For discussion: A basic python embedded examples

[matentzn]

# /// script
# requires-python = ">=3.10"
# dependencies = [
#   "linkml-transformer",   # pretend we’re using LinkML tooling
#   "pyyaml"
# ]
# [tool.mapping-metadata]
# id = "https://mapping-commons.github.io/rda-fair-mappings/example/personinfo.transform.yml"
# title = "PersonInfo to Agent example transformation"
# source_schema = "https://mapping-commons.github.io/rda-fair-mappings/example/personinfo.schema.yml"
# target_schema = "https://mapping-commons.github.io/rda-fair-mappings/example/personinfo.transform.yml"
# prefixes.sssom = "https://w3id.org/sssom/"
# prefixes.semapv = "https://w3id.org/semapv/vocab/"
# mapping_method = "semapv:ManualMappingCuration"
# mapping_tool_version = "0.3.8"
# license = "https://creativecommons.org/licenses/by/4.0/"
# creator_id = ["https://orcid.org/0000-0002-7356-1779"]
# author_id = ["https://orcid.org/0000-0002-7356-1779"]
# publication_date = "2025-08-14"
# ///

import json

# Example: input in "PersonInfo" schema style
personinfo = {
    "given_name": "Ada",
    "family_name": "Lovelace",
    "age": 36,
    "height_in_cm": 165
}

# Simple transformation logic (stand-in for LinkML transformer)
agent = {
    "id": "agent:1",
    "type": "Agent",
    "full_name": f"{personinfo['given_name']} {personinfo['family_name']}",
    "age": personinfo["age"],
    "height_in_m": round(personinfo["height_in_cm"] / 100.0, 2)
}

print(json.dumps(agent, indent=2))