Part 5: Using Python dataclasses
If you are not a developer, you can skip this section.
If you are a developer and favor a language other than python, you may still be interested in this section. The use of generated code is an optional but convenient part of LinkML. We are actively adding support for other languages.
Generating a Python datamodel
For illustration, we will take the schema we developed in the last section:
personinfo.yaml:
id: https://w3id.org/linkml/examples/personinfo
name: personinfo
prefixes: ## Note are adding 3 new ones here
linkml: https://w3id.org/linkml/
schema: http://schema.org/
personinfo: https://w3id.org/linkml/examples/personinfo/
ORCID: https://orcid.org/
imports:
- linkml:types
default_range: string
classes:
Person:
class_uri: schema:Person ## reuse schema.org vocabulary
attributes:
id:
identifier: true
full_name:
required: true
description:
name of the person
slot_uri: schema:name ## reuse schema.org vocabulary
aliases:
multivalued: true
description:
other names for the person
phone:
pattern: "^[\\d\\(\\)\\-]+$"
slot_uri: schema:telephone ## reuse schema.org vocabulary
age:
range: integer
minimum_value: 0
maximum_value: 200
id_prefixes:
- ORCID
Container:
attributes:
persons:
multivalued: true
inlined_as_list: true
range: Person
We can use a script that is distributed with LinkML to generate a python dataclasses model:
gen-python personinfo.yaml > personinfo.py
This creates a python datamodel:
@dataclass
class Person(NamedThing):
"""
A person (alive, dead, undead, or fictional).
"""
id: Union[str, PersonId] = None
full_name: Optional[str] = None
...
Note you don’t need to directly view the python - but your favorite IDE should be able to autocomplete and type check as expected
You can now write code like:
test.py:
from personinfo import Person
p1 = Person(id='ORCID:9876', full_name='Lex Luthor')
print(p1)
run this:
python test.py
Outputs:
Person(id='ORCID:9876', full_name='Lex Luthor', aliases=[], phone=None, age=None)
This is not very impressive in itself, but as your models get larger it can be very handy to have a python data model
LinkML runtime
The LinkML runtime is a separate python library that provides methods needed by your generated datamodel classes, and utilities for converting your python objects into YAML, JSON, RDF, and TSV:
test_runtime.py:
from linkml_runtime.dumpers import json_dumper
from personinfo import Person
p1 = Person(id='ORCID:9876', full_name='Lex Luthor', aliases=["Bad Guy"])
print(json_dumper.dumps(p1))
python test_runtime.py
Outputs:
{
"id": "ORCID:9876",
"full_name": "Lex Luthor",
"aliases": [
"Bad Guy"
],
"@type": "Person"
}
Further reading
-
This repo has the minimal runtime required for a generated dataclasses model to work
Generators:
Python dataclasses
Next
Next we will look at the enumerations feature of LinkML