TypeGraph is a Python library designed for type conversion between various types, including custom types, built-in types, and structural types (such as lists, sets, and dictionaries). It is also compatible with Pydantic Annotated[T, Field(...)]. The library supports both synchronous and asynchronous conversion methods.
蓦然回首，那人却在灯火阑珊处
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- Register type converters for synchronous and asynchronous functions.
- Automatically convert function arguments based on type annotations.
- Support for subclass, union types, and structural types conversion.
- Recursive Generic Calculation.
- Visualize the conversion graph using mermaid syntax.
Install the required dependencies with the following command:
pip install typegraph3Or
pdm add typegraph3Register and use a synchronous converter:
from typegraph import PdtConverter
converter = PdtConverter()
@converter.register_converter(int, str)
def int_to_str(value: int) -> str:
return str(value)
result = converter.convert(10, str) # "10"
print(result)Register and use an asynchronous converter:
import asyncio
from typegraph import PdtConverter
converter = PdtConverter()
@converter.async_register_converter(str, int)
async def str_to_int(value: str) -> int:
return int(value)
async def test_async_conversion():
result = await converter.async_convert("10", int) # 10
print(result)
asyncio.run(test_async_conversion())from typing import Protocol, TypedDict, runtime_checkable
from dataclasses import dataclass
from typegraph import PdtConverter
t = PdtConverter()
class Person(Protocol):
name: str
phone: str
address: str
def get_name(self) -> str:
...
class PersonDict(TypedDict):
name: str
phone: str
address: str
class A:
name: str
phone: str
address: str
def __init__(self, name: str, phone: str, address: str):
self.name = name
self.phone = phone
self.address = address
def get_name(self) -> str:
return self.name
@dataclass
class B:
name: str
phone: str
address: str
@t.register_converter(dict, PersonDict)
def convert_dict_to_persondict(data: dict):
return PersonDict(
name=data["name"],
phone=data["phone"],
address=data["address"]
)
@t.register_converter(Person, str)
def convert_person_to_str(data: Person):
return f"{data.name} {data.phone} {data.address}"
@t.register_converter(dict, A)
def convert_dict_to_a(data: dict):
return A(data["name"], data["phone"], data["address"])
@t.register_converter(dict, B)
def convert_dict_to_b(data: dict):
return B(data["name"], data["phone"], data["address"])
@t.auto_convert()
def test(a: str):
return a
d = {"name": "John", "phone": "123", "address": "123"}
t.convert([d], list[str], debug=True)t.show_mermaid_graph()
graph TD;
dict-->PersonDict
dict-->A
dict-->B
Person-->str
t.show_mermaid_graph()
graph TD;
dict-->PersonDict
dict-->A
dict-->B
Person-->str
A-.->Person
Converting dict[str, str] to <class 'str'> using [<class 'dict'>, <class '__main__.A'>, <class '__main__.Person'>, <class 'str'>], <function TypeConverter.get_converter.<locals>.<lambda>.<locals>.<lambda> at 0x7f1f3306fac0>
['John 123 123']Default recursion depth is two
from typing import Iterable, TypeVar, Annotated
from pydantic import Field
from typegraph import PdtConverter
t = PdtConverter()
K = TypeVar("K")
V = TypeVar("V")
P = Annotated[int, Field(ge=0, le=10)]
@t.register_generic_converter(dict[K, V], dict[V, K])
def convert_dict_to_dict(value: dict[K, V]) -> dict[V, K]:
return {v: k for k, v in value.items()}
@t.register_generic_converter(V, Iterable[V])
def convert_to_iterable(value: V) -> Iterable[V]:
return [value]
@t.register_converter(P, int)
def convert_p_to_int(value: P) -> int:
return value
try:
t.convert(11, P)
except Exception as e:
print(e)Pydantic Annotated[T, Feild(...)]
