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Aidantic-1.0.1
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مشاهده بیشترتوضیحات
Data parsing and validation with OneOf using Python type hints
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | Aidantic-1.0.1 |
| نام | Aidantic |
| نسخه کتابخانه | 1.0.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | AivanF. |
| ایمیل نویسنده | projects@aivanf.com |
| آدرس صفحه اصلی | https://github.com/AivanF/Aidantic |
| آدرس اینترنتی | https://pypi.org/project/Aidantic/ |
| مجوز | - |
# Aidantic
Aidantic is a tiny library inspired by Pydantic,
made to be more suitable for:
- Data parsing.
- Subclasses lookup (like `OneOf` in JSON-Schema).
- Input data validation:
- Exceptions store precise path.
- Plain types values validation, see `PlainWrapper`.
- Custom complex objects validation, see `ModelVisitorBase`.
The library isn't aimed to become a full replacement of Pydantic
(e.g, there is no planned support of JSON Schema, OpenAPI),
but in some cases it's worth using `:)`
ToDo List:
- 1: optional strict value type check
- 2: field name aliases
- 3: OneOf typing: pass inner class methods
- 4: probably some default types are missed and not supported
License: GPL 3
# Usage
Install with pip: `pip install Aidantic`
Or simply obtain a single source file from the repo.
## 1. Basics
```py
from typing import List
from aidantic import BaseModel
class Model(BaseModel):
name: str
data: List[int]
obj = Model(name="Foo", data=[61, 80, 33, 98])
assert obj.data[2] == 33
```
Simple yet boring, let's dive deeper!
## 2. OneOf
Sometimes an object may contain instance(s)
of plenty subclasses, and you, or your auxiliary lib,
have to decide which class to use during parsing process.
For this aim, Aidantic provides built-in support of
One-Of logic: all you need is to specify a key,
which should be used to subclass picking,
and declare its values for the subclasses.
No `Union` with boring enumeration is needed!
```py
from typing import List, Literal
from aidantic import BaseModel, OneOf
class RandomModel(BaseModel):
_discriminator = "key"
key: int
class EuropeModel(RandomModel):
key: Literal[271]
value: str
class PieModel(RandomModel):
key: Literal[314]
value: int
class PackageModel(BaseModel):
title: str
content: List[OneOf[RandomModel]]
data = dict(title="Bar42", content=[
dict(key=314, value=15926535),
dict(key=271, value="lol"),
])
package = PackageModel(**data)
package.validate()
assert package.content[1].value == "lol"
print(package)
```
Result:
```py
PackageModel(
title='Bar42',
content=[
PieModel(key=314, value=15926535),
EuropeModel(key=271, value='lol')
])
```
BTW, `_discriminator` may be a tuple of field names.
## 3. PlainWrapper
If you need to validate a set of values of plain types
(like str, int), you can utilise `PlainWrapper` class,
which can be used as a simple replacement of any plain type.
However, you should note that a wrapped value has
some limitations: typical `isinstance` usage won't work.
But comparison to plain values works well,
and you can easily access wrapper's `value` property.
There are a couple of use-cases:
### 3.1. Creation time validation
```py
from aidantic import PlainWrapper, BaseModel, CreationError, PathType
class StatusCode(PlainWrapper["str"]):
_allowed = {"foo", "bar", "lol"}
def __init__(self, code, path: PathType):
if code not in self._allowed:
raise CreationError(f"Unknown code '{code}'", path)
super().__init__(code, path)
class SomeModel(BaseModel):
code: StatusCode
obj = SomeModel(code="bar")
assert obj.code == "bar"
```
The same could be also written as a Union of Literals,
but this would be too verbose.
### 3.2. Validator class
If you have to perform more complex logic,
e.g load allowed values later, or compare values
from different objects, you can use a separate validator
that will traverse your data models:
```py
from typing import List
from aidantic import PlainWrapper, BaseModel, ModelVisitorBase, ValidationError
class StatusCode(PlainWrapper["str"]):
pass
class SomeModel(BaseModel):
codes: List[StatusCode]
class CrossValidator(ModelVisitorBase):
_label = "Cross"
_allowed = {"foo", "bar", "lol"}
def __init__(self,):
super().__init__()
self.collected_codes = set()
def visit(self, obj):
super().visit(obj)
unknown_codes = self.collected_codes - self._allowed
if unknown_codes:
raise ValidationError(f"Got {len(unknown_codes)} unknown codes", ())
def visit_wrapper(self, obj, _type, path):
if issubclass(_type, StatusCode):
self.collected_codes.add(str(obj))
obj = SomeModel(codes=["foo", "bar", "lol"])
CrossValidator().visit(obj)
```
## 4. Plain values parsing
Thanks to `from_plain` method,
it's pretty simple to write a parser that will translate
plain values like strings into objects:
```py
from aidantic import BaseModel
from expr import AnyExpr
class Message(BaseModel):
title: str
formula: AnyExpr
msg = Message(
title="Please calculate the attached expression",
formula="log(pi, 2.7182)",
)
print(msg)
```
```py
Message(
title="Please calculate the attached expression",
formula=ExprFunction(operator='FUNCTION', name='log', arguments=[
ExprField(operator='FIELD', name='pi'), ExprLiteral(operator='LITERAL', value=2.7182)
])
)
```
See `tests/test_expr.py` for implementation details!
# Historical intro
While I was developing CI and scenario scripts for
my GameDev pet-project [Destiny Garden](https://www.aivanf.com/destiny-garden-1),
I've met a need to parse and validate large scripts
written in custom YAML-based format.
I tried to use my beloved Pydantic for this problem,
but found it too focused on networking, ORM logic and too
rigid for game scenario scripting, so quickly I've
developed this library which is small yet powerful
for dealing with such specialised problems.
نحوه نصب
نصب پکیج whl Aidantic-1.0.1:
pip install Aidantic-1.0.1.whl
نصب پکیج tar.gz Aidantic-1.0.1:
pip install Aidantic-1.0.1.tar.gz