Takes source_data:Dict ( a single instance example of something like a JSON node) and self generates a pythonic data model with Alias to original source field names. This makes it easy to popuate or export to other systems yet handle the data in a pythonic way.
Being a pydantic datamodel all the richness of pydantic data validation is available and these models can easily be used in FastAPI and or a ORM
It does not process full JSON data structures but takes simple JSON document with basic elements
Provide a model_name, an example of JSON data and a dict of type overrides
Example:
source_data = {'Name': '48 Rainbow Rd',
'GroupAddressStyle': 'ThreeLevel',
'LastModified': '2020-12-21T07:02:51.2400232Z',
'ProjectStart': '2020-12-03T07:36:03.324856Z',
'Comment': '',
'CompletionStatus': 'Editing',
'LastUsedPuid': '955',
'Guid': '0c85957b-c2ae-4985-9752-b300ab385b36'}
source_overrides = {'Guid':{'type':uuid.UUID},
'LastModified':{'type':datetime },
'ProjectStart':{'type':datetime },
}
source_optionals = {"Comment":True}
#create Model
model_Project=pydanticModelGenerator(
model_name="Project",
source_data=source_data,
overrides=source_overrides,
optionals=source_optionals).generate_model()
#create instance using DynamicModel
project_instance=model_Project(**project_info)
Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about:
source_data is a snapshot of the default values produced by the class
params are the names of the actual keys required by __init__
