arjuna.tpi.data.entity module

arjuna.tpi.data.entity.data_entity(entity_name, *attrs, bases=(), **attrs_with_defaults)

Create a new Data Entity class with provided name and attributes.

Check Data Entities documentation for various use cases.

Parameters

  • entity_name – The class name for this new Data Entity type.

  • *attrs – Arbitrary names for Python attributes to be associated with objects of this entity.

Keyword Arguments

  • bases – Base data entities for this entity. Can be a string or tuple or list.

  • **attrs_with_defaults – Arbitrary attributes to be associated with objects of this entity, with the defaults that are provided.

Note

You can create objects of newly created data entity just like a normal Python class.

Person = data_entity("Person", "name age")
person = Person(name="SomeName", age=21)

Note

The defaults that are provided can be any of the following:

  • Any Python Object

  • A Python callable

  • Arjuna generator

  • Arjuna composite

Note

Data entity objects behave like Python dictionaries.

So you can retrieve an attribute value as:

entity.attr
# or
entity['attr']

Following dict-like operations are valid too. The key difference to note is that in these operations that attributes that have None value are excluded unlike a Python dictionary.

entity.keys()
entity.items()
**entity # Unpacking of key-values

# Iterating on keys
for attr in entity:
    pass

# Iterating on key-value pairs
for attr, value in entity.items():
    pass

To retain keys/attrs corresponding to None values, you can provide remove_none=False as argument:

entity.keys(remove_none=False)
entity.items(remove_none=False)
**entity # Unpacking of key-values

# Iterating on keys
for attr in entity.keys(remove_none=False):
    pass

# Iterating on key-value pairs
for attr, value in entity.items(remove_none=False):
    pass

Also note that because len() in Python is not flexible to allow for the above, you can use size method:

len(entity) # Will ignore attrs with None value
entity.size() # Will ignore attrs with None value
entity.size(remove_none=False) # Includes attrs with None value

All above mentioned methods also accept remove argument to explicitly exclude one or more attributes by name.

entity.keys(remove='some_key')
entity.keys(remove={'some_key1', 'some_key2'})

entity.items(remove='some_key')
entity.items(remove={'some_key1', 'some_key2'})

entity.size(remove='some_key')
entity.size(remove={'some_key1', 'some_key2'})

Note

When you provide one or more bases, the overriding order is B1 -> B2 -> B3 ….. -> This Entity.

At each stage of this chain, you can

  • Add one or more mandatory attributes.

  • Add one or more optional attributes

  • Make an optional attribute in base entity as mandatory.

  • Make a mandatory attribute as optional by assigning a default value.

  • Change the default for an existing default attribute to something else.

Note

Delete operation is disllowed on the data entity because it corresponds to attribute deletion. Use as_dict() method for representation that has one or more keys removed.

# Raises exception
del entity['some_attr']

Note

You can make an object of a data entity IMMUTABLE by passing freeze=True argument.

person = Person(name="SomeName", age=21, freeze=True)
# Raises Exception
person.age = 25