This generator uses spec case for all (object) property names and parameter names.
- So if the spec has a property name like camelCase, it will use camelCase rather than camel_case
- So you will need to update how you input and read properties to use spec case
Endpoint parameters are stored in dictionaries to prevent collisions (explanation below)
- So you will need to update how you pass data in to endpoints
Endpoint responses now include the original response, the deserialized response body, and (todo)the deserialized headers
- So you will need to update your code to use response.body to access deserialized data
All validated data is instantiated in an instance that subclasses all validated Schema classes and Decimal/str/list/tuple/frozendict/NoneClass/BoolClass/bytes/io.FileIO
- This means that you can use isinstance to check if a payload validated against a schema class
- This means that no data will be of type None/True/False
  - ingested None will subclass NoneClass
  - ingested True will subclass BoolClass
  - ingested False will subclass BoolClass
  - So if you need to check is True/False/None, instead use instance.is_true_oapg()/.is_false_oapg()/.is_none_oapg()
All validated class instances are immutable except for ones based on io.File
- This is because if properties were changed after validation, that validation would no longer apply
- So no changing values or property values after a class has been instantiated
String + Number types with formats
- String type data is stored as a string and if you need to access types based on its format like date, date-time, uuid, number etc then you will need to use accessor functions on the instance
- type string + format: See .as_date_oapg, .as_datetime_oapg, .as_decimal_oapg, .as_uuid_oapg
- type number + format: See .as_float_oapg, .as_int_oapg
- this was done because openapi/json-schema defines constraints. string data may be type string with no format keyword in one schema, and include a format constraint in another schema
- So if you need to access a string format based type, use as_date_oapg/as_datetime_oapg/as_decimal_oapg/as_uuid_oapg
- So if you need to access a number format based type, use as_int_oapg/as_float_oapg
Property access on AnyType(type unset) or object(dict) schemas
- Only required keys with valid python names are properties like .someProp and have type hints
- All optional keys may not exist, so properties are not defined for them
- One can access optional values with dict_instance['optionalProp'] and KeyError will be raised if it does not exist
- Use get_item_oapg if you need a way to always get a value whether or not the key exists
  - If the key does not exist, schemas.unset is returned from calling dict_instance.get_item_oapg('optionalProp')
  - All required and optional keys have type hints for this method, and @typing.overload is used
  - A type hint is also generated for additionalProperties accessed using this method
- So you will need to update you code to use some_instance['optionalProp'] to access optional property and additionalProperty values
The location of the api classes has changed
- Api classes are located in your_package.apis.tags.some_api
- This change was made to eliminate redundant code generation
- Legacy generators generated the same endpoint twice if it had > 1 tag on it
- This generator defines an endpoint in one class, then inherits that class to generate apis by tags and by paths
- This change reduces code and allows quicker run time if you use the path apis
  - path apis are at your_package.apis.paths.some_path
- Those apis will only load their needed models, which is less to load than all of the resources needed in a tag api
- So you will need to update your import paths to the api classes

Why are Oapg and _oapg used in class and method names?

Classes can have arbitrarily named properties set on them Endpoints can have arbitrary operationId method names set For those reasons, I use the prefix Oapg and _oapg to greatly reduce the likelihood of collisions on protected + public classes/methods. oapg stands for OpenApi Python Generator.

Object property spec case

This was done because when payloads are ingested, they can be validated against N number of schemas. If the input signature used a different property name then that has mutated the payload. So SchemaA and SchemaB must both see the camelCase spec named variable. Also it is possible to send in two properties, named camelCase and camel_case in the same payload. That use case should be support so spec case is used.

Parameter spec case

Parameters can be included in different locations including:

query
path
header
cookie

Any of those parameters could use the same parameter names, so if every parameter was included as an endpoint parameter in a function signature, they would collide. For that reason, each of those inputs have been separated out into separate typed dictionaries:

query_params
path_params
header_params
cookie_params

So when updating your code, you will need to pass endpoint parameters in using those dictionaries.

Endpoint responses

Endpoint responses have been enriched to now include more information. Any response reom an endpoint will now include the following properties: response: urllib3.HTTPResponse body: typing.Union[Unset, Schema] headers: typing.Union[Unset, TODO] Note: response header deserialization has not yet been added

Installation & Usage

pip install

If the python package is hosted on a repository, you can install directly using:

pip install git+https://github.com/GIT_USER_ID/GIT_REPO_ID.git

(you may need to run pip with root permission: sudo pip install git+https://github.com/GIT_USER_ID/GIT_REPO_ID.git)

Then import the package:

import dynamic_servers

Setuptools

Install via Setuptools.

python setup.py install --user

(or sudo python setup.py install to install the package for all users)

Then import the package:

import dynamic_servers

Getting Started

Please follow the installation procedure and then run the following:


import time
import dynamic_servers
from pprint import pprint
from dynamic_servers.apis.tags import usage_api
# Defining the host is optional and defaults to http://petstore.swagger.io:80/v2
# See configuration.py for a list of all supported configuration parameters.
configuration = dynamic_servers.Configuration(
    host = "http://petstore.swagger.io:80/v2"
)


# Enter a context with an instance of the API client
with dynamic_servers.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = usage_api.UsageApi(api_client)
    
    try:
        # Use custom server
        api_response = api_instance.custom_server()
        pprint(api_response)
    except dynamic_servers.ApiException as e:
        print("Exception when calling UsageApi->custom_server: %s\n" % e)

Documentation for API Endpoints

All URIs are relative to http://petstore.swagger.io:80/v2

Class	Method	HTTP request	Description
UsageApi	custom_server	get /custom	Use custom server
UsageApi	default_server	get /default	Use default server

Documentation For Models

Documentation For Authorization

All endpoints do not require authorization.

Author

Notes for Large OpenAPI documents

If the OpenAPI document is large, imports in dynamic_servers.apis and dynamic_servers.models may fail with a RecursionError indicating the maximum recursion limit has been exceeded. In that case, there are a couple of solutions:

Solution 1: Use specific imports for apis and models like:

from dynamic_servers.apis.default_api import DefaultApi
from dynamic_servers.model.pet import Pet

Solution 1: Before importing the package, adjust the maximum recursion limit as shown below:

import sys
sys.setrecursionlimit(1500)
import dynamic_servers
from dynamic_servers.apis import *
from dynamic_servers.models import *