No converter registered for <class 'int'> to typing.Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=10)])]t.convert(5, P)5dict[K,V]->dict[V,K]
t.convert({1: "2", 3: "4"}, dict[int, int], debug=True)Converting dict[int, str] to dict[str, int] using [dict[int, str], dict[str, int]], <function convert_dict_to_dict at 0x7f18e595c3a0>
{'2': 1, '4': 3}V->Iterable[V]
t.convert(1, Iterable[Iterable[Iterable[P]]], debug=True)Converting typing.Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=10)])] to typing.Iterable[typing.Iterable[typing.Iterable[typing.Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=10)])]]]] using [typing.Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=10)])], typing.Iterable[typing.Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=10)])]], typing.Iterable[typing.Iterable[typing.Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=10)])]]], typing.Iterable[typing.Iterable[typing.Iterable[typing.Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=10)])]]]]], <function PdtConverter.get_converter.<locals>.<lambda>.<locals>.<lambda> at 0x7f18e46ecf70>
[[[1]]]Visualization
t.show_mermaid_graph()graph TD;
node0["typing.Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=10)])]"] --> node1["int"]
node0["typing.Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=10)])]"] -.-> node2["typing.Iterable[typing.Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=10)])]]"]
node1["int"] -.-> node3["typing.Iterable[int]"]
node3["typing.Iterable[int]"] -.-> node4["typing.Iterable[typing.Iterable[int]]"]
node2["typing.Iterable[typing.Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=10)])]]"] -.-> node5["typing.Iterable[typing.Iterable[typing.Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=10)])]]]"]
node5["typing.Iterable[typing.Iterable[typing.Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=10)])]]]"] -.-> node6["typing.Iterable[typing.Iterable[typing.Iterable[typing.Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=10)])]]]]"]
node7["dict[int, str]"] -.-> node8["dict[str, int]"]
node7["dict[int, str]"] -.-> node9["typing.Iterable[dict[int, str]]"]
node8["dict[str, int]"] -.-> node7["dict[int, str]"]
node8["dict[str, int]"] -.-> node10["typing.Iterable[dict[str, int]]"]
node9["typing.Iterable[dict[int, str]]"] -.-> node11["typing.Iterable[typing.Iterable[dict[int, str]]]"]
node10["typing.Iterable[dict[str, int]]"] -.-> node12["typing.Iterable[typing.Iterable[dict[str, int]]]"]
node6["typing.Iterable[typing.Iterable[typing.Iterable[typing.Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=10)])]]]]"] -.-> node13["typing.Iterable[typing.Iterable[typing.Iterable[typing.Iterable[typing.Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=10)])]]]]]"]
node13["typing.Iterable[typing.Iterable[typing.Iterable[typing.Iterable[typing.Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=10)])]]]]]"] -.-> node14["typing.Iterable[typing.Iterable[typing.Iterable[typing.Iterable[typing.Iterable[typing.Annotated[int, FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=10)])]]]]]]"]
Automatically convert function arguments based on type annotations:
from typegraph import PdtConverter
converter = PdtConverter()
@converter.register_converter(str, int)
def str_to_int(value: str) -> int:
return int(value)
@converter.auto_convert()
def add_one(x: int) -> int:
return x + 1
result = add_one("10") # 11
print(result)from typegraph import PdtConverter
import asyncio
converter = PdtConverter()
@converter.async_register_converter(str, int)
async def str_to_int(value: str) -> int:
return int(value)
@converter.async_auto_convert()
async def add_one(x: int) -> int:
return x + 1
async def test_async():
result = await add_one("10") # 11
print(result)
asyncio.run(test_async())Unit tests are provided to ensure the library functions correctly. Run the tests:
pdm testTests cover:
- Registration and execution of synchronous converters.
- Registration and execution of asynchronous converters.
- Conversion capability checks.
- Automatic conversion of function arguments (both synchronous and asynchronous).
You can visualize the type conversion graph:
from typegraph import PdtConverter
t = PdtConverter()
class Test:
def __init__(self, t):
self.t = t
@t.register_converter(float, Test)
def str_to_Test(input_value):
return Test(input_value)
@t.register_converter(Test, float)
def B_to_float(input_value):
return float(input_value.t)
@t.register_converter(float, str)
async def float_to_str(input_value):
return str(input_value)
t.show_mermaid_graph()graph TD;
float-->Test
float-->str
Test-->float
The graph will be displayed using mermaid syntax, which can be rendered online or in supported environments like Jupyter Notebooks.
- Subclass type
- Union type
- Annotated type
Pydantic Annotated[T, Feild(...)] - Structural type
- Protocol type
- TypedDict type
- Generic type
- Dataclass (Dataclass/BaseModel)
This project is licensed under the MIT License.
Contributions are welcome! Please open an issue or submit a pull request for any changes.
If you have any questions or concerns, please open an issue in this repository.