_sequence",
                              data=[4, 8, 16])

# Insert values
dc.insert(0, 2)
dc.insert(0, 1)

# Item access and collection interface
assert 16 in dc
assert 32 not in dc
assert dc[0] == 1

for num in dc:
    print(num, end=", ")
# prints:  1, 2, 4, 8, 16,

Specializing the DataManager

This works in essentially the same way: A DataManager is specialized by adding data_loaders mixin classes.

import dantro as dtr
import dantro.data_mngr
from dantro.data_loaders import YamlLoaderMixin, PickleLoaderMixin


class MyDataManager(PickleLoaderMixin,
                    YamlLoaderMixin,
                    dtr.data_mngr.DataManager):
    """This is a dantro data manager specialization that can load pickle
    and yaml data.
    """

That’s all.

For more information, see The DataManager.

Note

As an example, you can have a look at data manager used in utopya.